HIGH INTENSITY or LOW-TO-MODERATE INTENSITY EXERCISE after CHEMOTHERAPY. For whom and how? Caroline S. Kampshoff

Transcription

1 HIGH INTENSITY or LOW-TO-MODERATE INTENSITY EXERCISE after CHEMOTHERAPY For whom and how? Caroline S. Kampshoff

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3 HIGH INTENSITY or LOW-TO-MODERATE INTENSITY EXERCISE after CHEMOTHERAPY: for whom and how? Caroline S. Kampshoff

4 This thesis was prepared within the Amsterdam Public Health research institute, at the Department of Public and Occupational Health of the VU University Medical Center, Amsterdam, the Netherlands. The Resistance and Endurance exercise After ChemoTherapy (REACT) study was supported by Alpe d HuZes/Dutch Cancer Society (Grant number:alpe ). The printing of this thesis was financially supported by the Dutch Society of Physical Therapy within Lymphology and Oncology (NVFL), the Royal Dutch Society for Physical Therapy (KNGF), Pro Education, Dutch Institute of Allied Health Care (NPi), ProCare B.V., Lode B.V., ChipSoft, and VU University Medical Center. Cover photo: Elroy Aguiar Cover design: Esther Scheide, Layout: Esther Scheide, Printing: Ridderprint B.V., Ridderkerk ISBN: , Caroline S. Kampshoff, the Netherlands. All rights reserved. No part of this thesis may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage of retrieval system, without prior written permission from the author.

5 VRIJE UNIVERSITEIT High intensity or low-to-moderate intensity exercise after chemotherapy: for whom and how? ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. V. Subramaniam, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Geneeskunde op donderdag 22 juni 2017 om uur in de aula van de universiteit, De Boelelaan 1105 door Caroline Stephanie Kampshoff geboren te Tilburg

6 promotoren: copromotoren: prof.dr. W. van Mechelen prof.dr.ir. J. Brug dr. L.M. Buffart prof.dr. M.J.M. Chin A Paw

7 CONTENT Chapter 1 General introduction 7 Chapter 2 Design of the Resistance and Endurance exercise After Chemo Therapy (REACT) study: a randomized controlled trial to evaluate the Effectiveness and cost-effectiveness of exercise interventions after chemotherapy on physical fitness and fatigue 17 Chapter 3 Randomized controlled trial of the effects of high intensity and low-to-moderate intensity exercise on physical fitness and fatigue in cancer survivors: results of the Resistance and Endurance exercise After ChemoTherapy (REACT) study 35 Chapter 4 Mediators of exercise effects on health-related quality of life in cancer survivors after chemotherapy 55 Chapter 5 Long-term effectiveness and cost-effectiveness of high versus low-to-moderate intensity resistance and endurance exercise among cancer survivors 71 Chapter 6 Determinants of exercise adherence and maintenance among cancer survivors: a systematic review 93 Chapter 7 Participation in and adherence to physical exercise after completion of primary cancer treatment 115 Chapter 8 Demographic, clinical, psychosocial, and environmental correlates of objectively assessed physical activity among breast cancer survivors 137 Chapter 9 General discussion 157 Summary Nederlandse samenvatting List of Publications Dankwoord About the author

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9 CHAPTER 1 General introduction

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11 General introduction 9 GENERAL INTRODUCTION Cancer According to current estimates in developed countries more than one in three people will be affected directly by cancer at some point in their lifetime [1]. The most prevalent cancer diagnoses in adults are breast, colon, prostate and lung cancer, representing 43% of all new cases worldwide [2]. Over the past few decades, major milestones have been achieved in early detection, diagnosis, and effective treatment regimens of cancer. As a result, the 5-year relative survival rates improved significantly, and currently rates are 68% for all cancers combined [1]. Given the increased incidence and survival rates, cancer survivors represent a growing population, reflecting anyone who has been diagnosed with cancer, from the time since diagnosis through the rest of life [3]. Physical and psychosocial problems Surviving cancer, however, is associated with long-term disease- and treatment-related problems including decreased cardiorespiratory fitness and muscle strength, increased risk of anxiety and depression, and/or severe feelings of fatigue [4,5]. These physical and psychosocial problems may persist for years after completing active treatments and reduce a person s health-related quality of life (HRQoL) [6]. Fatigue, in particular, has been identified as one of the most distressing problems reported by cancer survivors affecting more than 70% of the population [7]. Moreover, fatigue becomes a chronic condition in 30% of the cancer survivors [8]. Traditionally, cancer survivors were advised to rest and avoid exercise when they feel fatigued [4]. However, reducing daily physical activity may further reduce the compromised levels of cardiorespiratory fitness and muscle mass following cancer treatments, resulting in a self-perpetuating cycle of physical inactivity, contributing to poorer physical fitness and long term persistence of fatigue [4]. Physical activity and exercise programs Physical activity (i.e., any bodily movement that results in energy expenditure from muscle contraction) and exercise (i.e., form of physical activity that is planned, structured, and repetitive and that aims to improve or maintain physical fitness, performance or health) [9] have been increasingly recognized as promising interventions to break the self-perpetuating cycle of daily physical inactivity [4]. This, in turn, may assist cancer survivors to cope with and recover from the cancer- and treatment-related problems and contribute to a better quality of life [10,11]. Over the past few decades, the field of exercise oncology has

12 10 CHAPTER 1 evolved rapidly and the impact of exercise on specific health outcomes - including HRQoL - has been studied extensively [12]. Aiming to organize research on exercise and cancer survivorship, Courneya et al. proposed the Physical Activity and Cancer Control framework (PACC), distinguishing four time periods following cancer diagnosis (i.e., pre-treatment, treatment, survivorship, and end of life) [13]. A limited number of studies have examined the effects of exercise pre-treatment and at the end-of-life phases [14], while most research to date has investigated the effectiveness of exercise during and post treatment. Generally, during primary cancer treatment, exercise showed beneficial effects in minimizing decline in cardiorespiratory fitness [15] and muscle strength [16], limiting fatigue and improving quality of life [10]. After completion of primary cancer, exercise may reverse losses in physical fitness that have occurred during treatment and may prevent, manage or reduce long-term psychosocial problems. Moreover, observational studies have found that higher levels of physical activity after diagnosis might improve survival. Optimal exercise prescriptions Current systematic literature reviews have underlined the positive physical and psychosocial benefits of exercise programs among cancer survivors, but also have highlighted the importance to firmly establish the magnitude of positive effects in high-quality randomized controlled trials (RCT)s [10,11,17,18]. Furthermore, additional research is needed to move away from one-size fits all exercise programs and to define specific exercise prescriptions in terms of frequency, intensity, type and time (i.e., FITT factors) of exercise [19,20]. Studying exercise prescriptions for cancer survivors may facilitate the development of targeted interventions, which in turn is likely to contribute to more effective exercise programs [19]. The first RCTs evaluating the effects of different exercise doses and modes in breast cancer survivors during chemotherapy on physical functioning and HRQoL [21,22] have indicated that higher exercise doses (3 times 60 minutes per week at moderate-to-high intensity) resulted in significantly better physical functioning and less symptoms, compared to standard doses (3 times 30 minutes per week at moderate-to-high intensity) [22]. Furthermore, resistance exercises had superior effects on lower and upper body muscle strength compared to aerobic exercises and usual care, while, aerobic exercises had larger effects on peak oxygen uptake compared to resistance exercises and usual care [21]. Neither exercise modes prevented weight gain, but compared to usual care both, i.e., resistance and aerobic exercises, showed beneficial effects on body composition and self-esteem [21]. To date, only two relatively small RCTs examined the effects of different exercise intensities after completion of primary cancer treatment [23,24]. Burnham et al. have compared

13 General introduction 11 moderate versus low intensity aerobic exercise in breast cancer survivors (n=18) and have reported that both exercise programs improved cardiorespiratory fitness compared to usual care, with no differences in effects between the interventions [23], whereas Gibbs et al. reported larger improvements in cardiorespiratory fitness in breast cancer survivors (n=73) after high intensity resistance exercise compared to low intensity resistance exercise and usual care [24]. Due to the scarcity of studies, small sample sizes and a predominant focus on breast cancer survivors, future research is needed to define the optimal exercise intensity among cancer survivors. REACT study The Resistance and Endurance exercise After ChemoTherapy (REACT) study was developed to evaluate a 12-week high intensity (HI) and low-to-moderate intensity (LMI) resistance and endurance exercise program compared to a waiting list control (WLC) group in cancer survivors who had completed primary cancer treatment including chemotherapy [25]. The REACT study is one of the four RCTs of the Alpe d HuZes Cancer Rehabilitation clinical research program (A-CaRe). Primarily, A-CaRe Clinical Research aimed to develop, evaluate and implement state-of-the-art exercise programs among four different subgroups of cancer survivors [26]. A-CaRe clinical research program hypothesized that exercise improves cardiorespiratory fitness and muscle strength, thereby reducing fatigue and consequently improving HRQoL among cancer survivors (Figure 1). FIGURE 1 Conceptual model of the A-CaRe program Cancer and cancer treatments Physical activity or exercise Physical fitness Fatigue Health-related quality of life Further, implementation of exercise programs might be facilitated by a better understanding about the demographic, clinical, psychosocial, physical and environmental factors that influence participation and exercise adherence among cancer survivors [20]. More

14 12 CHAPTER 1 specifically, knowledge of demographic and clinical factors will identify subgroups that are at most risk for declining participation or withdrawing from an exercise program. Know ledge of psychosocial factors may identify relevant targets for additional strategies to support the improvement of participation and adherence rates. Objectives and outline of this thesis The overall objective of the current thesis is to evaluate (cost-)effectiveness and facilitate implementation of exercise programs in a mixed group of cancer survivors who had completed primary cancer treatment, including chemotherapy. In particular, this thesis addresses three primary objectives: I. To evaluate the (cost-)effectiveness of a HI and LMI resistance and endurance exercise intervention on physical fitness and fatigue; II. To test the hypothesis that resistance and endurance exercises improves cardiorespiratory fitness and muscle strength, thereby reducing fatigue and consequently improve global quality of life and physical function; III. To identify demographic, clinical, psychosocial, physical and environmental factors that are associated with exercise participation and exercise adherence. Chapter 2 describes the design of the REACT study, a randomized controlled trial evaluating the effectiveness and cost-effectiveness of exercise interventions in a large group of cancer survivors (n=277) who had recently completed treatment with curative intent, including chemotherapy. Chapter 3 presents the results of the REACT study at 12 weeks follow-up by reporting on the effectiveness of HI exercise and LMI exercise compared to a WLC group, with cardiorespiratory fitness, muscle strength, and fatigue as primary outcomes. Secondary outcomes included HRQoL, physical activity, daily functioning, body composition, mood, and sleep disturbances. Chapter 4 evaluates the hypothesis that resistance and endurance exercise improves cardiorespiratory fitness and muscle strength, thereby reducing fatigue and consequently improving global quality of life and physical function. Chapter 5 describes the effectiveness and cost-effectiveness of HI versus LMI exercise programs in the REACT study at 64 weeks follow-up. Chapter 6 presents a systematic review that summarized evidence on demographic, clinical, psychological, physical and environmental correlates of exercise intervention adherence and exercise maintenance after completion of an intervention. In this review, a distinction is made between correlates of exercise adherence before, during and after primary cancer treatment according to the PACC framework [13]. Chapter 7 explores correlates of participation in and adherence to exercise programs in cancer survivors. The

15 General introduction 13 differences between participants and non-participants of the REACT study were studied to identify subgroups of cancer survivors who are most likely to participate in exercise programs. Furthermore, this chapter studies the demographic, clinical, psychosocial, physical and environmental correlates of exercise intervention adherence, in which a distinction was made between correlates of session attendance and correlates of compliance to the prescribed protocol for HI exercise and LMI exercise. Chapter 8 examines demographic, clinical, psychosocial, and environmental correlates of objectively assessed physical activity among breast cancer survivors that participated in one of three RCTs, in order to facilitate the development of effective and targeted interventions aiming to improve physical activity. Chapter 9 describes the main findings of the current thesis, discusses the strengths and limitations of the work that is presented, as well as clinical implications and directions for future research.

16 14 CHAPTER 1 REFERENCES 1. Siegel RL, Miller KD, Jemal A: Cancer statistics, CA Cancer J Clin 2015, 65: Online Source: 3. Centers for Disease Control and Prevention (CDC): Cancer survivors- United States, MMWR Morb Mortal Wkly Rep 2011, 60: Lucia A, Earnest C, Perez M: Cancer-related fatigue: can exercise physiology assist oncologists? Lancet Oncol 2003, 4: Jones LW, Eves ND, Haykowsky M, Joy AA, Douglas PS: Cardiorespiratory exercise testing in clinical oncology research: systematic review and practice recommendations. Lancet Oncol 2008, 9: Curt GA: Impact of fatigue on quality of life in oncology patients. Semin Hematol 2000, 37: Curt GA, Breitbart W, Cella D, Groopman JE, Horning SJ, Itri LM et al.: Impact of cancer-related fatigue on the lives of patients: new findings from the Fatigue Coalition. Oncologist 2000, 5: Dimeo F: Radiotherapy-related fatigue and exercise for cancer patients: a review of the literature and suggestions for future research. Front Radiat Ther Oncol 2002, 37: Caspersen CJ, Powell KE, Christenson GM: Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep 1985, 100: Mishra SI, Schrerer RW, Snyder C, Geigle PM, Berlanstein DR, Topaloglu O: Exercise interventions on health-related quality of life for people with cancer during active treatment. Cochrane Database Syst Rev 2012, CD Mishra SI, Schrerer RW, Geigle PM, Berlanstein DR, Topaloglu O, Gotay CC et al.: Exercise interventions on health-related quality of life for cancer survivors. Cochrane Database Syst Rev 2012, CD Jones LW, Alfano CM: Exercise-oncology research: past, present, and future. Acta Oncol 2013, 52: Courneya KS, Friedenreich CM: Physical activity and cancer control. Semin Oncol Nurs 2007, 23: Speck RM, Courneya KS, Masse LC, Duval S, Schmitz KH: An update of controlled physical activity trials in cancer survivors: a systematic review and meta-analysis. J Cancer Surviv 2010, 4: Jones LW, Liang Y, Pituskin EN, Battaglini CL, Scott JM, Hornsby WE et al.: Effect of exercise training on peak oxygen consumption in patients with cancer: a meta-analysis. Oncologist 2011, 16: Strasser B, Steindorf K, Wiskemann J, Ulrich CM: Impact of Resistance Training in Cancer Survivors: a Meta-analysis. Med Sci Sports Exerc Knols R, Aaronson NK, Uebelhart D, Fransen J, Aufdemkampe G: Physical exercise in cancer patients during and after medical treatment: a systematic review of randomized and controlled clinical trials. J Clin Oncol 2005, 23: Cramp F, James A, Lambert J: The effects of resistance training on quality of life in cancer: a systematic literature review and meta-analysis. Support Care Cancer 2010, 18: Buffart LM, Galvao DA, Brug J, Chinapaw MJ, Newton RU: Evidence-based physical activity guidelines for cancer survivors: current guidelines, knowledge gaps and future research directions. Cancer Treat Rev 2014, 40: Courneya KS, Rogers LQ, Campbell KL, Vallance JK, Friedenreich CM: Top 10 research questions related to physical activity and cancer survivorship. Res Q Exerc Sport 2015, 86: Courneya KS, Segal RJ, Mackey JR, Gelmon K, Reid RD, Friedenreich CM et al.: Effects of aerobic and resistance exercise in breast cancer patients receiving adjuvant chemotherapy: a multicenter randomized controlled trial. J Clin Oncol 2007, 25: Courneya KS, McKenzie DC, Mackey JR, Gelmon K, Friedenreich CM, Yasui Y et al.: Effects of exercise dose and type during breast cancer chemotherapy: multicenter randomized trial. J Natl Cancer Inst 2013, 105:

17 General introduction Burnham TR, Wilcox A: Effects of exercise on physiological and psychological variables in cancer survivors. Med Sci Sports Exerc 2002, 34: Gibbs Z: Exercise for breast cancer patietns with lymphedema. Australian New Zealand Clinical Trials Registry. ACTRN www anzctr org au Kampshoff CS, Buffart LM, Schep G, van Mechelen W, Brug J, Chinapaw MJ: Design of the Resistance and Endurance exercise After ChemoTherapy (REACT) study: a randomized controlled trial to evaluate the effectiveness and cost-effectiveness of exercise interventions after chemotherapy on physical fitness and fatigue. BMC Cancer 2010, 10: Chinapaw MJ, Buffart LM, van Mechelen W, Schep G, Aaronson NK, van Harten WH et al.: Alpe d'huzes Cancer Rehabilitation (A-CaRe) Research: Four Randomized Controlled Exercise Trials and Economic Evaluations in Cancer Patients and Survivors. Int J Behav Med 2012, 19:

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19 CHAPTER 2 Design of the Resistance and Endurance exercise After ChemoTherapy (REACT) study: a randomized controlled trial to evaluate the effectiveness and cost-effectiveness of exercise interventions after chemotherapy on physical fitness and fatigue Caroline S. Kampshoff Laurien M. Buffart Goof Schep Willem van Mechelen Johannes Brug Mai J.M. Chinapaw BMC Cancer, 2010; 10: 658.

20 18 CHAPTER 2 ABSTRACT Background: Preliminary studies suggest that physical exercise interventions can improve physical fitness, fatigue and quality of life in cancer patients after completion of chemotherapy. Additional research is needed to rigorously test the effects of exercise programs among cancer patients and to determine optimal training intensity accordingly. The present paper presents the design of a randomized controlled trial evaluating the effectiveness and costeffectiveness of a high intensity (HI) exercise program compared to a low-to-moderate intensity (LMI) exercise program and a waiting list control (WLC) group on physical fitness and fatigue as primary outcomes. Methods: After baseline measurements, cancer patients who completed chemotherapy are randomly assigned to either a 12-week HI exercise program or a LMI exercise program. Next, patients from both groups are randomly assigned to immediate training or a waiting list (i.e., WLC group). After 12 weeks, patients of the WLC group start with the exercise program they have been allocated to. Both interventions consist of equal bouts of resistance and endurance interval exercises with the same frequency and duration, but differ in training intensity. Additionally, patients of both exercise programs are counselled to improve compliance and achieve and maintain an active lifestyle, tailored to their individual preferences and capabilities. Measurements will be performed at baseline (T=0), 12 weeks after randomization (T=1), and 64 weeks after randomization (T=2). The primary outcome measures are cardiorespiratory fitness and muscle strength assessed by means of objective performance indicators, and self-reported fatigue. Secondary outcome measures include health-related quality of life, physical activity, daily functioning, body composition, mood and sleep disturbances, and return to work. In addition, compliance and satisfaction with the interventions will be evaluated. Potential moderation by pre- and post-illness lifestyle, health and exercise-related attitudes, beliefs and motivation will also be assessed. Finally, the cost-effectiveness of both exercise interventions will be evaluated. Discussion: This randomized controlled trial will be a rigorous test of effects of exercise programs for cancer patients after chemotherapy, aiming to contribute to evidence-based practice in cancer rehabilitation programs.

21 Design of the REACT study 19 INTRODUCTION Cancer treatment has made substantial progress in the last decades. Survival rates after cancer treatment have improved up to 56% in male and 62% in female patients [1]. This is a major achievement; it is, however, important to acknowledge that cancer and cancer treatment are associated with long-term physical and psychosocial side effects. These sequelae include decreased muscle strength, reduced cardiorespiratory fitness, reduced lean body mass, bone loss, severe feelings of fatigue [2,3], depression, emotional distress, anxiety and decreased self-esteem [4]. Fatigue is one of the most common side effects of cancer treatment, affecting approximately 70% of the cancer population receiving radiation therapy and chemotherapy [5,6]. Even years after treatment, feelings of fatigue persist in 30% of cancer patients [7]. This has great impact on the patient s quality of life [5,8]. Cancer rehabilitation programs have become a great matter of interest as component of cancer patient care to reduce the side effects of cancer treatment and to enhance a patient s quality of life. In the Netherlands, two cancer rehabilitation programs have been evaluated in previous years. The first program was based on a biopsychosocial approach, combining physical exercise with psychosocial activities in a group format. Exercise started with low-to-moderate intensities, and workload increased gradually after four weeks training [9]. The second cancer rehabilitation program focused on high intensity (HI) resistance and endurance exercise [10]. Both rehabilitation programs were well tolerated by most patients and improvements in physical fitness and health-related quality of life (HRQoL) were reported, directly after completion of the programs [10,11] and after respectively 9 [12] and 12 months [13] follow-up. Systematic reviews of the literature [14-17] underline the positive physical and psychosocial benefits from exercise programs accordingly. Evidence suggests that exercise may result in improved physical fitness, reduced levels of fatigue and enhanced HRQoL. However, the results must be interpreted with caution [14,18]. Overall, the methodological quality of many of the studies reviewed was moderate and opportunities to improve the scientific methodology were evident. Authors of the reviews suggested including larger sample sizes, using appropriate control groups, and using a comparable set of valid and reliable outcome measures in future randomized controlled trials (RCTs). Moreover, certain aspects of the examined exercise programs were less than optimal: most exercise programs were relatively short in duration (less than 12 weeks), the programs did not stimulate the patients to stay physically active after the program, and studies frequently included aerobic exercises such as walking and cycling, but no resistance exercises.

22 20 CHAPTER 2 Accumulating evidence suggests that resistance exercises may have great potential as well [15,19]. A recent systematic review of twenty-four studies evaluating resistance exercise in cancer patients post-treatment [20] reported beneficial effects on cardiorespiratory fitness and muscle strength. Furthermore, the studies included in the review did not report adverse effects, indicating that resistance exercise was well-tolerated. Courneya et al. [21] compared aerobic exercise with resistance exercise in breast cancer patients. While aerobic exercises showed significant improvements in self-esteem, preserved aerobic fitness, and maintained body fat levels, resistance exercises significantly improved self-esteem, muscle strength, and lean body mass. A recent roundtable, organised by the American College of Sports and Medicine (ACSM), came to consensus that both aerobic and resistance exercise are recommended to be prescribed in cancer patients [22]. Current ACSM exercise recommendations for cancer patients include moderate intensity exercises with aerobic exercises at 40% to 60% of heart rate reserve (HRR) three to five times per week for 20 to 60 minutes and resistance exercises at 40% to 60% of one-repetition maximum (1-RM) two or three times per week with one to three sets of 8 to 12 repetitions per exercise [23]. However, the ACSM acknowledges the remaining gap in existing knowledge on the optimal mode, frequency, duration and intensity of exercise [22]. HI exercise has shown to improve physical fitness and enhance HRQoL in cancer patients who completed chemotherapy [10]. Also in patients with heart failure, HI exercise was feasible and resulted in greater improvements in physical fitness as compared to lower intensity exercise [24]. High quality scientific research is needed to firmly establish the range and magnitude of positive effects of exercise programs among cancer patients and to determine optimal exercise intensities in this population. This paper presents the design of a randomized controlled multicenter trial to evaluate the effectiveness and cost-effectiveness of a HI exercise program compared to a low-tomoderate intensity (LMI) exercise program and a waiting list control (WLC) group on physical fitness (cardiorespiratory fitness and muscle strength), and fatigue in cancer patients who completed chemotherapy. We hypothesize that patients in both exercise programs will achieve more muscle strength, greater gains in cardiorespiratory fitness and will report lower levels of fatigue compared to the patients who are allocated to the WLC group. Furthermore, we hypothesize these improvements to be greater in patients who completed the HI exercise program compared to patients who completed the LMI exercise program, both on the short and longer (at one year follow-up) term. Additionally, we compare the cost-effectiveness of the HI exercise program with the LMI exercise program.

23 Design of the REACT study 21 METHODS The Resistance and Endurance exercise After ChemoTherapy (REACT) study is one of four RCTs included in the Alpe d HuZes Cancer Rehabilitation (A-CaRe) clinical research program [25]. All four studies in this program have been designed to evaluate the effectiveness and cost-effectiveness of exercise-based rehabilitation programs in different cancer patient groups. Figure 1 shows the design of the REACT study and the flow of eligible patients through the trial. The Medical Ethics Committee of the Máxima Medical Center approved the study. FIGURE 1 Design and Procedures of the study Patients with primary breast, colon, or ovarian cancer or lymphomas who have completed (adjuvant) chemotherapy Patients not eligible T0 measurement 4-6 weeks after chemotherapy Patients not willing to participate Invitation to participate in a one-time survey Randomization HI exercise LMI exercise Immediate start Waiting list control group Immediate start Waiting list control group T1 measurement 12 weeks after randomization HI exercise LMI exercise T2 measurement 64 weeks after randomization

24 22 CHAPTER 2 Study sample Patients with histological confirmed primary breast, colon or ovarian cancer, or lymphomas with no indication of recurrent or progressive disease, who completed (adjuvant) chemotherapy with curative intention, and aged between 18 and 70 years are eligible for this study. Patients who are not able to perform basic activities of daily living such as walking or biking, who show cognitive disorders or severe emotional instability, who are suffering from other disabling comorbidity that might hamper physical exercise (e.g., heart failure, chronic obstructive pulmonary disease (COPD), orthopaedic conditions and neurological disorders), and patients who are unable to understand and read the Dutch language are excluded from the study. Recruitment and randomization The patients are recruited from three hospitals in the southern part of the Netherlands: Máxima Medical Center (Veldhoven/Eindhoven), Catharina Hospital (Eindhoven), and Elkerliek Hospital (Helmond). Expectations are that more hospitals in the southern part of the Netherlands will be invited to collaborate. In consultation with the treating medical oncologist, the oncology nurse determines if patients in their clinical setting are eligible for the study. All potentially eligible patients receive written information to take home. Next, patients are contacted by telephone and invited to query any question about the study. Patients who are willing to participate are asked to provide written informed consent. After completing all baseline measurements, patients are stratified by tumour type and hospital and randomly assigned to one of the following groups: 1) HI exercise program or 2) LMI exercise program. Next, patients from both groups are randomly assigned to immediate training or waiting list (i.e., WLC group). After 12 weeks, patients of the WLC group start with the exercise program they have been allocated to. The research assistant uses statistical software for randomization of the sample and informs patients about the results. Allocation sequence is concealed from the medical team. Study outcomes are assessed by a blinded professional and patients are instructed not to reveal their treatment allocation. Patients who choose not to participate in the REACT study are asked to complete a one-time survey. This questionnaire includes relevant characteristics, the reason for not participating and questions on the current attitudes towards and beliefs about exercise.

25 Design of the REACT study 23 Interventions This study includes three arms: a HI exercise program, a LMI exercise program, and a WLC group. Both interventions consist of equal bouts of resistance and endurance interval exercises with similar frequency and duration; the exercise programs differ in training intensity only. All patients train in groups of a maximum of eight persons, on specific resistance training equipment and ergometers (e.g., bicycle, treadmill), twice a week for 12 weeks under supervision of a physiotherapist. Additionally, a physical active lifestyle is stimulated in both interventions groups equally, using behavioral motivational techniques (see below; Behavioral motivation counselling program). The safety of both exercise programs is guaranteed by a comprehensive intake procedure performed by a sports physician or rehabilitation specialist. Medical history together with possible physical limitations is reported, and if necessary, adaptations in training methods or specific advice to patient and physiotherapists are provided. Intervention A; HI exercise program The HI resistance exercise session consists of six exercises targeting the large muscle groups as follows: 1) vertical row (focusing on m. longissimus, m. biceps brachii, m. rhomboideus); 2) leg press (m. quadriceps, m. glutei, m. gastrocnemius); 3) bench press (m. pectoralis major, m. triceps); 4) pull over (m. pectoralis, m. triceps brachii, m. deltoideus, m. trapezius); 5) abdominal crunch (m. rectus abdominis); 6) lunge (m. quadriceps, m. glutei, hamstring muscles). Resistance exercises are performed at 70 to 85% of 1-RM and consist of two sets of 10 repetitions. Every four weeks (week 5 and 9) the training progress is evaluated by means of an indirect 1-RM test, and the resistance is adjusted accordingly. The 1-RM is the greatest resistance that can be moved through the full range of motion in a controlled manner with good posture, and is considered to be the standard for dynamic strength assessment [26]. To minimize the risk of injury we apply an indirect 1-RM measurement. Following a warmup, the physiotherapist estimates a workload at which the patient is expected to perform approximately 4-8 repetitions, taking into consideration sex, height and age. In case that the physiotherapist s judgement regarding this workload proves to be incorrect, another assessment will be carried out a little later. The first four weeks, the HI endurance interval exercises consist of two times 8 minutes cycling, with alternating workloads: 30 seconds at a workload of 65% of the maximal workload assessed by the steep ramp test and 60 seconds at 30%. From the fifth week onwards, the duration of the latter block is reduced to 30 seconds. Every four weeks, the training progress is evaluated by means of the steep ramp test, and the workload is adjusted accordingly. The steep ramp test is an incremental bicycle ergometer test, in which the

26 24 CHAPTER 2 patient is instructed to cycle at a rate between 70 and 80 revolutions per minute (RPM), starting at 25 watt (W), after which the load is increased by 25W every 10 seconds. The test ends if cycling rate falls below 60 RPM. The obtained maximal workload during the steep ramp test, indicated as maximal short exercise capacity (MSEC) [27], the time cycled at that load and heart rate (HR) at the end of the test are recorded. The steep ramp test has shown to be a reliable (Intraclass Correlation Coefficient (ICC)=0.996) and valid (correlation with peakvo 2 =0.85) test to estimate maximal workload in cancer patients [27]. From the fifth week onwards, an additional endurance interval session is included in the program, in exchange for one block of 8 minutes cycling. This interval session consists of three bouts of 5 minutes with 1 minute of rest in between each bout. During the 5 minutes of exercise, patients train on ergometers (e.g., cycle ergometer or treadmill) at a constant workload in which the training HR is 80% of their heart rate reserve (HRR) or higher. Training HRR is determined by using the Karvonen formula [28], using the maximum heart rate (peak HR) obtained from baseline measurements and heart rate at rest (HR rest). Intervention B; LMI exercise program Resistance exercise session of the LMI exercise program consists of the same six exercises as the HI exercise program, but with lower intensity. All exercises are performed at 40 to 55% of 1-RM, with a frequency of two sets of 10 repetitions. The LMI endurance interval exercises start with two times cycling of 8 minutes as well. The alternating workloads are adjusted in 30 seconds at a workload of 45% of the MSEC assessed by the steep ramp test and 60 seconds at 30%. From week five onwards, the duration of the latter block is reduced to 30 seconds in a similar way. Every four weeks (week 5 and 9) training progress of the resistance and endurance interval exercises are evaluated and adjusted accordingly. Comparable to the HI exercise program, from the fifth week onwards one block of 8 minutes cycling is exchanged by an additional endurance interval session which consists of three bouts of 5 minutes, with 1 minute of rest in between each bout. Patients who follow the LMI exercise program should achieve 40-50% of their HRR during these three bouts of 5 minutes of exercise at a constant workload. Behavioral motivation counselling program A behavioral motivation component is included to improve compliance and stimulate physical activity outside the exercise program. Patients are encouraged to be moderately physically active for at least 30 minutes, three times per week in addition to the supervised exercise program. After completion of the 12-week exercise intervention, patients are encouraged

27 Design of the REACT study 25 to maintain an active lifestyle with the aim to be moderately physically active for at least 30 minutes three times per week as well as to continue with physical exercises at a higher intensity level for at least 20 minutes two times per week [23]. Specific program elements include the provision of general and motivational information about physical activity, both verbally and via folders, and discussing individual barriers and facilitators. Behavioral motivation counselling is offered by the physiotherapist in close collaboration with the sports physician or rehabilitation specialist. WLC group The control arm of this trial consists of a waiting list in order to control for spontaneous recovery over time. Patients from the WLC group start either with the HI exercise program or the LMI exercise program after 12 weeks. Study outcomes All studies within A-CaRe clinical research program use similar methodologies and a comparable set of outcome measures [25]. Within the REACT study, primary and secondary outcome measures are assessed at baseline (T=0) at the time of inclusion in the trial (4-6 weeks after ending chemotherapy), 12 weeks after randomization (T=1), and 64 weeks after randomization (T=2). For logistic reasons, all physical tests are conducted centrally at Máxima Medical Center in Eindhoven. All professionals follow detailed and standardized test protocols. The questionnaires can be completed at home or via internet. Primary outcome measures Cardiorespiratory fitness Cardiorespiratory fitness is measured during a maximal exercise test on an electronically braked cycle ergometer according to a ramp protocol [29], in which the resistance gradually increases every 6 seconds aiming to achieve the maximum within 8 to 12 minutes. All patients are instructed to cycle with a pedal frequency between 70 and 80 RPM, and are encouraged to continue exercising until exhaustion, or inability to maintain the pedal frequency of 70 RPM. Expired gases are collected and analysed breath by breath for O 2, CO 2, and volume. The average values of the last 30 seconds of exercise are used as measures for peak oxygen uptake (peakvo 2, in l/min), peak power output (peakw, in watt), and peak HR. Ventilatory threshold is determined by the oxygen equivalent method [30], using the average value obtained by two independent observers. HR and respiratory exchange ratio (RER) are used as objective criteria for peak exercise.

28 26 CHAPTER 2 Muscle strength Upper extremity muscle strength is assessed by using a JAMAR hand-grip strength dynamometer. Each patient is asked to grip first right-handed then left-handed three consecutive times. The maximum score in terms of kilograms is recorded for each side. Hand-grip dynamometry can be used to characterize general upper limb muscle strength dynamometer [31-33]. Hand-grip strength can increase after general resistance training of the upper extremities, consisting of exercises that did not specifically involve hand-grip strength [34]. Lower extremity muscle strength is assessed by the 30-seconds chair-stand test [35]. The patient is asked to stand upright from a chair with their arms folded across the chest, then to sit down again and then repeat the action at his or her fastest pace over a 30 seconds period. The final test score is the number of times that the subject rises to a full stand [35,36]. The 30-seconds chair-stand test is a valid and reliable measure of proximal lower limb strength in older adults [37]. Fatigue Two self-report questionnaires are used to assess fatigue: the Multidimensional Fatigue Inventory (MFI) [38,39] and the Fatigue Quality List (FQL) [40]. The MFI is a questionnaire consisting of 20 statements for which the person has to indicate on a 0-5 scale to what extent the particular statement applies to him or her. This self-report instrument consists of five subscales based on different dimensions: general fatigue, physical fatigue, reduced physical activity, reduced motivation and mental fatigue. The MFI subscales have exhibited adequate reliability for purposes of group comparisons and has good known group validity [38]. The patients perception and appraisal of experienced fatigue is assessed with the FQL. The FQL consists of 25 adjectives describing the fatigue experience, organized into four subscales: frustrating, exhausting, pleasant, and frightening. Secondary outcome measures The REACT study assesses the following secondary outcome measures: HRQoL, body composition, bone mineral density, neuropathy, objective and self-reported daily physical activity level, mood and sleep disturbances, functioning in daily life, return to work, cost from a social perspective, adverse events, compliance and satisfaction with the intervention. In addition, clinical data, disease status and treatment, sociodemographic characteristics, moderating variables of the exercise program and adverse events will be recorded. A complete overview of primary and secondary outcome measures is provided in Table 1. A small selection of these secondary measures is described in detail below. A detailed description of the secondary outcome measures, common to all four trials, are described in an overall design paper [25].

29 Design of the REACT study 27 TABLE 1 Overview primary and secondary outcome measures Outcome measures Instrument A. Primary outcome measures Cardiorespiratory fitness Maximum exercise test (peakvo 2 ) Muscular strength Fatigue 30-seconds chair-stand test, hand-grip strength Multidimensional Fatigue Inventory (MFI) [38] and the Fatigue Quality List (FQL) [40] questionnaires B. Secondary outcome measures Sociodemographic data Clinical data Medical history Disease status and treatment Adverse events Physical tests Physical examination Body composition and bone mineral density Questionnaires HRQoL Physical activity Age, education, marital status, living situation, comorbidities and life style variables (e.g., smoking) Date of diagnosis, subtype of disease, stage of disease, history of therapy Response to treatment, progression or relapse of disease and data on any additional treatment will be recorded from medical records Medical records, reports of the sports physician and physical therapist Height, weight, waist and hip circumferences, four skinfolds (biceps, triceps, suprailiacal and subscapular) Dual Energy X-ray (DXA) scan EORTC Quality of Life Questionnaire C30 (EORTC QLQ-C30) [43], EuroQol (EQ5D) [44], EORTC Chemotherapy-induced peripheral neuropathy module (QLQ-CIPN20) [45] Physical Activity Scale for the Elderly (PASE) [46], Recordings of the Actitrainer accelerometer (Actigraph, Fort Walton Beach Florida, USA) Mood disturbance Hospital Anxiety and Depression Scale (HADS) [47,48] Functioning in daily life Impact on Participation and Autonomy (IPA) [49] Quality of Sleep Pittsburgh Sleep Quality Index (PSQI) [50] Return to work Moderating variables Satisfaction with the intervention Cost questionnaires Compliance with the exercise program Return to work questionnaire Questionnaire about pre-illness lifestyle, current attitudes toward and beliefs about exercise in general Satisfaction questionnaire Cost dairies Self-report and objective measures (e.g., attendance, exercise logs, target intensity) Sociodemographic and clinical data Sociodemographic data such as age, level of education, marital status, living situation, medication use (including alternative medications or therapies) and lifestyle variables (e.g., smoking) are obtained by questionnaire. Clinical information, including date of diagnosis,

30 28 CHAPTER 2 stage and subtype of disease, and treatment history is obtained from medical records. During the follow-up period, data on disease status (response to treatment, progression or relapse) and data on any additional treatment are collected. Moderating variables At baseline, a series of questions is used to assess a number of potential moderating variables, including pre-illness lifestyle (frequency, nature and intensity of daily physical activity and exercise behavior, or avoidance thereof), current attitudes towards and beliefs about exercise and daily physical activity in general, and about exercising after chemotherapy. These questions are adapted from measures developed by Courneya and colleagues [41,42] for use in evaluating exercise in cancer survivors, and are based on established health behavior theories, in particular the Theory of Planned Behavior [42]. Costs from a societal perspective Besides the costs of the exercise programs, data on health care costs, patient and family costs, and costs of production losses are collected using cost diaries administered on a 3-monthly basis during the entire follow-up period. Health care costs include the costs of oncological care, general practice care and physiotherapy, additional visits to other health care providers, prescription of medication, professional home care and hospitalization. Patient and family costs include out-of-the-pocket expenses such as travel expenses, over-thecounter medication, and costs for paid and unpaid help. Costs related to production losses include work absenteeism for patients with paid jobs, and days of inactivity for patients without a paid job. Power calculations Power calculation is based on the effects on physical fitness and fatigue found in the study by De Backer et al. [13] examining the long-term effects of a HI resistance and endurance exercise program after cancer treatment compared with natural recovery. With a sample size of 80 we are able to detect a difference in fatigue of 9 points (EORTC QLQ-C30), with a standard deviation (SD) of 20, a power of 0.80 and two sided alpha of Additional power calculations (a power of 0.80 and alpha of 0.05) for muscle strength (vertical row) and cardiorespiratory fitness (peakvo 2 ) showed that this sample size enables us to detect a difference in vertical row of RM/kg (SD=0.20) and a difference in peakvo 2 of 3 ml/kg/min (SD=7). Both supplementary calculations are based on differences in results reported by the same research group [13]. To compensate for dropouts and taking into

31 Design of the REACT study 29 account the multi-level design we aim to enrol 40% more patients, therefore in total 120 subjects per group. Since we expect smaller differences between the HI exercise group and the LMI exercise group, we have decided to enlarge these two groups to 140 subjects per group. The WLC group will consist of 120 patients. Statistical analyses Baseline characteristics of the two intervention groups and control group with regard to the most important prognostic indicators and main outcome measures will be compared to assess the adequacy of the randomization. If necessary, adjustments will be made for baseline characteristics. In a similar way, we will assess differences between responders and non-responders with regard to the most important prognostic indicators in order to describe the generalizability of the results. Data are analyzed according to the intention-to-treat principle. In addition, per protocol analysis will be performed, in which only patients will be included who attained 75% of all exercise sessions. Scores on the self-report measures of fatigue, mood state and HRQoL will be calculated according to published scoring algorithms. Multilevel longitudinal regression analysis will be conducted to assess changes in each outcome measure. The follow-up measurements will be defined as dependent variable and the following levels are used, 1) hospital and 2) individual. Regression coefficients will indicate differences between interventions and control group. Regression models will be adjusted for age, gender, and baseline values. Missing values will be avoided as much as possible by asking participants to comply with the post-treatment and follow-up measurement even after they drop out from the exercise program. In the event of missing values, the mixed linear regression modelling will account for them. Cost-effectiveness analyses The economic evaluation includes cost-effectiveness and cost-utility analyses from a societal perspective, and will be performed according to the intention-to-treat principle. Detailed descriptions of the economic evaluation are described in the overall design paper [25].

32 30 CHAPTER 2 DISCUSSION This project aims to contribute to evidence-based practice in cancer rehabilitation programs. We evaluate the effectiveness of exercise in cancer patients with respect to improving physical fitness and reducing fatigue. Preliminary results in the literature are promising. Yet, the suggested positive physical and psychosocial outcomes of exercise programs among cancer patients need to be confirmed in large, well-designed trials. The REACT study evaluates the effectiveness and cost-effectiveness of a HI exercise program compared to a LMI exercise program, and a WLC group. In this way, we will obtain more insight in outcomes of different training intensities. Furthermore, if exercise appears effective, it becomes vital to evaluate cost-effectiveness and cost savings for health care utilization since exercise-based rehabilitation programs do not yet form part of standard cancer care for cancer patients and survivors. The following suggestions made by the systematic reviews have been incorporated to strengthen our methodology; applying a larger sample and longer-term follow-up measurements, incorporating randomization of patients to appropriate comparison groups, including concealed allocation and blinded outcome assessment, and using intention-to-treat and per protocol analyses. Most studies so far have focused on breast cancer patients [16-18]. The inclusion criteria of the present study include as many as four cancer diagnoses (primary breast, colon or ovarian cancer, or lymphomas) allowing to explore whether patients with different cancer types respond differently to exercise. Furthermore, detailed evaluation of current attitudes towards and beliefs about exercise of both responders and non-responders are obtained. This provides insight concerning the generalizability of the results from this RCT. Limitations of the study should be noted as well. Instead of a true non-exercising control group, the present study includes a WLC group. Due to the growing availability of cancer rehabilitation groups in daily clinical practices in the study region, patients expectations may not be met, and higher dropout rates could occur with a non-exercising control group. However, the use of a WLC group does not allow longer-term follow-up measurements, because after 12 weeks these patients start the exercise program they have been allocated to. The prescribed design of resistance and endurance interval exercises in the present study is originally based on the intervention assessed by De Backer et al. (2007) [9]. To improve the earlier exercise program and to be more closely aligned to the ACSM guidelines [23], we added an additional endurance interval session with a constant workload. Hence, the resulting combination of exercises is in our opinion state-of-the-art, included in a firm study design.

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35 Design of the REACT study Courneya KS, Friedenreich CM: Utility of the theory of planned behavior for understanding exercise during breast cancer treatment. Psychooncology 1999, 8: Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ, et al: The European Organization for Research and Treatment of Cancer QLQC30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst 1993, 85: Kopec JA, Willison KD: A comparative review of four preference-weighted measures of healthrelated quality of life. J Clin Epidemiol 2003, 56: Postma TJ, Aaronson NK, Heimans JJ, Muller MJ, Hildebrand JG, Delattre JY, et al: The development of an EORTC quality of life questionnaire to assess chemotherapy-induced peripheral neuropathy: the QLQ-CIPN20. Eur J Cancer 2005, 41: Washburn RA, Smith KW, Jette AM, Janney CA: The Physical Activity Scale for the Elderly (PASE): development and evaluation. J Clin Epidemiol 1993, 46: Spinhoven P, Ormel J, Sloekers PP, Kempen GI, Speckens AE, van Hemert AM: A validation study of the Hospital Anxiety and Depression Scale (HADS) in different groups of Dutch subjects. Psychol Med 1997, 27: Zigmond AS, Snaith RP: The hospital anxiety and depression scale. Acta Psychiatr Scand 1983, 67: Cardol M, Beelen A, van den Bos GA, De Jong BA, de G, de Haan RJ: Responsiveness of the Impact on Participation and Autonomy questionnaire. Arch Phys Med Rehabil 2002, 83: Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ: The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 1989, 28:

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37 CHAPTER 3 Randomized controlled trial of the effects of high intensity and low-to-moderate intensity exercise on physical fitness and fatigue in cancer survivors: results of the Resistance and Endurance exercise After ChemoTherapy (REACT) study Caroline S. Kampshoff Mai J.M. Chinapaw Johannes Brug Jos W.R. Twisk Goof Schep Marten R. Nijziel Willem van Mechelen Laurien M. Buffart BMC Medicine, 2015; 13:275

38 36 CHAPTER 3 ABSTRACT Background: International evidence-based guidelines recommend physical exercise to form part of standard care for all cancer survivors. However, at present, the optimum exercise intensity is unclear. Therefore, we aimed to evaluate the effectiveness of a high intensity (HI) and low-to-moderate intensity (LMI) resistance and endurance exercise program compared with a waiting list control (WLC) group on physical fitness and fatigue in a mixed group of cancer survivors who completed primary cancer treatment, including chemotherapy. Methods: Overall, 277 cancer survivors were randomized to 12 weeks of HI exercise (n=91), LMI exercise (n=95), or WLC group (n=91). Both interventions were identical with respect to exercise type, duration and frequency, and only differed in intensity. Measurements were performed at baseline (4 6 weeks after primary treatment) and post-intervention. The primary outcomes were cardiorespiratory fitness (peakvo 2 ), muscle strength (hand-grip strength and 30-seconds chair-stand test), and self-reported fatigue (Multidimensional Fatigue Inventory; MFI). Secondary outcomes included health-related quality of life, physical activity, daily functioning, body composition, mood, and sleep disturbances. Multilevel linear regression analyses were performed to estimate intervention effects using an intention-to-treat principle. Results: In the HI and LMI groups, 74% and 70% of the participants attended more than 80% of the prescribed exercise sessions, respectively (p=0.53). HI (β=2.2; 95%CI=1.2;3.1) and LMI (β=1.3; 95%CI=0.3;2.3) exercise showed significantly larger improvements in peakvo 2 compared to WLC group. Improvements in peakvo 2 were larger for HI than LMI exercise (β=0.9; 95%CI= 0.1;1.9), but the difference was not statistically significant (p=0.08). No intervention effects were found for hand-grip strength and the 30-seconds chair-stand test. HI and LMI exercise significantly reduced general and physical fatigue and reduced activity (MFI subscales) compared to WLC group, with no significant differences between both interventions. Finally, compared to WLC group, we found benefits in global quality of life and anxiety after HI exercise, improved physical functioning after HI and LMI exercise, and less problems at work after LMI exercise. Conclusions: Shortly after completion of cancer treatment, both HI and LMI exercise were safe and effective. There may be a dose response relationship between exercise intensity and peakvo 2, favouring HI exercise. HI and LMI exercise were equally effective in reducing general and physical fatigue.

39 Short-term effects of exercise 37 INTRODUCTION Exercise during and after cancer treatment is safe and may increase physical fitness [1], reduce fatigue [2], and enhance the health-related quality of life (HRQoL) [3]. International evidence-based guidelines have endorsed these findings and recommend physical exercise to be part of standard care for all cancer survivors [4]. However, current exercise recommendations remain rather generic. Defining the optimal mode, frequency, volume, and intensity of exercise in cancer survivors may help to further improve the effectiveness of exercise programs [5]. The effects of different exercise modes and volumes in breast cancer survivors during chemotherapy have been previously evaluated in large randomized controlled trials (RCT) [6,7]. Yet, only two [8,9] relatively small RCTs have studied the effects of different exercise intensities in cancer survivors after completion of primary cancer treatment. Burnham et al. [8] compared moderate versus low intensity aerobic exercise in breast cancer survivors (n=18) and reported that both exercise programs improved cardiorespiratory fitness, compared to usual care, with no differences in effects between the interventions [8]. Gibbs et al. [9] reported larger improvements in cardiorespiratory fitness in breast cancer survivors (n=73) after high intensity (HI) resistance exercise compared to low intensity exercise and usual care. Both high and low intensity exercise significantly improved muscle strength and reduced general fatigue compared to usual care, but no significant differences between the interventions were found [9]. Due to the scarcity of studies and small sample sizes, more insight into the effects of different exercise intensities is warranted to bridge this gap in existing knowledge. Herein, we report results of the Resistance and Endurance exercise After ChemoTherapy (REACT) study [10]. This is the largest RCT to date that has examined the effectiveness of a HI and a low-to-moderate intensity (LMI) resistance and endurance exercise program compared with a waiting list control (WLC) group in cancer survivors who had completed primary cancer treatment with cardiorespiratory fitness, muscle strength, and fatigue as primary outcomes. We included HRQoL, physical activity, daily functioning, body composition, mood, and sleep disturbances as secondary outcomes.

40 38 CHAPTER 3 METHODS Design The REACT study was a RCT including three study arms: HI exercise, LMI exercise, and a WLC group. The study was approved by the Medical Ethics Committee of the VU University Medical Center (Amsterdam) and the local ethical boards of all participating hospitals, including Máxima Medical Center (Eindhoven and Veldhoven), Catharina Hospital (Eindhoven), Elkerliek Hospital (Helmond), St. Anna Hospital (Geldrop), VieCuri Medical Center (Venray and Venlo), Zuwe Hofpoort Hospital (Woerden), St. Antonius Hospital (Utrecht and Nieuwegein), Academic Medical Center (Amsterdam), and Erasmus MC University Medical Center (Rotterdam). Participants Between 2011 and 2013, patients were recruited from nine Dutch hospitals. Patients aged 18 years with histologically confirmed breast, colon, ovarian, cervix or testis cancer, or lymphomas with no indication of recurrent or progressive disease, who had completed (adjuvant or neoadjuvant) chemotherapy were invited to participate. Exclusion criteria were (1) not being able to perform basic activities of daily living, (2) cognitive disorders or severe emotional instability, (3) other serious diseases that might hamper patients capacity of carrying out HI exercise (e.g., severe heart failure), and (4) inability to understand the Dutch language. Written informed consent was obtained from all patients prior to participation. Randomization and blinding After baseline assessments, participants were stratified by cancer type and hospital, and randomly assigned to one of the three study arms. An independent research assistant performed the randomization by using a table of random numbers generated from statistical software. Allocation sequence was concealed from the clinical and research staff. Following randomization, both HI and LMI groups commenced their 12-week exercise program. Participants from the WLC group were similarly randomly allocated to HI or LMI exercise. However, they started exercising after the post-test assessment. Study outcomes of objective physical assessments were assessed by trained and blinded assessors and participants were instructed not to reveal their group allocation.

41 Short-term effects of exercise 39 Exercise interventions Full details of the HI and LMI programs are described elsewhere [10]. Both interventions were identical with respect to exercise type, duration and frequency, and differed only in intensity (Table 1) [10]. After medical clearance by a sports physician, exercise sessions were given twice per week for 12 weeks under supervision of a physiotherapist. Both exercise programs included six resistance exercises targeting large muscle groups with a frequency of two sets of 10 repetitions. Workload per exercise was defined by an indirect one-repetition maximum (1-RM) measurement. HI resistance exercises started in the first week at 70% of 1-RM (Table 1) and gradually increased to 85% of 1-RM in week 12, whereas LMI resistance exercises started at 40% of 1-RM gradually increased to 55% of 1-RM. Every four weeks (weeks 5 and 9) the physiotherapist conducted the indirect 1-RM test and adjusted the workload accordingly. Furthermore, both programs included two types of endurance interval exercises, aiming to maximize improvements in cardiorespiratory fitness. In the first four weeks, patients cycled 2 8 minutes with alternating workloads. Workloads were defined by the maximum short exercise capacity (MSEC) estimated by the steep ramp test [11]. The HI group cycled 30 seconds at a workload of 65% of the MSEC and 60 seconds at 30%, and the LMI group cycled 30 seconds at a workload of 45% of the MSEC and 60 seconds at 30%. Once the first four weeks were accomplished, the duration of the latter block was reduced from 60 to 30 seconds in both exercise programs. Every four weeks, the physiotherapist evaluated training progress by means of the steep ramp test, and the workload was adjusted accordingly. From the fifth week onwards, one additional endurance interval session was performed in exchange for one block of 8 minutes cycling. This interval session consisted of 3 5 minutes cycling at constant workload, with 1 minute rest between each bout. Participants trained on ergometers (e.g., cycle ergometer or treadmill). The workload was defined by the heart rate reserve (HRR) using the Karvonen formula [12]. The HI group trained at 80% of HRR and the LMI group at 40 50% of HRR. The physiotherapists closely monitored individual session attendance. In addition, they applied behavioral motivation counselling techniques to overcome possible exercise barriers and to encourage participants to start or maintain a physically active lifestyle outside the exercise program. Participants were stimulated to be physically active at moderate intensity for at least 30 minutes, three times per week complementary to the supervised exercise program and regardless of their group allocation. The combination of supervised exercise, twice a week, and home-based exercises, three times a week, meets the recommendations of the evidence-based physical activity guidelines for cancer survivors [4].

42 40 CHAPTER 3 TABLE 1 Exercise intensities of the HI and LMI resistance and endurance exercise programs Resistance exercises (1-RM) a (6 exercises targeting the large muscle groups) Endurance interval exercises Part A (MSEC) a (8 min alternating workload) Endurance interval exercises Part B (HRR) a (3x5 min constant workload) Counseling High intensity 70-85% 30/65% 80% Participants were (HI) exercise b encouraged to start or Low-to-moderate intensity (LMI) exercise b 40-55% 30/45% 40-50% maintain a physically active lifestyle in addition to the supervised exercise sessions. Abbreviations: 1-RM, one repetition maximum; MSEC, maximum short exercise capacity; HRR, heart rate reserve; a Every four weeks (week 1, 5 and 9), the physiotherapist evaluated training progress, and adjusted the workload accordingly. b Exercises were accompanied with BORG scores and heart rate monitors to guide the physiotherapists. In the occasion that the training intensity seemed too high or too low, the 1-RM, MSEC or HRR were reassessed. Measurements All outcome measures were assessed at baseline (4 6 weeks after completion of primary cancer treatment) and after 12 weeks. Details on the validity and reliability of the different outcome measures have been described previously [13]. Primary outcome measures Cardiorespiratory fitness was measured during a maximal exercise test on an electronical braked cycle ergometer according to a ramp protocol, aiming to achieve peak oxygen uptake (peakvo 2, in ml/kg/min) within 8 12 minutes [14]. Expired gases were collected and analysed breath by breath to determine peakvo 2 [14]. PeakVO 2 was defined as the highest values of oxygen consumption averaged over a 15 seconds interval within the last minute of exercise. After each test, peakvo 2, peak power output (in watt), and the ventilatory threshold determined by the oxygen equivalent method were recorded. Upper body muscle strength was assessed using a JAMAR hand-grip dynamometer [15]. Participants were instructed to complete three measurements for each hand while alternating sides. The mean score of the three attempts of a participants dominant hand was used as indicator for upper body muscle strength. Lower body function was assessed using the 30-seconds chair-stand test [16]. Participants were instructed to rise to a full stand and return to the original seated position as quickly as possible. The total number of times that the participant raised to a full stand in 30 seconds was reported. Both the hand-grip strength and 30-seconds chair-stand tests are valid outcome measures and can be used to characterize upper body strength and lower body function [15,17].

43 Short-term effects of exercise 41 Fatigue was assessed using the Multidimensional Fatigue Inventory (MFI) questionnaire [18]. The MFI is a validated questionnaire and consists of 20 items divided into five subscales: general fatigue, physical fatigue, reduced physical activity, reduced motivation, and mental fatigue. Participants were asked to indicate, on a1 5 scale, to what extent the particular item applied to them, with a maximum sum score of 20 points per subscale. Secondary outcome measures HRQoL was measured using the European Organisation Research and Treatment of Cancer-Quality of Life questionnaire-core 30 [19], anxiety and depression by the Hospital Anxiety and Depression Scale [20], sleep disturbances with the Pittsburgh Sleep Quality Index [21], participation in daily life using the Impact on Participation and Autonomy (IPA) [22], and self-reported physical activity (PA) using the Physical Activity Scale for the Elderly questionnaire [23]. Objective measurement of PA was assessed with an accelerometer (Actitrainer, Actigraph, Fort Walton Beach, USA) using vertical accelerations converted into PA counts per minute. Participants were instructed to wear the accelerometer around the hips for seven consecutive days during all waking hours. Raw data was recorded in epochs of 60 seconds. A valid day of wearing-time was defined as 10 hours and non-wearing time was defined as 90 minutes of consecutive zero counts. Raw data were processed using ActiLife Software version (ActiGraph, Pensacola, Florida, USA). Body weight was measured to the nearest 0.1 kilogram on a digital scale with light cloths on and no shoes. Body height was measured to the nearest 0.1 centimetres without shoes. Body mass index was calculated from the measured body weight and height accordingly. Thickness of four skinfolds in millimetres (biceps, triceps, suprailiac, and subscapular) was measured using a Harpenden skinfold caliper. The mean of two consecutive measurements was used for further analyses. Assessments of covariates, session attendance, adverse events, and contamination Sociodemographic data were collected by self-report. Clinical information was obtained from medical records. Physiotherapists monitored session attendance in the exercise logs, as well as possible adverse events during the intervention period. In addition, adverse events were documented from the medical records for the intervention and WLC groups. Contamination was assessed by asking participants from the WLC group at the post-test assessment if they had attended supervised exercise outside the study [24].

44 42 CHAPTER 3 Power calculations Power calculations were based on a previous uncontrolled trial evaluating the effectiveness of a HI resistance and endurance exercise program in 119 cancer survivors after completion of chemotherapy [25]. To be able to detect a difference in peakvo 2 of 3 ml/kg/min (SD=5.8), with a power of 0.80 and two-sided alpha of 0.05, 60 participants per group were needed at post-test assessment. To compensate for loss to follow-up (20 40%) and taking into account the multilevel design, a sample size of 280 was required. Additional power calculations (a power of 0.80 and alpha of 0.05) for fatigue (MFI) and hand-grip strength demonstrated that this sample size of 280 would also be sufficient to detect a clinically relevant difference of two points [26] on the MFI questionnaire and a difference of 3 kilogram (10% difference) in hand-grip strength. Statistical analyses Differences in age, sex, and diagnosis between participants and non-participants were examined using multivariable logistic regression analyses. For all outcome measures, we used multivariable multilevel linear regression analyses to evaluate differences in effects between the HI, LMI, and WLC groups. Possible clustering of data within hospitals was taken into account using a two-level structure with hospital as the first level and participants as the second. Both interventions were simultaneously regressed on the post-test value of the outcome, adjusted for the baseline value, with age and sex as covariates. All analyses were performed according to an intention-to-treat principle. In addition, exploratory analyses were conducted to check for effect modification by age, sex, and diagnosis (breast cancer vs. other). To determine whether missing data were selective, univariable logistic regression analyses were conducted to examine baseline differences in the primary outcomes between participants who completed post-test assessments and those who did not (dropouts). We found no significant differences between the groups, and consequently, we considered missing values to be at random. Since also dropout rate was 10%, we did not use imputation strategies [27]. We considered p<0.05 to be statistically significant. The statistical analyses were performed using MLwiN (version 2.22) and Statistical Package of Social Sciences (SPSS, version 20.0).

45 Short-term effects of exercise 43 RESULTS Of the 757 patients who were eligible, 277 (37%) participated (Figure 1). No significant differences in age, sex, and diagnosis were found between the participants and nonparticipants (Table 2). Furthermore, sociodemographic and clinical data of the participants in the intervention and WLC groups were balanced at baseline (Table 2). In the HI and LMI groups, 74% and 70% of the participants attended more than 80% of the prescribed exercise sessions, respectively (p=0.53; Figure 1). FIGURE 1 Patients flowchart of the REACT study Screened (n=793) Patients not eligible (n=38; 5%) (cognitive disorders or severe emotional instability n=7; serious diseases that hampers patients capacity of carrying out HI exercise n=21; inability to understand the Dutch language n=7; already participating in an exercise study n=1; complications due to cancer treatments n=1) (n=757; 100%) Non-participants (n=480; 63%) (forgotten n=16; too much n=144; already exercising n=80; study design n=47; not interested n=69; abroad n=5; unknown n=119) Participants stratified by diagnosis and hospital, randomly assigned (n=277; 37%) HI exercise (n=91) Adherence Attendance 80%: n=67; 74% Attendance 80%: n=24; 26% LMI exercise (n=95) Adherence Attendance 80%: n=66; 70% Attendance 80%: n=29; 30% WLC (n=91) Contamination Supervised exercising on their own initiative: n=7; 8% Lost to follow-up Lost to follow-up Lost to follow-up No Physical fitness (n=3) Unwell (n=1) Too much burden (n=1) Recurrence (n=1) Neither (n=7) Unwell (n=3) Too much burden (n=3) No response (n=1) Post-test Assessment Physical fitness (n=81;89%) PROs (n=84;92%) No Physical fitness (n=3) Too much burden (n=1) No response (n=1) Recurrence (n=1) No PRO (n=5) Too much burden (n=2) No response (n=2) Questionnaire lost (n=1) Neither (n=12) Unwell (n=1) Too much burden (n=7) No response (n=1) Recurrence (n=3) Post-test Assessment Physical fitness (n=79;83%) PROs (n=77;81%) No Physical fitness (n=1) Too much burden (n=1) Neither (n=1) Too much burden (n=1) Post-test Assessment Physical fitness (n=89;98%) PROs (n=90;99%) Participants included in intention-to-treat analysis n=91; 100% Participants included in intention-to-treat analysis n=95; 100% Participants included in intention-to-treat analysis n=91; 100% Abbreviations: HI, High intensity exercise; LMI, Low-to-moderate intensity exercise; WLC, Waiting list control group; PRO, Patient reported outcomes

46 44 CHAPTER 3 Exercise effects on primary outcomes HI (β=2.2; 95%CI=1.2;3.1) and LMI (β=1.3; 95%CI=0.3;2.3) exercise showed significantly larger improvements in peakvo 2 compared to WLC group (Table 3). Improvement in peakvo 2 was larger for HI than LMI exercise (β=0.9;95%ci= 0.1;1.9), but the difference was not statistically significant (p=0.08). Relative improvements in peakvo 2 were 20% and 15% for HI and LMI exercise, respectively, which is in line with the relative improvements in healthy adults after a 12-week exercise program [28]. No significant intervention effects were found for hand-grip strength and 30-seconds chair-stand tests. Compared to WLC group, both HI and LMI exercise showed significant improvements in general fatigue (HI: β= 1.3;95%CI= 2.2;0.4 and LMI: β= 1.1; 95%CI= 2.0; 0.2), physical fatigue (HI: β= 2.0; 95%CI= 2.9; 1.1 and LMI: β= 1.4; 95%CI= 2.3; 0.5), and reduced activity (HI: β= 1.1; 95%CI= 1.9; 0.2 and LMI: β= 1.2; 95%CI= 2.1; 0.3), with no significant differences between both interventions. HI exercise showed a beneficial effect on motivation compared to LMI exercise (β= 0.8; 95%CI= 1.5; 0.03) and WLC group (β= 1.2; 95%CI= 1.9; 0.4), with no significant differences between LMI exercise and WLC group. Furthermore, HI exercise showed a significant reduction in mental fatigue compared to WLC group (β= 0.9; 95%CI= 1.7; 0.2). The effects on peakvo 2 were modified by age (HI: β interaction = 0.2; 95%CI= 0.3; 0.1; p=0.000 and LMI:β interaction = 0.1; 95%CI= 0.2; 0.01; p=0.03), indicating larger effects for younger participants. No significant interaction effects for gender or diagnosis were found for physical fitness or fatigue. Exercise effects on secondary outcomes HI exercise showed significantly larger improvements in global quality of life (QoL) (β=5.9; 95%CI= ) and reduced anxiety (β= 1.0; 95%CI= 1.7; 0.3) compared to WLC group (Table 4). Significantly larger improvements in physical functioning were found for both exercise programs compared to WLC group (HI: β=3.1; 95%CI=0.7;5.5 and LMI: β=4.1; 95%CI=1.6;6.6), with no significant differences between the exercise programs. The effects of HI exercise on global QoL were larger for younger participants (β interaction = 0.4; 95%CI= 0.8; 0.04; p=0.03) and for participants with breast cancer (β interaction =9.5; 95%CI=1.4;17.8; p=0.02). Women showed larger improvements after HI exercise in global QoL (β interaction =11.1; 95%CI=1.8;20.4; p=0.02) and physical functioning (β interaction =7.1; 95%CI=1.2;13.0; p=0.02) than men. No significant between-group differences were found for role, emotional, cognitive, and social functioning, body composition, sleep disturbances, physical activity levels, and depression, nor for the IPA questionnaire, except for significantly lower scores on the problems at work subscale after LMI exercise (β= 0.3; 95%CI= 0.6; 0.02) compared to WLC group (Table 4, 14 subscales of IPA are not presented).

47 Short-term effects of exercise 45 TABLE 2 Baseline characteristics of the participants and non-participants Characteristics HI n=91 LMI n=95 WLC n=91 Non-participants n=480 Socio-demographic Age, mean (SD) years 54 (11.0) 53 (11.3) 54 (10.9) 55 (11.6) Gender, n (%) male a 18 (20) 17 (18) 20 (22) 77 (16) Married/living together, n (%) yes 73 (80)* 87 (92)* 72 (79)* Education, n (%) b Low Intermediate High Being employed, n (%) Employed Not employed Retirement 19 (21) 37 (41) 34 (38) 54 (59) 25 (28) 12 (13) 12 (13) 43 (46) 38 (40) 56 (58) 22 (23) 17 (18) 16 (18) 42 (46) 33 (36) 57 (63) 19 (21) 15 (17) Smoking, n (%) yes c 7 (8) 5 (5) 5 (6) Comorbidity, n (%) yes 12 (13) 8 (8) 10 (11) Sport history, n (%) yes d 45 (50) 61 (65) 49 (54) Exercise during chemotherapy, n (%) yes b 21(23) 21 (22) 10 (11) Clinical Diagnosis, n (%) e Breast Colon Ovarian Lymphoma Cervix Testis Stage of disease, n (%) Stage I-II Stage III-IV Type of treatment, n (%) yes Surgery Radiation therapy 62 (68) 15 (17) 4 (4) 9 (10) 0 (0) 1 (1) 68 (75) 23 (25) 83 (91) 46 (51) 62 (65) 19 (20) 3 (3) 9 (9) 2 (2) 0 (0) 57 (60) 38 (40) 87 (92) 41 (43) 57 (63) 15 (17) 5 (6) 8 (9) 2 (2) 4 (4) 62 (68) 29 (32) 80 (88) 48 (53) Surgery + Radiation therapy 41 (45) 39 (41) 46 (51) Immunotherapy 16 (18) 25 (26) 18 (20) Hormone therapy 45 (50) 40 (42) 43 (47) Type of chemotherapy, n (%) TAC 39 (43) 33 (34) 31 (34) FEC TAC/FEC combinations Capecitabine en Oxaliplatin Oxaliplatin combinations Carboplatin en Paclitaxel CHOP ABVD Cisplantin BEP Other 7 (8) 15 (17) 8 (9) 7 (8) 4 (4) 5 (6) 4 (4) 0 1 (1) 1 (1) 7 (7) 21 (22) 11 (12) 8 (8) 4 (4) 6 (6) 2 (2) 2 (2) 0 1 (1) 5 (6) 17 (19) 7 (8) 7 (8) 10 (11) 7 (8) 2 (2) 1 (1) 3 (3) 1 (1) 309 (65) 85 (18) 23 (5) 47 (10) 12 (3) 4 (1) Abbreviations: n, number; FEC, fluorouracil, epirubicin, cyclophosphamide; TAC, taxotere, adriamycin, cyclophosphamide; CHOP, cyclophosphamide, doxorubicin, vincristine, prednisone; ABVD, doxorubicin, bleomycin, vinblastine, dacarbazine; BEP, bleomycin, etoposide, cisplatin;*(p<0.05); a n-4 (non-participants); b n-3; c n-4; d n-1; e n-1 (non-participants).

48 46 CHAPTER 3 TABLE 3 Mean (SD) values of baseline and post-test measurements differences in effects on primary outcomes physical fitness and fatigue between groups a HI LMI WLC HI vs. WLC LMI vs. WLC HI vs. LMI Baseline mean (SD) Post-test mean (SD) Baseline mean (SD) Post-test mean (SD) Baseline mean (SD) Post-test mean (SD) β (95%CI) β (95%CI) β (95%CI) PRIMARY OUTCOMES Cardiorespiratory fitness b VO 2 peak (ml/kg/min) 21.9 (6.5) 26.3 (7.6) 22.3 (5.9) 25.6 (6.5) 21.5 (5.5) 23.8 (5.9) 2.2 (1.2;3.1)* 1.3 (0.3;2.3)* 0.9 (-0.1;1.9) WMax (W) 136 (46) 163 (53) 134 (43) 154 (45) 135 (42) 150 (43) 12.6 (7.7;17.5)* 5.0 (0.01;9.9)* 7.6 (2.5;12.7)* Ventilatory threshold (ml/kg/min) 15.6 (4.1) 18.8 (4.7) 16.2 (4.8) 18.8 (5.2) 15.5 (4.8) 17.3 (5.6) 1.5 (0.4;2.5)* 1.1 (0.1;2.2)* 0.4 (-0.7;1.4) Muscle strength Sit to stand (stands) c 17 (4.4) 19 (4.9) 16 (3.6) 19 (4.8) 16 (3.6) 18 (3.9) 0.2 (-0.8;1.1) 0.6 (-0.3;1.5) -0.5 (-1.4;0.4) Hand-grip strength (kg) d 32.5 (9.7) 34.4 (10.5) 32.9 (9.8) 34.9 (9.8) 33.5 (9.5) 35.5 (10.6) -0.3 (-1.3;0.7) 0.3 (-0.7;1.3) -0.6 (-1.6;0.5) Fatigue (Range 1-20) e General fatigue f 12.8 (3.8) 10.0 (3.3) 12.6 (4.1) 10.1 (3.4) 12.7 (4.2) 11.3 (4.1) -1.3 (-2.2;-0.4)* -1.1 (-2.0;-0.2)* -0.2 (-1.1;0.7) Physical fatigue f 12.8 (3.9) 9,0 (3.2) 12.3 (3.9) 9.4 (3.6) 13.2 (4.0) 11.2 (3.9) -2.0 (-2.9;-1.1)* -1.4 (-2.3;-0.5)* -0.6 (-1.6;0.3) Reduced activity g 12.2 (3.8) 9.6 (3.2) 11.5 (3.6) 9.1 (3.5) 11.8 (3.6) 10.5 (3.6) -1.1 (-1.9;-0.2)* -1.2 (-2.1;-0.3)* 0.1 (-0.8;1.0) Reduced motivation g 9.4 (3.2) 7.9 (2.6) 9.0 (3.0) 8.5 (3.1) 8.6 (3.1) 8.7 (3.2) -1.2 (-1.9;-0.4)* -0.4 (-1.1;0.4) -0.8 (-1.5;-0.03)* Mental Fatigue f 11.1 (4.2) 9.8 (3.7) 10.9 (4.0) 9.9 (3.6) 10.7 (4.1) 10.5 (4.1) -0.9 (-1.7;-0.2)* -0.7 (-1.5;0.1) -0.2 (-1.1;0.6) Abbreviations: SD, standard deviation; n, number; *(p<0.05); (0.05 p<0.10) ; a adjusted model, corrected for age and sex; b missings due to technical problems (n=5), musculoskeletal problems (n=1) or discomfort (n=6); c missings due to musculoskeletal problems (n=2); d missings due to technical problems (n=3) or musculoskeletal problems (n=2); e higher score means a higher level of self-reported fatigue in all subscales; f missing due to incomplete questionnaire (n=1); g missing due to incomplete questionnaire (n=2).

49 Short-term effects of exercise 47 TABLE 4 Baseline and post-test measurements and adjusted between group differences on secondary outcomes health-related quality of life, body composition, sleep disturbances, physical activity and distress a HI LMI WLC HI vs. WLC LMI vs. WLC HI vs. LMI Baseline mean (SD) Post-test mean (SD) Baseline mean (SD) Post-test mean (SD) Baseline mean (SD) Post-test mean (SD) β (95% CI) β (95% CI) β (95% CI) Health-related quality of life (Range 0-100) b Global QoL c 72.8 (15.3) 82.0 (13.6) 73.6 (17.2) 79.7 (16.1) 71.0 (16.5) 75.3 (15.4) 5.9 (2.0;9.8)* 3.3 (-0.6;7.2) 2.6 (-1.4;6.6) Physical functioning 82.0 (13.8) 88.1 (9.5) 83.0 (12.2) 89.6 (10.2) 80.2 (15.4) 84.1 (13.1) 3.1 (0.7;5.5)* 4.1 (1.6;6.6)* -1.0 (-3.5;1.5) Role functioning c 73.7 (25.1) 82.5 (21.4) 69.2 (25.8) 83.5 (21.1) 67.4 (25.6) 82.0 (21.4) -2.1 (-7.6;3.3) 0.8 (-4.7;6.3) -3.0 (-8.6;2.7) Emotional functioning c 86.0 (15.9) 88.3 (15.3) 82.9 (16.3) 84.0 (17.3) 83.5 (17.0) 83.3 (17.5) 3.3 (-0.4;7.1) 1.1 (-2.7;4.9) 2.2 (-1.7;6.1) Cognitive functioning c 79.9 (22.5) 84.3 (17.5) 78.0 (21.4) 80.3 (19.5) 76.7 (22.7) 80.6 (21.1) 2.0 (-2.4;6.3) -0.9 (-5.3;3.6) 2.8 (-1.7;7.3) Social functioning c 78.3 (22.0) 89,6 (15.1) 78.2 (20.0) 86.1 (20.0) 75.6 (24.6) 85.2 (21.7) 3.1 (-1.7;7.9) -0.2 (-5.1;4.7) 3.3 (-1.7;8.3) Body composition BMI, kg/m (4.0) 26.8 (4.0) 26.3 (4.3) 26.5 (4.4) 27.7 (4.8) 27.7 (4.8) (-0.3;0.3) 0.2 (-0.1;0.4) -0.2 (-0.4;0.1) Sum of skinfolds, d mm 73.0 (20.7) 72.2 (22.7) 72.2 (30.1) 73.1 (29.6) 77.7 (32.0) 78.2 (31.6) -1.7 (-5.3;1.8) -0.3 (-3.9;3.4) -1.5 (-5.2;2.2) Sleep disturbances (Range 0-21) e,f 10.3 (3.3) 9.9 (3.3) 10.9 (3.1) 10.7 (3.7) 10.1 (3.2) 9.9 (3.6) -0.2 (-1.0;0.6) 0.1 (-0.8;0.9) -0.2 (-1.1;0.6) Physical activity Self-reported PA g 93.0 (71.5) (87.7) (80.9) (68.5) 96.2 (66.2) (72.9) -2.0 (-20.7;16.7) 1.4 (-17.7;20.4) -3.4 (-22.8;16.0) Accelerometer, 246 (95.8) 248 (106.3) 229 (91.0) 247 (76.6) 239 (89.5) 258 (87.0) (40.3;14.5) -5.7 counts per minute h,i (-33.0;21.5) -7.2 (-35.4;21.0) Distress (Range 0-21) j Anxiety k 4.0 (3.0) 3.2 (2.9) 4.0 (3.0) 3.9 (3.3) 3.8 (2.8) 4.1 (3.0) -1.0 (-1.7;-0.3)* -0.4 (-1.0;0.3) -0.6 (-1.3;0.1) Depression l 3.1 (2.7) 2.5 (2.6) 3.5 (3.2) 2.7 (2.8) 3.3 (2.8) 3.0 (3.2) -0.4 (-1.1;0.2) -0.4 (-1.1;0.2) (-0.7;0.6) Abbreviations: SD, standard deviation; n, number; *(p<0.05); (0.05 p<0.10) ; a adjusted model, corrected for age and sex; b higher score means a higher level of self-reported HRQoL in all subscales; c missing due to incomplete questionnaire (n=1); d missings due to skin problems (n=3); e higher score means poorer self-reported sleep quality; f missings due to incomplete questionnaire (n=58); g missings due to incomplete questionnaire (n=1); h average counts for Y-Axis; i missings due to technical problems/insufficient wearing-time (n=85); j higher score means a higher level of anxiety and depression in both subscales; k missing due to incomplete questionnaire (n=2); l missing due to incomplete questionnaire (n=3).

50 48 CHAPTER 3 Adverse events No adverse events directly related to the exercise programs were reported. Nevertheless, five participants reported disease recurrence and withdrew from the study, four participants withdrew from the study because of comorbidities not related to the interventions (i.e., heart failure, hernia nuclei pulposi, ankle fracture, and abdominal adhesions), six participants withdrew from the study because two exercise sessions per week was too much, and 11 participants reported musculoskeletal problems at the start of the exercise program and they continued with a modified program (despite program modifications, four of these participants withdrew). DISCUSSION We performed a head-to-head comparison of a 12-week HI and LMI exercise program compared to WLC group shortly after completion of primary cancer treatment in a large group of cancer survivors with mixed diagnoses. This allowed us to determine differences in effects of exercise intensity on physical fitness, fatigue, and HRQoL. Both HI and LMI exercise significantly improved peakvo 2 compared to WLC group. We found mean peakvo 2 improvements of 4.4 ml/kg/min after HI exercise and 3.3 ml/kg/min after LMI exercise, which is in line with the 3.3 ml/kg/min increase reported in a meta-analysis of three RCTs among patients who completed cancer treatment [1]. Improvements in peakvo 2 tended to be larger after HI exercise than LMI exercise, suggesting a dose response relationship for exercise intensity. However, this should be confirmed in future studies. Improving peakvo 2 of cancer survivors is particularly important because, compared to healthy adults, their peakvo 2 levels are very poor [29]. Higher peakvo 2 levels in cancer survivors have been associated with lower fatigue and higher HRQoL [25,30]. In addition, results from observational studies showed a positive association between peakvo 2 and survival [31], but causality needs to be established. In contrast to a meta-analysis examining effects of resistance exercises on muscle strength [32], we found no significant intervention effects on the hand-grip strength and 30-seconds chair-stand tests. However, the indirect 1-RM tests that were conducted every 4 weeks as part of the exercise programs indicated an improvement of 37% on the leg press and 34% on the vertical row. Therefore, the current lack of intervention effects may be related to our choice of outcome measures. Although hand-grip strength is a reliable and valid measure of general upper body muscle strength [15], it may not be sensitive enough

51 Short-term effects of exercise 49 to detect improvements in muscle strength of the upper arm and shoulder [33]. Comparably, the 30-seconds chair-stand test is a reliable and valid functional test [16], but may be prone to ceiling effects [34]. More direct measures of muscle strength, such as isokinetic dynamometers, may be more sensitive to detect changes over time [35]. Compared to WLC group, both HI exercise and LMI exercise resulted in significant and clinically meaningful reductions in general fatigue, physical fatigue, and reduced activity. These results support previous results of a meta-analysis [2], showing that exercise significantly reduced cancer-related fatigue compared to non-exercise control groups. Interestingly, our results showed that exercise is beneficial in reducing both general and physical components of fatigue, regardless of the training intensity. From a physiological point of view, it is most likely that exercise counteracts physical fatigue [36]. Yet, HI exercise also significantly reduced mental components of fatigue, compared to WLC group. However, the intervention effects on reduced motivation and mental fatigue were small and may not be clinically relevant. Further research is needed to investigate whether combinations of exercise with cognitive behavioral therapy, stress management, or sleep therapy may have larger benefits on mental fatigue [37]. HI exercise showed a significant and clinically meaningful (>10 points) increase on global QoL compared to WLC group. Furthermore, HI and LMI exercise significantly improved self-reported physical functioning. However, the improvements (6 points) may have small clinical meaning [38]. Both findings support the positive effect on global QoL and inconsistent findings on physical functioning reported in a previous meta-analysis [3]. A better understanding of the mechanisms underlying the exercise effects on HRQoL in cancer survivors may help to further target exercise interventions to specific HRQoL outcomes [39]. Previous meta-analyses reported small significant reductions in depression [40] and anxiety [3] after exercise. Our study supports these findings for anxiety, but not for depression. It has been suggested that larger effects on depression may only be expected in cancer survivors with higher levels of depression [40]. Our baseline data showed low mean values on the Hospital Anxiety and Depression Scale for depression and anxiety, leaving little room for improvement. Likewise, not many of our participants reported sleep disturbances or problems in daily functioning at baseline. Furthermore, we found no increase in PA levels in both exercise groups. However, our interventions included only a small component of behavioral motivation counselling and accomplishing behavior change may require more specific PA promotion strategies [41]. Finally, the lack of significant reductions in body fat was consistent with previous research [42] and not unanticipated, because both interventions focused on physical exercise only and did not aim at losing body weight by including a dietary component.

52 50 CHAPTER 3 Strengths of this study include the direct comparison between HI and LMI exercise, a well-designed (e.g., blinded outcome assessment, concealed allocation) multicenter RCT including a large sample size, the use of valid and reliable outcome measures, and intentionto-treat analyses. However, some limitations should be noted. First, compared to WLC group, the reported effect sizes could be interpreted as modest. Nevertheless, the results from the current study highlight that twice-a-week, supervised exercise for 12 weeks is superior to natural recovery. Since adherence rates might have played a role in the magnitude of the effect sizes, a full report on adherence and compliance rates is needed to provide further insight on whether and how exercise components were delivered. Furthermore, participants of WLC group were asked to maintain their habitual daily PA pattern; however, 8% of the WLC participants engaged in weekly supervised exercise sessions and this may have reduced the intervention effects as well. Secondly, although we recruited 277 patients, 65% of the participants were diagnosed with breast cancer and only small groups of other diagnosis were included. Therefore, potential differences in effects across different cancer types could not be established. Yet, except for global QoL, we found no differences in intervention effects between survivors of breast cancer or other types of cancer. Finally, slightly higher dropout rates were observed in LMI group compared to the other groups. This can be partly explained by higher recurrence of disease rates in LMI group, which was most likely coincidental. Conclusion In conclusion, the current study demonstrates that supervised HI exercise can be safely recommended to cancer survivors shortly after completion of cancer treatment. Because we found some indication for a dose response relationship for peakvo 2, HI exercise may be preferred to LMI exercise when aiming to improve peakvo 2 levels in cancer survivors. Yet, HI and LMI exercise were equally beneficial in counteracting general and physical fatigue. Additional research should further disentangle the effects of different exercise modes, frequencies, volumes, and intensities among different subgroups of patients to optimize evidence-based exercise recommendations for cancer survivors.

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57 CHAPTER 4 Mediators of exercise effects on health-related quality of life in cancer survivors after chemotherapy Joeri Kalter Caroline S. Kampshoff Mai J.M. Chinapaw Willem van Mechelen Francisca Galindo-Garre Goof Schep Irma M. Verdonck-de Leeuw Johannes Brug Laurien M. Buffart Medicine & Science in Sports & Exercise, 2016; 48:1859

58 56 CHAPTER 4 ABSTRACT Purpose: We investigated the hypothesis that combined resistance and endurance exercise improves cardiorespiratory fitness and muscle strength, thereby reducing fatigue and improving global quality of life (QoL) and physical function among cancer survivors who completed curative treatment including chemotherapy. Methods: Cancer survivors were assigned to a 12-week exercise intervention (n=186) or a waiting list control group (WLC, n=91). Data were collected at baseline and after 12 weeks. Path analyses using follow-up values adjusted for baseline values, age, and gender were conducted to test if the exercise effects on global QoL and physical function (European Organization Research and Treatment of Cancer-Quality of Life questionnaire-core 30) were mediated by changes in cardiorespiratory fitness (peakvo 2 ), hand-grip strength, lower body muscle function (30-seconds chair-stand test), and fatigue (Multidimensional Fatigue Inventory). Results: Compared with WLC group, exercise increased cardiorespiratory fitness (β=1.7, 95% confidence interval (CI)=0.9;2.6 ml/kg/min) and reduced general (β=-1.0, 95%CI= -1.8;-0.2) and physical fatigue (β=-1.4, 95%CI=-2.2;-0.6). The exercise effect on physical fatigue was mediated by change in cardiorespiratory fitness (β=-0.1, 95%CI=-0.2;0.0). Higher hand-grip strength was significantly associated with lower physical fatigue, and better lower body muscle function with lower physical and general fatigue. Lower general and physical fatigue were significantly associated with higher global QoL (β=-1.7, 95%CI= -2.2;-1.1 and β=-1.7, 95%CI=-2.3;-1.2, respectively), and physical function (β=-1.0, 95%CI=-1.3;-0.7 and β=-1.1, 95%CI=-1.5;-0.8, respectively). The models explained 44-61% of the variance in global QoL and physical function. Conclusion: Beneficial effects of exercise on global QoL and physical function in cancer survivors were mediated by increased cardiorespiratory fitness, and subsequent reductions in fatigue.

59 Mediators of exercise on HRQoL 57 INTRODUCTION Recent systematic reviews and meta-analyses showed beneficial effects of exercise interventions on physical fitness, fatigue, and health-related quality of life (HRQoL) in cancer survivors [20,28,35]. However, reported effect sizes were small to moderate. To improve the effectiveness of exercise, it is important to gain more insight into the mechanisms by which an exercise intervention achieves its effect. Mediators may help identify effective intervention components. By keeping effective intervention components and by removing ineffective ones, the cost-effectiveness and participant burden of the interventions can be improved [12]. Furthermore, identification of mediators may support in the building and refining of intervention theory [27]. It is hypothesized that physical inactivity induces muscle catabolism and causes further detraining, which may result in a self-perpetuating detraining state with easily induced fatigue. Physical exercise may break this self-perpetuating cycle by improving physical fitness, and consequently reducing fatigue and improving HRQoL [16,26]. Few previous studies investigated mediators of exercise effects on HRQoL in cancer survivors. They showed that the association between improved cardiorespiratory fitness and improved HRQoL was mediated by fatigue [8,10,11,33]. In a randomized controlled trial (RCT) among 57 prostate cancer survivors, Buffart et al. [8] showed that upper body muscle strength and walking speed mediated the effects of a 12-week combined resistance and endurance exercise intervention on physical health and that fatigue and walking speed mediated the effects on general health. Lower body muscle strength also mediated the effects of resistance and endurance exercise on global QoL and physical function in older long-term prostate cancer survivors. However, no mediating effects were found for cardiorespiratory fitness and fatigue [9]. To further build the knowledge of mechanisms underlying the exercise intervention effect on HRQoL, we tested the hypothesis that a combination of resistance and endurance exercises improves cardiorespiratory fitness and muscle strength, thereby reducing fatigue, and improving global QoL and physical function [13]. To test this hypothesis, we used data from the Resistance and Endurance exercise After ChemoTherapy (REACT) study [21,22], that was conducted in a large group of cancer survivors (n=277) who had recently completed treatment with curative intent, including chemotherapy.

60 58 CHAPTER 4 METHODS Patient recruitment and allocation The REACT study was a multicenter RCT which evaluated the effectiveness of a 12-week high intensity exercise program (HI) and a low-to-moderate intensity (LMI) exercise program compared to a waiting list control (WLC) group on physical fitness, fatigue, and HRQoL [22]. A detailed description of the study procedures has been published previously [22]. The medical ethics committees of the VU University Medical Centre and the local ethical boards of the participating hospitals had approved the study and written informed consent was obtained from all cancer survivors prior to participation [22]. Cancer survivors were eligible for the study if they were aged 18 years, were treated for histologically confirmed breast, colon, ovarian, lymphatic, cervical or testicular cancer, had completed primary cancer treatment with curative intent including chemotherapy, and had no indication for recurrent or progressive disease [22]. Cancer survivors were not eligible for the study if they were unable to perform basic activities of daily life, had cognitive disorders or severe emotional instability, had other serious diseases that might hamper the capacity of carrying out high intensity exercise (e.g., severe heart failure), or were unable to understand and read Dutch [22]. Cancer survivors were recruited between 2011 and 2013 from 9 hospitals in the Netherlands. Baseline measurements were performed 4-6 weeks after completion of primary cancer treatment. After baseline measurements, participants were stratified by cancer type and hospital, and were randomly assigned into one of the three study arms. Both HI and LMI exercise groups started with their 12-week exercise program. Participants from the WLC group were offered the intervention, that they were randomly allocated to, after 12 weeks. In total, 277 cancer survivors (response rate 37%) participated in the study. We previously reported that both HI and LMI exercise were able to increase cardiorespiratory fitness, reduce fatigue, and improve quality of life and physical function compared with WLC group [22]. The current analyses examine the mechanisms underlying the intervention effects on global QOL and physical function. Because we assumed that the intervention effects follow the same path as proposed in the hypothesized model, and to increase statistical power, we combined both intervention groups into one group. Therefore, 186 cancer survivors were allocated to the exercise intervention and 91 to the WLC group. Measurements were performed at baseline and after 12 weeks.

61 Mediators of exercise on HRQoL 59 Interventions A detailed description of the exercise interventions has been published elsewhere [22]. In short, the exercise interventions took place twice a week for 12 weeks and were identical with respect to exercise type, frequencies and durations, and differed only in intensity. Resistance exercises included vertical row, leg press, bench press, pull over, abdominal crunch and lunges, and these were performed at 70 to 85% of 1 repetition maximum (1-RM) in the HI exercise group and at 40 to 55% of 1-RM in the LMI exercise group. Aerobic interval training aimed to improve cardiorespiratory endurance and included two times 8 minutes of cycling in the first four weeks, with an alternating workload of 30% and 65% of the maximal short exercise capacity (estimated by the steep ramp test [15]) in the HI exercise group and 30% and 45% in the LMI exercise group. From the fifth week onwards, an additional aerobic interval session was included in exchange for 8 minutes cycling. This interval session consisted of three times 5 minutes cycling at constant workload, with 1 minute rest between each bout. The constant workload was defined by means of heart rate reserve based on the Karvonen formula [23], and was at least 80% of heart rate reserve for HI exercise and 40-50% for LMI exercise. On average, 70% of the cancer survivors had high adherence rates, defined as attending 80% of the prescribed supervised exercise sessions [22]. Outcome measure HRQoL was measured with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 [1], with higher scores representing a higher global QoL and better function. We used the global QoL and physical function scales for further analyses. Potential mediators Cardiorespiratory fitness was measured with a maximal exercise test on an electronically braked cycle ergometer according to a ramp protocol in which the resistance gradually increased every 6 seconds, aiming to achieve the maximum peak oxygen uptake (peakvo 2 ) within 8-12 minutes [4,19]. PeakVO 2 was defined as the highest VO 2 value averaged over a 15-second interval within the last minute of exercise, and was expressed in ml/kg/min. Hand-grip strength was assessed with a JAMAR hand-grip dynamometer [6], and was expressed in kilograms. The mean score of the three attempts with the dominant hand was used in the statistical analyses. We used the 30-seconds chair-stand test as a measure for lower body muscle function [18]. The total number of times participants raised to a full stand in 30 seconds was used in the statistical analyses.

62 60 CHAPTER 4 Self-reported fatigue was measured with the Multidimensional Fatigue Inventory [34]. We used the general and physical fatigue subscales for further analyses, with higher scores indicating higher levels of fatigue. Covariates Demographic characteristics were collected at baseline with a self-reported questionnaire and included age, gender, education level, marital status, and smoking. We categorized education level into low (elementary and lower vocational education), medium (secondary and secondary vocational education), and high (higher vocational and university education). Clinical characteristics were collected from medical records and included cancer type, stage of disease, and treatment history (i.e., radiation therapy, immunotherapy, hormone therapy and/or surgery) [22]. Statistical analyses Baseline characteristics and pre- and post-intervention values of the outcome assessments are presented as means and standard deviations (SD), or as numbers and percentages. To test the hypothesis that exercise improves physical fitness (i.e., cardiorespiratory fitness, hand-grip strength, and lower body muscle function), which is associated with lower general and physical fatigue and higher global QoL and physical function (Figure 1), we conducted path analyses using maximum likelihood estimation with MPlus. Path analysis allows the simultaneous assessment of multiple regression equations [38]. Four separate path models were built using follow-up values of the mediators and outcome variables, adjusting for their baseline values, age and gender: 1) physical fitness and general fatigue as mediators in the intervention effects on global QoL; 2) physical fitness and physical fatigue as mediators in the intervention effect on global QoL; 3) physical fitness and general fatigue as mediators in the intervention effects on physical function; and 4) physical fitness and physical fatigue as mediators in the intervention effect on physical function. Bootstrapping techniques were applied to calculate the 95% confidence interval (CI) around the estimates of the direct and indirect effects using 10,000 bootstrap samples. The model fit was evaluated using the root mean square error of approximation (RMSEA), with values below 0.05 for good fit (acceptable fit: ), the Comparative Fit Index (CFI), and the Tucker-Lewis (TLI) index with values above 0.95 as good fit (adequate fit:>0.90) [25]. These tests were used because they are least sensitive to sample size, and provide unbiased and consistent model specifications [17]. The path analyses were based on complete cases. Because we pooled data from the HI and LMI exercise groups including a heterogeneous group of cancer survivors, we conducted sensitivity analyses to test

63 Mediators of exercise on HRQoL 61 whether the mediator effects were similar between the two intervention groups and between survivors of breast cancer (n=181) or other (n=96) cancer types. RESULTS The mean age of the participants in the exercise group was 53.6 (SD=11.1) years, 81% was female, and 67% was diagnosed with breast cancer (Table 1). Participants in the WLC group were on average 53.5 (SD=10.9) years old, 78% was female, and 63% was diagnosed with breast cancer. Descriptive values of all outcomes for the exercise and WLC groups at pre-intervention and post- intervention are presented in Table 2. TABLE 1 Sociodemographic and clinical characteristics of the exercise and waiting list control group (n=277) Exercise group n=186 Waiting list control group n=91 Sociodemographic Age, mean (SD) years 53.6 (11.1) 53.5 (10.9) Gender, n (%) male 35 (19) 20 (22) Married/living together, n (%) 160 (86) 72 (79) Education Low 31 (17) 16 (18) Medium 80 (44) 42 (46) High 72 (39) 33 (36) Smoking, n (%) 12 (7) 5 (6) Clinical Type of cancer, n (%) Breast 124 (67) 57 (63) Colon 34 (18) 15 (17) Ovarian 7 (4) 5 (6) Lymphatic 18 (10) 8 (9) Cervical 2 (1) 2 (2) Testicular 1 (1) 4 (4) Cancer Stage of cancer, n (%) Stage (67) 62 (68) Stage (33) 29 (32) Type of treatment, n (%) Surgery only 170 (91) 80 (88) Radiotherapy only 87 (47) 48 (53) Surgery and radiotherapy 80 (43) 46 (51) Immunotherapy 41 (22) 18 (20) Hormone therapy 85 (46) 43 (47) Abbrevation: SD, standard deviation.

64 62 CHAPTER 4 TABLE 2 Pre- and post-intervention values of mediator and outcome variables in the exercise and waiting list control groups. Pre-test mean (SD) Exercise group Post-test mean (SD) Waiting list control group Pre-test mean (SD) Post-test mean (SD) Health-related quality of life Global quality of life a 73.2 (16.2) 80.9 (14.9) 71.0 (16.5) 75.3 (15.4) Physical function 82.5 (13.0) 88.8 (9.8) 80.2 (15.4) 84.1 (13.1) Fatigue b General fatigue 12.7 (3.9) 10.1 (3.4) 12.7 (4.2) 11.3 (4.1) Physical fatigue 12.6 (3.9) 9.2 (3.4) 13.2 (4.0) 11.2 (3.9) Cardiorespiratory fitness c PeakVO 2 (ml/kg/min) 22.1 (6.2) 26.0 (7.1) 21.5 (5.5) 23.8 (5.9) Hand-grip strength d Hand-grip strength (kg) 32.7 (9.7) 34.6 (10.1) 33.5 (9.5) 35.5 (10.6) Lower body muscle function e Sit to stand (stands) 16.7 (4.0) 19.0 (4.8) 15.6 (3.6) 17.6 (3.9) Abbreviations: a Missing due to incomplete questionnaire (n=1); b Missing due to incomplete questionnaire (n=1); c Missing due to technical problems (n=5), musculoskeletal problems (n=1), or discomfort (n=6). Eight percent did not achieve the objective end criteria of respiratory exchange ratio 1.10 at baseline and follow-up; d Missing due to technical problems (n=3) or musculoskeletal problems (n=2); e Missing due to musculoskeletal problems (n=2); kg, kilograms; ml, milliliters; min, minutes; peakvo 2, maximum peak oxygen uptake; SD, standard deviation. We found a significant beneficial effect of exercise on cardiorespiratory fitness, but not on hand-grip strength or lower body muscle function (Figure 1, Table 3). In addition, higher cardiorespiratory fitness was significantly associated with lower physical fatigue (Figure 1b and 1d), but not with general fatigue (Figure 1a and 1c). Better lower body muscle function test was significantly associated with lower general and physical fatigue. Higher hand-grip strength was significantly associated with lower physical fatigue (Figure 1b and 1d). We also found a direct effect of the exercise on general and physical fatigue. Both lower general and physical fatigue were significantly associated with higher global QoL and physical function. Higher cardiorespiratory fitness was significantly associated with higher physical function (Figure 1c and 1d), but not with global QoL (Figure 1a and 1b). The paths explained 44-61% of the total variance in global QoL or physical function and the models had an adequate fit (RMSEA<0.08; CFI>0.98; TLI>0.95, Figure 1). Sensitivity analyses showed larger effects on global QoL for HI compared to LMI exercise and for survivors of breast cancer compared to survivors of other cancer types. Other paths were comparable across subgroups.

65 Mediators of exercise on HRQoL 63 TABLE 3 Unstandardized regression coefficients of the total and indirect effects and their 95% confidence intervals (CI) of the exercise intervention effect on global quality of life (QoL) and physical function, with cardiorespiratory fitness, hand-grip strength, lower body muscle function, and fatigue (either general or physical) as potential mediators Model results General fatigue Physical fatigue Global QoL Effect from intervention on fatigue Estimate (95% CI) Estimate (95% CI) Total effect -1.1 * (-1.9; -0.3) -1.6 * (-2.4; -0.8) Total indirect effect -0.1 (-0.3; 0.0) -0.2 * (-0.4; -0.1) Specific indirect effect via: Cardiorespiratory fitness -0.1 (-0.2; 0.0) -0.2 * (-0.4; -0.1) Hand-grip strength 0.0 (-0.0; 0.1) -0.0 (-0.1; 0.1) Lower body muscle function -0.0 (-0.2; 0.0) -0.0 (-0.2; 0.0) Effect from intervention on global QoL Total effect 4.5 * (1.2; 7.8) 4.1 * (0.8; 7.4) Total indirect effect 2.2 * (0.8; 3.8) 3.0 * (1.5; 4.8) Specific indirect effect via: Fatigue 1.6 * (0.4; 3.1) 2.4 * (1.1; 4.2) Cardiorespiratory fitness 0.3 (-0.1; 0.9) 0.1 (-0.3; 0.7) Hand-grip strength -0.0 (-0.2; 0.1) 0.0 (-0.1; 0.2) Lower body muscle function 0.1 (-0.1; 0.6) 0.1 (-0.1; 0.6) Cardiorespiratory fitness and fatigue 0.1 (-0.1; 0.4) 0.3 * (0.1; 0.7) Hand-grip strength and fatigue -0.0 (-0.1; 0.1) 0.0 (-0.2; 0.1) Lower body muscle function and fatigue 0.1 (-0.0; 0.3) 0.1 (-0.0; 0.3) Physical function Effect from intervention on fatigue Total -1.1 * (-1.9; -0.3) -1.6 * (-2.4; -0.8) Total indirect -0.1 (-0.3; 0.0) -0.2 * (-0.4; -0.1) Specific indirect Cardiorespiratory fitness -0.1 (-0.2; 0.0) -0.2 * (-0.4; -0.1) Hand-grip strength 0.0 (-0.0; 0.1) -0.0 (-0.1; 0.1) Lower body muscle function -0.0 (-0.2; 0.0) -0.0 (-0.2; 0.0) Effect from intervention on physical function Total effect 3.3 * (1.2; 5.5) 3.2 * (0.9; 5.3) Total indirect effect 1.5 * (0.7; 2.6) 2.2 * (1.2; 3.5) Specific indirect effect via: Fatigue 0.9 * (0.2; 1.9) 1.6 * (0.7; 2.7) Cardiorespiratory fitness 0.4 * (0.1; 0.9) 0.3 # (0.0; 0.7) Hand-grip strength 0.0 (-0.1; 0.1) -0.0 (-0.1; 0.1) Lower body muscle function 0.1 (-0.0; 0.3) 0.1 (-0.0; 0.4) Cardiorespiratory fitness and fatigue 0.1 (-0.0; 0.2) 0.2 * (0.1; 0.5) Hand-grip strength and fatigue -0.0 (-0.1; 0.0) 0.0 (-0.1; 0.1) Lower body muscle function and fatigue 0.0 (-0.0; 0.2) 0.0 (-0.0; 0.2) Abbreviations: SE, standard error; Path analyses using maximum likelihood estimation with MPlus adjusted for baseline scores of the mediator, age and gender; * p<0.05; # 0.05 p<0.10.

66 64 CHAPTER 4 FIGURE 1 Path models showing cardiorespiratory fitness, hand-grip strength, lower body muscle function, and fatigue as hypothesized mediators of the effect of the exercise intervention on global quality of life (QoL) and physical function 1a 0.2 (-0.1; 0.4) 1b Cardiorespiratory fitness 1.7 (0.9; 2.6)* 1.7 (0.9; 2.6)* -0.0 (-0.1; 0.0) -1.0 (-1.8; -0.2)* 0.1 (-0.2; 0.3) Cardiorespiratory fitness -0.1 (-0.2; -0.0)* -1.4 (-2.2; -0.6)* -0.0 (-0.9; 0.9) -0.0 (-0.1; 0.0) -1.7 (-2.2; -1.1)* Exercise intervention Hand-grip strength General fatigue Global QoL 0.0 (-0.9; 0.9) -0.1 (-0.1; -0.0)* -1.7 (-2.3; -1.2)* Exercise intervention Hand-grip strength Physical fatigue Global QoL 0.1 (-0.1; 0.3) 0.1 (-0.1; 0.3) 0.4 (-0.3; 1.1) Lower body muscle function -0.1 (-0.2; -0.0)* 0.2 (-0.3; 0.6) 0.4 (-0.3; 1.2) Lower body muscle function -0.1 (-0.2; -0.0)* 0.2 (-0.3; 0.6) 2.3 (-0.8; 5.4) 1.1 (-2.0; 4.2) 1c 0.2 (0.1; 0.4)* 1d Cardiorespiratory fitness 1.7 (0.9; 2.6)* 1.7 (0.9; 2.6)* -0.0 (-0.1; 0.0) -1.0 (-1.8; -0.2)* 0.2 (0.0; 0.3)* Cardiorespiratory fitness -0.1 (-0.2; -0.0)* -1.4 (-2.2; -0.6)* -0.0 (-0.9; 0.9) -0.0 (-0.1; 0.0) -1.0 (-1.3; -0.7)* Exercise intervention Hand-grip strength General fatigue Physical function -0.0 (-0.9; 0.9) -0.1 (-0.1; -0.0)* -1.1 (-1.5; -0.8)* Exercise intervention Hand-grip strength Physical fatigue Physical function 0.4 (-0.4; 1.1) Lower body muscle function -0.1 (-0.2; -0.0)* -0.0 (-0.2; 0.1) 0.2 (-0.0; 0.4) # 0.4 (-0.3; 1.2) Lower body muscle function -0.1 (-0.2; -0.0)* -0.1 (-0.2; 0.1) 0.2 (-0.0; 0.4) # 1.8 (-0.2; 3.8) # 1.0 (-1.1; 3.0) Note: Numbers represent unstandardized regression coefficients and their 95% confidence intervals (CI). Dotted lines represent non-significant associations, solid lines represent significant associations. Abbreviations: CFI, Comparative Fit Index; RMSEA, Root Mean Square Error of Approximation; TLI, Tucker Lewis index; a The model fitted the data: RMSEA=0.067, 90%CI=0.041;0.093, CFI=0.981, TLI= Explained total variance in global QoL=0.4; b The model fitted the data: RMSEA=0.073, 90%CI=0.048;0.098, CFI=0.977, TLI= Explained total variance in global QoL=0.4; c The model fitted the data: RMSEA=0.065, 90%CI=0.039;0.091, CFI=0.983, TLI= Explained total variance in physical function=0.6; d The model fitted the data: RMSEA=0.080, 90%CI=0.056;0.105, CFI=0.975, TLI= Explained total variance in physical function=0.6; * p<0.05; # 0.05 p<0.10

67 Mediators of exercise on HRQoL 65 DISCUSSION The current study found support for the hypothesis that a combined resistance and endurance exercise intervention improves cardiorespiratory fitness, which is associated with lower physical fatigue, and higher global QoL and physical function. Further, we found that higher hand-grip strength was significantly associated with lower physical fatigue, and better lower body muscle function with lower general and physical fatigue. We previously reported beneficial effects of the exercise intervention on cardiorespiratory fitness, fatigue, and HRQoL [22], which supports previous reviews and meta-analyses [14,20,28]. The current study further elucidates these findings by providing insight into the mechanisms underlying the beneficial effects of resistance and endurance exercise on HRQoL. Our finding that improved cardiorespiratory fitness mediated the exercise effects on physical fatigue, but not on general fatigue indicates that improving cardiorespiratory fitness is an important intervention strategy to reduce physical fatigue. The lack of mediating effect of improved cardiorespiratory fitness on general fatigue is in line with previous findings in prostate [8] and breast cancer survivors [30]. This may be explained by the fact that general fatigue does not only include physical aspects, but also mental aspects, which are perhaps more likely influenced by concepts other than or additional to cardiorespiratory fitness. It is possible that psychological factors such as depression and anxiety may mediate exercise effects on general fatigue [30]. Furthermore, exercise effects on fatigue could also be mediated by biological factors (e.g., improved body composition, and increased proinflammatory cytokines [31], or other psychosocial factors, such as reduced sleep quality, mastery, and self-efficacy [10,30]. These factors may also explain the direct beneficial effect of exercise on general fatigue in the current study as well as in a previous study [10]. In line with findings from previous studies [8,29], we found that higher hand-grip strength and better lower body muscle function was significantly associated with lower fatigue. We further found that better lower body muscle function tended to be associated with higher physical function. This indicates that muscle strength and function might be important intervention targets when aiming to reduce fatigue and improving physical function. However, due to the lack of a significant intervention effect on hand-grip strength and 30-seconds chair-stand test, we could not confirm that muscle strength and function mediated the exercise effects on fatigue and physical function. The lack of significant effects of exercise on muscle strength is in contrast with a previous meta-analysis [36] and a systematic review [24] summarizing the effects of exercise on muscle strength, and may

68 66 CHAPTER 4 be related to our choice of instruments used to assess the outcomes. Despite being valid and reliable measures of hand-grip strength [25] and lower body muscle function [5], they may have been less sensitive to detect exercise-induced changes. Future studies are needed to clarify the mediating role of muscle strength in the exercise-intervention effect on fatigue and physical function. We further found that the effects of exercise on global QoL can be explained by reduced fatigue, which supports findings from previous studies [8,10,11,33]. In older long term prostate cancer survivors, lower general fatigue was associated with higher global QoL [9]. However, in this study lower general fatigue was not a mediator of the exercise effect [9]. Furthermore, our results demonstrate that the effects of exercise on physical function can be explained by reduced general and physical fatigue. This is in contrast to a study in prostate cancer survivors [8], which reported that general fatigue mediated the effects of exercise on global QoL but not on physical function. This lack of mediating effects of general fatigue on global QoL or physical function in these studies may be related to the lower baseline values of fatigue, leaving less room for improvement. In contrast, our study clearly suggests that reducing fatigue can be important to improve global QoL and physical function, and that exercise is an effective strategy to do so. In addition to its effect via fatigue, we also found a direct association between improved cardiorespiratory fitness and improved physical function. The mediating role of cardiorespiratory fitness in the intervention effect on physical function was not found in studies among prostate cancer survivors [8,9]. Differences in mediating effects may be related to differences in study population, or to the type of instrument used to measure cardiorespiratory fitness [22]. Instead of the submaximal exercise test, the current study used a gold standard maximum exercise test to assess cardiorespiratory fitness, which may be more sensitive to detect changes and less prone to measurement error [2]. Baseline peakvo 2 values of our population were low compared to normative values [32], which may interfere with daily life functioning [37]. Our study showed that this can be (partially) counteracted by a training program of 12 weeks that improves cardiorespiratory fitness. The strengths of the present study are the examination of mediators in a well-designed RCT with a relatively large sample size, the use of valid and reliable instruments to assess outcome measures, and the use of path analyses enabling the simultaneous evaluation of multiple mediators [7]. However, despite the use of an RCT design, one should still be cautious when making inferences about causality, because the mediator and outcome variables were measured at the same time [39]. Consequently, we studied associations rather than temporal relationships between these variables, and the reverse that higher

69 Mediators of exercise on HRQoL 67 global QoL and physical function were associated with lower levels of fatigue may also be true. However, fatigue was found to be the strongest predictor of HRQoL and physical function [3], supporting the direction of the association studied. Another limitation is the use of indirect measures to assess muscle strength. Both hand-grip strength and 30-seconds chair-stand test are valid and reliable measures to assess hand-grip strength (25) and lower body muscle function [5]. In addition, the use of (indirect) 1-RM tests would introduce learning bias in the intervention group because these tests were included as part of the intervention. However, hand-grip strength and 30-seconds chair-stand test may not have been sensitive enough to detect exercise-induced changes. Finally, to increase statistical power, and because we hypothesized that the intervention effects had similar mechanisms underlying beneficial effects on global QoL and physical function, we pooled the data from both intervention groups. Our sensitivity analyses indicated that paths were comparable across subgroups, except for the intervention effect on global QoL, which was larger for HI than LMI exercise and for survivors of breast cancer compared to other cancer types [22]. As a result of pooling, we were unable to distinguish differences in strengths of mediator effects between HI and LMI exercise. Current results contribute to the understanding of the mechanisms by which a resistance and endurance exercise intervention achieves its effect on global QoL and physical function in cancer survivors. These results will help to further tailor interventions to the desired outcome. Supported by previous studies showing beneficial effects of exercise on cardiorespiratory fitness [20], it is recommended to improve cardiorespiratory fitness in order to reduce fatigue. Furthermore, reducing fatigue helps to improve the cancer survivors global QoL and physical function. In conclusion, this study found support for the hypothesis that exercise increases cardiorespiratory fitness, and consequently reduces physical fatigue and improves global QoL and physical function in cancer survivors shortly after completion of primary cancer treatment. Improving cardiorespiratory fitness could therefore be an important intervention target to reduce fatigue and to improve cancer survivors global QoL and physical function.

70 68 CHAPTER 4 REFERENCES 1. Aaronson NK, Ahmedzai S, Bergman B et al.: The European Organization for Research and Treatment of Cancer QLQ-C30: a Quality-of-Life Instrument for Use in International Clinical Trials in Oncology. J Natl Cancer Inst 1993, 85(5): American College of Sports Medicine. ACSM's guidelines for exercise testing and prescription. 9 ed.: Lippincott Williams & Wilkins Arndt V, Stegmaier C, Ziegler H, Brenner H: A population-based study of the impact of specific symptoms on quality of life in women with breast cancer 1 year after diagnosis. Cancer 2006, 107(10): Balady GJ, Arena R, Sietsema K et al.: Clinician's Guide to cardiopulmonary exercise testing in adults: a scientific statement from the American Heart Association. Circulation 2010, 122(2): Baruth M, Wilcox S, Wegley S et al.: Changes in physical functioning in the Active Living Every Day program of the Active for Life Initiative(R). Int J Behav Med 2011, 18(3): Bohannon RW: Hand-grip dynamometry provides a valid indication of upper extremity strength impairment in home care patients. J Hand Ther 1998, 11(4): Bryan A, Schmiege SJ, Broaddus MR: Mediational analysis in HIV/AIDS research: estimating multivariate path analytic models in a structural equation modeling framework. AIDS Behav 2007, 11(3): Buffart LM, Galvao DA, Chinapaw MJ et al.: Mediators of the resistance and aerobic exercise intervention effect on physical and general health in men undergoing androgen deprivation therapy for prostate cancer. Cancer 2014, 120(2): Buffart LM, Newton RU, Chinapaw MJ et al.: The effect, moderators, and mediators of resistance and aerobic exercise on health-related quality of life in older long-term survivors of prostate cancer. Cancer 2015, 121(16): Buffart LM, Ros WJ, Chinapaw MJ et al.: Mediators of physical exercise for improvement in cancer survivors' quality of life. Psychooncology 2014, 23(3): Buffart LM, Thong MS, Schep G, Chinapaw MJ, Brug J, van de Poll-Franse LV: Selfreported physical activity: its correlates and relationship with health-related quality of life in a large cohort of colorectal cancer survivors. PLoS One 2012, 7(5):e Cerin E, Mackinnon DP: A commentary on current practice in mediating variable analyses in behavioural nutrition and physical activity. Public Health Nutr 2009, 12(8): Chinapaw MJ, Buffart LM, van Mechelen W et al.: Alpe d'huzes cancer rehabilitation (A-CaRe) research: four randomized controlled exercise trials and economic evaluations in cancer patients and survivors. Int J Behav Med 2012, 19(2): Cramp F, Byron-Daniel J: Exercise for the management of cancer-related fatigue in adults. Cochrane Database Syst Rev 2012, 11:CD De Backer IC, Schep G, Hoogeveen A, Vreugdenhil G, Kester AD, van Breda E: Exercise testing and training in a cancer rehabilitation program: the advantage of the steep ramp test. Arch Phys Med Rehabil 2007, 88(5): Dimeo FC: Effects of exercise on cancer-related fatigue. Cancer 2001, 92(6 Suppl): Hooper D, Coughlan J, Mullen MJ: Structural equation modelling: Guidelines for determining model fit. EJBRM 2008, 6: Jones CJ, Rikli RE, Beam WC: A 30-s chairstand test as a measure of lower body strength in community-residing older adults. Res Q Exerc Sport 1999, 70(2): Jones LW, Eves ND, Haykowsky M, Joy AA, Douglas PS: Cardiorespiratory exercise testing in clinical oncology research: systematic review and practice recommendations. Lancet Oncol 2008, 9(8): Jones LW, Liang Y, Pituskin EN et al.: Effect of exercise training on peak oxygen consumption in patients with cancer: a meta-analysis. Oncologist 2011, 16(1):

71 Mediators of exercise on HRQoL Kampshoff CS, Buffart LM, Schep G, van Mechelen W, Brug J, Chinapaw MJ: Design of the Resistance and Endurance exercise After ChemoTherapy (REACT) study: a randomized controlled trial to evaluate the effectiveness and cost-effectiveness of exercise interventions after chemotherapy on physical fitness and fatigue. BMC Cancer 2010, 10: Kampshoff CS, Chinapaw MJ, Brug J et al.: Randomized controlled trial of the effects of high intensity and low-to-moderate intensity exercise on physical fitness and fatigue in cancer survivors: results of the Resistance and Endurance exercise After ChemoTherapy (REACT) study. BMC Med 2015, 13: Karvonen J, Vuorimaa T: Heart rate and exercise intensity during sports activities. Practical application. Sports Med 1988, 5(5): Keogh JW, MacLeod RD: Body composition, physical fitness, functional performance, quality of life, and fatigue benefits of exercise for prostate cancer patients: a systematic review. J Pain Symptom Manage 2012, 43(1): Lamers I, Kelchtermans S, Baert I, Feys P: Upper limb assessment in multiple sclerosis: a systematic review of outcome measures and their psychometric properties. Arch Phys Med Rehabil 2014, 95(6): Lucia A, Earnest C, Perez M: Cancer-related fatigue: can exercise physiology assist oncologists? Lancet Oncol 2003, 4(10): MacKinnon DP, Luecken LJ: Statistical analysis for identifying mediating variables in public health dentistry interventions. J Public Health Dent 2011, 71 Suppl 1:S Mishra SI, Scherer RW, Geigle PM et al.: Exercise interventions on health-related quality of life for cancer survivors. Cochrane Database Syst Rev 2012, 8:CD Schmidt ME, Semik J, Habermann N, Wiskemann J, Ulrich CM, Steindorf K: Cancer related fatigue shows a stable association with diurnal cortisol dysregulation in breast cancer patients. Brain Behav Immun 2016, 52: Schneider CM, Repka CP, Brown JM et al.: Demonstration of the need for cardiovascular and pulmonary normative data for cancer survivors. Int J Sports Med 2014, 35(13): Schwartz AL: Fatigue mediates the effects of exercise on quality of life. Qual Life Res 1999, 8(6): Smets EM, Garssen B, Bonke B, De Haes JC: The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res 1995, 39(3): Speck RM, Courneya KS, Masse LC, Duval S, Schmitz KH: An update of controlled physical activity trials in cancer survivors: a systematic review and meta-analysis. J Cancer Surviv 2010, 4(2): Strasser B, Steindorf K, Wiskemann J, Ulrich CM: Impact of resistance training in cancer survivors: a meta-analysis. Med Sci Sports Exerc 2013, 45(11): Sweeney C, Schmitz KH, Lazovich D, Virnig BA, Wallace RB, Folsom AR: Functional limitations in elderly female cancer survivors. J Natl Cancer Inst 2006, 98(8): Tomarken AJ, Waller NG: Structural equation modeling: strengths, limitations, and misconceptions. Annu Rev Clin Psychol 2005, 1: Van der Weele T: Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press; Rogers LQ, Hopkins-Price P, Vicari S et al.: A randomized trial to increase physical activity in breast cancer survivors. Med Sci Sports Exerc 2009, 41(4): Rogers LQ, Vicari S, Trammell R et al.: Biobehavioral factors mediate exercise effects on fatigue in breast cancer survivors. Med Sci Sports Exerc 2014, 46(6):

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73 CHAPTER 5 Long-term effectiveness and cost-effectiveness of high versus low-to-moderate intensity resistance and endurance exercise among cancer survivors Caroline S. Kampshoff Johanna M. van Dongen Willem van Mechelen Goof Schep Art Vreugdenhil Jos W.R. Twisk Judith E. Bosmans Johannes Brug Mai J.M. Chinapaw Laurien M. Buffart Submitted

74 72 CHAPTER 5 ABSTRACT Background: This study aimed to evaluate the long-term effectiveness and cost-effectiveness of high intensity (HI) versus low-to-moderate intensity (LMI) resistance and endurance exercise on physical fitness, fatigue, and health-related quality of life (HRQoL) in cancer survivors. Methods: 277 cancer survivors participated in the Resistance and Endurance exercise After ChemoTherapy (REACT) study and were randomized to 12 weeks of HI (n=139) or LMI exercise (n=138), that had similar exercise types, durations and frequencies, but different intensities. Measurements were performed at baseline (4-6 weeks after primary treatment including chemotherapy), 12 (i.e., short-term) and 64 (i.e., longer-term) weeks after randomization. Outcomes included cardiorespiratory fitness (peakvo 2 ), muscle strength (hand-grip strength and 30-seconds chair-stand test), self-reported fatigue (MFI), HRQoL (EORTC-QLQ-C30), Quality-Adjusted Life Years (QALYs) and societal costs. Linear mixed models were conducted to study (a) the difference in effects between HI and LMI exercise at longer-term; (b) within-group changes in outcomes from short-term to longer-term; and (c) the cost-effectiveness from a societal perspective. Results: At longer-term, intervention effects on role function (β=5.9, 95%CI=0.5;11.3) and social function (β=5.7, 95%CI=1.7;9.6) were larger for HI exercise compared to LMI exercise. No significant between-group differences were found for physical fitness and fatigue. Intervention-induced improvements in cardiorespiratory fitness and HRQoL were maintained between week 12 and 64, but not for fatigue. From a societal perspective, the probability that HI exercise was cost-effective compared to LMI exercise was 0.91 at 20,000/QALY gained and 0.95 at 52,000/QALY gained, mostly due to significant lower healthcare costs in HI exercise. Conclusions: At longer-term follow-up, we found a larger intervention effect on role and social function for HI than LMI exercise. Furthermore, HI exercise was cost-effective with regard to QALYs compared to LMI exercise.

75 Longer-term effects of exercise 73 INTRODUCTION Supervised exercise can contribute to counteracting the negative side effects of cancer and its treatments [1]. Systematic reviews demonstrated safety and beneficial effects of exercise on physical fitness [2], fatigue [3], and health-related quality of life (HRQoL) [4], during and after cancer treatment. However, previous studies predominantly reported short-term effects [4,5]. The few studies that included a longer-term follow-up ( 6 months) showed that the benefits of exercise were maintained for HRQoL [4], but not for fatigue [5]. For other outcomes (e.g., physical fitness), longer-term effects are unclear. Therefore, more research on the longer-term effectiveness of exercise in cancer survivors is warranted. As resources are scarce, decisions on the implementation of healthcare programs are not only guided by their health effects, but also by their additional costs in relation to these effects (i.e., cost-effectiveness). Therefore, it is important that state-of-the-art cost-effectiveness analyses of healthcare programs are performed [6]. Cost-effectiveness analyses of exercise interventions in cancer survivors are scarce [7,8]. A systematic review compared exercise interventions to usual care in patients with various diseases, including cancer, found acceptable incremental cost-effectiveness ratios or cost savings [7]. Similar results were found by a systematic review evaluating the cost-effectiveness of multidimensional cancer rehabilitation programs [8]. However, despite the fair methodological quality of the reviewed studies, the heterogeneity across interventions hampered solid conclusions about their cost-effectiveness. The present study reports the (cost-)effectiveness of the randomized controlled Resistance and Endurance exercise After ChemoTherapy (REACT) study at longer-term (i.e., 64 weeks) [9]. At short-term (i.e., 12 weeks), high intensity (HI) and low-to-moderate intensity (LMI) exercise significantly improved cardiovascular fitness and HRQoL, and reduced fatigue compared to a waiting list control (WLC) group, with some indication for a dose-response relationship for exercise intensity on cardiorespiratory fitness [9]. Also, HI and LMI exercise were equally beneficial in counteracting fatigue [9]. This study aimed to evaluate the longerterm (cost-)effectiveness of HI exercise versus LMI exercise for physical fitness, fatigue, and HRQoL.

76 74 CHAPTER 5 METHODS Setting and participants Detailed methods, including sample size calculations, of the REACT study have been reported previously [9,10]. Briefly, REACT is a multicenter randomized controlled trial (RCT) in cancer patients recruited from nine Dutch hospitals between 2011 and The Medical Ethics Committee of the VU University Medical Centre approved the study. Patients aged 18 years with histologically confirmed breast, colon, ovarian, cervix or testis cancer, or lymphomas with no indication of recurrent or progressive disease who had completed chemotherapy were eligible and invited to participate. Exclusion criteria were: being unable to perform daily activities; presence of cognitive disorders, severe emotional instability; diseases that hamper patients capacity of carrying out HI exercise; being unable to read and write Dutch. Written informed consent was obtained from all participants prior to participation. Randomization Following baseline assessments, participants were stratified by cancer type and hospital, and randomly assigned to HI, LMI, or WLC using random numbers tables [9]. Shortly after randomization, HI and LMI participants commenced their 12-week exercise program. WLC participants were also randomly allocated to HI or LMI exercise, but started exercising after the 12-week follow-up assessment. Allocation sequence was concealed from the clinical and research staff. Due to the interventions nature, participants and physiotherapists were not blinded. Exercise interventions HI and LMI exercise had similar exercise types, durations and frequencies, but differed in intensity (Table 1). Exercise sessions were given twice per week during 12 weeks, and supervised by a trained physiotherapist. Both exercise programs included six resistance exercises targeting large muscle groups (frequency:2x10 repetitions). Workload per exercise was defined by an indirect one repetition maximum (1-RM) measurement. Furthermore, both programs included two types of endurance interval exercises. During week 1-4, patients cycled 2x8 minutes with alternating workloads (defined by the maximum short exercise capacity estimated by the steep ramp test [11]). During week 5-12, one additional endurance interval session was added substituting one 8-minute interval of cycling. This interval session consisted of 3x5 minutes cycling at constant workload (defined by the heart rate reserve using the Karvonen formula) [12]. Physiotherapists applied behavioral

77 Longer-term effects of exercise 75 motivation counselling techniques to overcome possible exercise barriers and to encourage participants to obtain and maintain a physically active lifestyle. At 4, 10 and 18 weeks after intervention completion, three booster sessions were provided to motivate participants to maintain their exercise engagements. TABLE 1 Exercise intensities of the HI and LMI resistance and endurance exercise programs Resistance exercises (1-RM) a (6 exercises targeting the large muscle groups) Endurance interval exercises Part A (MSEC) a (8 min alternating workload) Endurance interval exercises Part B (HRR) a (3x5 min constant workload) Counseling High intensity 70-85% 30/65% 80% Participants were (HI) exercise b encouraged to Low-to-moderate intensity (LMI) exercise b 40-55% 30/45% 40-50% start or maintain a physically active lifestyle in addition to the supervised exercise sessions. Abbreviations: 1-RM, one repetition maximum; MSEC, maximum short exercise capacity; HRR, heart rate reserve; a Every four weeks (week 1, 5 and 9), the physiotherapist evaluated training progress, and adjusted the workload accordingly. b Exercises were accompanied with BORG scores and heart rate monitors to guide the physiotherapists. In the occasion that the training intensity seemed too high or too low, the 1-RM, MSEC or HRR were reassessed. Measurements Socio-demographic data were collected by self-report. Clinical information was obtained from medical records. Physiotherapists documented session attendance in exercise logs. Outcomes were assessed at baseline, and after 12 and 64 weeks, except for dual energy X-ray absorptiometry (DXA), which was only performed at baseline and 64 weeks. Detailed descriptions of the assessments and their measurement properties are provided elsewhere [10,13]. Physical tests were performed by an independent assessor. Primary outcomes Cardiorespiratory fitness was measured during a maximal cyclometer exercise test aiming to achieve peak oxygen uptake (peakvo 2, in ml/kg/min) within 8-12 minutes [14] following a ramp protocol, in which breath-by-breath gas exchange was measured continuously. After each test, peakvo 2 (i.e., highest oxygen consumption values averaged over a 15-second interval within the last 60 seconds), peak power output (peakw, in watt), and the ventilatory threshold (determined by the oxygen equivalent method [14]) were recorded. Hand-grip strength was assessed using a JAMAR hand-grip dynamometer [15] and the mean score

78 76 CHAPTER 5 (in kilogram) of three attempts with the participants dominant hand was used for further analyses. Lower body function was assessed using the 30-second chair-stand test [16]. The total number of times participants raised to a full stand in 30 seconds was reported. Fatigue was assessed using the Multidimensional Fatigue Inventory (MFI) [17], including five subscales: general fatigue; physical fatigue; reduced physical activity; reduced motivation; mental fatigue. Secondary outcomes HRQoL was measured using the European Organisation Research and Treatment of Cancer- Quality of Life questionnaire-core 30 (EORTC-QLQ-C30) [18] and anxiety and depression by the Hospital Anxiety and Depression Scale (HADS) [19]. Physical activity (PA) was objectively assessed by accelerometers (Actigraph) using vertical accelerations converted into counts/minute. Body mass index (BMI) was calculated from measured body height and weight. Body composition was determined using percentage of total body fat mass (%FM), lean mass (%LM), and lumbar spine (L1-L4) bone mineral density (BMD), measured by DXA with a Hologic Discovery DXA scanner. Quality Adjusted Life Years (QALYs) were estimated using the EQ-5D-3L [20]. EQ-5D-3L health states were converted into utilities using the Dutch tariff [21]. QALYs were calculated using linear interpolation between measurement points. Cost measures Intervention costs were micro-costed [22,23]. Attendance of exercise and booster sessions were registered, intervention providers time investments were valued using their gross hourly salaries (including overhead), and material costs were estimated using invoices. All other cost categories were assessed using 3-monthly questionnaires, with 3-month recall periods. Healthcare costs included costs due to primary and secondary healthcare use, and medication. Dutch standard costs were used to value healthcare use [23]. Medication use was valued using unit prices of the Royal Dutch Society of Pharmacy [24]. Informal care (i.e., care by family/friends) was valued using a shadow price [23]. Absenteeism was assessed using participants reports of their number of absence days and, in case of partial absence, their percentage of normal working hours worked. Using the friction cost approach (FCA), absenteeism costs were valued with age- and gender-specific price weights [23,25]. The FCA assumes that costs are limited to the friction period (i.e., period needed to replace a sick-listed worker=23 weeks) [23,25]. Unpaid productivity (e.g., volunteer work) losses were valued using the aforementioned shadow price [23]. Sports costs included expenses on memberships and equipment. All costs were converted to 2012 Euros [26].

79 Longer-term effects of exercise 77 Statistical analyses Differences in outcomes between HI and LMI exercise at longer-term follow-up were assessed using linear mixed model analyses with a two-level structure (i.e., participants were clustered within hospitals). Both interventions were simultaneously regressed on the longer-term value of the outcome, adjusted for the baseline value, age, gender, and timing of intervention (i.e., direct start or WLC group). To check whether missing data affected the results, sensitivity analyses were conducted on an imputed dataset for peakvo 2, hand-grip strength, and fatigue (SA1). Missing data were multiple imputed using Predictive Mean Matching, stratified by group allocation [27]. The imputation model was specified according to White et al. [27]. Twenty different datasets were created. Pooled estimates were calculated using Rubin s rules [27]. To evaluate within-group changes in HI and LMI exercise from short-term to longer-term follow-up, we conducted linear mixed models for repeated measurements (i.e., repeated measurements were clustered within patients, which were clustered within hospitals). This model simultaneously regressed the intervention effect on short-term and longer-term, and included time and the interaction between time and exercise group as determinants and age, gender and the outcome s baseline value as covariates. Cost-effectiveness analyses were performed from the societal perspective using the multiple imputed datasets [27]. Between-group differences were estimated for total and disaggregated costs. Total cost and effect differences were estimated using linear mixed model analyses, adjusted for baseline, age, gender, and intervention timing. Incremental cost-effectiveness ratios (ICERs) were calculated by dividing the adjusted total cost differences by those in effects [6]. Uncertainty around cost differences and ICERs was estimated using bias-corrected (BC) bootstrap intervals (5000 replications, stratified by hospital) [28]. Costeffectiveness planes [29] and cost-effectiveness acceptability (CEA) curves were constructed [30]. A post-hoc analysis was performed applying a healthcare perspective and a sensitivity analysis (SA2) was conducted assuming that all scheduled exercise sessions needed to be paid for, rather than only those attended. As disease recurrence, -which may influenced quality of life and healthcare costsoccurred more often during follow-up in LMI exercise, additional sensitivity analyses (SA3-4) were performed. We excluded patients with disease recurrence (n=17) in order to check whether disease recurrence affected the results of the main effectiveness (i.e., betweengroup difference SA3) and cost-effectiveness analyses (SA4). All primary analyses were performed according to intention-to-treat. Costs and effects beyond one year were discounted at a rate of 4% and 1.5%, respectively [23]. Effectiveness

80 78 CHAPTER 5 analyses were performed in SPSS (v22.0) and multiple imputation and cost-effectiveness analyses in STATA (v12.0). p<0.05 was considered significant. RESULTS Of the 757 eligible patients, 277 (37%) participated (Figure 1). Age, gender, and cancer type did not differ significantly between participants and non-participants [9]. The participants baseline characteristics were balanced across groups (Table 2). On average, participants in HI and LMI groups attended 20.2 (SD=8.8) and 21.8 (SD=6.2) of 24 exercise sessions and 1.5 (SD=1.2) and 1.7 (SD=0.9) of 3 booster sessions, respectively (Figure 1). There were no adverse events directly related to the interventions. Complete physical fitness and patient-reported outcome data were obtained from 116 (80%) and 223 (81%) participants, respectively. Furthermore, 211 (76%), 185 (66%), 179 (65%), 173 (63%), and 176 (64%) participants had complete cost data at 3, 6, 9, 12, and 15 months, respectively. At longer-term, intervention effects on role function (β between-group difference =5.9, 95%CI=0.5;11.3) and social function (β between-group difference =5.7, 95%CI=1.7;9.6) were larger for HI exercise than LMI exercise (Table 3). No other significant between-group differences were found at longer-term (Table 3). Results of the sensitivity analyses (SA3) were comparable (data not shown). No significant within-group changes were found for peakvo 2 and HRQoL between short- and longer-term for both HI and LMI exercise, indicating that the intervention-induced improvements at short-term were maintained at longer-term (Table 3). For HI exercise, role function (β within-group change =5.5, 95%CI=0.3;10.6), hand-grip strength (β within-group change =1.4, 95%CI=0.6;2.2), and BMI (β within-group change =0.3, 95%CI=0.03;0.5) increased from shortto longer-term, and lower body muscle function increased both in HI and LMI exercise (HI:β within-group change =1.4, 95%CI=0.7;2.2, LMI:β within-group change =1.2, 95%CI=0.5;1.9). For both groups, significant within-group changes were found for fatigue and anxiety, such that they returned to baseline levels. No significant within-group changes from short- to longer-term were found for depression and objectively measured PA.

81 Longer-term effects of exercise 79 FIGURE 1 Patients flowchart of the REACT study Screened (n=793) Patients not eligible (n=38; 5%) (cognitive disorders or severe emotional instability n=7; serious diseases that hampers patients capacity of carrying out HI exercise n=21; inability to understand the Dutch language n=7; already participating in an exercise study n=1; complications due to cancer treatments n=1) (n=757; 100%) Non-participants (n=480; 63%) (forgotten n=16; too much n=144; already exercising n=80; study design n=47; not interested n=69; abroad n=5; unknown n=119) Participants stratified by diagnosis and hospital, randomly assigned (n=277; 37%) HI exercise (n=139) Adherence Session attendance, mean (SD): 21.8 (6.2) Counselling sessions attendance, mean (SD): 1.7 (0.9) LMI exercise (n=138) Adherence Session attendance, mean (SD): 20.2 (8.8) Counselling sessions attendance, mean (SD): 1.5 (1.2) Lost to follow-up Post-test Assessment Lost to follow-up Post-test Assessment No Physical fitness (n=6) Comorbidities (n=1) Recurrence (n=3) Too much burden (n=2) No PRO (n=5) No response (n=5) Neither (n=18) Data lost (n=1) Died (n=1) Comorbidities (n=3) No response (n=4) Recurrence (n=1) Too much burden (n=8) Physical fitness (n=115;83%) PROs (n=116;83%) Participants included in intention-to-treat analysis n=139; 100% No Physical fitness (n=9) Recurrence (n=2) Too much burden (n=7) No PRO (n=3) No response (n=2) Questionnaire lost (n=1) Neither (n=28) Comorbidities (n=3) Died (n=5) No response (n=5) Recurrence (n=2) Too much burden(n=13) Physical fitness (n=101;73%) PROs (n=107;78%) Participants included in intention-to-treat analysis n=138; 100% Abbreviations: HI, high intensity exercise; LMI, low-to-moderate intensity exercise; WLC, waiting list control group; PRO, patient reported outcomes.

82 80 CHAPTER 5 TABLE 2 Baseline characteristics of the participants Characteristics LMI n=138 HI n=139 Socio-demographic Age, mean (SD) years 53 (11.4) 54 (10.7) Gender, n (%) male 26 (19) 29 (21) Partner, n (%) yes 120 (87) 112 (81) Education, n (%) b Low Intermediate High Being employed, n (%) Employed Not employed Retired 19 (14) 64 (47) 53 (39) 82 (59) 30 (22) 26 ( 19) 28 (20) 58 (42) 52 (38) 85 (61) 36 (26) 18 (13) Smoking, n (%) yes c 8 (6) 9 (7) Comorbidities 2, n (%) yes 14 (10) 16 (12) Sport history, n (%) yes d 83 (61) 72 (52) Exercise during chemotherapy, n (%) yes b 25 (18) 27 (20) Clinical Cancer type, n (%) Breast Colon Ovarian Lymphoma Cervix Testis Cancer stage, n (%) Stage I-II Stage III-IV Type of treatment, n (%) yes Surgery Radiation therapy 89 (65) 24 (17) 4 (3) 16 (12) 4 (3) 1 (1) 84 (61) 54 (39) 123 (89) 61 (44) 92 (66) 25 (18) 8 (6) 10 (7) 0 4 (3) 103 (74) 36 (26) 127 (91) 74 (53) Surgery and Radiation therapy 58 (42) 68 (49) Immunotherapy 36 (26) 23 (17) Hormone therapy 61 (44) 67 (48) Type of chemotherapy, n (%) TAC 47 (34) 56 (40) FEC TAC/FEC combinations Capecitabine and Oxaliplatin Oxaliplatin combinations Carboplatin and Paclitaxel CHOP ABVD Cisplantin BEP Other 9 (7) 30 (22) 14 (10) 10 (7) 8 (6) 11 (8) 4 (3) 3 (2) 1 (1) 1 (1) 10 (7) 23 (17) 12 (9) 12 (9) 10 (7) 7 (5) 4 (3) 0 3 (2) 2 (1) Abbreviations: n, number; FEC, fluorouracil, epirubicin, cyclophosphamide; TAC, taxotere, adriamycin, cyclophosphamide; CHOP, cyclophosphamide, doxorubicin, vincristine, prednisone; ABVD, doxorubicin, bleomycin, vinblastine, dacarbazine; BEP, bleomycin, etoposide, cisplatin; b n-3; c n-4; d n-1.

83 Longer-term effects of exercise 81 TABLE 3 Mean (SD) values at baseline and follow-up and differences in effects on primary and secondary outcomes between groups a LMI (n=138) HI (n=139) HI vs. LMI HI vs. LMI b Time LMI Time HI Baseline mean (SD) 64 weeks mean (SD) Baseline mean (SD) 64 weeks mean (SD) β (95%CI) β (95% CI) β (95% CI) β (95% CI) Cardiorespiratory fitness c PeakVO 2 (ml/kg/min) 22.1 (5.8) 25.6 (6.8) 22.0 (6.5) 26.3 (8.1) 0.7 (-0.3;1.7) 0.5 (-0.9;1.9) 0.1 (-0.8;0.9) -0.5 (-1.3;0.3) WMax (W) 136 (43) 155 (48) 137 (45) 162 (55) 6.4 (0.6;12.3)* 2.2 (-1.6;5.9) 1.8 (-1.8;5.4) Anaerobic threshold (ml/kg/min) 16.1 (4.6) 18.0 (5.2) 15.9 (4.9) 18.5 (5.7) 0.6 (-0.4;1.6) -0.7 (-1.6;0.1) -1.1 (-1.9;-0.2) * Muscle strength Sit to stand (stands) d 16 (3.7) 20 (5.2) 17 (4.2) 20 (5.2) -0.4 (-1.4;0.5) 1.2 (0.5;1.9) * 1.4 (0.7;2.2) * Hand-grip strength (kg) e 33.2 (9.5) 35.9 (11.0) 32.8 (10.0) 35.6 (11.4) -0.4 (-1.6;0.8) -0.2 (-2.1;1.8) 0.1 (-0.7;0.9) 1.4 (0.6;2.2) * Fatigue (Range 1-20) f General fatigue g 12.9 (4.2) 11.7 (1.3) 12.7 (3.8) 11.7 (1.6) -0.1 (-0.4;0.3) -0.2 (-0.6;0.3) 1.5 (0.7;2.3) * 2.0 (1.2;2.6) * Physical fatigue g 12.8 (4.0) 13.0 (1.4) 12.9 (3.9) 12.8 (1.6) -0.2 (-0.6;0.1) 3.8 (3.0;4.6) * 4.1 (3.3;4.8) * Reduced activity h 11.7 (3.5) 12.5 (1.2) 12.0 (3.5) 12.4 (1.5) -0.1 (-0.5;0.3) 3.5 (2.7;4.2) * 3.0 (2.3;3.7) * Reduced motivation i 8.7 (3.1) 12.1 (1.7) 9.0 (3.0) 12.1 (1.8) 0.02 (-0.4;0.5) 3.7 (3.0;4.4) * 4.6 (3.9;5.2) * Mental fatigue g 10.8 (4.1) 11.8 (1.1) 11.0 (4.0) 11.7 (1.4) (-0.3;0.3) 1.8 (1.0;2.6) * 2.1 (1.3;2.9) * Health-related quality of life (Range 0-100) j Global QoL 73.2 (16.7) 80.0 (16.5) 71.3 (15.8) 83.0 (15.6) 3.7 (-0.3;7.7) 0.7 (-2.7;4.0) 0.4 (-2.9;3.7) Physical function 82.1 (12.9) 87.6 (14.8) 80.4 (15.3) 89.7 (11.9) 2.9 (-0.1;5.9) -0.4 (-2.8;1.9) 2.2 (-0.1;4.5) Role function 70.9 (25.1) 83.5 (24.5) 68.5 (26.7) 88.8 (19.4) 5.9 (0.5;11.3) * 1.1 (-4.2;6.3) 5.5 (0.3;10.6) * Emotional function 83.5 (16.3) 85.3 (18.1) 85.4 (16.5) 87.4 (17.4) 0.9 (-2.8;4.6) 1.0 (-2.4;4.3) -0.8 (-4.1;2.4) Cognitive function 77.7 (23.0) 83.8 (17.9) 79.5 (21.6) 83.9 (21.2) -0.7 (-4.8;3.4) 5.7 (2.0;9.4) * 2.1 (-1.6;5.7) Social function 78.8 (21.3) 87.2 (19.0) 76.7 (24.1) 92.2 (15.5) 5.7 (1.7;9.6) * 1.1 (-2.6;4.8) -0.5 (-1.3;0.3) Distress (range 0-21) k i Anxiety 3.9 (2.8) 3.9 (3.1) 3.8 (3.0) 3.9 (3.5) 0.2 (-0.4;0.9) -0.1 (-0.7;0.6) 0.7 (0.1;1.3) * l Depression 3.1 (2.8) 2.8 (3.3) 3.2 (2.7) 2.6 (3.0) -0.3 (-0.9;0.4) 0.1 (-0.5;0.6) 0.1 (-0.4;0.7)

84 82 CHAPTER 5 LMI (n=138) HI (n=139) HI vs. LMI HI vs. LMI b Time LMI Time HI Baseline mean (SD) 64 weeks mean (SD) Baseline mean (SD) 64 weeks mean (SD) β (95%CI) β (95% CI) β (95% CI) β (95% CI) Body composition BMI, kg/m (4.3) 26.9 (4.5) 26.9 (4.5) 27.0 (4.6) (-0.4;0.4) 0.1 (-0.2;0.3) 0.3 (0.03;0.5) * Percentage fat mass m 31.7 (7.4) 33.5 (7.4) 32.1 (6.9) 33.1 (8.3) -0.7 (-1.7;0.3) Percentage lean mass 64.6 (7.5) 63.5 (7.0) 64.9 (6.5) 63.3 (9.2) -0.4 (-1.7;0.9) BMD Lumbar spine (g/cm 2 ) n 1.0 (0.2) 1.0 (0.2) 1.0 (0.2) 1.0 (0.2) (-0.02;0.01) Physical activity Accelerometer (CPM) o,p (96.1) (165.4) (100.4) (139.9) (-65.3;20.5) (-61.8;17.9) (-65.6;13.0) Abbreviations: LMI, low-to-moderate intensity exercise; HI, high intensity exercise; SD, standard deviation; n, number; kg, kilogram; W, watt; BMI, body mass index; BMD, bone mineral density; CPM, counts per minute; *(p<0.05); (0.05 p<0.10) ; a adjusted model, corrected for age and gender; b sensitivity analysis imputed dataset; c missings due to technical problems (n=2), or discomfort (n=1); d missings due to musculoskeletal problems (n=3); e missings due to musculoskeletal problems (n=11); f higher score means a higher level of self-reported fatigue in all subscales; g missing due to incomplete questionnaire (n=2); h missing due to incomplete questionnaire (n=1); i missing due to incomplete questionnaire (n=3); j higher score means a higher level of self-reported HRQoL in all subscales; k higher score means a higher level of anxiety and depression in both subscales; l missing due to incomplete questionnaire (n=8); m missings due to no show (n=2); n missings due to no show (n=2) or technical problems (n=2); o average counts for Y-Axis; p missings due to technical problems/insufficient wearing-time (n=37).

85 Longer-term effects of exercise 83 Total societal costs did not differ significantly between HI and LMI exercise (β=-2429, 95%CI=-5798;933). In HI exercise, healthcare costs were significantly lower (β=-2056, 95%CI=-3816;-443) and intervention costs were significantly higher (β=40, 95%CI=8;75) than in LMI exercise (Table 4). For QALYs, an ICER of -87,831 was found, indicating that HI exercise was associated with a cost saving of 87,831 /QALY gained, compared with LMI (Table 5). When societal decision-makers are not willing to pay anything per unit of effect gained, the probability of HI exercise being cost-effective compared with LMI exercise was This probability increased to 0.91 at a willingness-to-pay of 20,000 /QALY and reaching 0.95 at 52,000 /QALY. For hand-grip strength, the probability of cost-effectiveness increased as the willingness-to-pay increased, from 0.87 to 0.95 at /kilogram, while it decreased for peakvo 2 and general fatigue (data not shown). From a healthcare perspective, results were more favorable for HI exercise as shown by higher probabilities of cost-effectiveness, e.g., if healthcare decision-makers are not willing to pay anything per unit of effect gained, the probability of cost-effectiveness was 0.97 for all outcome measures. When we assumed that all scheduled exercise sessions needed to be paid for (SA2), we found comparable results. When patients who had a disease recurrence during follow-up were excluded from the analyses, (SA4), the mean difference in total societal costs between HI and LMI exercise was smaller (i.e., versus -2429). Additionally, HI exercise had slightly lower probabilities of being cost-effective in comparison with LMI (i.e., 0.89 versus 0.96 at 80,000 /QALY). However, the societal cost difference was in favor of HI exercise, in both the main analysis and SA4, and the differences in effect were comparable.

86 84 CHAPTER 5 TABLE 4 Mean costs per participant in the high intensity (HI) and low-to-moderate intensity (LMI) exercise groups and cost differences between both groups during follow-up Cost category LMI n=138; mean (SEM) HI n=139; mean (SEM) Mean cost difference Model 1 a (95%CI) Mean cost difference Model 2 b (95%CI) Intervention costs 815 (15) 858 (11) 43 (8;77) 40 (7;76) 42 (8;75) Mean cost difference Model 3 c (95%CI) Healthcare costs 6232 (993) 4148 (522) (-3816;-464) (-3851;-438) (-3816;-443) Primary care 2494 (385) 2127 (384) -370 (-1102;471) -333 (-1073;491) -342 (-1056;493) Secondary care 2644 (657) 1515 (226) (-2237;-204) (-2295;-201) (-2274;-200) Medication 1093 (227) 505 (75) -584 (-917;-280) -578 (-915;-276) -584 (-917;-268) Informal care costs 1964 (344) 2095 (478) 136 (-590;949) 163 (-566;969) 151 (-552;954) Absenteeism costs 7527 (942) 6759 (845) -696 (-2630;1241) -523 (-2462;1369) -523 (-2450;1394) Unpaid productivity costs 264 (35) 197 (29) -67 (-140;6) -63 (-137;8) -67 (-138;5) Sports costs 552 (73) 566 (90) 18 (-138;192) 25 (-132;197) 26 (-128;197) Total costs (1720) (1327) (-5983;767) (-5850;942) (-5798;933) Abbreviations: n, number; CI, confidence interval; SEM, standard error of the mean, a solely corrected for follow-up duration; c Corrected for follow-up duration, age, and gender; d random intercept for hospital and corrected for follow-up duration, age, and gender.

87 Longer-term effects of exercise 85 TABLE 5 Differences in pooled mean costs and effects (95% confidence intervals), incremental cost-effectiveness ratios, and the distribution of incremental cost-effect pairs around the quadrants of the cost-effectiveness planes Analysis Sample size Outcome C (95% CI) E (95% CI) ICER Distribution CE-plane (%) LMI HI Points /point NE 1 SE 2 SW 3 NW 4 Main analysis Imputed dataset QALYs (Range: 0-1) (-5798;933) (-0.006;0.061) General fatigue (0-20) (-5798;933) (-0.61;0.29) Hand-grip strength (kg) (-5798;933) 0.14 (-1.72;2.01) PeakVO 2 (ml/kg/min) (-5798;933) (-1.40;1.37) Post-hoc analysis Healthcare perspective QALYs (Range: 0-1) (-3786;-412) (-0.006;0.061) General fatigue (0-20) (-3786;-412) (-0.61;0.29) Hand-grip strength (kg) (-3786;-412) 0.14 (-1.72;2.01) PeakVO 2 (ml/kg/min) (-3786;-412) (-1.40;1.37) Sensitivity analysis Fixed intervention costs QALYs (Range: 0-1) (-5849;907) (-0.006;0.061) General fatigue (0-20) (-5849;907) (-0.61;0.29) Hand-grip strength (kg) (-5849;907) 0.14 (-1.72;2.01) PeakVO 2 (ml/kg/min) (-5849;907) (-1.40;1.37) Sensitivity analysis Patients with disease recurrence excluded (SA4) QALYs (Range: 0-1) (-4692;2063) (-0.009;0.059) General fatigue (0-20) (-4692;2063) (-0.63;0.26) Hand-grip strength (kg) (-4692;2063) 0.16 (-1.58;1.90) PeakVO 2 (ml/kg/min) (-4692;2063) (-1.31;1.25) Abbreviations: C, costs; E, effects; ICER, incremental cost-effectiveness ratio; CE-plane, cost-effectiveness-plane; QALYs, quality adjusted life years 1 Refers to the northeast quadrant of the CE-plane, indicating that high intensity training is more effective and more costly than low-to-moderate intensity training 2 Refers to the southeast quadrant of the CE-plane, indicating that high intensity training is more effective and less costly than low-to-moderate intensity training 3 Refers to the southwest quadrant of the CE-plane, indicating that high intensity training is less effective and less costly than low-to-moderate intensity training 4 Refers to the northwest quadrant of the CE-plane, indicating that high intensity training is less effective and more costly than low-to-moderate intensity training

88 86 CHAPTER 5 DISCUSSION At longer-term (i.e., 64 weeks), effects on role and social function were significantly larger for HI than for LMI exercise. Within-group changes showed that intervention-induced improvements in cardiorespiratory fitness and HRQoL found at short-term, were successfully maintained at longer-term for HI and LMI exercise, whereas fatigue returned to baseline levels. Also, HI exercise was cost-effective for QALYs, compared to LMI exercise. The mean improvements in peakvo 2 after exercise (HI:4.3 ml/kg/min, LMI:3.5 ml/ kg/min) at longer-term were in line with the mean improvement of 3.3 ml/kg/min found in cancer survivors after supervised exercise, as reported in a previous meta-analysis [2]. In contrast with the tendency of a dose-response relationship of exercise intensity at shortterm [9], no significant differences in peakvo 2 were found at longer-term between HI and LMI exercise. Nevertheless, the exercise-induced benefits on peakvo 2 at short-term were successfully maintained over time in both exercise groups. Although, this is hopeful, we should acknowledge that, compared to healthy adults, the patients level of peakvo 2 at longer-term was still poor [14]. Apparently, a 12-week exercise program is too short for patients to fully recover to normative values. At longer-term, hand-grip strength and lower body muscle function were not significantly different between HI and LMI exercise. A previous meta-regression analysis revealed that the effects of resistance training on muscle strength may be more dependent on volume than on intensity [31]. Additional head-to-head comparisons of exercise programs with different exercise parameters (i.e., frequency, intensity, type, time) are therefore warranted to define the optimal exercise dose on muscle strength for cancer survivors. Furthermore, increases in both strength outcomes between short- and longer-term for both groups suggest that these improvements result from increased uptake of daily activities during follow-up. At longer-term, self-reported fatigue did not differ significantly between HI and LMI exercise, and in both groups, it returned to baseline values between short- and longer-term. This lack of sustainable improvements in fatigue is in line with previous studies [3], and may be related to the patients low self-efficacy in managing fatigue, particularly while resuming daily activities without supervision and support from a physiotherapist [32]. On the other hand, self-reported fatigue in a longitudinal study is also susceptible to response-shift bias, resulting from a change in the internal standard of fatigue perception throughout the cancer continuum [33]. We found a significant better social and role function for HI exercise compared to LMI exercise at longer-term. In addition, longer-term effects on global QoL and physical function

89 Longer-term effects of exercise 87 tended to be larger for HI than LMI exercise, but this was not significant. Overall, current findings reveal a possible dose-response relationship of exercise intensity for some HRQoL domains among cancer survivors. Hence, a previous meta-analysis reported significant exercise effects on global QoL and social function, but not on role function [4]. Based on our significant effects on role function it may be hypothesized that participants gain confidence from completing a HI exercise program [34] resulting in improvements in a person s role in society. Furthermore, the exercise-induced benefits on HRQoL were successfully maintained over time in both interventions, despite the return to baseline levels of fatigue. This indicates that besides fatigue, which is found to mediate the exercise effect on HRQoL [35], other factors also contribute to HRQoL. The lack of a significant difference between HI and LMI exercise in psychological distress at longer-term is in contrast with a previous meta-analysis reporting small but significant reductions in depression and anxiety after exercise at short- and longer-term, compared to usual care [4,36]. Yet, our study lacked a non-exercise group and the mean values for both outcomes were already low at baseline, leaving little room for improvement. Furthermore, from short- to longer-term, anxiety returned to baseline in both groups, despite the beneficial short-term intervention effects on anxiety after HI exercise [9]. So, HI exercise might be more effective in reducing anxiety compared to LMI exercise, however, sustainability is lacking which may reflect the vulnerability of psychosocial recovery [37]. Comparable with our short-term findings [9] there were no significant differences between HI and LMI exercise in body composition and objectively measured PA at the longer-term. This may be related to the design of our exercise interventions. To successfully reduce fat mass, complementary dietary changes may be required [38] and improving and maintaining PA may require specific behavioral change techniques (e.g., motivational interviewing [39], goal setting [40]). Our finding that BMI significantly increased from short- to longer-term in HI exercise is unexpected, and its clinical meaningfulness may be questioned, as it was not supported by changes in %FM and %LM. At the lower bounds of the Dutch and UK willingness-to-pay threshold (i.e., 20,000 and 24,400 /QALY gained, respectively), the probability of HI exercise being cost-effective compared to LMI exercise was 0.91 and increased even more with increasing willingnessto-pay values. Thus, at longer-term, HI exercise can be considered cost-effective compared with LMI exercise for QALYs, if decision makers are willing to accept a probability of costeffectiveness of 0.91 and to pay 20,000 /QALY. The relatively high probabilities of costeffectiveness seemed to be related to lower healthcare costs in HI exercise. Although smaller, the healthcare costs were still lower after excluding patients with disease recurrence, and

90 88 CHAPTER 5 HI exercise remained cost-effective. Current results support previous results of a systematic review showing acceptable cost-effectiveness ratios for cancer rehabilitation programs that produced significant health gains [8] compared to usual care. As willingness-to-pay thresholds are lacking for peakvo 2, hand-grip strength and general fatigue, strong conclusions about HI s cost-effectiveness as compared to LMI exercise for these outcomes cannot be made. Strengths of this study include the direct comparison between HI and LMI exercise, longer-term (cost-)effectiveness analyses, multicenter RCT design, large sample size, use of valid and reliable outcome measures, and the use of state-of-the-art statistical methods. However, some limitations are noteworthy. First, to limit non-participation and minimize contamination, a WLC group was included instead of a non-exercising control group. This hampered the exercise interventions longer-term effectiveness evaluation, because all participants had received an exercise intervention at 64 weeks. Second, cost data were collected using self-report, which may have caused social desirability and/or recall bias. Third, a relatively large number of participants had missing cost data. To deal with this limitation, missing data were multiply imputed [41]. Finally, it should be acknowledged the CEA results might not be generalized to other countries with different healthcare systems and/or payment structures [42]. In conclusion, at longer-term follow-up, we found a larger intervention effect on role and social function for HI than LMI exercise. Exercise-induced benefits in peakvo 2 and HRQoL were successfully maintained between short- and longer-term, but not for fatigue. Furthermore, HI exercise was cost-effective for QALYs compared to LMI exercise, mostly due to significant lower healthcare costs in HI exercise. Hence, the current findings advocate the implementation of supervised exercise as part of standard cancer care, and if possible HI exercise.

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95 CHAPTER 6 Determinants of exercise adherence and maintenance among cancer survivors: a systematic review Caroline S. Kampshoff Femke Jansen Willem van Mechelen Anne M. May Johannes Brug Mai J.M. Chinapaw Laurien M. Buffart International Journal Behavioural Nutrition and Physical Activity, 2014; 11: 80.

96 94 CHAPTER 6 ABSTRACT Background: For an exercise intervention to be successful, it is important that cancer survivors adhere to the prescribed program. To be able to improve adherence and to preserve achieved beneficial effects, insights into the relevant and modifiable determinants is important. Therefore, we aimed to systematically review determinants of exercise adherence and maintenance in cancer survivors using a socio-ecological approach. Methods: Studies were identified in PubMed, Embase, PsycINFO and SPORTDiscus up to July We included full-text articles that: 1) were conducted among adult cancer survivors; 2) quantitatively assessed factors associated with intervention adherence and maintenance, and 3) were published in English. The methodological quality of the selected studies was examined. A best evidence synthesis was applied. Results: Eighteen studies were included. Median methodological quality was 53% and ranged from 21-78% of maximum score. Twelve studies focused on determinants of exercise adherence and evaluated 71 potential determinants: 29 demographic and clinical, 27 psychological, ten physical, four social factors, and one environmental factor. Six studies focused on determinants of exercise maintenance after completion of an intervention, and investigated 63 factors: 22 demographic and clinical, 28 psychosocial, nine physical, three social and one environmental factor. We found moderate evidence for a positive association between exercise history and exercise adherence. Inconsistent findings were found for age, gender and education as well as for psychological factors such as stage of change, perceived behavioral control, self-efficacy, extraversion, attitude, intention, fatigue, and quality of life, and physical factors including cardiovascular fitness, body mass index, and baseline physical activity. Discussion: Exercise history is positively associated with exercise adherence. Future trials should further study the influence of social and environmental determinants on exercise adherence and maintenance in addition to demographic, psychological and physical determinants.

97 Systematic review of exercise adherence and maintenance 95 INTRODUCTION The 5-year survival rates across all cancers have increased in the United States from 49% in to 68% in [1]. In the Netherlands, the 5-year survival rates across all cancers have increased to 54% for men and 62% for women in [2]. Besides these major advances in disease free and overall survival rates, many cancer survivors face physical and psychological problems such as reduced physical fitness [3] and quality of life [4]. Physical activity (PA i.e., any bodily movement that results in energy expenditure [5]) and exercise (i.e., specific type of PA that is planned, structured, and repetitive and aims to improve or maintain physical fitness, performance or health [5]) are increasingly recognized as promising interventions aiming to counteract cancer- and treatment-related problems [6]. Systematic reviews and meta-analysis showed beneficial effects of exercise programs on aerobic fitness [7], muscle strength [8], quality of life [9-11], fatigue [12] and depression [13], however the reported effect sizes are generally small to moderate, varying from 0.10 to 0.54 [14-16]. International evidence-based PA guidelines recommend exercise programs as a conditional part of care for all cancer survivors [16-20]. For an exercise program to be successful, it is important that cancer survivors adhere to the prescribed program. Yet, exercise adherence during and after cancer treatment is reported as challenging [21]. Adherence can be defined as the degree of attendance or completion of prescribed exercise sessions [22]. To be able to improve adherence, insights into its relevant and modifiable determinants is important. Previous reviews showed that cancer survivors exercise stage of change, exercise intention and perceived behavioral control were significantly associated with exercise intervention adherence [23,24]. Furthermore, demographic determinants such as lower age and lower body mass index (BMI) were found to be associated with exercise intervention adherence [25]. In order to receive a better understanding of exercise adherence, socio-ecological models of determinants of health behaviors posit that potential social and environmental determinants should be taken into account in addition to demographic, physical, and psychological determinants [26,27]. However, previous reviews on determinants of exercise adherence among cancer survivors lack a complete overview of different types of determinants, a thorough methodological quality assessment, or a presentation of findings from multivariate analysis [23,25]. Furthermore, Courneya and colleagues [28] suggested that determinants of adherence to exercise during cancer treatment may differ from determinants after completion of primary cancer treatment. The Physical Activity and Cancer Control (PACC) framework [6] distinguishes four time periods after a cancer diagnosis:

98 96 CHAPTER 6 pretreatment, during treatment, survivorship and end-of-life. Little is known about the most important determinants in the different time periods. To be able to preserve achieved beneficial effects on physical and psychological outcomes, cancer survivors need to maintain exercising after completion of an exercise intervention. Maintaining higher levels of exercise may also reduce the risk of cancer death and recurrence [29-31]. Despite beneficial effects, for many cancer survivors it appears to be difficult maintain sufficient levels of PA [32]. Therefore, a better understanding of determinants of exercise maintenance is needed. In summary, in cancer survivors, little is known about the determinants of exercise adherence and maintenance in the different phases of cancer survivorship. Identifying these determinants provides insight into possible opportunities to optimize adherence to exercise interventions for cancer survivors, and may help health care professionals to personalize future interventions and target specific patient groups who need additional support (e.g., low adherers or maintainers). Therefore, the aim of this systematic literature review is to identify determinants of exercise adherence and exercise maintenance. In addition, we aim to differentiate between determinants of exercise adherence in cancer survivors before, during and after primary cancer treatment according to the PACC framework [33]. METHODS Literature search The databases, PubMed (dates of coverage: 1950-present), Embase (1947-present), PsycINFO (1880-present) and SPORTDiscus (1800-present), were searched from inception to July An information specialist of the VU University Medical Center was consulted for the development of the search strategy. Relevant keywords included terms related to the intervention (e.g., PA, exercise, sports, training) AND the participants (e.g., cancer, neoplasm, tumor) AND adherence (e.g., adherence, adaptation) AND relevant personal and environmental factors (e.g., correlates, determinants). The full search strategy is available on request. In addition, studies were identified from reference lists of relevant studies retrieved from the primary search. Eligibility criteria Studies were included if: 1) they were performed in adult ( 18 years) cancer survivors before, during and/or after primary cancer treatment; 2) they quantitatively assessed factors associated with exercise intervention adherence or factors associated with exercise

99 Systematic review of exercise adherence and maintenance 97 maintenance after completion of an intervention; 3) original full-text was available in English. Studies were excluded if they reported on an exercise intervention consisting of a PA recommendation only, factors associated with adherence to a lifestyle intervention that combined exercise with other behaviors (e.g., diet) or a yoga intervention consisting of breathing techniques, relaxation or meditation only. Selection process and data extraction Screening of all four databases was performed in two phases. First, titles and abstracts of identified articles were screened by two independent reviewers (CK and FJ) to exclude articles out of scope. In case of disagreement, the full-text was screened for eligibility. Second, fulltexts of the retrieved articles were screened for eligibility by both reviewers. Disagreement between the two reviewers was resolved by discussion. When necessary, a third reviewer (LB) was consulted. Next, data was extracted using a standardized form including the following items: cancer diagnosis, study population (including the number, age and gender of patients), type of exercise intervention, cancer-related time period, adherence or maintenance rates and definitions, and results (i.e., potential determinants of exercise adherence or maintenance). Determinants of exercise adherence and exercise maintenance were assessed separately. Each factor was scored as positively (+) or negatively associated ( ) if the association was statistically significant (p<0.05), or borderline significant (p<0.10), otherwise, we labelled the factor as no evidence for an association (0). In case included studies evaluated the associations using both univariable and multivariable analyses, we used the results from the multivariable analysis. Categorization of determinants Determinants were categorized into five groups according to the ecological model of health behavior; (i) demographic and clinical (e.g., age, stage of disease and date of diagnosis), (ii) psychological (e.g., Trans Theoretical Model (TTM) stage of change and health-related quality of life), (iii) physical (e.g., past exercise behavior, muscle strength and body composition), (iv) social (e.g., family support), and (v) environmental factors (e.g., location of fitness center). Determinants of exercise adherence were categorized into three time periods after cancer diagnosis according to the PACC framework: pre-treatment, during treatment and after treatment (survivorship and end-of-life care). Methodological quality assessment The methodological quality of the included studies was assessed using an 11-item methodological quality assessment tool adapted from existing quality criteria lists [34-36].

100 98 CHAPTER 6 The quality list included items on (i) study population and participation (three items); (ii) study attrition (two items); (iii) data collection (three items) and (iv) data analysis (three items) (Table 2). Further, the items distinguished between informativeness (I, three items) and validity/precision (V/P, eight items) [34]. Two reviewers (CK and FJ) independently conducted the quality assessment. If the study provided information on a quality item and met the criterion, we gave a positive score. If the study provided information on a quality item but did not meet the criterion, we gave a negative score. In case of no or insufficient information, we scored the quality item with a question mark. When an article referred to another study containing relevant information for scoring the quality items, the study of interest was retrieved. If the additional study did not provide the requested information, a question mark was given. For items on reliability and validity of a measurement tool (items F and G), we separately evaluated the reliability and validity of the measurement tool used for each individual factor, and weighed the scores. For example, if a study assessed 20 singular associated factors of which 11 were measured with a reliable tool, a score of 0.55 (11/20) was given for reliability. Therefore, the total score for item F and G ranged from 0 to 1. Disagreements in the methodological quality assessment were resolved by discussion and, if necessary, by consulting the third reviewer (LB). For each study, we calculated a total methodological quality score by counting the number of items scored positively on the validity/ precision (V/P) criteria divided by the total number of validity/ precision criteria (i.e., 8). According to Chinapaw and colleagues [34] the three informativeness (I) criteria were omitted from our calculation, because these criteria represent descriptive information only. Therefore, the total score of methodological quality could range from 0 to 8. We defined a study to be of high methodological quality when it scored 70% of the criteria as positive (+) and of low methodological quality when it scored <70% of the criteria as positive [37]. Level of evidence To synthesize the methodological quality of the studies and to be able to draw conclusions regarding the determinants of exercise adherence and maintenance, we applied a bestevidence synthesis. This rating system consists of three levels and takes into account the number, methodological quality and consistency of outcomes of the studies as follows [37,38]: A) strong evidence: consistent findings in multiple ( 2) high-quality studies; B) moderate evidence: consistent findings in one high quality study and at least one low-quality study, or consistent findings in multiple ( 2) low-quality studies; C) insufficient evidence: only one study available or inconsistent findings in multiple ( 2) studies. Results were considered to be consistent when at least 75% of the studies showed results in the same direction.

101 Systematic review of exercise adherence and maintenance 99 RESULTS The electronical database search yielded 11,839 records. After removing duplicates, 9,012 titles and abstracts were screened and 213 potentially relevant articles were retrieved in fulltext. Finally, 18 articles met the in- and exclusion of the present review (Figure 1). Main study characteristics, including the type of cancer, study population, exercise intervention, and definition and results of exercise adherence or maintenance are presented in Table 1. One study focused on determinants of exercise intervention adherence before treatment [39], four studies during treatment [40-43], five studies after treatment [44-48], and two studies during and after treatment [49,50]. Six studies focused on determinants of exercise maintenance [51-56]. Three studies examined determinants of exercise adherence [40,44,49] and maintenance [51-53] in the same sample, but published in separate articles. FIGURE 1 Flowchart of conducted literature search and study inclusion Total search: 11,839 2,827 duplicates removed 9,012 records screened on title and abstract 8,797 records excluded that were out of scope 213 full-text articles assessed for eligibility 18 studies included 195 full-text articles excluded: - no PA intervention (n=144); - factors associated with adherence not reported (n=11); - design (n=18): qualitative design (n=11), mediation analysis (n=1), protocol (n=1); review (n=5); - no full-tekst article (n=18): dissertation/book (n=6), abstract (n=12); - not performed in patients with cancer (n=4)

102 100 CHAPTER 6 TABLE 1 Characteristics of the included studies (n=18) First author, year Cancer diagnosis Study population (number of patients (n); mean age ± SD; %female) Pretreatment Peddle, 2009 [39] Lung cancer n=19; 64 ± 10y; 68% female During treatment Courneya, 2008 [40] Breast cancer n=160; 49y; 100% female Klepin, 2011 [41] Acute myelogenous leukemia Shang, 2012 [42] Mixed (34% breast cancer) n=24; 65.1 ± 7.8y; 62.5% female n=68; 59.8 ± 10.8y; 39.7% female Swenson, 2010 [43] Breast cancer n=29; 46.9y (range: 40 54); 100% female After treatment Courneya, 2004 [44] Colorectal cancer n=62; 59.9 ± 10.7y; 45.2% female Courneya, 2004 [45] Prostate cancer n=82; 68.2 ± 7.9y; 0% female Latka, 2009[46] Breast cancer n=37; 56.5 ± 9.5y; 100% female McGuire, 2011 [47] Breast cancer n=120; 58.7y, 100% female Study design Single-group trial Three-armed RCT Single-group trial Two-armed RCT Two-armed RCT Two-armed RCT Two-armed RCT Two-armed RCT Two-armed RCT Exercise intervention AET 5 times a week for the duration of surgical wait time (range 4 13 weeks) 1) Supervised AET or 2) RET, 3 times a week for the duration of CT (ranging from 12 to 24 weeks) Supervised AET and strength training 3 times a week for 4 weeks Home-based walking intervention 5 times a week for the duration of RT/CT (ranging from 5 to 35 weeks) Tools and advise to perform minimal 10,000 steps per day (PA assessed over 12 months) Home-based AET 3 5 times a week for 16 weeks RET at fitness center 3 times a week for 12 weeks Supervised AET 3 times a week and home-based AET 2 times a week for 6 months Home-based strength training for 8 months and strength training in a fitness center for the following 16 months, both 2 times a week Outcome measures adherence or maintenance Percentage of the prescribed number of sessions attended Percentage of the expected number of sessions attended Number of exercise sessions completed Percentage of patients meeting the personalized exercise prescription > 2/3 of the study period Percentage of patients meeting the exercise prescription of 10,000 steps per day Average min/week of moderatestrenuous AET performed Number of observed exercise session attended Average min/week of moderateintensity AET performed (prescribed 150 min.) Percentage of the prescribed number of sessions performed Adherence/ maintenance (% or mean ± SD) 73 ± 35% Group 1: 72.0 ± 30.1%; Group 2: 68.2 ± 28.4% 2.7 ± % 74% 91.5 ± min/week 28.2 ± ± 52.4 min/week 62%

103 Systematic review of exercise adherence and maintenance 101 Pinto, 2009 [48] Breast cancer n=43; 53.4 ± 9.1; 100% female Two-armed RCT Home-based walking intervention 2 5 days a week for 12 weeks Percentage of patients meeting the exercise prescription 54-91% During and after treatment Courneya, 2002 [49] Mixed (41% breast cancer) n=51; 52.5 ± 10.2y; 84.4% female Courneya, 2010 [50] Lymphoma n=60; 52.8y (range: 18 77); 38.3% female Two-armed RCT Two-armed RCT Home-based AET 3 5 times per week for 10 weeks Supervised AET 3 times a week for 12 weeks Average min/week of moderatestrenuous AET performed Percentage of the expected number of sessions attended ± min/week 78 ± 30% Maintenance Courneya, 2004 [51] Mixed (40% breast cancer) n=30; 54.9 ± 8.0y; 77% female Courneya, 2009 [52] Breast cancer n=201; 49y; 100% female Courneya, 2011 [53] Lymphoma n=110; 44 pt <55y and 66 pt 55y; 43.6% female Loprinzi, 2012 [54] Breast cancer n=69; 70.6 ± 1.2y; 100% female Rogers, 2011 [55] Breast cancer n=36; 53 ± 9y; 100% female Vallance, 2010 [56] Breast cancer n=266; 57y (range 36 90); 100% female Two-armed RCT Three-armed RCT Two-armed RCT Three-armed RCT Two-armed RCT Four-armed RCT Home-based AET 3 5 times per week for 10 weeks 1) Supervised AET; 2) RET 3 times a week for the duration of CT (ranging from 12 to 24 weeks) or 3) a delayed 1 month supervised program for usual care patients 1) Supervised AET 3 times a week for 12 weeks or 2) a delayed 1 month supervised program for usual care 1) Supervised AET; 2) supervised RET or 3) supervised stretching and relaxation exercise 3 times a week for 12 months 1) 12 individual supervised exercise sessions, 6 discussion group sessions and 3 individual face-toface counseling sessions over a 3 month period or 2) information on PA after a cancer diagnosis Exercise recommendation and 1) nothing, 2) exercise for health book, 3) pedometer or 4)exercise for health book and pedometer Average min/week of moderatestrenuous exercise Meeting AET and/or RET guidelines Percentage of patients meeting ACSM guideline Activity status based on TTM stages Daily minutes of activity of moderate-strenuous activity Percentage of patients meeting ACSM guideline ± min/week Neither: 42.3%; either: 36.8%; both: 20.9% 55.5% Sufficiently active: 57%; Insufficiently active: 43% Group 1: min/day; Group 2: 92 min/day 49.2% Abbreviations: ACSM, American College of Sports Medicine; AET, aerobic exercise training; CT, chemotherapy; PA, physical activity; RE, resistance exercise training; RT, radiotherapy; TTM, Transtheoretical Model.

104 102 CHAPTER 6 Outcome measures of adherence and maintenance In four studies [39,40,47,50], exercise adherence was defined as a percentage of the prescribed number of sessions attended (mean: 62-78%), three studies [44,46,49] used the average minutes of exercise per week (mean: min/week), three studies [42,43,48] used the percentage of survivors meeting the exercise prescriptions (mean: 54-74%), and two studies [41,45] used the number of completed exercise sessions (mean: 3 of 12 [41] and 28 of 36 [45] sessions), see Table 1. Maintenance was defined by average minutes of PA per week (mean: ) in two studies [51,55], by percentage of survivors meeting the PA guideline (mean: 37-56%) in three studies [52,53,56], and the number of survivors in the action or maintenance stage of change compared to the number of survivors in the precontemplation, contemplation or preparation stage (i.e., 57% and 43%) in one study [54]. Methodological quality The median methodological quality score of the included studies was 53% and the range was 21% to 78% (Table 2). One study [40] was of high methodological quality. Of all studies, 84% had shortcomings related to the selection of the study sample (item C), 74% had shortcomings related to the sample size (item D), and 63% had shortcomings related to the assessment of adherence (item H), and analysis (item J). Determinants of exercise adherence Twelve studies focused on determinants of exercise adherence and evaluated 71 factors: 29 demographic and clinical, 27 psychological, ten physical, four social factors, and one environmental factor. In total, 19 demographic and clinical, 18 psychological, and eight physical factors, and one environmental factor were examined in two or more studies (Table 3). We found moderate evidence that exercise history was positively associated with exercise adherence during and after cancer treatment (Table 3). Due to inconsistent findings, there was insufficient evidence for an association of gender, type of treatment, perceived behavioral control, stage of change, self-efficacy, extraversion, cardiovascular fitness, and fitness center with exercise adherence. Education level, income, time since diagnosis or treatment, tumor localization, type of surgery, radiotherapy, chemotherapy, comorbidity, attitude, social norms, quality of life, anxiety, baseline PA, body composition and muscle strength were examined in three or more studies, which all found no significant association with exercise adherence (Table 3). Insufficient evidence was also found for ten demographic and clinical, nine psychological, two physical, and four social factors that were studied in one single article (Table 4).

105 Systematic review of exercise adherence and maintenance 103 TABLE 2 Methodological quality assessment tool and quality score of the included studies (n=18) Exercise intervention adherence Exercise maintenance after completion of an intervention pre during after during/after Items/Reference [39] [40] [42] [43] [41] [45] [47] [44] [46] [48] [49] [50] [56] [53] [52] [51] [55] [54] score (%) Study population and participation Topic A. Description of cancer type, stage and treatment I B. Description of inclusion and exclusion criteria I C. Positive if the participation rate at baseline was at V/P ? least 80%, or if the non-response was not selective a Study attrition D. Number of patients included in the analysis 100 V E. Positive if the response at first follow-up was at least 80%, or if the non-response at first follow-up was not selective b V/P Data collection F. Positive if determinants of adherence were V/P measured with a reliable tool c G. Positive if determinants of adherence were V/P measured with a valid tool d H. Adherence was measured by an objective tool e V/P Data analysis I. Multivariate analysis techniques was used. V/P J. Results were presented as point estimates (mean differences/beta s/correlation coefficients) and measures of variability (SD, standard error or CI) I K. Positive if number of samples is at least 10 times the number of independent variables V/P Total quality score (%) f Abbreviations: 1, study provided information on the quality item and met the criterion; 0, study provided information on the quality item but did not meet the criterion;?,study provided no or insufficient information on the quality item. I: informativeness; V: validity/p: precision. a attrition analyses were performed and results showed no significant differences between baseline study sample and population of eligible subjects; b attrition analyses were performed and results showed no significant differences between dropouts and follow-up participants; c associated factors showed internal consistency of Cronbach's alpha 0.70 or test-retest correlations of 0.80 or κ/icc For clinical factors a standardized protocol was followed by trained researchers; d associated factors showed correlations of 0.80 or κ/icc 0.70 with similar constructs. For physical variables (i.e., past physical activity and past sedentary behavior) an objective measurement instrument (i.e., accelerometer/pedometer) was used. For clinical variables a standardized protocol was followed by trained researchers; e For walking interventions: adherence or maintenance was measured by accelerometer or pedometer read out by the researcher. For supervised exercise : the trainer reported presence of the participant; f the number of items scored positively on the validity/precision (V/P) criteria divided by the total number of validity/precision criteria (i.e., 8).

106 104 CHAPTER 6 TABLE 3 Determinants of exercise adherence Overall During treatment After treatment N N + (ref) N- (ref) N0 (ref) LoE N N + (ref) N- (ref) N0 (ref) N N + (ref) N- (ref) N0 (ref) Demographic & Clinical Age 12 1 [50] 1 [45] 10 [39-49] C 4 4 [40-43] 5 1 [45] 4 [44,46-48] Being married 10 1 [47] 1 [42] 8 [40,41,44-46,48-50] C 3 1 [42] 2 [40,41] 5 1 [47] 4 [44-46,48] Education 9 9 [40-42,44-46,48-50] C 3 3 [40-42] 4 4 [44-46,48] Employment 8 1 [44] 7 [40,42,43,45,47,49,50] C 3 3 [40,42,43] 3 1 [44] 2 [45,47] Gender 6 2 [39,49] 4 [41,42,44,50] C 2 2 [41,42] 1 1 [44] Income 5 5 [40,44,45,49,50] C 1 1 [40] 2 2 [44,45] Smoking 4 1 [50] 3 [39,40,45] C 1 1 [40] 1 1 [45] Race 2 2 [41,42] C 2 2 [41,42] 0 Disease stage 10 1 [40] 9 [42-50] C 3 1 [40] 2 [42,43] 5 5 [44-48] Time since diagnosis/ treatment 7 7 [44-50] C [44-48] Type of treatment 5 2 [44,50] 3 [42,46,47] C 1 1 [42] 3 1 [44] 2 [46,47] Tumor localization 4 4 [42,44,49,50] C 1 1 [42] 1 1 [44] Type of surgery 4 4 [40,43,44,46] C 2 2 [40,43] 2 2 [44,46] Radiotherapy 4 4 [43-45,49] C 1 1 [43] 2 2 [44,45] Chemotherapy 3 3 [44,49,50] C [44] Comorbidity 3 3 [39,41,47] C 1 1 [41] 1 1 [47] Chemotherapy cycle Type of chemotherapy 2 2 [43,50] C 1 1 [43] [40,50] C 1 1 [40] 0 Surgery 2 2 [45,49] C [45] Psychological Attitude 6 6 [39,40,44,45,49,50] C 1 1 [40] 2 2 [44,45] Intention 6 1 [45] 5 [39,40,44,49,50] C 1 1 [40] 2 1 [45] 1 [44] Perceived behavioral control 6 3 [39,44,49] 3 [40,45,50] C 1 1 [40] 2 1 [44] 1 [45]

107 Systematic review of exercise adherence and maintenance 105 Social norms 6 6 [39,40,44,45,49,50] C 1 1 [40] 2 2 [44,45] Quality of life 6 6 [40,41,43,45,46,50] C 3 3 [40,41,43] 2 2 [45,46] Stage of change 5 3 [44-46] 2 [45,48] C [44-46] 2 [45,48] Fatigue 5 1 [42] 4 [40,43,45,50] C 3 1 [42] 2 [40,43] 1 1 [45] Depression 4 1 [40] 3 [41,46,50] C 2 1 [40] 1 [41] 1 1 [46] Self-efficacy 3 1 [48] 2 [39,50] C [48] Anxiety 3 3 [40,46,50] C 1 1 [40] 1 1 [46] Extraversion 2 1 [49] 1 [44] C [44] Distress 2 2 [41,42] C 2 2 [41,42] 0 Neuroticism 2 2 [44,49] C [44] Openness 2 2 [44,49] C [44] Agreeable 2 2 [44,49] C [44] Conscientiousness 2 2 [44,49] C [44] Self-esteem 2 2 [40,50] C 1 1 [40] 0 Happiness 2 2 [46,50] C [46] Physical Exercise history 3 3 [42,47,50] B 1 1 [42] 1 1 [47,50] Body mass index 10 2 [46,50] 8 [39-45,47] C 4 4 [40-43] 4 1 [46] 3 [44,45,47] PA at baseline 7 7 [40,41,43,44,46,48,49] C 3 3 [40,41,43] 3 3 [44,46,48] Body composition 6 6 [40,43,44,46,49,50] C 2 2 [40,43] 2 2 [44,46] Cardiovascular fitness Physical functioning 5 2 [40,42] 3 [44,49,50] C 2 2 [40,42] 1 1 [44] 4 1 [41] 3 [42,43,50] C 3 1 [41] 2 [42,43] 0 Muscle strength 3 3 [40,41,45] C 2 2 [40,41] 1 1 [45] Flexibility 2 2 [44,49] C [44] Environmental Fitness center 2 1 [40] 1 [45] C 1 1 [40] 1 1 [45] Abbreviations: N+, number of studies showing a positive association; N-, number of studies showing a negative association; N0, number of studies showing no association; LoE, Level of Evidence: A=strong evidence; B=moderate evidence; C=insufficient evidence. PA, physical activity.

108 106 CHAPTER 6 TABLE 4 Determinants of exercise adherence or maintenance examined in one single study (insufficient evidence) Demographic & Clinical Psychological Physical Social Environmental Children at home A [43] Barriers M [55] Exercise frequency M [51] Having exercise partner M [55] Fitness center M [52] Drinking A [45] Behavioral beliefs A [49] Exercise limitations M [56] Having exercise role model M [55] Gender M [50] Control beliefs A [49] Exertion during PA A [43] Providing feedback A [47] Rural versus urban A [47]/M [56] Controllability M [56] General health A [50]/M [50] Promoting knowledge A [47] ADT therapy A [45] Decision balance A [48] Physical functioning M [50] Promoting self-efficacy A [47] Chemotherapy M [56] Expectations M [55] Support by friend/family M [55] Chemotherapy A [43] Expected success M [51] Support by health day professional Hormone A [46]/M [56] Happiness M [50] treatment Lymphoma A [46]/M [50] Locus M [51] symptoms Premenopausal M [56] Mood disturbance A [42] Radiotherapy M [56] Negative affect M [51] Relapse disease A [41]/M [50] Normative beliefs A [49] Chemotherapy M [50] PA enjoyment M [55] response Serological A [41] PA fear M [55] parameter Time since M [50] PA preference M [50] diagnosis Treatment status M [50] PA pros/cons M [54] Treatment regime M [50] Perceived stress A [46] Type of biopsy A [43] Perceived success M [51] Type of surgery M [52] Personal control M [51] Planning M [56] Positive affect M [51] Self esteem M [52] Stability M [51] Symptoms A [43] Sleep disturbance A [42] Stage of change M [54] View on PA amount A [43] A [47] Abbreviations: A=determinants of exercise adherence; M=determinants of exercise maintenance. ADT, Androgen Deprivation Therapy; PA, physical activity.

109 Systematic review of exercise adherence and maintenance 107 Determinants of exercise maintenance Six studies focused on determinants of exercise maintenance after completion of an intervention, and investigated 63 factors: 22 demographic and clinical, 28 psychosocial, nine physical, three social and one environmental factor. In total, nine demographic and clinical, ten psychological, and five physical factors were examined in two or more studies (Table 5). Due to inconsistent findings, there was insufficient evidence for an association of age, education, self-efficacy, instrumental and affective attitude, fatigue, quality of life, intention, PA intervention adherence, body mass index, baseline PA and cardiovascular fitness with exercise maintenance. Being married, income, employment, disease stage and social norms were examined in three studies, which all found no significant association with exercise maintenance (Table 5). There was insufficient evidence of 13 demographic and clinical, 18 psychological, four physical, three social and one environmental factor that were evaluated in one single study (Table 4). TABLE 5 Determinants of exercise maintenance Demographic & Clinical N N+ (ref) N- (ref) N0 (ref) LoE Age 3 2 [52,56] 1 [53] C Education 3 1 [56] 2 [52,53] C Being married 3 3 [52,53,56] C Income 3 3 [52,53,56] C Employment 3 3 [52,53,56] C Smoking 2 2 [52,53] C Disease stage 3 3 [52,53,56] C Chemotherapy cycle 2 2 [52,53] C Type of chemotherapy 2 2 [52,53] C Psychological Self-efficacy 4 2 [54,56] 2 [53,55] C Instrumental attitude 3 2 [52,56] 1 [53] C Affective attitude 3 1 [56] 2 [52,53] C Fatigue 3 2 [52,56] 1 [53] C Quality of life 3 1 [56] 2 [52,53] C Intention 3 2 [53,56] 1 [52] C Perceived behavioral control 2 2 [52,53] C Social norms 3 3 [52,53,56] C Anxiety 2 2 [52,53] C Depression 2 2 [52,53] C Physical PA intervention adherence 4 2 [51,56] 2 [52,53] C Body mass index 4 2 [52,54] 2 [53,56] C PA at baseline 3 2 [52,56] 1 [53] C Body composition 2 2 [52,53] C Cardiovascular fitness 2 1 [54] 1 [50] C Abbreviations: N+, number of studies showing a positive association; N-, number of studies showing a negative association; N0, number of studies showing no association. LoE, Level of Evidence: A=strong evidence; B=moderate evidence; C=insufficient evidence. PA, physical activity.

110 108 CHAPTER 6 DISCUSSION This study provides a comprehensive overview of determinants of exercise intervention adherence and exercise maintenance after completion of an intervention in cancer survivors. Eighteen studies were evaluated using a socio-ecological model of determinants of health behaviors, taking into account demographic and clinical, psychological, physical, social and environmental factors. Most studies examined demographic and clinical, psychological and physical factors, whereas few studies investigated social and environmental factors. We found moderate evidence for a positive association between exercise history and exercise intervention adherence. For most demographic and clinical factors, we found insufficient evidence of an association with exercise adherence or maintenance. For exercise adherence, inconsistent findings were found for gender, type of treatment, as well as for psychological factors including perceived behavioral control, stage of change, self-efficacy, extraversion, the physical factor cardiovascular fitness and the environmental factor location of the fitness center. For exercise maintenance, we found inconsistent findings for age, education, selfefficacy, fatigue, attitude, quality of life, intention, PA intervention adherence, body mass index, baseline PA and cardiovascular fitness. Similar to the review of Szymlek-Gay and colleagues [25], lower age, lower body mass index, more advanced disease stage, higher degree of readiness to change PA behavior, higher self-efficacy, higher physical fitness, and higher baseline PA were identified as possible determinants of exercise adherence. However, according to our best evidence synthesis, the level of evidence was insufficient mainly due to inconsistent findings across studies. In contrast to our review, Husebø et al. found exercise stage of change, intention, perceived behavioral control, and subjective norm to be a significant determinant of exercise adherence in their meta-analysis [23]. However, although statistically significant, the strength of the associations were low (<0.3). They extracted their results from univariate analysis instead of multivariate analysis which may have overestimated the strength of the associations. Most demographic and clinical factors were not significantly associated with exercise adherence or maintenance. The lack of statistically significant associations may be related to small sample sizes and the relatively low variability of exercise adherence and maintenance. Most studies were conducted as efficacy trials, evaluating the effects of exercise in ideal circumstances, in which usually a more homogenous group of patients participated with a relatively high adherence [57]. On the contrary, effectiveness trials evaluating intervention effects under real-world conditions, generally have lower adherence levels [57]. More well-powered studies are needed on determinants of exercise adherence in real-world

111 Systematic review of exercise adherence and maintenance 109 circumstances. Although, most demographic and clinical factors, such as age, gender and type of cancer, are unmodifiable, insight into these factors provide valuable information about which subgroups of patients that are more or less likely to adhere to exercise programs or maintain exercise behaviors. From previous research, it is well known that social factors including social support, having an exercise partner or role model, may influence exercise behavior [55] or exercise behavior change [58]. From studies in the general population it is also known that the physical or built environment improving the availability, accessibility, and attractiveness of exercise opportunities (e.g., sidewalks, bicycle lanes, safe road crossings, availability of green spaces and recreation facilities) are related to exercise behavior [26]. Because cancer survivors may experience even more barriers than the general population, social support, as well as attractive and easily accessible exercise facilities may even be more important determinants for cancer survivors compared to the general population. However, only few studies have evaluated the association of social and environmental factors with exercise adherence and maintenance in cancer survivors. The few studies published to date suggest that feedback from trainers or nurses was positively associated with exercise adherence [47], whereas no significant association was found of social support, having an exercise partner or role model [55] and the location of the fitness center [52] with exercise maintenance. Future studies are needed to further build the evidence for the influence of social and environmental factors on exercise adherence and maintenance. Methodological quality Overall, the methodological quality of the reviewed studies was low, with only one study of high quality [40]. A major concern regarding the quality of most included studies was the high likelihood of selection bias and small sample sizes. The included studies conducted secondary data analysis of RCTs that were not designed to evaluate determinants of exercise adherence. Further, many studies did not report point estimates and measures of variability. Another frequent methodological shortcoming was the lack of valid and reliable measures of adherence and maintenance. We recommend to systematically report session attendance in a supervised exercise intervention and/or using accelerometers of pedometers to assess PA levels. Strengths and limitations Strengths of this systematic review include the extensive literature search in multiple relevant databases, the in-depth methodological quality assessment and best evidence synthesis,

112 110 CHAPTER 6 as well as the presentation of determinants within ecological framework categorizing demographic and clinical, psychological, physical, social and environmental factors. Another strength is the attempt to differentiate determinants of adherence to exercise interventions at different time points during cancer survivorship according to the PACC framework. However, due to the limited number of studies we were unable to study differences in determinants of exercise adherence before, during and after cancer treatment. The limited number of studies also hampered us to examine whether determinants of exercise adherence vary across cancer types and exercise modalities such as mode (e.g., aerobic versus resistance exercises), delivery (e.g., supervised versus home-based), intensity and frequency. Further work is necessary to determine the most important determinants of exercise adherence and maintenance, and to study differences across cancer types and exercise modalities. Another limitation is the variety of definitions of exercise adherence, with some studies exclusively focusing on adherence, whereas other studies also incorporated a measure on compliance, i.e., whether the PA was conducted at the prescribed intensity [22]. As a result, we could not differentiate between determinants of exercise adherence and determinants of compliance. Therefore, future studies should more clearly distinguish exercise adherence and compliance. Finally, similar to other reviews and meta-analysis, publication bias cannot be ruled out. Conclusion This systematic review showed that exercise history was positively associated with exercise adherence. Further, inconsistent findings were found for age, gender and education as well as for psychological factors such as stage of change, attitude, intention, perceived behavioral control, self-efficacy, extraversion, fatigue, and quality of life, and physical factors including cardiovascular fitness, body mass index, and baseline PA. Future effectiveness trials are needed on the influence of social and environmental factors on exercise adherence and maintenance in addition to demographic, psychological and physical factors. In addition, future studies should provide insight into differences in determinants across timing of exercise interventions (e.g., before, during and after cancer treatment), cancer types and exercise modalities.

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117 CHAPTER 7 Participation in and adherence to physical exercise after completion of primary cancer treatment Caroline S. Kampshoff Willem van Mechelen Goof Schep Marten R. Nijziel Lenja Witlox Lisa Bosman Mai J.M. Chinapaw Johannes Brug Laurien M. Buffart International Journal Behavioural Nutrition and Physical Activity, 2016; 13(1):100.

118 116 CHAPTER 7 ABSTRACT Background: The purpose of this study was to identify demographic, clinical, psychosocial, physical and environmental factors that are associated with participation in and adherence to a combined resistance and endurance exercise program among cancer survivors, shortly after completion of primary cancer treatment. Data from the randomized controlled Resistance and Endurance exercise After ChemoTherapy (REACT) study were used for this study. Methods: The participants of the REACT study were randomly allocated to either a high intensity (HI) or low-to-moderate intensity (LMI) exercise program. Patients participation rate was defined as the cancer survivors decision to participate in the REACT study. Exercise adherence reflected participants attendance to the scheduled exercise sessions and their compliance to the prescribed exercises. High session attendance rates were defined as attending at least 80% of the sessions. High compliance rates were defined as performing at least of 90% of the prescribed exercise across all sessions. Correlates of exercise adherence were studied separately for HI and LMI exercise. Demographic, clinical, and physical factors were assessed using self-reported questionnaires. Relevant clinical information was extracted from medical records. Multivariable logistic regression analyses were applied to identify correlates that were significantly associated with participation, high session attendance, high compliance with resistance and high compliance with endurance exercises. Results: Participants were more likely to have higher education, be non-smokers, have lower psychological distress, higher outcome expectations, and perceive more exercise barriers than non-participants. In HI exercise, higher self-efficacy was significantly associated with high session attendance and high compliance with endurance exercises, and lower psychological distress was significantly associated with high compliance with resistance exercises. In LMI exercise, being a non-smoker was significantly associated with high compliance with resistance exercises and higher BMI was significantly associated with high compliance with resistance and endurance exercises. Furthermore, breast cancer survivors were less likely to report high compliance with resistance and endurance exercises in LMI exercise compared to survivors of other types of cancer. The discriminative ability of the multivariable models ranged from 0.62 to Conclusion: Several demographic, clinical and psychosocial factors were associated with participation in and adherence to exercise among cancer survivors. Psychosocial factors were more strongly associated with adherence in HI than LMI exercise.

119 Correlates of exercise participation and adherence 117 INTRODUCTION Supervised exercise programs following cancer diagnosis show significant and clinically relevant beneficial effects on cardiorespiratory fitness [1], general and physical fatigue [2] and quality of life (QoL) [3]. More specifically, exercise can improve cancer survivors cardiorespiratory fitness, thereby reducing fatigue and improve global QoL and physical function [4]. In addition, observational studies have reported positive associations of physical activity [5] and fitness [6] with cancer-free and overall survival, yet, randomized controlled trials (RCTs) need to establish causality. The success of RCTs evaluating exercise programs depends largely on patients participation and exercise adherence rates. Patients participation rate reflects the decision by cancer survivors whether or not to participate in a randomized controlled trial evaluating exercise interventions. Exercise adherence reflects participants session attendance rates and their compliance to the prescribed exercises. A better understanding of the modifiable and unmodifiable correlates that are associated with participation in and adherence to exercise interventions may inform future interventions and facilitate successful implementation of exercise programs among cancer survivors. Modifiable correlates (e.g., psychosocial) provide insights into intervention target components via which improvements in participation or adherence might be achieved. Unmodifiable correlates, such as demographics (e.g., age) or clinical variables (e.g., treatment type) indicate which subgroups of patients are most at risk for non-participation or low exercise adherence rates and can thus help to identify relevant target populations for intervention. Correlates of participation in exercise trials during primary cancer treatment have been investigated by two previous trials [7,8] and both studies reported that participants who perceived higher levels of fatigue were less likely to participate in the exercise trials. Furthermore, minimizing practical barriers to participation such as travel distances to practices and flexible training schedules were suggested as promising strategies to enhance participation in forthcoming studies [7,8]. However, correlates of participation in exercise trials after completion of primary cancer treatment have not been studied yet, and may differ from those during primary cancer treatment. In a recent systematic review, we identified correlates of exercise adherence among cancer survivors [9], and found exercise history to be significantly associated with exercise adherence. Other important demographic, clinical, psychosocial and environmental correlates of exercise adherence could not be distinguished due to the limited number of studies, or the inconsistency of findings across the reviewed manuscripts. Moreover, the

120 118 CHAPTER 7 definition of exercise adherence varied across the reviewed studies. Some studies exclusively focused on session attendance rate, while other studies also incorporated a measure on compliance. Therefore, more research is warranted. The current study aimed to identify demographic, clinical, psychosocial, physical and environmental factors that are associated with participation in an exercise program and exercise adherence among cancer survivors, shortly after completion of primary cancer treatment. We used data of the Resistance and Endurance exercise After ChemoTherapy (REACT) study [10], a RCT that evaluated the effectiveness of a high intensity (HI) and low-to-moderate intensity (LMI) exercise compared to a waiting list control (WLC) group shortly after completion of primary cancer treatment on physical fitness, fatigue and healthrelated quality of life (HRQoL). We found that HI and LMI exercise significantly improved cardiovascular fitness, reduced fatigue and improved quality of life [11]. METHODS Detailed procedures of the REACT study have been reported elsewhere [11]. Briefly, the REACT study was a multicenter RCT in which 277 cancer survivors were randomized into three study arms: HI exercise, LMI exercise, and a WLC group. Between 2011 and 2013, patients were recruited from 9 hospitals in the Netherlands. Patients aged 18 years with histologically confirmed breast, colon, ovarian, cervix or testis cancer, or lymphomas with no indication of recurrent or progressive disease, who had completed ((neo-)adjuvant) chemotherapy were eligible. Patients were excluded if they were unable to perform basic physical activity, had cognitive disorders, severe emotional instability, comorbidities that might hamper capacity of carrying out HI exercise, or were unable to understand and read the Dutch language. This study was approved by the Medical Ethics Committee of the VU University Medical Centre [2011/240] and the local ethical boards of all participating hospitals. (Non-)Participation Patients who were willing to participate were invited for baseline measurements, 4-6 weeks after completion of primary cancer treatment. After baseline measurements, participants were stratified by cancer type and hospital, and randomly assigned to one of the three study arms. HI and LMI groups started with their 12-week exercise program directly after randomization (i.e., direct start). Participants from the WLC group were also randomly

121 Correlates of exercise participation and adherence 119 allocated to HI or LMI exercise. However, they started exercising 12 weeks later. Patients who chose not to participate (i.e., non-participants) were invited to complete a one-time survey that was similar in content and timing to the baseline questionnaire of the REACT participants. Written informed consent was obtained from both non-participants and participants, including permission to extract relevant information from their medical records. Finally, from patients who chose to refrain from any participation, only age, gender and cancer type were documented. Exercise interventions Full details of the 12-week HI and LMI exercise programs have been described previously [10]. In short, both interventions included two one-hour supervised resistance and endurance exercise sessions per week and were identical with respect to exercise frequency, type, and duration, and differed only in exercise intensity (Table 1). Both exercise programs included six resistance exercises targeting large muscle groups with a frequency of two sets of 10 repetitions. Workload per exercise was defined by an indirect one repetition maximum (1- RM) measurement. Furthermore, both programs included two types of endurance interval exercises, aiming to maximize improvements in cardiorespiratory fitness. In the first four weeks patients cycled 2x8 minutes with alternating workloads. Workloads were defined by the maximum short exercise capacity (MSEC) estimated by the steep ramp test. From the fifth week onwards, one additional endurance interval session was added, substituting 8 minutes of cycling. This interval session consisted of three times 5 minutes cycling at a constant workload. Here, the workload was defined by the heart rate reserve (HRR), using the Karvonen formula. Twenty-one local physiotherapists supervised all training sessions. In the Netherlands, people are generally used to short travel distances to their health care providers and therefore patients trained at local physiotherapists practices close to the patients homes. The availability of flexible training hours and the possibility to join a rehabilitation group differed per practice. Furthermore, the start of the exercise programs was linked to the time point of completion of the primary cancer treatment of the individual cancer survivor. Consequently, the training hours and the availability of group sessions varied.

122 120 CHAPTER 7 TABLE 1 Exercise intensities of the HI and LMI resistance and endurance exercise programs Resistance exercises (1-RM) a (6 exercises targeting the large muscle groups) Endurance interval exercises Part A (MSEC) a (8 min alternating workload) Endurance interval exercises Part B (HRR) a (3x5 min constant workload) Counseling High intensity 70-85% 30/65% 80% Participants were (HI) exercise b encouraged to start or Low-to-moderate intensity (LMI) exercise b 40-55% 30/45% 40-50% maintain a physically active lifestyle in addition to the supervised exercise sessions. Abbreviations: 1-RM, one repetition maximum; MSEC, maximum short exercise capacity; HRR, heart rate reserve; a Every four weeks (week 1, 5 and 9), the physiotherapist evaluated training progress, and adjusted the workload accordingly. b Exercises were accompanied with BORG scores and heart rate monitors to guide the physiotherapists. In the occasion that the training intensity seemed too high or too low, the 1-RM, MSEC or HRR were reassessed. Adherence Adherence was defined as attendance to the prescribed number of sessions and compliance with the prescribed intensity, frequency and duration of the prescribed resistance and endurance exercises [12,13]. Both session attendance rates and compliance rates were retrieved from exercise logs completed by the physiotherapists. Session attendance was defined as the number of supervised exercise sessions attended, divided by the number of supervised exercise sessions offered. Compliance with resistance exercises was defined in terms of intensity and volume (Table 2), in which compliance with the intensity of the resistance exercises was calculated by the performed training load, divided by the prescribed training load and compliance with the volume of the resistance exercises was calculated by the performed number of repetitions, divided by the prescribed number of repetitions. The average value of compliance with intensity and volume provided the overall measure for compliance with the resistance exercises. Compliance with endurance exercises was defined as exercise duration (in minutes), divided by the prescribed exercise duration (Table 2). The average of this parameter provided the overall measure for compliance with the endurance exercises. Next, the normality assumption was tested for session attendance, compliance with resistance training, and compliance with endurance training. Since they were skewed, and to facilitate clinically meaningful interpretation, we dichotomized adherence outcome variables based on clinically-relevant cut-off points. In line with previous studies, high session attendance was defined as attending at least 80% of the sessions [14]. We defined high compliance rates as performing at least 90% of the resistance and endurance exercises according to the prescribed dosage. This cut-off point of 90% allowed some deviation due

123 Correlates of exercise participation and adherence 121 to the rounded weights and settings of the local training equipment, while maintaining a sufficient distinction between HI and LMI exercise. TABLE 2 Outcome measures of compliance to the prescribed exercises Compliance Resistance exercises Endurance interval exercises Intensity Volume Duration Used load a Prescribed load x Performed repetitions 100% x Prescribed repetitions 100% Performed duration b Prescribed duration x 100% a load in kilograms, b time in minutes Assessment of correlates Demographic data were collected using a self-report questionnaire and included age at baseline (in years), gender (0=male; 1=female), marital status (0=no partner; 1=married or de facto), education (0=low/intermediate; 1=high), employment status (0=no paid employment; 1=paid employment), smoking status (0=non-smoker; 1=smoker) and sport history (0=no; 1=yes). Furthermore, participants travel distance to the exercise program (in kilometres) was calculated based on zip codes of the patient s home and location of training facility. Clinical information was retrieved from medical records and included cancer type (0=breast cancer; 1=other (i.e., colon, ovarian, cervix or testis cancer, or lymphomas)), stage of disease (0=stage I-II, 1=stage III-IV), previous treatment with surgery (0=no; 1=yes), radiation therapy (0=no; 1=yes), immunotherapy (0=no; 1=yes), hormone therapy (0=no; 1=yes), and two or more of the following comorbidities (0=no; 1=yes) including heart disease, lung disease, diseases of the digestive system, diseases of the nervous system, endocrine disease, mental disorder, rheumatism or arthritis, or chronic pain [15]. In addition, body weight of the REACT participants was measured to the nearest 0.1 kilogram on a digital scale, with light clothes on and no shoes. Body height was measured to the nearest 0.1 centimetres without shoes. Body mass index (BMI) was calculated from the measured body weight and height accordingly. Patient-reported outcomes have been reported elsewhere [16] and included general fatigue (subscale of the Multidimensional Fatigue Inventory (MFI) [17]), global quality of life (subscale of the European Organisation Research and Treatment of Cancer - Quality of Life Questionnaire C30 (EORTC QLQ C-30) [18]), psychological distress (Hospital Anxiety and

124 122 CHAPTER 7 Depression Scale (HADS) [19]) and self-reported physical activity using the Physical Activity Scale for the Elderly questionnaire (PASE) [20]. Patient-reported behavioral and attitudinal factors towards exercise included a series of questions that were based on health behavior theories, in particular the Theory of Planned Behavior [21]. Current attitude towards exercise participation was measured by one item In my opinion regular exercise is.. rated on 5-point Likert scale (1=very bad to 5=very good) [21]. Barriers to exercise behavior (Cronbach s α=0.85) were measured using 18 items (e.g., my disease, insufficient motivation, lack of energy), rated on 5-point Likert scale (1=never to 5=very often) [21,22]. Outcome expectations regarding exercise participation (Cronbach s α=0.91) included 12 items (e.g., increase my health, feel better about myself, and be more physically fit), rated on a 5-point Likert scale (1=strongly disagree to 5=strongly agree) [21,22]. Exercise self-efficacy (Cronbach s α=0.83) was assessed with the following question How confident are you that you will be physically active in the following situations, including feeling tired, bad mood, do not have the time, on vacation, and, want to be active outside, but bad weather, rated on a 10-point Likert scale (1=absolutely no confidence, 10=completely confident) [23]. Social support for exercise (Cronbach s α=0.92) was assessed using the statement: The following people are supportive of my regular PA, followed by: family, friends, and other cancer patients, rated on a 5-point Likert scale (1=strongly disagree to 5=strongly agree) [24]. Mean scores were calculated for potential correlates that comprised more than one item. After that, all factors were linearly transformed to a scale. Exercise stage was measured using the response options derived from the Transtheoretical Model [25]: (1) no intention to exercise; (2) intention to exercise; (3) irregular exercise; (4) started exercising 30 minutes a day in last 6 months; or (5) exercising 30 minutes a day for longer than 6 months, that were dichotomized into nonexerciser (response options 1-3) and exerciser (response options 4-5). Statistical analyses The statistical analyses were performed using IBM SPSS Statistics (SPSS Inc., Evanston, IL, version 22.0). Descriptive statistics (mean and standard deviations (SD)) were calculated for all outcome variables. Data on BMI and travel distance to the exercise program for the non-participants were not available due to trial logistics. Correlations between all potential correlates were checked for multicollinearity (r 0.60). Multicollinearity was present between cancer type and gender. Because cancer type was most strongly associated with exercise adherence, this variable was included in the model instead of gender. Differences in session attendance rates and compliance rates between HI and LMI exercise were tested using chi-

125 Correlates of exercise participation and adherence 123 square tests. To examine whether correlates of exercise adherence differed between HI and LMI exercise groups, we added an interaction term of the correlate with the interventions into a regression model, separately for each correlate. As significant interaction terms were found for education, cancer type, self-efficacy, and psychological distress, we performed stratified analyses for HI and LMI exercise. All analyses on exercise adherence were performed according to an intention-to-treat principle. Univariable and multivariable logistic regression analyses were conducted to identify factors that were significantly correlated with participation and exercise adherence. Separate multivariable logistic regression analyses with a forward selection procedure were carried out for each outcome variable: participation, high session attendance and high compliance with the resistance and endurance exercises. By default, timing of intervention (i.e., direct start or WLC group) was retained as covariate in the univariable and multivariable models of exercise adherence. First the independent variables with a p-value 0.25 in the univariable analyses were selected for further analyses. After that, a multivariable stepwise forward selection procedure was undertaken by identifying the correlates that was most strongly associated with the dependent variable. Subsequently, the next strongest related correlate was then selected after controlling for the first correlate. Only variables with a p-value of 0.05 were retained in the final multivariable model. The regression coefficients (β) and odds ratio (OR) with 95% confidence interval (CI) were reported accordingly. In addition, the model fit was evaluated by the area under the receiver operating characteristic curve (AUC) with 95% CI. RESULTS In total, 277 out of 757 eligible patients (37%) participated. Furthermore, 179 patients (24%) did not participate in the trial, but completed the one-time survey (i.e., non-participants). Selfreported reasons for non-participation were having too many things on one s mind (n=72), already exercising (n=30), not wanting to be randomized (n=20), and not interested to participate in a clinical trial (n=13). For 44 non-participants the reason of non-participation was unknown. Baseline demographic, clinical, psychosocial and physical characteristics of the participants and non-participants are presented in Table 3. 90% of the participants and non-participants underwent surgery to treat cancer, revealing very little variability within our population. Therefore, we omitted surgery as a potential correlate for participation and exercise adherence from the multivariable regression analyses.

126 124 CHAPTER 7 High session attendance was found in 76% and 67% of the participants in HI and LMI groups respectively (p=0.10). High compliance with resistance exercises was found in 69% of the participants in HI and in 67% of participants in LMI (p=0.80). High compliance with endurance exercises was found in 47% of the participants in HI and in 42% of the participants in LMI (p=0.40). TABLE 3 Baseline characteristics of (non-) participants Non-participants (n=179) HI (n=139) Participants (n=277) LMI (n=138) Demographic Age, mean (SD) years 55 (10.6) 54 (10.7) 53 (11.4) Gender, n (%) male 26 (15) 29 (21) 26 (19) Marital status, n (%) having a partner 150 (84) 112 (81) 120 (87) Education, n (%) high 43 (25) 52 (38) 53 (39) Employment status at baseline, n (%) yes 91 (51) 85 (61) 82 (59) Smoking status at baseline, n (%) yes 27 (15) 9 (7) 8 (6) Sport history, n (%) yes 100 (57) 72 (52) 83 (61) Clinical Cancer type, n (%) Breast 120 (67) 92 (66) 89 (65) Colon 31 (17) 25 (18) 24 (17) Ovarian 6 (3) 8 (6) 4 (3) Lymphoma 16 (9) 10 (7) 16 (12) Cervix 4 (2) 0 4 (3) Testis 2 (1) 4 (3) 1 (1) Stage of disease, n (%) Local 123 (69) 103 (74) 84 (61) Advanced 55 (31) 36 (26) 54 (39) Type of treatment, n (%) Surgery 161 (90) 127 (91) 123 (89) Radiation therapy 87 (49) 74 (53) 61 (44) Immunotherapy 33 (18) 23 (17) 36 (26) Hormone therapy 81 (45) 67 (48) 61 (44) Comorbidities 2, n (%) yes 26 (15) 16 (12) 14 (10) BMI in kg/m 2, mean (SD) n.a (4.5) 26.6 (4.2) Psychosocial General fatigue (MFI), mean (SD) a 12.3 (4.4) 12.8 (3.8) 12.9 (4.2) Global HRQoL (QLQ-C30), mean (SD) b 70.9 (18.2) 71.3 (16.2) 73.1 (16.1) Psychological distress (HADS), mean (SD) c 8.2 (6.4) 7.4 (5.7) 7.6 (5.3) Attitude, mean (SD) 83.8 (21.2) 85.2 (22.3) 87.3 (18.8) Perceived barriers, mean (SD) 23.1 (12.4) 28.6 (12.3) 25.8 (12.0) Outcome expectations, mean (SD) 71.8 (17.8) 75.0 (15.7) 76.0 (13.7)

127 Correlates of exercise participation and adherence 125 Non-participants (n=179) HI (n=139) Participants (n=277) LMI (n=138) Self-efficacy, mean (SD) 64.8 (17.9) 59.9 (16.6) 61.1 (16.4) Social support, mean (SD) 79.3 (21.1) 81.9 (20.6) 81.0 (20.1) Physical Exercise stage, n (%) d 85 (50) 66 (49) 67 (49) Self-reported PA (91.3) 96.7 (69.0) (83.1) Environmental Travel distance to the exercise program (in kilometres), mean (SD) n.a. 7.3 (6.1) 6.5 (4.8) Adherence High session attendance, n (%) (76) 93 (67) High compliance with resistance exercises, n (%) - 93 (69) 87 (67) High compliance with endurance exercises, n (%) - 64 (47) 55 (42) Abbreviations: SD, standard deviation; n, number; HI, high intensity exercise; LMI, low-to-moderate intensity exercise; BMI, body mass index, n.a., not applicable; PA, physical activity; n.a., not applicable; a Range 4-20, higher score means a higher level of self-reported general fatigue; b Range 0-100, higher score means a higher level of self-reported global HRQoL; c Range 0-36, higher means a higher level of anxiety and/or depression; d currently exercising. Correlates of participation The results of the univariable and multivariable logistic regression analyses are presented in Table 4. The multivariable regression model showed that participants were more likely to have higher education, (OR=1.79, 95%CI:1.14;2.82), non-smoking habits (OR=0.46, 95%CI:0.23;0.92), lower psychological distress (OR=0.94, 95%CI:0.91;0.98), higher outcome expectations (OR=1.02, 95%CI:1.01;1.04), and perceive more exercise barriers (OR=1.05, 95%CI:1.03;1.07) than non-participants. The AUC for this model was 0.69 (95%CI:0.64;0.74). No significant associations were found between participation and treatment-related or physical characteristics. Correlates of adherence in HI In HI exercise, higher self-efficacy was significantly associated with high session attendance (OR=1.06, 95%CI:1.03;1.09; AUC=0.75, 95%CI:0.66;0.84) and high compliance with endurance exercises (OR=1.05, 95%CI:1.02;1.07; AUC=0.68, 95%CI:0.59;0.77) (Table 5). Furthermore, less psychological distress (OR=0.87, 95%CI:0.81;0.94) was significantly correlated with high compliance with resistance exercises in HI (AUC=0.69, 95%CI:0.60;0.79). Demographic, treatment-related, physical and environmental characteristics were not significantly associated with exercise adherence in HI exercise.

128 126 CHAPTER 7 TABLE 4 Odds ratios and their 95% confidence intervals as results from univariable and multivariable logistic regression analyses with participation as dependent variable and demographic, clinical, psycho social and physical variables as independent variables Demographic Univariable, OR (95% CI) Age 0.99 (0.97;1.00) Gender 0.69 (0.41;1.14) Marital status 1.00 (0.60;1.66) Multivariable, OR (95% CI) Education 1.89 (1.24;2.89) 1.79 (1.14;2.82) Employment status 1.47 (1.01;2.15) Smoking status 0.37 (0.19;0.70) 0.46 (0.23;0.92) Sport history 0.99 (0.67;1.44) Clinical Cancer type 1.08 (0.73;1.61) Stage of disease 1.08 (0.72;1.61) Type of treatment Radiation therapy 1.01 (0.69;1.46) Immunotherapy 1.20 (0.75;1.93) Hormone therapy 1.04 (0.71;1.52) Comorbidities ( 2) 0.72 (0.41;1.25) Psychosocial General fatigue 1.03 (0.99;1.08) Global HRQoL 1.01 (0.99;1.02) Psychological distress 0.98 (0.95;1.01) 0.94 (0.91;0.98) Attitude 1.01 (1.00;1.02) Perceived barriers 1.03 (1.01;1.05) 1.05 (1.03;1.07) Outcome expectations 1.02 (1.00;1.03) 1.02 (1.01;1.04) Self-efficacy 0.99 (0.97;1.00) Social support 1.01 (1.00;1.01) Physical Exercise stage 0.97 (0.66;1.42) Self-reported PA 1.00 (1.00;1.00) Abbreviations: p 0.05 in bold; OR, odds ratio; CI, confidence interval; HRQoL, Health-related quality of life; PA, physical activity. Correlates of adherence in LMI In LMI exercise, being a non-smoker (OR=0.16, 95%CI:0.03;0.91) was significantly associated with high compliance with resistance exercises (Table 5), and higher BMI was significantly associated with high compliance with resistance exercises (OR=1.11, 95%CI:1.00;1.23) and endurance exercises (OR=1.11, 95%CI:1.01;1.21). Furthermore, breast cancer survivors were less likely to report high compliance with resistance exercises (OR=3.25, 95%CI:1.31;8.02) and high compliance with endurance exercises (OR=2.94, 95%CI:1.38;6.27) in LMI than survivors of other types of cancer. The AUC for the models of high compliance with resistance and endurance exercises were 0.69 (95%CI:0.60;0.79) and 0.67 (95%CI:0.58;0.77), respectively. Treatment-related, psychosocial, physical and environmental characteristics were not significantly associated with exercise adherence in LMI exercise.

129 Correlates of exercise participation and adherence 127 TABLE 5 Odds ratios and their 95% confidence intervals as results from univariable and multivariable logistic regression analyses with session attendance and compliance with the resistance and endurance exercises as dependent variables and demographic, clinical, psychosocial and physical variables as independent variables HI LMI Session attendance Compliance with the resistance exercises Compliance with the endurance exercises Session attendance Compliance with the resistance exercises Compliance with the endurance exercises Univariable, OR (95% CI) Multivariable, OR (95% CI) Univariable, OR (95% CI) Multivariable, OR (95% CI) Univariable, OR (95% CI) Multivariable, OR (95% CI) Univariable, OR (95% CI) Multivariable, OR (95% CI) Univariable, OR (95% CI) Multivariable, OR (95% CI) Univariable, OR (95% CI) Multivariable, OR (95% CI) Demographic Age 1.01 (0.98;1.05) 1.00 (0.97;1.03) 1.01 (0.97;1.04) 1.00 (0.97;1.04) 1.03 (0.99;1.06) 1.01 (0.98;1.05) Gender 0.62 (0.22;1.78) 0.51 (0.19;1.37) 0.97 (0.42;2.20) 0.71 (0.27;1.83) 0.45 (0.16;1.30) 0.41 (0.17;1.01) Marital status 1.15 (0.44;3.04) 1.22 (0.49;3.02) 1.29 (0.54;3.07) 0.70 (0.23;2.14) 0.64 (0.19;2.16) 0.40 (0.14;1.20) Education 1.29 (0.56;2.94) 1.84 (0.84;4.05) 1.67 (0.83;3.38) 0.66 (0.32;1.37) 0.49 (0.23;1.05) 0.45 (0.21;0.94) Employment status 1.66 (0.75;3.66) 0.96 (0.45;2.03) 1.38 (0.69;2.78) 0.84 (0.41;1.75) 0.43 (0.19;0.94) 0.67 (0.33;1.35) Smoking status 0.62 (0.14;2.64) 1.66 (0.33;8.42) 0.90 (0.23;3.52) 0.27 (0.06;1.19) 0.18 (0.03;0.97) 0.16 (0.03;0.91) 0.22 (0.03;1.87) Sport history 1.61 (0.73;3.55) 0.95 (0.46;1.98) 1.22 (0.62;2.41) 1.40 (0.67;2.91) 1.54 (0.72;3.28) 0.92 (0.45;1.89) Clinical Cancer type 1.21 (0.52;2.81) 1.90 (0.83;4.35) 0.94 (0.46;1.93) 2.16 (0.97;4.80) 2.71 (1.16;6.36) 3.25 (1.31;8.02) 2.66 (1.27;5.56) 2.94 (1.38;6.27) Stage of disease 1.39 (0.54;3.56) 1.73 (0.71;4.22) 0.77 (0.36;1.69) 0.82 (0.40;1.69) 0.85 (0.40;1.82) 1.30 (0.63;2.66) Type of treatment Radiation therapy 2.04 (0.92;4.54) 0.79 (0.38;1.65) 1.39 (0.71;2.76) 0.99 (0.49;2.04) 0.90 (0.43;1.88) 1.17 (0.58;2.37) Immunotherapy 0.67 (0.25;1.82) 1.05 (0.40;2.79) 1.96 (0.78;4.91) 0.58 (0.26;1.28) 0.85 (0.37;1.93) 1.11 (0.50;2.44) Hormone therapy 1.17 (0.53;2.58) 0.79 (0.38;1.64) 1.31 (0.67;2.59) 1.32 (0.64;2.72) 0.58 (0.28;1.23) 0.69 (0.34;1.41)

130 128 CHAPTER 7 HI LMI Session attendance Compliance with the resistance exercises Compliance with the endurance exercises Session attendance Compliance with the resistance exercises Univariable, OR (95% CI) Multivariable, OR (95% CI) Univariable, OR (95% CI) Multivariable, OR (95% CI) Univariable, OR (95% CI) Multivariable, OR (95% CI) Univariable, OR (95% CI) Multivariable, OR (95% CI) Univariable, OR (95% CI) Multivariable, OR (95% CI) Comorbidities 0.68 (0.22;2.12) 0.66 (0.22;1.99) 0.73 (0.24;2.17) 0.63 (0.20;1.96) 0.75 (0.23;2.48) BMI 0.93 (0.86;1.02) 0.99 (0.91;1.08) 0.94 (0.87;1.02) 1.05 (0.96;1.15) 1.10 (0.96;1.22) 1.11 (1.00;1.23) Psychosocial General fatigue 0.92 (0.83;1.02) 0.92 (0.83;1.01) 0.97 (0.89;1.06) 1.00 (0.92;1.09) 1.03 (0.94;1.13) Global HRQoL 1.01 (0.99;1.03) 1.02 (1.00;1.04) 1.01 (0.98;1.03) 1.00 (0.98;1.02) 1.00 (0.98;1.02) Psychological distress 0.93 (0.87;0.99) 0.87 (0.81;0.94) 0.87 (0.81;0.94) 0.91 (0.85;0.98) 1.00 (0.94;1.07) 1.01 (0.94;1.08) Attitude 1.03 (1.01;1.04) 1.02 (1.01;1.04) 1.02 (1.00;1.04) 1.01 (0.99;1.03) 1.00 (0.98;1.02) Perceived barriers 0.96 (0.92;0.99) 0.97 (0.94;1.00) 0.98 (0.95;1.01) 0.99 (0.96;1.02) 0.98 (0.95;1.01) Outcome expectations 1.01 (0.99;1.04) 1.00 (0.98;1.03) 1.01 (0.99;1.03) 1.00 (0.98;1.03) 1.02 (0.99;1.05) Self-efficacy 1.06 (1.03;1.09) 1.06 (1.03;1.09) 1.02 (1.00;1.05) 1.05 (1.02;1.07) 1.05 (1.02;1.07) 1.00 (0.98;1.02) 1.00 (0.98;1.03) Social support 1.00 (0.98;1.02) 1.00 (0.99;1.02) 1.00 (0.98;1.02) 1.00 (0.98;1.02) 1.00 (0.98;1.02) Physical Exercise stage 2.96 (1.26;6.93) 1.08 (0.52;2.26) 1.66 (0.83;3.33) 0.86 (0.42;1.77) 1.23 (0.58;2.60) Self-reported PA 1.00 (0.99;1.01) 1.00 (1.00;1.01) 1.00 (1.00;1.01) 1.00 (0.99;1.00) 1.00 (0.99;1.00) Environmental Travel distance to the exercise program 1.03 (0.96;1.11) 0.95 (0.89;1.03) 0.99 (0.92;1.06) 1.00 (0.94;1.07) 1.06 (0.99;1.15) Abbreviations: p 0.05 in bold; OR, odds ratio; CI, confidence interval; HRQoL, Health-related quality of life; BMI, body mass index; PA, physical activity. Compliance with the endurance exercises Univariable, OR (95% CI) Multivariable, OR (95% CI) 1.62 (0.51;5.18) 1.09 (1.00;1.19) 1.11 (1.01;1.21) 1.05 (0.96;1.14) 1.01 (0.99;1.03) 1.00 (0.93;1.07) 1.01 (0.99;1.03) 0.98 (0.95;1.01) 1.00 (0.98;1.03) 1.01 (0.99;1.03) 1.01 (0.99;1.02) 0.65 (0.32;1.32) 1.00 (1.00;1.00) 1.02 (0.96;1.08)

131 Correlates of exercise participation and adherence 129 DISCUSSION The current study identified important demographic, clinical, psychosocial, physical and environmental factors that may influence participation and exercise adherence, aiming to facilitate successful exercise participation among cancer survivors. We found that some demographic and psychosocial factors were significantly associated with exercise participation and adherence. Additionally, we found that psychosocial factors such as psychological distress and self-efficacy were more strongly associated with adherence to HI than LMI exercise. (Non-) participation The current participation rates of 37% are in line with previous exercise trials among cancer survivors, reporting that 35-50% of the eligible patients participated [26,27]. Our finding that patients with a high level of education were more likely to participate supports previous findings in cancer survivors during active cancer treatment [8]. Accordingly, it has repeatedly been found in non-clinical populations that people who attained higher education are more likely to participate in health behavior change interventions [28]. Furthermore, the current study identified non-smoking as a significant correlate of participation. This was in contrast with a study in the Netherlands, who found no significant association between smoking and participation in an exercise trial during cancer treatment. Possibly, cancer survivors who chose to participate in an exercise trial after completion of primary cancer treatment might have experienced a teachable moment, or a need to change, during cancer treatment, including quitting smoking and participating in an exercise program [29]. Finally, we found that lower psychological distress, higher outcome expectations and experiencing more exercise barriers were significantly associated with participation. Our findings that patients with higher psychological distress and lower outcome expectations were more likely to decline participation, is in line with the previous study evaluating exercise programs in breast cancer survivors during chemotherapy [8]. Aiming to successfully target those subgroups of patients, previous studies suggested that clinical practice may benefit from behavior change strategies such as motivational interviewing [30]. Yet, further evidence is needed to determine which approaches are most efficacious among cancer survivors. Our finding that REACT participants reported more exercise barriers than non-participants seems paradoxical. However, it is possible that non-participants were not interested in exercise shortly after completion of primary cancer treatment and consequently perceived fewer barriers to obtain and maintain exercise, or that participants were more open to support from healthcare professionals in overcoming their exercise barriers, compared to the non-participants.

132 130 CHAPTER 7 Adherence In the HI and LMI groups, 76% and 67% of the participants showed high attendance rates, which is within the range reported by other exercise trials following cancer diagnosis [9]. However, comparing studies is limited by the scarcity of studies reporting on session attendance rates to supervised exercise after primary cancer treatment [9]. The compliance rates with the resistance and endurance exercises did not differ significantly between HI and LMI exercise. This suggests that the exercise prescriptions were equally feasible to perform when the participants attended the session. Regarding exercise types, compliance rates with resistance exercises were higher than compliance rates with endurance exercises in both groups. This may suggest that resistance exercises are more feasible for cancer survivors than endurance exercises. However, it may also reflect a lower accuracy of defining the maximum workload by MSEC and HRR for the endurance exercises, compared to the 1-RM measurement for the resistance exercises. Although the steep ramp test is a short maximal exercise capacity test, which has proven to be a reliable and valid method to estimate cardiorespiratory fitness in cancer survivors [31], its anaerobic nature may have overestimated the workload for the endurance exercises. Comparably, the Karvonen formula including HRR is a commonly used method to calculate training workload for endurance exercises [32], however, someone s resting heart rate is prone to day-to-day fluctuations. Psychosocial variables were significantly associated with high session attendance and high compliance with resistance and endurance exercises in HI exercise, but not in LMI exercise. This suggests that an individual s self-efficacy and distress levels are important characteristics while accomplishing a HI exercise program. Hence, including behavioral motivational strategies aiming to improve these psychosocial variables may support cancer survivors in achieving their exercise goals, especially for participants with less favorable scores in these variables to begin with. Participants with less favorable scores could also be recommended to start with LMI exercise, and -after gaining further confidence in exercisingthe exercise intensity could gradually increase over time [33]. In LMI exercise, only being a non-smoker or clinical factors were significantly associated with high compliance with the resistance or endurance exercises. Previous studies in nonclinical populations have suggested that health-related behaviors such as a physically active lifestyle and being a non-smoker tend to cluster [34]. This may explain why non-smokers had higher compliance rates. In contrast, the significant association between higher BMI and high compliance with resistance and endurance exercises seems counterintuitive but may indicate that participants with a lower BMI generally had better physical health and found the training intensity of the LMI exercise program is less challenging. A similar explanation

133 Correlates of exercise participation and adherence 131 could be suggested for breast cancer survivors; LMI exercise might have been too low for them compared to the other five cancer diagnosis with generally lower 5-year survival rates, contributing to lower compliance rates. Though, previous studies also report that breast cancer survivors are more likely to experience difficulties in accomplishing resistance and endurance exercises due to a limited range of motion in the shoulders after surgery [35], cardiorespiratory problems after radiation therapy [36] or joint stiffness as a result of hormone therapy [37]. Yet, in our data we found no significant associations between type of treatment and exercise adherence. Future insight in the role of cancer treatment in exercise adherence is warranted. Strengths and limitations To the best of our knowledge, the current study is the first study assessing correlates of participation in exercise after completion of primary cancer treatment, facilitated by an extensive non-responder questionnaire completed by 179 non-participants. In addition, we assessed factors associated with participants exercise adherence taking into account both session attendance, as well as compliance to the prescribed exercises. We included large sample sizes, a relatively large number of potential demographic, clinical, psychosocial and physical correlates, allowing multivariable regression analyses. Yet, the following limitations should be taken into account. First, 301 of our non-participants (63%) did not complete the extensive non-responder survey, which limits the generalizability of the current findings. Nevertheless, no significant differences in age, gender and cancer type were found between the participants of the one-time survey and the non-responders. Second, the discriminative ability of the models was moderate [38], ranging from 0.62 to This indicates that there may be other variables that were not included in our study that are important to explain differences in participation and adherence rates among cancer survivors. For example, previous research showed that low socioeconomic status was negatively associated with adherence rates in cardiopulmonary rehabilitation [39] and may warrant further investigation among cancer survivors. Moreover, in general, studies that have investigated social and environmental correlates of participation and adherence rates among cancer survivors are scarce. Previous studies in the healthy population showed that social and environmental factors including peer support, physician influence, and access to facilities at flexible time points [40] were significantly associated with exercise participation. Therefore, future studies should examine whether social and environmental factors are associated with participation in and exercise adherence to an exercise program among cancer survivors. Finally, theory-based interventions have shown to be more effective in

134 132 CHAPTER 7 changing behavior than non-theory based interventions [41]. Since the current study showed significant associations of outcome expectations and self-efficacy with participation in or adherence to an exercise program, forthcoming studies might consider a role for behavioral theory such as social cognitive theory or self-determination theory [42], to facilitate a better understanding of exercise behavior in cancer survivors. Conclusion This study showed that cancer survivors who attained a higher level of education, were non-smokers, perceived less psychological distress, had higher outcome expectations and perceived more exercise barriers were more likely to participate in a combined resistance and endurance exercise trial. This is worth acknowledging when promoting exercise participation as part of usual cancer care. Furthermore, the current study found several demographic, clinical and psychosocial factors to be significantly associated with exercise adherence in which, psychosocial factors, such as psychological distress and self-efficacy were more strongly associated with HI than LMI exercise. When offering HI exercise, it may therefore be recommended to screen these variables, and if needed, include additional behavioral motivational strategies or consider starting at a lower training intensity.

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139 CHAPTER 8 Demographic, clinical, psychosocial, and environmental correlates of objectively assessed physical activity among breast cancer survivors Caroline S. Kampshoff Fiona Stacey Camille E. Short Willem van Mechelen Mai J.M. Chinapaw Johannes Brug Ronald Plotnikoff Erica L. James Laurien M. Buffart Supportive Cancer Care, 2016; 24: 3333

140 138 CHAPTER 8 ABSTRACT Background: The aim of this study was to identify demographic, clinical, psychosocial, and environmental correlates of objectively assessed physical activity among breast cancer survivors. Methods: Baseline data were utilized from 574 female breast cancer survivors who participated in three different intervention studies: Resistance and Endurance exercise After ChemoTherapy (REACT), Exercise and Nutrition Routine Improving Cancer Health (ENRICH), and Move More for Life (MM4L). Participants were eligible if they were aged 18 years and had completed primary cancer treatment. Physical activity was objectively assessed by accelerometers or pedometers. Participants completed self-reported questionnaires on demographic, psychosocial, and environmental factors. Information regarding clinical factors was obtained from medical records or patient self-report. Multivariable linear regression analyses were applied on the pooled dataset to identify factors that were significantly correlated with physical activity. In addition, the explained variance of the model was calculated. Results: The multivariable regression model revealed that older age, (β= 0.01, 95%CI= 0.02; 0.003), higher body mass index (β= 0.05, 95%CI= 0.06; 0.03), lower self-efficacy (β=0.2, 95%CI=0.08;0.2), and less social support (β=0.1, 95%CI=0.05;0.2) were significantly correlated with lower physical activity. This model explained 15% of the variance in physical activity. Conclusion: Age, body mass index, self-efficacy, and social support were significantly correlated with objectively assessed physical activity in breast cancer survivors. It may therefore be recommended that physical activity intervention studies in these women target those who are older, and have a higher body mass index, and should operationalize behavior change strategies designed to enhance self-efficacy and social support.

141 Correlates of physical activity 139 INTRODUCTION Physical activity (PA), both during and after cancer treatment, has shown to improve cardiorespiratory fitness [1] and enhance quality of life [2] and has been associated with improved survival in breast cancer survivors (BCS) [3]. Therefore, BCS are advised to avoid physical inactivity, encouraged to engage in aerobic exercises for at least 150 minutes per week and include resistance exercises twice a week [4]. However, shortly after diagnosis, PA tends to decline in many BCS [5], and consequently, most survivors have insufficient levels of PA [6]. Given the beneficial effects of PA on health outcomes, effective interventions to obtain and maintain sufficient PA among BCSs should be pursued. Identifying modifiable and unmodifiable correlates of PA in BCS may facilitate the development of PA interventions. Modifiable correlates (e.g., psychosocial) provide insights of intervention components (i.e., mediators) via which PA improvement might be achieved. Non-modifiable correlates, such as demographics (e.g., age) or clinical variables (e.g., treatment type) indicate which subgroups of patients are most at risk for physical inactivity and can thus help to identify relevant target populations for intervention. Theoretical behavior change frameworks such as the social cognitive theory [7] and socio-ecological models [8] strongly recommend taking a broad range of correlates into account while investigating PA behavior, including demographic, clinical, psychosocial, and environmental factors. Previous studies that have examined correlates of self-reported PA among BCS found a significant association of older age [9,10], higher body mass index (BMI) [9], lower education level [11], and lower social support [10] with lower PA. Self-reported measures of PA have a predominant position in the existing literature, yet, these instruments are prone to over-reporting due to social desirability bias and/or misinterpretation of the survey questions [12]. Objective PA monitoring using accelerometers or pedometers overcomes these limitations and provides a more valid estimate of PA as well as more precise associations with potential correlates [13]. Few recent studies have examined correlates of objectively assessed PA in cancer survivors [14 16]. Boyle et al. reported that demographic factors including older age and lower level of education and clinical factors including no family history of breast cancer were associated with lower PA in BCS [14]. Additionally, in colon cancer and non-hodgkin lymphoma survivors, older age and higher BMI or larger waist circumference were found to be significantly associated with lower PA [15,16]. So while some correlates identified via self-report and objective assessment correspond, there are too few studies in BCS with objective assessments to draw firm conclusions.

142 140 CHAPTER 8 To facilitate the development of effective and targeted interventions aiming to improve PA among BCS, we aimed to identify demographic, clinical, psychosocial, and environmental correlates of objectively assessed PA in a large group of BCS. METHODS Study design The current study has a cross-sectional design and to ensure a large sample size, we utilized baseline data from BCS who had participated in three different intervention studies, i.e., the Resistance and Endurance exercise After ChemoTherapy (REACT) study [17]; Exercise and Nutrition Routine Improving Cancer Health (ENRICH) study [18], and Move More for Life (MM4L) study [19] (n total =574). All three studies were randomized controlled trials that evaluated the effectiveness of a PA or a healthy lifestyle intervention on objectively assessed PA as outcome measure, compared to usual care or a waiting list control group. The REACT study was approved by the Medical Ethics Committee of the VU University Medical Centre. The ENRICH and MM4L studies were approved by the Human Research Ethics Committee of the University of Newcastle (ENRICH: H and MM4L: H ). Full details of the three study designs including the recruitment strategy utilized, inclusion and exclusion criteria, have been described in earlier protocol papers [17 19] and summarized in Figure 1. Eligibility criteria The current analysis included female BCS aged 18 years who had completed primary cancer treatment. Exclusion criteria were (1) being unable to perform basic activities such as self-care and walking, (2) having other serious diseases that hamper PA (e.g., heart failure and cognitive disorders), and (3) being unable to understand and read the first language of the country of recruitment. Participants in the REACT study were recruited from nine hospitals in the Netherlands. In consultation with the treating medical oncologist, the oncology nurse determined if patients in their clinical setting were eligible for the study. Participants in the ENRICH and MM4L studies were recruited via community advertising, health professional, cancer charity, and self-referral (Figure 1).

143 Correlates of physical activity 141 FIGURE 1 In- and exclusion criteria, recruitment strategies and differences between participants and non-participants of the included studies REACT study ENRICH study MM4L study Inclusion criteria 1) histologically confirmed breast, colon, ovarian, cervix or testis cancer, or lymphomas with no indication of recurrent or progressive disease, who had completed primary cancer treatment including chemotherapy; 2) aged 18 years or older; 3) able to perform basic activities of daily living; 4) fluent in Dutch. 1) cancer survivor; 2) signed medical clearance from their General Practitioner; 3) aged 18 years or older; 4) able to perform basic activities of daily living; 5) fluent in English. 1) breast cancer survivor, who had completed primary cancer treatment; 2) aged 18 years or older; 3) able to perform basic activities of daily living; 4) fluent in English. Recruitment Participants were recruited from nine hospitals in the Netherlands. In consultation with the treating medical oncologist, the oncology nurse determined if patients in their clinical setting were eligible for the study. Participants were recruited via multiple methods, including referrals from health professionals, medical centers, professional organizations (such as the Dieticians Association of Australia, New South Wales Oncology Groups), community health centers, cancer support groups, local media, and various Cancer Council NSW resources (website, mailing lists, and publications). Participants were recruited via multiple methods, including referrals from health professionals, medical centers, professional organizations (such as The Breast Cancer Network Australia, The Cancer Council), cancer support groups, and snowballing recruitment (inviting participants to pass on study information to potentially eligible friends and acquaintances). Screened: 757 Screened: 275 Number of participants: 277 Number of participants included in present study: 180 Number of non-participants: 480 Number of participants: 133 Number of participants included in present study: 64 Number of non-participants: 142 Number of participants: 330 Number of participants included in present study: 330 Number of nonparticipants: unable to be estimated Differences participants vs. nonparticipants Age, gender and diagnosis did not differ between the participants and non-participants. Participants were more likely to be female and have a high socioeconomic status compared to nonparticipants. Participants were more likely to be married, educated, and moderately physical active compared to the general breast cancer population. Yet, participants represented the target population of interest in terms of age, disease stage, treatments received, fatigue and health-related quality of life.

144 142 CHAPTER 8 Physical activity In the REACT study, objective PA was assessed using accelerometers (ActiTrainer; Actigraph, Fort Walton Beach Florida, USA). This is a lightweight PA monitor measuring PA using vertical accelerations that were converted into activity counts per minute (cpm; sum of counts for y-axis, divided by valid wear time). Raw data was recorded in epochs of 60 seconds and non-wearing time was defined as 90 minutes of consecutive zero counts. Accelerometer data were processed using ActiLife software version (ActiGraph, Pensacola, Florida, USA). The ENRICH and MM4L studies used pedometers (Yamax Digi-Walker, SW200) to assess PA. A pedometer is also a lightweight device that assesses daily step counts (sum of steps, divided by the number of days worn). Participants were instructed to wear the accelerometer/ pedometer at the hip for seven consecutive days during all waking hours. Both PA monitors are recognized as reliable and valid tools to objectively assess PA in adults [20, 21] and have been used in previous studies among cancer survivors [22]. While accelerometers provide more detailed information on PA than pedometers do, pedometers are much less expensive and therefore more financially feasible for larger studies [23]. Strong convergent validity between accelerometers and pedometers has been demonstrated [13]. Despite the common use of accelerometers and pedometers, a standardized way to process and summarize the collected data is currently lacking. Following the suggestions of Masse et al., we calculated within our own datasets the minimum number of days needed to obtain a reliable measure of objectively measured PA [24]. First, we computed the betweenday intraclass correlation coefficient and 95% confidence interval (CI). Next, we calculated the required days of monitoring needed to achieve reliabilities of 0.70, 0.80, and 0.90 respectively, by using the between-day intraclass correlation coefficient according to the Spearman-Brown prophecy formula [25]. To obtain 75%reliability, the minimum number of days needed for the accelerometer (REACT data) and pedometer data (MM4L data) was 5 days in both studies, in which a valid day of wearing time was defined as 10 h. We were unable to apply this data reduction rule to the data of the ENRICH study; the pedometers in this study were sealed and therefore day-specific step counts were not available. Demographic factors The demographic factors were collected using a self-report questionnaire and included age at baseline (in years), marital status (0=no partner; 1=married or de facto), education (0=low/ intermediate (i.e., elementary, and lower and secondary vocational education); 1=high (i.e., higher vocational and university education)), and employment status (0=no paid (i.e., retired, unemployed, household duties, or student); 1=paid (i.e., full-time or part-time)).

145 Correlates of physical activity 143 Clinical factors Clinical information was retrieved from medical records (REACT study) or collected using self-report questionnaires (ENRICH study and MM4L study) and included previous treatment with chemotherapy, surgery, radiation therapy, immunotherapy, hormone therapy, history of cancer (i.e., a previous cancer diagnosis prior to most recent diagnosis), and time since breast cancer diagnosis (in months). The number of comorbidities was the sum of each of the following conditions: heart disease, lung disease, diseases of the digestive system, diseases of the nervous system, endocrine disease, mental disorder, rheumatism or arthritis, chronic pain, and other conditions. Furthermore, participants BMI was calculated from measured body weight and height. In the REACT study, the participants body weight and height was measured by a health professional. In the ENRICH study and MM4L study, participants were asked to measure and report their own current body weight and height. Clinical measurements and self-report body weight and height have shown strong correlations between each other; however, self-reports may overestimate height and underestimate weight [26]. Psychosocial factors Self-efficacy. In all three studies, self-efficacy was assessed with the following question: How confident are you that you will be physically active in the following situations? In the REACT study, participants were asked to respond on a 10-point Likert-type scale, and the following five situations were described: feeling tired; bad mood; do not have the time; on vacation; bad weather (Cronbach s α=0.80). This five-item questionnaire has been reported as a reliable outcome measure [27]. In the ENRICH study, nine situations were rated on a 5-point Likert scale: feeling tired; bad mood or feeling depressed; when you have to do it by yourself; when it becomes boring; there are no noticeable improvements in fitness; having other demands; feeling stiff or sore; bad weather; or having to get up early even on weekends (Cronbach s α=0.89). This nine-item questionnaire has demonstrated validity and reliability for use in a population of diabetes patients [28]. In the MM4L study, the following 12 situations were rated on a 5-point Likert-type scale: feeling tired; a little ill; little stiff or sore; bad mood or feeling depressed; when you have to do it by yourself; bad weather; lacking discipline; not a priority; lacking time; not enjoying exercise; no encouragements to exercise; there are no noticeable improvements in fitness (Cronbach s α=0.94). Two previous self-efficacy scales were combined in the MM4L study; and reliability and validity of both scales have been established [28,29]. For all studies, a higher score indicated higher self-efficacy scores of the individual.

146 144 CHAPTER 8 Social support. In the REACT study, social support was assessed using a questionnaire with the statement The following people are supportive of my regular PA, followed by the following persons: family; friends; co-workers; and other cancer patients (Cronbach s α=0.92). The participants were asked to score how much they agreed with the statement for each person on a 5-point Likert-scale. In the ENRICH study, social support was assessed using a two-item questionnaire. Participants were asked to rate on a 5-point Likert scale whether people in their social network are likely to help them participate in regular PA, and whether they felt that someone in their social network will provide the support they need in order to be regularly physically active (Cronbach s α=0.90). In the MM4L study, social support was assessed using a questionnaire starting with the statement During the past 4 months, my family and friends, followed by the following suggestions: exercised with me; encouraged me to stick with my program; changed their schedule to exercise together; offered to exercise with me; reminded me to exercise; planned exercise on recreational outings; discussed exercise with me; talked about exercise; plan activities around exercise; asked me for ideas on exercise; took over chores; made positive comments about my physical appearance; got angry at me for exercising; criticized me for exercising; gave me rewards for exercising (Cronbach s α=0.92). Participants were asked to score how often they perceived social support from their friends and family on a 5-point Likert-type scale (1, none to 5, very often). The social support questionnaire included in the MM4L study is a valid and reliable instrument for perceived social support in adults, specific to healthrelated exercise behaviors [30]. The social support questionnaires included in the REACT and ENRICH studies are frequently used questionnaires among cancer survivors [31]. For all studies, a higher score indicated a higher social support. Outcome expectations. In the REACT study, outcome expectations was assessed using an eight-item scale starting with the statement: When I am physically active, then I will, followed by eight suggestions including increase my health; feel better about myself; be more physical fit; improved performance of daily activities; lose weight; meet new people; feel better and increase my well-being; cope better with stress (Cronbach s α=0.88). Participants were asked to score how much they agreed with each statement on a 5-point Likert-type scale. In both ENRICH and MM4L studies, the questionnaire on outcome expectations started with To what extent do you agree or disagree that participating in regular PA in the next eight weeks would do for you, followed with five statements which included help reduce tension or manage stress; feel more confident about my health; have better sleep; a more positive outlook; control weight (ENRICH Cronbach s α=0.92; MM4L Cronbach s α=0.80). Participants were asked to score how much they agreed with each statement on a

147 Correlates of physical activity point Likert scale. For all studies, higher scores on outcome expectations indicated higher expectations of perceived benefits. Environmental factor geographic location In the current study, geographic location was dichotomized into currently living in an urban area versus living in rural or remote areas. In the REACT study and ENRICH study, classification was based on country-defined zip codes. In the MM4L study, participants were asked to report whether they were currently living in a city or in a rural/remote area. Data treatment and synchronization of variables across studies To synchronize variables across studies, the following conversion procedure was applied: (1) the negatively keyed items of the MM4L social support questionnaire were reversecoded; (2) the sum scores of the three psychosocial questionnaires were linearly transformed to a scale; (3) the normality assumption was tested, and three continuous variables were found to be highly skewed. Consequently, we dichotomized comorbidity into the presence (1) or absence (0) of two or more comorbidities, and time since diagnosis into longer (1) or shorter (0) than three years ago. Outcome expectations was categorized into four groups based on quartiles; (4) PA, self-efficacy, and social support were normally distributed; however, because different outcome measurements were used across studies, we computed standardized or z -scores (mean=0 and a standard deviation=1) in each study by subtracting the mean score at baseline from the individual score, divided by the mean standard deviation at baseline; (5) transformation of the variable names of the original studies into current project s coding scheme; and (6) export of all variables of interest into a final data file for the proposed statistical analyses. Statistical analyses Descriptive statistics (mean and standard deviation (SD)) were calculated for all study variables. Univariable and multivariable linear regression analyses were conducted on the pooled data to identify factors significantly correlated with PA. The potential correlates of PA were regressed on PA ( z -transformed). By default, study as covariate was retained in the univariable and multivariable models to account for varying sample size and clustering of data within the studies. The multivariable regression analyses included a backward selection procedure in which factors with the highest p value were removed from the model one by one. Only variables with a p value of 0.05 were retained in the final multivariable model. The unstandardized and standardized regression coefficients (β) with 95% CI and the

148 146 CHAPTER 8 explained variance (R 2 ) of the model were reported accordingly. Due to z-transformations, interpretations of the regression models were based on standardized β s. Prior to the multivariable regression analyses, the possibility of multicollinearity between the potential correlates was checked. Because the variance inflation factors of the included factors were small ( 2), we concluded that multicollinearity would not occur in the final model. The statistical analyses were performed using Statistical Package of Social Sciences (SPSS, Inc., Evanston, IL) version RESULTS Baseline characteristics In total, 484 BCS provided complete baseline data on all variables of interest (84% of the total sample (n=574)). There were no significant differences (p>0.05) between women with complete data and 90 women with incomplete baseline data on PA or the potential correlates of PA, except for marital status (i.e., participants with a partner were more likely to have complete baseline data). Participants were on average 54.5 years old (SD=9.2); 77% had a partner, 43% had completed a bachelor or master degree, and 60% were employed (Table 1). At least 95% of the participants underwent surgery to treat breast cancer, revealing very little variability within our population; therefore, we omitted surgery as a potential correlate of PA from the multivariable regression analyses. In the REACT study, an average (SD) of (97.8) activity cpm were measured. The ENRICH study and MM4L study reported mean (SD) step counts of (3485.5) and (2831.6), respectively (Table 1). Correlates of PA The results of the univariable and multivariable linear regression analyses are presented in Table 2. The multivariable regression model showed significant associations for higher age (β= 0.01, 95%CI= 0.02; 0.003) and higher BMI (β= 0.05, 95%CI= 0.06; 0.03) and lower self-efficacy (β=0.2, 95%CI=0.08;0.2) and lower social support (β=0.1, 95%CI=0.05;0.2) with lower PA. Based on the standardized β s, BMI had the strongest association with PA. The final model explained 15% of the variance in PA. No significant correlations were found between treatment-related characteristics or geographic location and PA.

149 Correlates of physical activity 147 TABLE 1 Baseline characteristics REACT n=180 ENRICH n=64 MM4L n=330 Pooled n=574 Physical activity Mean daily activity counts, mean (SD) (97.8) Mean daily steps, mean (SD) (3485.5) (2831.6) } z -score Demographic Age, mean (SD) years 51.8 (9.4) 54.8 (11.0) 55.9 (8.3) 54.5 (9.2) Married or de facto, n(%) 148 (82) 41 (64) 250 (76) 439 (77) Education, Low/intermediate, n(%) High, n(%) Being employed, Employed, n(%) Not employed, n(%) Clinical Type of treatment, 106 (59) 72 (40) 121 (67) 59 (33) 34 (53) 29 (45) 32 (50) 31 (48) 184 (56) 146 (44) 189 (57) 141 (43) 324 (57) 247 (43) 342 (60) 231 (40) Chemotherapy, (yes) n(%) 180 (100) 52 (81) 231 (70) 463 (81) Radiation therapy, (yes) n(%) 124 (69) 49 (77) 225 (68) 398 (69) Surgery, (yes) n(%) 177 (98) 62 (97) 306 (93) 545 (95) Immunotherapy, (yes) n(%) 35 (19) 0 15 (5) 371 (65) Hormone therapy, (yes) n(%) 125 (69) 45 (70) 201 (61) 50 (9) Time between date of diagnosis and baseline, mean (SD) months 7.9 (1.5) 44.2 (83.5) 69.0 (50.9) 47.6 (55.1) Cancer in the past, (yes) n(%) 15 (8) 4 (6) 19 (6) 38 (7) Sum of comorbidities, mean (SD) 0.5 (0.7) 2.3 (1.7) 2.3 (1.7) 1.4 (1.5) BMI (kg/m 2 ), mean (SD) 27.2 (4.8) 27.0 (6.1) 26.9 (5.2) 27.0 (5.2) Psychosocial (Range 0-100) Self-efficacy, mean (SD) 60.0 (16.7) 52.5 (17.8) 50.7 (22.3) z -score Social support, mean (SD) 80.3 (20.4) 48.8 (32.2) 30.1 (15.6) z -score Outcome expectations, mean (SD) 74.7 (15.2) 73.4 (16.6) 81.8 (13.9) 78.6 (15.0) Environmental Urban area, (yes) n(%) 45 (25) 64 (100) 155 (47) 264 (46) Abbreviations: SD, standard deviation; n, number.

150 148 CHAPTER 8 TABLE 2 Unstandardized and standardized regression coefficients and their 95% confidence intervals as results from univariate and multivariate regression analyses with PA z-scores as dependent variable and demographic, clinical, psychosocial and environmental variables as independent variables Univariable Unstandardized β (95% CI) Multivariable Unstandardized β (95% CI) Multivariable Standardized β (95% CI) Demographic Age, years (-0.02;-0.01) * (-0.02;-0.003) * Married or de facto 0.1 (-0.1;0.3) Education 0.1 (-0.1;0.3) Being employed 0.1 (-0.1;0.3) Clinical Type of treatment, Chemotherapy -0.1 (-0.3;0.1) Radiation therapy -0.2 (-0.4;0.01) Immunotherapy 0.1 (-0.3;0.4) Hormone therapy 0.1(-0.1;0.3) Time between diagnosis and baseline 0.1(-0.02;0.3) Cancer in the past -0.1 (-0.5;0.2) Presence of two or more comorbidities -0.3 (-0.4;-0.1) * BMI (kg/m 2 ) -0.1 (-0.1;-0.04) * (-0.06;-0.03) * Psychosocial Self-efficacy 0.2 (0.1;0.3) * 0.2 (0.08;0.2) * 0.17 Social support 0.2 (0.1;0.3) * 0.1 (0.05;0.2) * 0.13 Outcome expectations quartile 1 vs. 2 quartile 1 vs. 3 quartile 1 vs. 4 Environmental 0.1 (-0.2;0.3) 0.01 (-0.2;0.2) 0.1 (-0.2;0.3) Urban area 0.01 (-0.2;0.2) Abbreviations: β, regression coefficients; CI, confidence interval; *(p<0.05).

151 Correlates of physical activity 149 DISCUSSION The current study examined possible demographic, clinical, psychosocial, and environmental correlates of objectively assessed PA in a large group of BCS. We found that age, BMI, selfefficacy and social support were significantly correlated with PA. Our finding that older age was significantly associated with lower PA is in line with previous research in BCS using self-reported PA questionnaires [9,10]. Demographic factors, such as age, are non-modifiable factors and highlight those subpopulations that are more likely to be physically inactive and may thus have a greater need for intervention. To date, previous studies predominately reached younger BCS, and consequently, PA interventions in older female BCS remain understudied [32]. Future studies should develop PA interventions appealing to older BCS and evaluate the effectiveness of such interventions in this subpopulation. Higher BMI was also found to be significantly associated with lower objectively assessed PA. This finding supports previous studies using subjective and objective PA assessments [33]. The current cross-sectional study design does not allow us to detangle the causal direction of the association between BMI and PA (i.e., whether BCS with higher BMI have lower PA or vice versa, BCS with lower PA have higher BMI). Nevertheless, the current study underlines the importance of targeting overweight and obese BCS. Such PA interventions for overweight and obese BCS may also contribute to achieving and maintaining weight loss in cancer survivors, especially when combined with a dietary intervention [34]. This may particularly be important given the independent relationship between obesity and breast cancer recurrence [35] as well as inactivity and breast cancer recurrence [36] reported in observational studies. We found that lower self-efficacy to overcome PA-related barriers was significantly associated with lower PA. This finding is consistent with previous studies using self-reported PA questionnaires in a mixed group of cancer survivors [37,38]. Hence, interventions aiming at increased PA levels may be more effective by including strategies to improve the individuals self-efficacy, especially for BCS who do not feel confident about their abilities to undertake new activities and stay engaged. Motivational interviewing has been identified as a promising strategy to improve a person s self-efficacy [39] and consequently improve PA. Bennett et al. [40] conducted a randomized controlled trial and reported a significant increase in PA after a motivational interviewing intervention among cancer survivors [40]. They also reported that cancer survivors with lower self-efficacy scores at baseline reported less PA improvement compared to those with higher self-efficacy scores, further indicating that boosting self-efficacy is of great importance. Additional components such as goal setting, skill development, or selfmonitoring [41] may also be effective strategies to improve self-efficacy.

152 150 CHAPTER 8 Finally, less social support from family and friends was significantly associated with lower PA among our population of BCS. This is in line with a previous review among healthy individuals reporting a strong correlation between less social support from family and friends and lower self-reported PA [42]. Likewise, Pinto et al. reported a significant correlation of social support with self-reported PA among BCS [10]. Yet, studies in other cancer diagnosis such as colon [43] and head and neck [44] and lung cancer [45] reported no significant association of social support with PA. A possible explanation for the differences among the cancer survivor groups may be related to gender [46]. On the other hand, despite the use of valid questions [30], the phrasing of the items in the current study might have caused a bias toward a positive association. Future studies should further examine the role of social support in BCS and in which way social support could be successfully promoted in PA interventions. In contrast with our findings, two previous studies have reported conflicting findings on associations between geographical location (i.e., living in urban versus rural or remote areas) and PA [47,48]. Weaver et al. showed that rural cancer survivors from the USA were less likely to be physically active compared to urban survivors [47]. Whereas, Lynch et al. found that urban colorectal cancer survivors from Australia were less likely to achieve or maintain a physical healthy lifestyle after diagnosis compared to rural survivors [48]. Conflicting results may be related to the urban and rural/remote classification or differences in context or regional and related community factors. Given higher mortality [49] and additional barriers to supportive care [50,51] faced by rural cancer survivors, further exploration of PA correlates for this target group is warranted. The remaining factors that were considered in the current study showed no significant association with PA, including marital and employment status, level of education, all treatment-related characteristics, and comorbidity. The fact that these non-modifiable factors were not associated with PA suggests that no additional subgroups that are at risk for physical inactivity can be distinguished in BCS, at least not based on these variables included in the present study. Our findings on those remaining variables are in line with previous research; however, the current literature on correlates of PA among BCS is limited. Our final multivariable model explained 15% of the variance in PA, indicating that there may be other variables explaining PA behavior that were not included in the current study are important to explain differences in PA among BCS. The current findings on selfefficacy and social support may suggest a role for behavioral theory such as social cognitive theory in developing interventions for BCS. Theory-based interventions have shown to be more effective in changing behavior than non-theory based interventions. However, current

153 Correlates of physical activity 151 theories do not incorporate potential cancer- or treatment-related factors (e.g., chemotherapy dose and neurotoxic side effects) and there is a need for refining current theories to BCS. Forthcoming studies will benefit from examination of in-depth information on clinical factors and a broader set of contextual or environmental factors (such as the availability of and accessibility to PA opportunities including sidewalks, bicycle lanes, and sports facilities). The following limitations are worth noting. First, the cross-sectional design of the study does not allow drawing conclusions on causal relationships. Hence, in a previous review among healthy individuals, self-efficacy was found to be the most important mediator [52] of the exercise intervention effect on PA, supporting findings of the current study. Yet, future experimental studies are warranted to investigate the mediating role of psychosocial variables of the intervention effect on PA in cancer survivors. Second, although objective assessments of PA with accelerometers and pedometers are the preferred method to assess PA, they may have underestimated PA because the monitors were worn around the hip and can therefore not accurately assess upper body activities, nor can they account for swimming activities or activities that require extra effort (e.g., walking uphill). However, accelerometers and pedometers are able to adequately distinguish between individuals with higher or lower PA levels [13]. Furthermore, in order to pool data from both PA outcome measures, we computed standardized or z -scores which hamper the interpretation of the reported effect sizes. Nevertheless, the level of significance and the directions of the reported associations are informative. Third, despite the strong correlations between clinical measurements and self-reported body weight and height [26], self-reports may underestimate weight and overestimate height. Aiming to avoid additional bias, BMI was included as a continuous variable in the current study [53]. Fourth, the definition and impact of living in a rural area compared to a metropolitan area varies enormously between Australia and the Netherlands. Since no current gold standard is available for pooling data from different countries, the use of the urban versus pooled rural/remote classification in the current study was a first attempt to acknowledge the international differences. Last, the participants who provided data for this analysis have been recruited to take part in behavior change trials rather than being drawn from a population sample. Therefore, eligible patients who declined participation may impact the generalizability of the results. Yet, each study checked whether the participants and non-participants differed on demographic and clinical factors [54 56], which revealed that patients who decided to participate were more likely to be higher educated, have a higher socioeconomic status, and be more physically active compared to those who declined participation (Figure 1) [55,56]. Worth noting, the three studies were conducted predominantly among Caucasian women, and therefore, ethnicity

154 152 CHAPTER 8 was unable to be explored as a possible correlate. As a consequence, the findings of the current study may not be generalizable to other ethnic groups, including ethnic minorities (e.g., indigenous Australians). In conclusion, we found that female BCS who are older, reported higher BMI scores, lower self-efficacy scores, or less social support from family and friends may be at higher risk of being physically inactive. This information should facilitate the development of targeted interventions aiming to improve PA among BCS. Future studies should gain a better understanding of the specific determinants that are unique to older or heavier BCS, which provides important information for further intervention design. In addition, forthcoming studies in BCS should further investigate the effectiveness of behavior change strategies on PA.

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159 CHAPTER 9 General discussion

160

161 General discussion 159 GENERAL DISCUSSION The first and main research objective of this thesis was to evaluate the effectiveness and cost-effectiveness of a 12-week high intensity (HI) and low-to-moderate intensity (LMI) exercise intervention in cancer survivors who completed primary cancer treatment including chemotherapy. Furthermore, we tested the underlying hypothesis that exercise improves cardiorespiratory fitness and muscle strength, thereby reducing fatigue and consequently improving global quality of life (QoL) and physical function. Finally, to facilitate implementation, we studied demographic, clinical, psychosocial and environmental factors associated with participation in and adherence to exercise programs in cancer survivors, and with daily physical activity (PA), aiming to identify intervention targets as well as subgroups at highest need for improving exercise and PA. This general discussion will start with a summary of the main findings of this thesis. Furthermore, methodological issues related to the studies will be discussed, as well as the clinical implications and suggestions for future research. MAIN FINDINGS Effectiveness and cost-effectiveness of exercise after completion of primary cancer treatment Compared to a waiting list control (WLC) group, both HI and LMI exercise resulted in signi ficant and clinically meaningful improvements in cardiorespiratory fitness, reductions in fatigue and improved health-related quality of life (HRQoL) in cancer survivors who completed cancer treatments including chemotherapy (Chapter 3). Moreover, a head-to-head comparison between HI and LMI exercise showed a potential dose-response relationship regarding exercise intensity for cardiorespiratory fitness (i.e., peak oxygen uptake (peakvo 2 )), favoring HI over LMI exercise, but not for fatigue. Furthermore, compared to WLC, improved physical function was found after HI and LMI exercise, improved global QoL and reduced anxiety after HI exercise, and less problems at work after LMI exercise. Chapter 4 found support for the hypothesis that a 12-week resistance and endurance exercise program improved cardiorespiratory fitness, leading to lower physical fatigue, and consequently to higher global QoL and physical function, illustrating the importance of improving cardiorespiratory fitness. At 64 weeks follow-up (Chapter 5), gain in cardiorespiratory fitness after the 12-week intervention was successfully maintained, but there was no significant difference between HI and LMI exercise. Furthermore, in both HI and LMI exercise, general and physical fatigue

162 160 CHAPTER 9 returned to their baseline levels, and differences between exercise groups were statistically not significant. Finally, at 64 weeks follow-up, improvements in global QoL and physical function tended to be larger for HI than for LMI exercise. In the Netherlands, the informal societal willingness-to-pay threshold ranges from 20,000 per Quality Adjusted Life Years (QALY) to 80,000 per QALY depending on the severity of a disease. The probability that HI exercise was cost-effective compared to LMI exercise analyzed from a Dutch societal perspective ranged from 0.91 at 20,000 per QALY to 0.95 at 52,000 per QALY, and therefore, HI exercise should be considered as being cost-effective compared to LMI exercise (Chapter 5). However, the willingness-to-pay thresholds were not reached for cardiorespiratory fitness, hand-grip strength and general fatigue. Adherence to and participation in exercise The systematic literature review presented in Chapter 6 showed that exercise history was significantly associated with adherence to exercise interventions. Other important demographic, clinical, psychosocial and environmental correlates of exercise adherence could not be distinguished due to the limited number of studies, the inconsistency of findings across the studies, and variations in the definition of adherence. In the study described in Chapter 7 we found that psychosocial factors, such as lower psychological distress and higher selfefficacy were significantly associated with better exercise adherence in HI exercise, but not in LMI exercise. Furthermore, cancer survivors who attained a higher level of education were non-smokers, perceived less psychological distress, had higher outcome expectations regarding exercise participation and perceived more exercise barriers. They were also more likely to participate in a randomized controlled trial (RCT) evaluating effects of a combined resistance and endurance exercise program. The research described in Chapter 8 showed that female breast cancer survivors who were younger, reported lower body mass index scores, higher self-efficacy scores or higher social support from family and friends were more likely to be physically active. METHODOLOGICAL CONSIDERATIONS In this thesis, various study designs were used to answer our study questions. Each chapter contains a discussion of the methodological issues related to the specific study design in that particular chapter. Here, we will discuss some general methodological considerations of this thesis.

163 General discussion 161 Study population, participation rate and generalizability The Resistance and Endurance exercise After ChemoTherapy (REACT) study was a prospec tive multicenter RCT in which participants were recruited from nine hospitals in the Netherlands. The eligibility criteria included as many as six cancer diagnoses (primary breast, colon, ovarian, cervix and testis cancer, or lymphomas). Although a mixed group of cancer survivors were recruited, breast cancer survivors represented 65% of the study population, while 1-18% participants represented the other cancer types. As a consequence, there was not sufficient statistical power to explore whether patients with other cancer types responded different to exercise. The participation rate in the REACT study was 37%, which is in line with previous RCTs evaluating the effects of exercise interventions in cancer survivors [1,2]. The most frequently reported reason for non-participation was having too many things on one s mind (Chapter 7), which is a common finding in cancer survivors shortly after completion of primary cancer treatments [3]. Perhaps cancer survivors who lack a history of sports or exercise might become overwhelmed with a schedule of twice per week supervised exercise sessions shortly after cancer treatments. However, sport history and current PA level did not differ between the participants and non-participants of the REACT study. Yet, assessments of sport history and current PA level relied on self-report measures, which are prone to either over- or under-estimation due to inaccurate recall, social desirability and misinterpretation of the survey questions [4]. The low response rates may hamper the generalizability of the study findings. However, no significant differences in age, gender and cancer type were found between participants and non-participants, supporting generalizability. Though, cancer survivors who attained a lower or intermediate level of education, were smokers, perceived more psychological distress, had lower outcome expectations or perceived less exercise barriers were less likely to participate in the REACT study. Therefore, one should be cautious to generalize the results from the REACT study to all cancer survivors treated with chemotherapy. Study designs and statistical power Different study designs were conducted to address the three primary research objectives of this thesis. First, a RCT was conducted to evaluate the (cost-) effectiveness of HI and LMI exercise compared with WLC group on physical fitness, fatigue and HRQoL. An RCT is considered the most rigorous study design to evaluate the effectiveness of interventions, as it controls for selection bias and confounding [5]. While setting up the REACT study (Chapter 2), the availability of cancer rehabilitation groups in clinical practices increased rapidly in the

164 162 CHAPTER 9 Netherlands, and the Dutch guideline for cancer rehabilitation [6] and the international PA guidelines for cancer survivors [7] were developed. Both emphasize the importance of exercise during and after cancer treatments. To limit non-participation and minimize the possibility of contamination (i.e., undertaking supervised exercise on a person s own initiative) whilst allowing optimal care for all participants, a WLC group was included, instead of a true non-exercising control group. Despite these advantages, the WLC group hampered the evaluation of longer-term effectiveness, because all participants had received the 12-week intervention at 64 weeks follow-up. In addition, patients may have been disappointed when they were assignment to a WLC group, which may have caused patients to belief that they would not improve as quickly as possible, and thereby slowing down natural recovery or increase the risk for contamination [8]. Yet, in the REACT study, the WLC group showed natural recovery on most outcomes (Chapter 3, Table 4), and contamination rates were low (8%). Therefore, it seems unlikely that the intervention effects were either overestimated or underestimated as a result of the choice for a WLC group. A priori power calculations based on a previous uncontrolled trial evaluating the effectiveness of a HI resistance and endurance exercise program in 119 cancer survivors post-treatment [9], estimated a total sample size of 280 participants on peakvo 2 as primary outcome measure. Although statistically powered to show a main intervention effect on peakvo 2 (Chapter 3 and 5), the between-group differences on peakvo 2 for HI and LMI exercise were smaller than anticipated and therefore we may have failed to show statistical significance. Second, mediation analyses were conducted to identify which exercise intervention components (e.g., muscle strength, cardiorespiratory fitness) were most relevant for reducing fatigue and consequently improving HRQoL (Chapter 4). Studying causal mechanisms underlying intervention effects requires data from a well-designed RCT, including a relatively large sample size. Despite using a RCT design, the mediator variables and the outcome variables were assessed at the same time-points, and therefore, inferences about causality between mediators and outcome variables could not be made. Third, a cross-sectional study design was used to identify correlates of participation in a combined resistance and endurance exercise program and daily PA, however, this type of study design is limited in its ability to draw conclusions about causality. Nevertheless, studying associations in cross-sectional studies may help to generate hypotheses for future research [10] which are useful in the development of targeted interventions to improve exercise participation and PA levels, and consequently improving outcomes.

165 General discussion 163 Primary outcome measures The REACT study is one of four RCTs included in the Alpe d HuZes Cancer Rehabilitation (A-CaRe) clinical research program [11]. All RCTs within A-CaRe were based on a similar conceptual model in which increasing physical fitness and reducing fatigue were both expected to improve HRQoL. The primary and secondary outcome measures were carefully chosen, based on the International Classification of Functioning, Disability and Health (ICF), and the validity and reliability of the instrumentation was established [11]. PeakVO 2 was measured during a maximal exercise test, including a continuous gas exchange analysis and electrocardiography monitoring. Such a test is widely acknowledged as the gold standard for assessing cardiorespiratory fitness [12]. Furthermore, a maximum exercise test in cancer survivors provides important diagnostic information before the start of an exercise program [13] and could detect cardiac or pulmonary toxicities resulting from chest irradiation or chemotherapeutic agents, such as anthracyclines [14] and bleomycin [15]. However, there might be a difference in responsiveness between maximal and submaximal exercise testing. Because submaximal exercise testing assesses functional capacity more directly, larger improvements may be detected following training, representing a lower physical strain during the same absolute level of daily activities and decreased dependence on anaerobic metabolism [16]. Nonetheless, the international guidelines on cardiopulmonary testing recommend that clinical studies in exercise oncology should aim to include a maximum exercise test when possible, particularly given the wealth of clinical information it can obtain [17]. Upper body muscle strength and lower body function were assessed using a handgrip dynamometer and the 30-seconds chair-stand test, both established as valid outcome measures [18,19]. Whereas the indirect 1-RM tests, conducted by the physiotherapists to evaluate training progress, showed 37% improvements on the leg press and 34% improvements on the vertical row, no significant intervention effects were found on hand-grip strength or lower body function. Perhaps the hand-grip strength and 30-seconds chair-stand test might have been limited to detect changes [20,21]. Therefore, in future studies, it would be worthwhile to consider using outcome measures that more directly assess the strength of targeted muscle groups, and therefore are more likely to detect changes. For example, a handheld dynamometer is a user-friendly tool for clinical practice, and is able to measure muscle strength of various upper and lower body muscles [1]. Fatigue was assessed with the Multidimensional Fatigue Inventory (MFI), specially designed for use in clinical trials focusing on cancer survivors and the psychometric properties are well documented [22]. Although, both exercise interventions achieved significant and

166 164 CHAPTER 9 clinically meaningful reductions in general and physical fatigue at 12 weeks follow-up, the exercise-induced benefits were not maintained at 64 weeks follow-up. The lack of longerterm effects may suggest that cancer survivors gain more confidence in managing cancer and treatment-related problems during supervised exercise program. However, when chores of everyday life resume and supervision and support from a physiotherapist is finished, one may struggle to remain confident, particularly in the self-management of fatigue [23]. On the other hand, while evaluating fatigue in a longitudinal study design, the possibility of a response shift, defined as a recalibration of a participant s internal standard used to judge one s current fatigue experience, should also be taken into account [24]. Fatigue is a subjective outcome based on self-report, and the internal standard of fatigue perception may change throughout the cancer trajectory [24]. To gain a better understanding how exerciseinduced benefits on fatigue can successfully be maintained on the longer term, future studies are warranted to identify mediators of the exercise intervention effects on fatigue. CLINICAL IMPLICATIONS Supervised HI and LMI exercise shortly after completion of cancer treatment is safe and superior to natural recovery on cardiorespiratory fitness, fatigue and HRQoL. We therefore recommend implementation of exercise as part of standard cancer care. HI exercise may be preferred to LMI exercise when aiming to improve peakvo 2 levels in cancer survivors, because some indication for a dose-response relationship was found. Improving cardiorespiratory fitness of cancer survivors is particularly important because, compared to reference values of healthy adults, their peakvo 2 levels were very poor, which increases the risk of reduced ability to carry out activities of daily living [25]. Therefore, a 5-10% gain in peakvo 2 from supervised exercise can be of great clinical importance for the individual patient. Moreover, results from observational studies showed a positive association between peakvo 2 and survival [26], but causality needs to be established. Further, HI exercise may also be preferred over LMI exercise when aiming to improve HRQoL. Yet, decisions about implementation of exercise programs as standard and reimbursed cancer care are not only guided by their effectiveness on health outcomes, but also by their additional costs in relation to these effects (i.e., cost-effectiveness) [27]. In line with the results on HRQoL, HI exercise was cost-effective in terms of QALY compared to LMI exercise, mostly due to lower medical costs.

167 General discussion 165 Studying correlates of adherence may identify intervention targets to further improve adherence. The finding that higher self-efficacy was significantly associated with high session attendance and high compliance with endurance exercises, and lower psychological distress was significantly associated with high compliance with resistance exercises in HI exercise, but not in LMI exercise, suggests that an individual s self-efficacy and distress levels are important characteristics while accomplishing a HI exercise program. Therefore, for improving adherence rates, additional programs may be required for patients with low self-efficacy and/or high distress. Cognitive behavioral techniques, such as motivational interviewing [28] and goal setting [29], could be included to improve self-efficacy and may support cancer survivors in achieving their exercise goals. Hence, patients with lower self-efficacy or higher psychological distress could also be recommended to start with LMI exercise, and -after gaining further confidence in exercise- the exercise intensity could gradually increase over time [30]. The Dutch evidence-based guideline Cancer rehabilitation published in 2011 [31], underlined the recommendations of the international exercise guidelines for cancer survivors (i.e., being as physically active as their abilities and conditions allow and avoid being physically inactivity [7]) and included a structured action plan for all disciplines involved in cancer care. Health care professionals (i.e., medical specialist, (specialized) nurse, and/ or general practitioner) are appointed to screen cancer survivors on cancer and treatmentrelated problems, such as fatigue, psychological distress and reduced physical function. Furthermore, the guideline differentiates between single and multiple or complex cancer and treatment-related problems, and informs health care professionals whether survivors should be referred to either monodisciplinary or multidisciplinary care. Most likely, the majority of the cancer survivors with a request for assistance report a single problem suggesting monodisciplinary care, such as supervised exercise to be sufficient. Based on the results of the REACT study, HI and LMI exercise should be considered as effective monodisciplinary strategy to improve cardiorespiratory fitness, fatigue and HRQoL in cancer survivors after completing primary treatments.

168 166 CHAPTER 9 FUTURE RESEARCH To further optimize effectiveness and efficiency of exercise programs for cancer survivors, it is necessary to move away from current one-size fits all approaches and to develop targeted interventions that meet the capabilities, characteristics and needs of cancer survivors. This requires more insight into optimal exercise prescriptions, as well as moderators and mediators of intervention effects. Optimal exercise prescription Exercise prescriptions should include specific guidelines on four main parameters; frequency, intensity, type and duration of exercise (i.e., exercise FITT parameters). However, at present, exercise prescriptions for cancer survivors are rather generic. The REACT study was the largest RCT to date that primarily evaluated the effects of different exercise intensities after completion of primary cancer treatments. Two previous RCTs evaluated the effects of different exercise types [32] and doses [2] in breast cancer survivors during chemotherapy and reported that the effect of aerobic exercises on peakvo 2 was superior to a resistance exercises, whereas, the effect of the resistance exercises on upper and lower body muscle strength was superior to the aerobic exercises [32]. Further, higher exercise doses (3 times 60 minutes per week at moderate-to-high intensity) resulted in significantly better physical function and less symptoms, compared to standard doses (3 times 30 minutes per week at moderate-to-high intensity) [2]. In order to define the optimal exercise prescription for cancer survivors, additional head-to-head comparisons on the FITT parameters are needed to further detangle their effects on a given outcome, for a given cancer type, in a particular phase of the cancer trajectory (e.g., during treatment, after treatment, end of life [33]). Moderators of exercise intervention effects Furthermore, to maximize benefits of interventions for the individual, it is important to determine which exercise program works, for whom, and under what circumstances (i.e., moderators of intervention effects [34]). Moderators identify subgroups of cancer survivors that are most responsive to certain exercise programs, and those that are less responsive [35]. In the REACT study, larger intervention effects on peakvo 2 of both interventions were found for younger participants (Chapter 3). Additionally, the intervention effects of HI exercise on global QoL were larger for younger participants and for participants with breast cancer, compared to other types of cancer, and women showed larger improvements after HI exercise in global QoL and physical function than men (Chapter 3). Few previous studies

169 General discussion 167 found that demographic (e.g., age [36], marital status [36,37]) and clinical variables (e.g., treatment type [37,38]) moderate the physical exercise effects on HRQoL in cancer survivors. Hence, none of the forgoing studies (including the REACT study) were designed or powered to analyze moderating effects and to conduct subsequent stratified analyses. Aiming to overcome these limitations, the Predicting OptimaL cancer RehabIlitation and Supportive care (POLARIS) study [39] was launched in which individual patient data from RCTs evaluating the effects of PA and/or psychosocial interventions exercise on HRQoL in cancer survivors are pooled to identify moderators of intervention effects. Identifying patient subgroups at highest need for improving exercise and PA, enables health care professionals to target subgroups in the cancer population more efficiently. Mediators of exercise intervention effects Finally, conducting targeted interventions requires a good understanding of the mediators (i.e., working mechanisms) underlying the exercise intervention effects on fatigue and HRQoL in cancer survivors. The REACT study found support for the hypothesis that a 12-week resistance and endurance exercise program improves cardiorespiratory fitness, leading to lower physical fatigue, and consequently to higher global QoL and physical function (Chapter 4). However, cardiorespiratory fitness did not mediate the intervention effect on general fatigue, which was in line with previous studies [40,41]. Because general fatigue comprises physical as well as mental aspects, only improving cardiorespiratory fitness might not be sufficient and concepts other than or additional to cardiorespiratory fitness should be taken into account when aiming to reduce general fatigue. Forgoing research in cancer survivors reported that the exercise effects on fatigue may be mediated by psychosocial factors, such as reduced sleep quality, psychological distress and self-efficacy [40,42]. The importance of self-efficacy in relation to fatigue has been acknowledged in earlier reports among cancer survivors [42,43] and may suggest a role for behavior change theories, such as social cognitive theory in developing interventions for cancer survivors. In addition to psychosocial working mechanisms, biological factors (e.g., function of immune and metabolic systems) and physiological factors (e.g., neuromuscular function) may mediate the effect of PA and exercise on relevant health outcomes, including fatigue and HRQoL [44]. Future studies that further investigate the mediating role of psychosocial, biological and physiological factors on exercise intervention effects of health outcomes may successfully identify the critical intervention components, and therefore, be a support in building and refining (cost-)effective exercise programs for cancer survivors.

170 168 CHAPTER 9 Physical activity assessments In addition to the ultimate aim to optimize the effectiveness and efficiency of exercise programs and provide targeted interventions, PA assessments in cancer survivors require further investigation as well. The outcome measures for PA in the REACT study included self-reported PA using the Physical Activity Scale for the Elderly questionnaire (PASE) and objectively measured PA using accelerometers. Although, self-reported measures of PA are widely used, these instruments are prone to to either over or under-estimation due to inaccurate recall, social desirability and misinterpretation of the survey questions [4]. Objective PA monitoring using accelerometers overcomes these limitations and provides a more valid estimate of PA. An accelerometer measures vertical accelerations that are converted into activity counts per minute (cpm; sum of counts for y-axis, divided by valid wear time). Based on the continuous data derived from the accelerometers, multivariable linear regression analyses were conducted to evaluate the effectiveness of HI and LMI exercise compared to WLC group (Chapter 3 and 5) on PA, and to identify possible correlates of PA (Chapter 8). However, more often, count cut-points are applied in objective PA monitoring, categorizing the counts into sedentary time, and light, moderate and vigorous intensity PA [45]. These cutpoints have been validated in the general population [45], which generally have a higher fitness level, and may therefore be less appropriate for cancer survivors. Future research should explore the validity of these accelerometer cut-points in cancer survivors. A more accurate estimate of PA provides more precise estimates of intervention effects as well as associations with potential determinants. CONCLUSION This thesis showed that exercise interventions can improve cardiorespiratory fitness, reduce fatigue and enhance HRQoL in cancer survivors who recently completed treatment with curative intent, including chemotherapy. Our results advocate the implementation of exercise as part of standard cancer care for cancer survivors. Moreover, the current thesis provides several directions to optimize exercise programs. First, when offering exercise programs aiming to improve peakvo 2 and HRQoL among cancer survivors, HI exercise may be preferred. Second, cancer survivors who attained a higher level of education were non-smokers, perceived less psychological distress, had higher outcome expectations and perceived more exercise barriers; they were also more likely to participate in a combined resistance and endurance exercise trial. This finding is worth acknowledging when promoting

171 General discussion 169 exercise participation as part of cancer care. Third, several demographic, clinical and psychosocial factors were found to be significantly associated with exercise adherence in which psychosocial factors, such as psychological distress and self-efficacy, were more strongly associated with HI than LMI exercise. When offering HI exercise, it may therefore be recommended to screen these variables and if needed, include additional behavioral motivational strategies or consider starting at a lower training intensity.

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177 Summary Nederlandse samenvatting List of Publications Dankwoord About the author

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179 English summary 177 ENGLISH SUMMARY Over the past decades survival rates in cancer survivors have improved significantly, as a result of improved screening and treatment. However, it is important to acknowledge that cancer and cancer treatment are associated with long-term physical and psychosocial problems. These problems include decreased cardiorespiratory fitness and muscle strength, increased risk of anxiety and depression, and/or severe feelings of fatigue, which negatively affects a patient s quality of life. Chapter 1 of the thesis introduces supervised exercise as a promising strategy to assist cancer survivors in coping with, and recovering from, cancer and treatment related problems. Previous systematic reviews reported physical and psychosocial benefits of exercise among cancer survivors and highlighted the importance to define optimal exercise prescriptions in terms of frequency, intensity, type and time (i.e., FITT factors) of exercise. Furthermore, implementation of exercise interventions might be facilitated by a better understanding of the demographic, clinical, psychosocial, physical, and environmental factors that influence participation and exercise adherence among cancer survivors. Therefore, this thesis aimed to evaluate and compare the (cost-)effectiveness of a high intensity (HI) and a low-to-moderate intensity (LMI) exercise intervention on physical fitness and fatigue. In addition, the hypothesis was tested that resistance and endurance exercise improves cardiorespiratory fitness and muscle strength, thereby reducing fatigue and consequently improving global quality of life (QoL) and physical function. Finally, this thesis aimed to identify demographic, clinical, psychosocial, physical, and environmental factors that are associated with exercise participation and exercise adherence in order to facilitate the development of effective and targeted interventions for cancer survivors. Chapter 2 presents the design of the Resistance and Endurance exercise After ChemoTherapy (REACT) study, a multicenter randomized controlled trial (RCT) evaluating the effects on physical fitness and fatigue, and the cost-effectiveness of a HI exercise intervention compared to a LMI exercise intervention and a waiting list control (WLC) group in cancer survivors who had recently completed primary treatment with curative intent, including chemotherapy. Aiming to determine differences in effectiveness of different exercise intensities, both exercise interventions comprised similar exercise types, durations and frequencies, and only differed in exercise intensity. Both interventions included two one-hour exercise sessions per week during 12 weeks, supervised by a physiotherapist. Immediately after baseline assessments and randomization, participants in the HI and LMI exercise groups commenced their 12-week exercise intervention. Participants from the WLC group were also randomly allocated to HI

180 178 English summary or LMI exercise, however they started exercising after the 12-week follow-up assessment. Consequently, at 64 weeks all participants had received an exercise intervention. Chapter 3 presents the short-term (i.e., 12 weeks) effectiveness of HI and LMI exercise compared to a WLC group on cardiorespiratory fitness (peakvo 2 ), muscle strength (handgrip strength and 30-second chair-stand test), and self-reported fatigue (Multidimensional Fatigue Inventory; MFI). Secondary outcomes included health-related quality of life (HRQoL), physical activity, daily functioning, mood and sleep disturbances, and body composition. Compared to the WLC group, both HI and LMI exercise interventions resulted in significant and clinically meaningful improvements in cardiorespiratory fitness, reductions in fatigue and improved HRQoL shortly after completion of primary cancer treatment. A potential doseresponse relationship regarding exercise intensity was found for peakvo 2, favouring HI over LMI exercise. No significant intervention effects were found for hand-grip strength and the 30-second chair-stand test. HI and LMI exercise were equally beneficial in counteracting fatigue and physical function. Furthermore, compared to WLC, benefits in global quality of life and anxiety were found in the HI exercise, improved physical functioning in both the HI and LMI exercise, and less problems at work after LMI exercise. Chapter 4 studied the hypothesis that a combined resistance and endurance exercise intervention improves cardiorespiratory fitness and muscle strength, thereby reducing fatigue and improving global quality of life (QoL) and physical function among cancer survivors who had completed curative treatment including chemotherapy. The results showed that cardiorespiratory fitness mediated the exercise intervention effects on physical fatigue, global QoL and physical function. Thus, improving cardiorespiratory fitness could be an important intervention target to reduce fatigue and to improve cancer survivors global QoL and physical function. Furthermore, higher hand-grip strength was associated with lower physical fatigue and better lower body muscle function with lower general and physical fatigue. This indicates that muscle strength and function might be important intervention targets when aiming to reduce fatigue. Finally, reducing fatigue was found to be important to improve global QoL and physical function, and exercise is an effective strategy to do so. Chapter 5 evaluated (a) the difference in cardiorespiratory fitness, muscle strength, fatigue, and HRQoL between HI and LMI exercise interventions at longer-term (i.e., 64 weeks after baseline); (b) changes in these outcomes between short-term to longer-term; and (c) the cost-effectiveness from a societal perspective. At longer-term follow-up, significant better

181 English summary 179 social and role function were found after HI exercise compared to LMI exercise. In addition, longer-term effects on global QoL and physical function were slightly better in HI than LMI exercise, but this was not statistically significant. Also, no significant differences between HI and LMI exercise were found for physical fitness and fatigue at longer-term follow-up. Furthermore, no significant changes between short- to longer-term follow-up were found in peakvo2 and HRQoL in both HI and LMI exercise, indicating that intervention-induced benefits were successfully maintained at longer-term. However, fatigue returned to baseline values between week 12 and 64 in both groups. Cost-effectiveness analyses from a societal perspective showed that the probability that HI exercise was cost-effective compared to LMI exercise was 0.91 at 20,000/Quality-Adjusted Life Years (QALY) gained and 0.95 at 52,000/QALY gained, mostly due to significant lower healthcare costs in HI exercise. Chapter 6 provides a comprehensive summary of previous studies on determinants of exercise intervention adherence and exercise maintenance after completion of an intervention in cancer survivors. Insight into the relevant and modifiable determinants of adherence is an important first step to improve intervention adherence. This literature review showed that exercise history is positively associated with exercise adherence. Other important demographic, clinical, psychosocial, physical, and environmental correlates of exercise adherence and maintenance could not be detected due to the limited number of studies and the inconsistency of findings across the studies. Moreover, the definition of exercise adherence varied across the included studies. Some studies exclusively focused on session attendance rate, while other studies also incorporated a measure on compliance. In conclusion, future studies are needed to further build the evidence for the influence of demographic, clinical, psychosocial, physical, and environmental factors on exercise adherence and maintenance. Additionally, it is recommended that future studies make a clear distinction between exercise attendance at supervised sessions and compliance to the prescribed type, time and intensity of exercises. To further build the evidence on determinants of exercise intervention adherence, Chapter 7 aimed to identify demographic, clinical, psychosocial, physical, and environmental factors associated with participation in and adherence to an exercise intervention among cancer survivors, using data from the REACT study. Results showed that cancer survivors with higher education, who were non-smokers, had lower psychological distress, higher outcome expectations, and who perceived more exercise barriers were more likely to participate in exercise interventions. These findings are worth acknowledging when promoting exercise

182 180 English summary participation as part of cancer care. Furthermore, in HI exercise, participants with higher self-efficacy had higher session attendance rates, and higher compliance to the prescribed endurance exercises, and participants with lower psychological distress had higher compliance to the prescribed resistance exercises. In LMI exercise, non-smoking participants had higher compliance to resistance exercises, and participants with a higher body mass index had higher compliance to the prescribed resistance and endurance exercises. Additionally, breast cancer survivors had lower compliance with resistance and endurance exercises in LMI exercise than survivors of other types of cancer. These findings suggest that an individual s psychosocial factors, such as psychological distress and self-efficacy are important characteristics while performing HI exercise compared to LMI exercise. When offering a HI exercise intervention, we recommend to screen on these variables and if needed, include behavioral motivational strategies to improve compliance or consider starting at a lower training intensity. Given the beneficial effects of physical activity on health outcomes among cancer survivors, effective interventions to obtain and maintain sufficient levels of physical activity are warranted. In Chapter 8 we explored demographic, clinical, psychosocial, and environmental correlates of physical activity among from 574 female breast cancer survivors who had participated in three different intervention studies: REACT, Exercise and Nutrition Routine Improving Cancer Health (ENRICH), or Move More for Life (MM4L). Results indicated that female breast cancer survivors who were older, had a higher body mass index, lower self-efficacy, or less social support from family and friends may be at higher risk of being physically inactive. Therefore, future interventions to promote physical activity among breast cancer survivors should specifically target patients who are older, and have a higher body mass index, and operationalize behavior change strategies designed to enhance selfefficacy and social support. In Chapter 9 the main findings of the studies were presented and interpreted. Furthermore, the methodological considerations including study population, participation rates and generalizability of the results, study designs and statistical power and the choice of outcome measures were discussed. Overall, this thesis advocates the implementation of exercise interventions as part of standard cancer care for cancer survivors, because results showed that exercise interventions can improve cardiorespiratory fitness, reduce fatigue and enhance HRQoL in cancer survivors shortly after completion of cancer treatment. HI exercise may be preferred over LMI exercise when aiming to improve peakvo 2 and HRQoL,

183 English summary 181 because it may yield larger effects. Moreover, HI exercise was cost-effective in terms of QALY compared to LMI exercise. In the final part of the chapter, future research suggestions were presented. Additional research should further disentangle the effects of different exercise frequency, intensity, type and time (i.e., FITT factors) among different subgroups of patients to optimize evidence-based exercise recommendations for cancer survivors. Furthermore, future studies should focus on identifying intervention moderators explaining 'for whom' or 'when' interventions are most effective? Finally, more insight into the working mechanisms of exercise interventions (i.e., mediators) on health outcomes in cancer survivors is needed to improve the efficacy and efficiency of interventions.

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185 Nederlandse samenvatting 183 NEDERLANDSE SAMENVATTING Dankzij verbeterde diagnostiek en behandeling van kanker zijn de overlevingskansen voor patiënten met kanker in de afgelopen decennia toegenomen. Echter, het is belangrijk dat wij ons realiseren dat kanker en de behandeling daarvan gepaard kunnen gaan met langdurige fysieke en psychosociale problemen. Het gaat vaak om een verminderde fysieke fitheid, een verhoogd risico op angst en depressieve klachten en/of ernstige vermoeidheid, en een verminderde kwaliteit van leven. Hoofdstuk 1 gaat over fysieke training als een veelbelovende aanpak tot herstel en leren omgaan met fysieke en psychosociale problemen die zijn ontstaan door de diagnose kanker en de behandelingen. Eerdere systematisch literatuurstudies rapporteerden gunstige effecten van fysieke training op fysieke en psychosociale problemen bij patiënten met kanker en benadrukten het belang van het bepalen van de optimale frequentie, intensiteit, duur en tijd (zogenaamde FITT factoren) van trainingsinterventies. Bovendien is er meer kennis nodig over factoren die van invloed zijn op deelname aan en volhouden van trainingsinterventies. Daarom heeft dit proefschrift als doel inzicht te krijgen in de (kosten-)effectiviteit van een hoog intensief (HI) trainingsinterventie en een laag tot matig intensief (LMI) trainingsinterventie op de fysieke fitheid en de vermoeidheid van patiënten die kortgeleden hun behandeling voor kanker hebben afgerond. Ook wordt de hypothese getoetst dat kracht- en duurtraining door de fysieke fitheid te verbeteren, leidt tot minder vermoeidheid en vervolgens tot een verbeterde kwaliteit van leven en fysiek functioneren. Tot slot beschrijft dit proefschrift welke demografische, klinische, psychosociale, fysieke en omgevingsfactoren samenhangen met deelname aan en volhouden van een trainingsinterventies. Dit inzicht draagt bij aan de ontwikkeling van effectieve en doelmatige trainingsinterventies voor patiënten met kanker. Hoofdstuk 2 gaat over het onderzoeksdesign van de Resistance and Endurance exercise After ChemoTherapy (REACT-)studie; een multicenter gerandomiseerd en gecontroleerd onderzoek naar de effectiviteit en kosteneffectiviteit van HI trainingsinterventie ten opzichte van LMI trainingsinterventie en een wachtlijstcontrolegroep (WLC) op fysieke fitheid en vermoeidheid van patiënten met kanker, kort na afronding van een in opzet curatieve behandeling met chemotherapie. Om verschillen in effectiviteit tussen verschillende trainingsintensiteiten aan te kunnen tonen waren beide trainingen uniform qua type oefeningen, duur en frequentie van herhalingen en verschilden deze enkel in trainingsintensiteit. De trainingen bestonden uit twee trainingssessies van een uur per week, gedurende 12 weken, onder begeleiding van een fysiotherapeut. Direct na de baselinemetingen en de randomisatie

186 184 Nederlandse samenvatting startten de HI en LMI trainingsgroepen met het 12 weken durende trainingsinterventie. De deelnemers van de WLC groep waren ook gerandomiseerd naar HI of LMI training, maar, zij startten pas met trainen nadat de eerste follow-up meting op korte termijn (i.e. 12 weken) was volbracht (figuur 1, pagina 21). Hoofdstuk 3 presenteert de resultaten van HI training en LMI training ten opzichte van de WLC groep op korte termijn (i.e. 12 weken follow-up). De primaire uitkomstmaten waren cardiorespiratoire fitheid (VO 2 max, in de volksmond ook wel conditie genoemd), spierkracht (handknijpkracht en 30 seconden sit-to-stand test), en vermoeidheid (multidimensional fatigue inventory; MFI). De secundaire uitkomstmaten waren de kwaliteit van leven, dagelijkse lichamelijk activiteit en functioneren, gemoedstoestand, slaapkwaliteit en lichaamssamenstelling. Ten opzichte van de WLC groep, resulteerden de HI en LMI trainingen tot een significante en klinisch relevante toename in de cardiorespiratoire fitheid, minder vermoeidheid en een betere kwaliteit van leven. Er leek een mogelijke dosis-respons relatie te zijn voor VO 2 max met grotere effecten in de HI trainingsgroep ten opzichte van LMI trainingsgroep. Er waren geen significante effecten op handknijpkracht en de 30 seconden sit-to-stand test. HI en LMI trainingen bleken even effectief in het verminderen van de vermoeidheid. Bovendien werden er ten opzichte van de WLC groep positieve effecten gevonden op algemene kwaliteit van leven en angstklachten na HI training, een verbeterd fysiek functioneren na HI en LMI trainingen, en minder problemen op het werk na LMI training. Hoofdstuk 4 bestudeert de hypothese dat door een verbeterde cardiorespiratoire fitheid en spierkracht, de vermoeidheid vermindert en vervolgens de kwaliteit van leven en het fysiek functioneren verbetert bij patiënten met kanker kort na afronding van een in opzet curatieve be handeling met chemotherapie. De trainingseffecten op fysieke vermoeidheid, algemeen kwali teit van leven en fysiek functioneren werden inderdaad gedeeltelijk verklaard door een verbeterde cardiorespiratoire fitheid. Daarom kan het vergroten van cardiorespiratoire fitheid bij kankerpatiënten een belangrijk interventiedoel zijn om daarmee de vermoeidheid te verminderen of de kwaliteit van leven en het fysiek functioneren te verbeteren. Daarnaast was een hogere handknijpkracht en een betere spierfunctie van de benen gerelateerd aan een lagere vermoeidheid, en was een betere spierfunctie van de benen gerelateerd aan een hoger fysiek functioneren. Deze resultaten geven aan dat het verbeteren van spierkracht en spierfunctie belangrijk kan zijn om vermoeidheid te verminderen. Tot slot toonden de resultaten aan dat vermindering van vermoeidheid belangrijk is voor de kwaliteit van leven en het fysiek functioneren, en dat dit bereikt kan worden door fysieke training.

187 Nederlandse samenvatting 185 Hoofdstuk 5 beschrijft (a) het verschil in cardiorespiratoire fitheid, spierkracht, vermoeidheid en kwaliteit van leven tussen HI en LMI training op de langere termijn (i.e. na 64 weken); (b) veranderingen in deze uitkomstmaten tussen korte en langere termijn follow-up (i.e. 12 en 64 weken); en (c) de kosteneffectiviteit van HI training ten opzichte van LMI training vanuit een maatschappelijk perspectief. Bij de langere termijn follow-up liet de HI trainingsgroep een grotere toename in sociaal- en rol-functioneren zien ten opzichte van de LMI trainingsgroep. Bovendien leken de langere termijn effecten groter op de kwaliteit van leven en het fysiek functioneren bij HI training vergeleken met LMI training, maar dit verschil was niet statistisch significant. Op de langere termijn waren geen significante verschillen tussen HI en LMI training in fysieke fitheid en vermoeidheid. De verbeteringen in cardiorespiratoire fitheid en kwaliteit van leven bereikt na 12 weken training beklijfden op de langere termijn follow-up in zowel de HI als LMI trainingsgroep. Daarentegen was in beide groepen de vermoeidheid bij de follow-up meting van 64 weken teruggekeerd naar de baselinewaarde. De resultaten van de kosteneffectiviteitsanalyse lieten zien dat HI training vergeleken met de LMI training een kans van 0.91 had om kosteneffectief te zijn bij een drempelwaarde van /QALY, en dat deze kans zelfs 0.95 was bij een drempelwaarde van /QALY. Dit resultaat was voornamelijk het gevolg van lagere gezondheidszorgkosten in de HI trainingsgroep. Hoofdstuk 6 presenteert een literatuurstudie naar de determinanten van therapietrouw en het volhouden van een actieve leefstijl, na afloop van een trainingsinterventie bij patiënten met kanker. Inzicht in de relevante en veranderbare determinanten van therapietrouw aan een trainingsinterventie is een belangrijke eerste stap om aanknopingspunten te vinden voor het verbeteren van therapietrouw. Patiënten met een sportverleden waren meer therapietrouw aan een trainingsinterventie. Over andere mogelijk belangrijke demografische, klinische, psychosociale, fysieke en omgevingsfactoren die samenhingen met therapietrouw en het vol houden van een actieve leefstijl, konden geen conclusies worden getrokken vanwege een te klein aantal studies en/of inconsistentie tussen de verschillende studies. Daarbij hanteerden de studies verschillende definities van therapietrouw. Sommige studies richtten zich uitsluitend op aanwezigheid bij een trainingssessie, terwijl andere studies ook rapporteerden of patiënten het voorgeschreven trainingsschema opvolgden. Daarom zijn er in de toekomst studies nodig die meer inzicht geven in demografische, klinische, fysieke, psychosociale en omgevingsfactoren die van invloed zijn op therapietrouw en het volhouden van een actieve leefstijl. Bovendien is het belangrijk dat toekomstige studies een duidelijk onderscheid maken in definitie tussen het aanwezig zijn bij een trainingssessie en het opvolgen van het voorgeschreven type, duur en intensiteit van een training.

188 186 Nederlandse samenvatting Om meer inzicht te krijgen in determinanten van therapietrouw aan trainingsinterventies, onderzocht hoofdstuk 7 welke demografische, klinische, psychosociale, en omgevingsfactoren gerelateerd zijn aan het deelnemen aan en het volhouden van fysieke training bij patiënten met kanker na afloop van de in opzet curatieve behandeling met chemotherapie. Hierbij is gebruikgemaakt van de verzamelde onderzoeksgegevens van de REACT-studie. Resultaten toonden aan dat patiënten met kanker vaker deelnamen aan de REACT-studie in geval van een hoger opleidingsniveau, niet roken, minder angst en/of depressieve klachten, een hogere verwachting van de trainingen en het ervaren van meer barrières voor een actieve leefstijl. Met deze factoren dient men dus rekening te houden om deelname aan trainingsinterventies na chemotherapie te verhogen. In de HI trainingsgroep waren de deelnemers met een groter zelfvertrouwen vaker aanwezig bij de trainingen en zij hielden de krachttraining beter vol. Deelnemers met minder angst en/of depressieve klachten hielden de duurtraining beter vol. In de LMI trainingsgroep hielden de deelnemers die niet rookten de krachttraining beter vol en de deelnemers met een hogere body mass index hielden zowel kracht- als duurtraining beter vol. Bovendien hadden patiënten met borstkanker in de LMI trainingsgroep vaker moeite met het volhouden van de kracht- en duurtraining dan patiënten met andere vormen van kanker. Concluderend kan gesteld worden dat individuele psychosociale factoren zoals zelfvertrouwen en angst en/of depressieve klachten belangrijker zijn voor het volhouden van HI training dan voor het volhouden van LMI training. Het is daarom raadzaam om deze factoren in kaart te brengen voorafgaand aan het starten van HI training en indien nodig aanvullende counseling aan te bieden om het zelfvertrouwen te vergroten en de angst en/of depressieve klachten te verminderen, of om training te starten met een lagere trainingsintensiteit. In hoofdstuk 8 werden bij 574 vrouwen met borstkanker demografische, klinische, psychosociale en omgevingsfactoren bestudeerd die van invloed kunnen zijn op de lichamelijke activiteit. De vrouwen hadden deelgenomen aan één van volgende drie verschillende interventiestudies: REACT-studie, Exercise and Nutrition Routine Improving Cancer Health (ENRICH) of Move More for Life (MM4L). Resultaten toonden aan dat vrouwen met borstkanker die ouder waren, een hogere body mass index hadden, een lager zelfvertrouwen hadden of minder sociale steun van familie en vrienden ervoeren, minder lichamelijk actief waren. Het is daarom aan te bevelen dat toekomstige interventiestudies die als doel hebben om de lichamelijke activiteit van patiënten met borstkanker te vergroten, zich richten op vrouwen die ouder zijn, en op degenen met een hoger body mass index, en daarbij counseling toepassen om het zelfvertrouwen en de sociale steun te vergroten.

189 Nederlandse samenvatting 187 Hoofdstuk 9 presenteert en interpreteert de belangrijkste bevindingen van de zes studies. Tevens bespreekt dit hoofdstuk de methodologische aspecten van deze studies waaronder de studiepopulatie, het responspercentage, de generaliseerbaarheid van de resultaten, de opzet van de studies, de berekening van benodigde groepsgrootte en de keuze van de uitkomstmaten. Het proefschrift onderschrijft het belang van het implementeren van trainingsinterventies in de zorg voor patiënten met kanker. Trainingsinterventies verbeteren de cardiorespiratoire fitheid, verminderen de vermoeidheid en verbeteren de kwaliteit van leven van patiënten die hun behandeling met chemotherapie hebben afgerond. Als het doel is om fysieke fitheid en kwaliteit van leven te verbeteren, dan heeft HI training de voorkeur boven LMI training vanwege de grotere effecten. Bovendien is HI training kosteneffectief ten opzichte van LMI training in termen van QALYs. Tenslotte presenteert dit hoofdstuk enkele aanbevelingen voor toekomstig onderzoek. Toekomstig onderzoek zou de effecten van verschillen in frequentie, intensiteit, type en duur (zogenaamde FITT factoren) van trainingen bij verschillende subpopulaties verder moeten ontrafelen om specifiekere bewezen effectieve richtlijnen voor lichamelijke activiteit en training bij patiënten met kanker te kunnen formuleren. Bovendien is toekomstig onderzoek naar moderatoren van interventieeffecten nodig om meer inzicht te krijgen in welke trainingsinterventie het meest effectief is voor welke patiënt en onder welke omstandigheden. Tot slot is er meer onderzoek nodig naar de werkingsmechanismen van trainingsinterventies (zogenaamde mediatoren) op gezondheidsuitkomsten van patiënten met kanker om zo de effectiviteit en doeltreffendheid van trainingen te vergroten.

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191 List of publications 189 LIST OF PUBLICATIONS Kampshoff CS, van Dongen JM, van Mechelen W, Schep G, Vreugendenhil G, Twisk TWR, Bosmans J, Brug J, Chinapaw MJ, Buffart LM. Long-term effectiveness and cost-effectiveness of high versus low-to-moderate intensity resistance and endurance exercise among cancer survivors. Submitted for publication. Stuiver MM, Kampshoff CS, Persoon S, Groen W, van Mechelen W, Chinapaw MJ, Brug J, Nollet F, Kersten MJ, Schep G, Buffart LM. Validation and refinement of prediction models to estimate exercise capacity in cancer survivors using the Steep Ramp Test. Archives of Physical Medicine and Rehabilitation 2017, Epub ahead of print. Kampshoff CS, van Mechelen W, Schep G, Nijziel MR, Witlox L, Bosman L, Chinapaw MJ, Brug J, Buffart LM. Participation and adherence to physical exercise after completion of primary cancer treatment. International Journal Behavioural Nutrition and Physical Activity 2016, 13: 100. Kampshoff CS, Stacey F, Short CE, Chinapaw MJ, Brug J, van Mechelen W, Plotnikoff R, James EL, Buffart LM. Demographic, clinical, psychosocial and environmental correlates of objectively assessed physical activity amongst breast cancer survivors. Supportive Care in Cancer 2016, 24: Kalter J, Kampshoff CS, Chinapaw MJ, van Mechelen W, Galindo-Garre F, Verdonck-de Leeuw IM, Brug J, Buffart LM. Mediators of the effects of resistance and endurance exercise on global quality of life and physical function in cancer survivors who completed primary cancer treatment. Medicine & Science in Sports & Exercise 2016, 48: Kampshoff CS, Chinapaw MJ, Brug J, Twisk TWR, Schep G, Nijziel MR, van Mechelen W, Buffart LM. Randomized controlled trial of the effects of high intensity and low-to-moderate intensity exercise on physical fitness and fatigue in cancer survivors: Results of the Resistance and Endurance exercise After ChemoTherapy (REACT) study. BMC Medicine 2015, 13: 275. Kampshoff CS, Jansen F, van Mechelen W, May AM, Brug J, Chinapaw MJ, Buffart LM. Determinants of exercise adherence and maintenance among cancer survivors: a systematic review. International Journal Behavioural Nutrition and Physical Activity 2014, 11: 80.

192 190 List of publications Verheyden G, Kampshoff CS, Burnett ME, Cashell J, Martinelli L, Nicholas A, Stack EL, Ashburn A. Psychometric properties of 3 functional mobility tests for people with Parkinson disease. Physical Therapy 2014, 94: Ashburn A, Kampshoff CS, Burnett M, Stack E, Verheyden G. Sequence and onset of wholebody coordination when turning in response to a visual trigger: comparing people with Parkinson's disease and healthy adults. Gait Posture 2014, 39: Janssen MA, van Achterberg T, Adriaansen MJ, Kampshoff CS, Schalk DM, Mintjes-de Groot J. Factors influencing the implementation of the guideline triage in emergency departments: a qualitative study. Journal of Clinical Nursing 2012, 21: Janssen MA, van Achterberg T, Adriaansen MJ, Kampshoff CS, Mintjes-de Groot J. Adherence to the guideline 'Triage in emergency departments': a survey of Dutch emergency departments. Journal of Clinical Nursing 2011, 20: Kampshoff CS, Buffart LM, Schep G, van Mechelen W, Brug J, Chinapaw MJ. Design of the Resistance and Endurance exercise After ChemoTherapy (REACT) study: a randomized controlled trial to evaluate the effectiveness and cost-effectiveness of exercise interventions after chemotherapy on physical fitness and fatigue. BMC Cancer 2010;30;10:658. Effing TW, Kampshoff CS, van der Valk PDLPM, Kerstjens HAM, Zielhuis GA, van der Palen J. Immediate in hospital reactivation of patients with an exacerbation of COPD: PULOMOFIT- MST. Thesis TW Effing ISBN: Publications in Dutch Kampshoff CS, Buffart LM, Schep G, Nijziel MR, van Mechelen W, Brug J, Chinapaw MJ. Een gerandomiseerd onderzoek naar de effecten van training na chemotherapie op fysieke fitheid en vermoeidheid: Resistance and Endurance exercise After ChemoTherapy (REACT). Nederlands Tijdschrift voor Oncologie 2011, 8: Kampshoff CS, Poot E, Janssen MA, Mintjes-de Groot J. Richtlijn Triage op de spoedeisende hulp. Landelijk Expertisecentrum Verpleging & Verzorging (LEVV), 2008.

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195 Dankwoord 193 The experience of truth is indispensible for the experience of beauty and the sense of beauty is guided by a leap in the dark - Arthur Koestler. DANKWOORD De foto op de voorkant van dit proefschrift toont de Merewether Ocean Baths, gelegen in de stad Newcastle, Australië. Deze openluchtzwembaden liggen direct naast de oceaan en worden middels de golfslag van de oceaan gevuld met zeewater. De foto is gemaakt bij een rustige en zomerse zonsopkomst, maar het kan er in deze zwembaden ook heel stormachtig aan toegaan. Voor mij verbeelden deze zwembaden pure schoonheid en een denkbeeldige sprong in het diepe (leap in the dark). Aan dit proefschrift heb ik met groot plezier en enthousiasme gewerkt. Het was voor mij een sprong in het diepe en het is mij een waar genoegen geweest om met zoveel mensen samen te werken. Allereerst dank ik de REACT-deelnemers. Gedurende een periode van 15 maanden heeft het REACT-team u mogen volgen in de tijd. Deze 15 maanden volgden kort op een periode van vele medische behandelingen in het ziekenhuis. U heeft mij en het team in deze roerige tijden geholpen met conditietesten op een fiets, invullen van vragenlijsten en kostendagboekjes, dragen van stappentellers, DXA scans, en nog veel meer. Ik bewonder uw veerkracht en doorzettingsvermogen. Dank voor uw openheid, betrokkenheid en de fijne gesprekken. Het promotorenteam. Laurien, ik kwam bij jou met de wens om heel veel te leren en die kans heb jij mij gegeven; ik had mij geen betere postdoc als begeleider kunnen wensen. Ik heb grote bewondering voor jouw harde werken, scherpe analyses en passie voor de weten schap. Dank voor jouw support, gezelligheid en de fijne samenwerking. Willem, jij was er altijd wanneer ik je nodig had. Je belde op, vroeg of ik in drie zinnen wilde toelichten waar jij mij bij kon helpen en we sloten af met een oplossing, zodat ik verder kon. Dank voor het delen van jouw expertise, het vertrouwen en de kansen die je mij geeft. Grondleggers van het Alpe d HuZes Cancer Rehabilitation (A-CaRe) onderzoek, Mai en Hans, jullie expertise, aan vullingen en de puntjes op de i maakten het team en dit proefschrift compleet. Dank jullie wel dat ik altijd een beroep op jullie kon doen. Tenslotte, grondlegger van het hoog intensieve trainingsprogramma voor patiënten met kanker, Goof, dank voor jouw gastvrijheid, tijd en inzet.

196 194 Dankwoord Het REACT-team; Karen, Charlotte, Julie, Joep en Michiel. Jullie zijn stuk voor stuk kanjers. Wat hebben jullie hard gewerkt en wat ben ik trots op jullie. Ik kon op jullie rekenen en die wetenschap was goud waard. Naast al dat harde werken, was er ook ruimte voor een lach en wederzijdse betrokkenheid. Dank je wel. De oncologen en de oncologieverpleegkundigen. Een studie in een ziekenhuis staat en valt met de inclusie deelnemers. Mijn directe contactpersonen en collega s van het Máxima Medisch Centrum in Eindhoven en Veldhoven, Catharina Ziekenhuis in Eindhoven, Elkerliek Ziekenhuis in Helmond, St. Anna Ziekenhuis in Geldrop, VieCuri Ziekenhuis in Venlo en Venray, Zuwe Hofpoort Ziekenhuis in Woerden, St. Antonius Ziekenhuis in Nieuwegein en Utrecht, Academisch Medisch Centrum in Amsterdam en het Erasmus Medisch Centrum in Rotterdam; dank voor het beantwoorden van de klinische vragen, het uitdelen van de informatiebrieven en -folders op de afdelingen en jullie betrokkenheid bij de REACT-studie. De fysiotherapeuten. Het trainen kort na afloop van de medische behandelingen vraagt bijzonder veel van mensen en het vraagt dus ook veel van jullie. Ik heb bewondering voor jullie passie voor het vak fysiotherapie en actieve bijdrage aan de wetenschap. Mijn directe contactpersonen en collega s van B-Fysic Kastelenplein in Eindhoven, Fysio- en Manuele Therapie Van Hoof in Westerhoven en Dommelen, Fysiotherapie Heikant in Veldhoven, Revalidatiecentrum Blixembosch in Eindhoven, St. Anna Zorggroep afdeling Fysiotherapie in Geldrop, Elkerliek ziekenhuis afdeling Fysiotherapie in Helmond, Fysionova in Oirschot en Liempde, LifeStyle health & prevention in Weert, Praktijk voor fysiotherapie en trainingscentrum Paul van der Weerden in Someren, Fysiomotion in Panningen, Fysiotherapiepraktijk Westsingel Horst, Funqtio in Steyl, St. Antonius Ziekenhuis afdeling Fysiotherapie in Utrecht en Nieuwegein, Fysiotherapeuten Maatschap Woerden, Fysiotherapie Woudrichem, Fysiotherapie van der Kley en Kuiper in Krimpen aan de IJssel, Fysio Oosterpark in Amsterdam, Fysiowave in Zevenbergen, Physiomotion in Rotterdam, FysioMaatwerk Heeswijk in Heeswijk-Dinther; dank voor jullie flexibiliteit, extra inzet en ruimte voor bezoekjes van het REACT-team. De sportartsen, de revalidatieartsen en de assistenten, de radiodiagnostisch laboranten, de osteo poroseverpleegkundigen en de radiologen. Petje af voor alle 277 sportmedische intakes en controle afspraken, 742 conditie- en krachtmetingen en 493 DXA scans die jullie mogelijk hebben gemaakt. Mijn directe contactpersonen en collega s van Sport Medisch Centrum SportMáx in Eindhoven en Veldhoven, afdeling Radiologie van het Máxima Medisch Centrum in Eindhoven, Libra Revalidatie & Audiologie locatie Blixembosch in Eindhoven, afdeling Sportgeneeskunde

197 Dankwoord 195 van het Elkerliek Ziekenhuis in Helmond, TopSupport Eindhoven, Sportgeneeskunde VieCuri in Venlo, afdeling Revalidatie van het St. Antonius ziekenhuis in Utrecht, afdeling Radiologie van het St. Antonius ziekenhuis in Nieuwegein, Topsport Medisch centrum SportsClinic in Utrecht, Sport geneeskunde Woerden, afdeling Revalidatiegeneeskunde en afdeling Radiologie van het Academisch Medisch Centrum in Amsterdam en afdeling Revalidatiegeneeskunde van het Erasmus Medisch Centrum in Rotterdam; dank voor jullie flexibiliteit in de planningen, hulp bij de medische en logistieke vraagstukken en het ontvangen van de REACT-deelnemers. De REACT-stagiaires; Femke, Maike, Lisa, Lenja, Viviana, Anne, Kim en Gabrielle. Wat vind ik het gaaf om te zien dat de REACT-studie als kick-off kon dienen voor jullie wetenschappelijke carrière. Dank voor het harde werken en hulp bij de publicaties. De onmisbare collega s op de universiteit en de afdeling; Inge, Joske, Eline, Patrick, Len, Brahim, Antoine, Annemiek, Sergio en Marjan. Naast de gezellige small talk, dank voor jullie tijd en hulp om tot oplossingen te komen voor alle grote en kleine issues rondom de REACT-studie. De coauteurs; Jos Twisk, Joeri Kalter, Hanneke van Dongen, Marten Nijziel, Art Vreugdenhil, Anne May en Judith Bosmans. Dank voor het kritisch meelezen van de manuscripten, de adviserende rol, en jullie tijd voor consultaties en extra toelichting. Colleagues of the University of Newcastle, Australia. Talking about my time in Newcastle, always brings a smile to my face. Thank you for your warm welcome, the opportunity to combine our datasets, and successfully publishing our manuscript on physical activity in breast cancer survivors. I will never forget the breath-taking coastline, the smell of coffee beans in the early morning (6 AM), Hunter Valley wines, and Christmas team lunch on the beach; many thanks to all of you. De leescommissie; Prof. dr. Henk Verheul, Prof. dr. Epie Boven, Prof. dr. Maria Hopman, Prof. dr. Henk Stam, Prof. dr. Hans Knoop, Dr. Martijn Stuiver en Prof. dr. Neil Aaronson. Dank voor de tijd en aandacht die jullie aan mijn proefschrift hebben besteed. Ik kijk er naar uit om met jullie allen van gedachten te wisselen over de inhoud van mijn proefschrift. De A-CaRe ladies; Saskia, Hanna, Katja en Alice. Ik heb samen met jullie een berg beklommen; letterlijk en figuurlijk. Dank voor de tips, het steuntje in de rug, het uitwisselen van ervaringen en het geschater en getetter tijdens de A-CaRe etentjes.

198 196 Dankwoord De Alpe d HuZes organisatie en de wielrenners. De REACT-studie is gefinancierd door het Alpe d HuZes fonds/kwf Kankerbestrijding en mijn dank gaat uit naar iedereen die dit grootse event mogelijk maakt en/of de berg op fietst om geld in te zamelen. Kamergenoten, H0-G0 gang collega s, PROVU bestuur 2012, ADH6-team 2013, SGgaatuitcommissie 2013, de paperclub collega s, Sport, Lifestyle and Health team, EMGO + secretariaat, collega s van het KNGF in het bijzonder team Kwaliteitsbeleid, ISBNPA roomies en Exercise and Cancer team. Dank voor de inhoudelijke discussies, de sociale afleiding en support, de koffietjes, de borrels en de vriendschappen. Jullie zorgen er mede voor dat ik met heel veel plezier naar mijn werk ga. De vrijwilligers van Nationale Vereniging Zonnebloem, Afdeling Moergestel. Ik kan niet tippen aan jullie warmte, tijd en liefde voor de gasten, maar wat fijn dat ik als een benjamin deel uitmaak van jullie team. Mijn vriendinnen. Best friends make bad times good and good times unforgettable. Wat voel ik mij een rijk mens met zoveel fijne vriendinnen om mij heen. Ik koester alle hilarische en memo rabele gebeurtenissen die ik met jullie heb beleefd. Zeer veel dank voor jullie support in de afgelopen jaren en de belangstelling in mijn onderzoek. De paranimfen; Karen en Marieke. Ik voel mij zeer vereerd dat jullie beiden aan mijn zijde staan tijdens de promotieplechtigheid. Dank voor jullie support! Mijn familie. Like branches on a tree, we all grow in different directions, yet our roots remain as one. De creativiteit van mijn tantes, de humor van mijn ooms en de vriendschappen met mijn neven en nichtjes. Ik vind het heel fijn om jullie nichtje te zijn. Oma. Mijn eerste woordjes op schrift werden geschreven aan uw keukentafel. Dank voor alle kaarsjes die u voor mij hebt aangestoken voor een beetje geluk en positieve gedachten bij de reeks aan tentamens. Ik ben heel trots op u. De viervoeter; Bibi. Dank voor de fijne wandelingen samen.

199 Dankwoord 197 Edu. Seven billion people on the planet and you re my favourite. Wat hebben we het ongelooflijk fijn samen. Dank voor alle lol en liefde en dat je mij stimuleert om mijn hart te volgen. Mijn ouders. Lieve papa en mama, dit boekje is voor jullie. Dank voor jullie onvoorwaardelijke liefde, grenzeloze vertrouwen, steun en vrijheid die jullie mij altijd hebben gegeven. Jullie zijn trots op mij, ik ben ongelooflijk trots op jullie.

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201 About the author 199 ABOUT THE AUTHOR Caroline Kampshoff was born on 11 th of September, 1979 in Tilburg, the Netherlands. After graduating from secondary school at Koning Willem II College in Tilburg, she studied Physical Therapy at the HU University of Applied Sciences in Utrecht. She completed her bachelor degree in 2002 and she enrolled in the masters program of Biomedical Sciences at the Radboud University in Nijmegen, including a two year specialisation in human movement sciences. As part of her traineeship, she visited the University of Southampton in the United Kingdom to study turn tests as performed by healthy adults. Furthermore, Caroline joined the research team of pulmonary medicine at Medisch Spectrum Twente hospital in Enschede and evaluated the effectiveness of an exercise program in hospitalized patients with acute exacer bation of COPD for her masters thesis. After her studies, Caroline started as a junior researcher at the HAN University of Applied Sciences in Nijmegen studying triage in the emergency department. In 2008, she returned to the University of Southampton to coordinate a research project on eye, head and body coordination during turning in people with Parkinson s Disease and healthy controls. In 2010, she started her PhD on exercise interventions after chemotherapy at the department of Public and Occupational Health of the VU University Medical Center in Amsterdam. During her PhD, she obtained her masters degree in Epidemiology at the VU University. In 2013, she was awarded an EMGO+ Travel Grant which enabled her to visit the Hunter Medical Research Institute of the University of Newcastle, Australia to study correlates of objectively assessed physical activity amongst breast cancer survivors. Currently, Caroline works at the Royal Dutch Society for Physical Therapy and she coordinates the revision of the physiotherapy guideline on knee and hip arthrosis.

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