Centralised physical activity feedback

Transcription

1 Centralised physical activity feedback ENCOURAGING EMPLOYEES WORKING IN AN OFFICE SETTING TO BE SUFFICIENTLY PHYSICALLY ACTIVE SIMONE T. BOEREMA Master Thesis Biomedical Engineering August 26, 2009 BME 024 & BSS COMMITTEE: Prof. Dr. Ir. H.J. Hermens University of Twente & Roessingh Research and Development Ir. T. M. Tönis University of Twente & Roessingh Research and Development Dr. E.M.A.G. van Dijk University of Twente, Human Media Interaction UNIVERSITY OF TWENTE Department of Electrical Engineering, Mathematics and Computer Science Biomedical Signals and Systems Enschede, The Netherlands ROESSINGH RESEARCH AND DEVELOPMENT Non-Invasive Neuromuscular Assessment Enschede, The Netherlands

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3 Abstract Interest in the field of sports and physical activity has risen in general population as well as in politics in the Netherlands. Sports and physical activity play an important role in public health and labour productivity, while active employees are less absent and recover faster from illness. Moreover, physical inactivity is seen as one of the most important factors in lower life expectancy due to heart and vascular diseases and other chronic conditions. Roessingh Research and Development (RRD) is working on a feedback system that stimulates desk workers to be more physically active during office hours. The primary goal of this research is to develop a User Interface for the physical activity feedback system that is already developed at the RRD. Subsequently this system is evaluated on its ability to change behaviour of subjects to become sufficiently physically active. To evaluate the influence of the feedback system on behaviour, first a User Interface had to be developed. Literature on physical activity feedback, tailoring and health promotion have been implemented to create the first prototype. Via several design iterations (e.g. findings emerging from a focus group meeting and an usability study) the final User Interface was created. Several types of feedback could be chosen by the user, thereby making it possible to evaluate user preference of type of physical activity feedback. Evaluation of behavioural intention was done by a questionnaire based on the Theory of Planned Behaviour, specially designed for the experiment with the physical activity feedback system. The experiment was done with 20 subjects, in two groups of 10, each for a duration of 3 weeks. Subjects wore a 3Daccelerometer at their hip to measure physical activity during working hours. These sensors send their data wireless to bridges in the building. When they are in the proximity of the coffee corner, users get physical activity feedback on a screen above the coffee machine. The first week is the baseline measurement without feedback followed by 2 weeks of feedback (the actual intervention). During the intervention it became clear that the feedback on the User Interface was a misrepresentation of the actual physical activity of users. Several phenomena were seen resulting in both over- and underestimation of physical activity by the feedback system. Extensive research to the causes of these phenomenons did not pinpoint the factual cause, but much of

4 ii the errors can be explained by the loss of data, resulting in long time steps between consecutive data samples, making the total dataset unreliable. The misrepresentation made it impossible to measure actual physical activity and biased all proximal behaviour measures. Many measures like Intention to perform a specific behaviour did not change compared to baseline, although subjects became more aware of their personal physical activity level. Which is a promising result for further research. Future research should solve the missing data issue, possibly by saving data locally on the sensor. This can create a buffer for out of reach issues. The other developed measures can be applied in future experiments to gather new insights in the mechanisms of physical activity feedback and its effect on health behaviour.

5 Samenvatting De interesse in sport en fysieke activiteit in Nederland neemt steeds verder toe, door hun belangrijke rol in gezondheid en arbeidsproductiviteit. Werknemers die actief zijn, verzuimen minder en korter. Fysieke inactiviteit is een van de belangrijkste onafhankelijke factoren van een lagere levensverwachting, ten gevolge van hart- en vaatziekten en andere chronische aandoeningen. Het Roessingh Research and Development (RRD) werkt daarom aan feedback systemen die werknemers met een kantoorbaan motiveren meer fysiek actief te zijn het werk. Het doel van dit onderzoek is om een gebruikers interface te ontwikkelen voor het feebdack systeem dat al is ontwikkeld binnen het RRD. Vervolgens zal deze interface getest worden in hoeverre het mogelijk is om hiermee gedrag van gebruikers te beïnvloeden, zodat zij voldoende fysiek actief zijn. Om het feedback systeem te kunnen evalueren, is eerst een gebruikers interface ontwikkeld. Literatuur over feedback op fysieke activiteit, op maat gemaakte informatie, en gezondheids interventies zijn toegepast in het eerste ontwerp. De uiteindelijke interface is via meerdere ontwerp stappen (zoals een focusgroep discussie en gebruikerstesten) tot stand gekomen. Gebruikers hadden de mogelijkheid om verschillende representaties van hun fysieke activiteit te kiezen, waardoor het mogelijk was om gebruikers voorkeur vast te stellen. Evaluatie van de intentie om gedrag aan te passen is gemeten met een vragenlijst op basis van de Theory van Gepland Gedrag (Theory of Planned Behaviour), en speciaal ontworpen voor de evaluatie van een feedback systeem voor fysieke activiteit. Het experiment is uitgevoerd met 20 proefpersonen met een kantoorbaan, verdeeld over twee groepen van 10 elk gedurende 3 weken. Proefpersonen droegen een 3D-accelerometer op hun heup, waarmee fysieke activiteit kon worden gemeten tijdens kantooruren. Deze sensor zendt bewegingsdata draadloos naar ontvangers in het gebouw. Wanneer een gebruiker dicht bij de koffie automaat is, krijgt de gebruiker feedback op zijn fysieke activiteit via een beeldscherm boven de koffie automaat. Tijdens de eerste week werd gedrag gemeten zonder feedback en tijdens week 2 en 3 kregen proefpersonen feedback (de eigenlijke interventie). Tijdens de interventie werd duidelijk dat de feedback beelden niet overeen kwamen met het daadwerkelijke bewegingsgedrag van de proefpersonen, het systeem gaf zowel over- als

6 iv onderschattingen weer op het scherm. Uitgebreid onderzoek naar de oorzaak hiervan, heeft niet kunnen leiden naar de exacte oorzaak van dit probleem, maar veel foute weergaven zijn te verklaren door het ontbreken van data, mogelijk door dataverlies bij het proces van draadloos verzenden en ontvangen. De foutieve weergave van de persoonlijke feedback maakt dat alle vergaarde data gekleurd door de invloed op motivatie van gebrukers. Uitkomstmaten zoals intentie om gedrag te veranderen bleken niet te veranderen tijdens het experiment. Wel lijkt het dat proefpersonen door de interventie zich meer bewust zijn geworden van hun eigen beweegsgedrag, wat een veelbelovende uitkomst is voor verder onderzoek. Toekomstig onderzoek zal zich eerst moeten richten op het probleem van de ontbrekende data, mogelijk door data ook lokaal op te slaan op de sensor. Wanneer die stap is gemaakt, kan dit experiment met de ontwikkelde meetinstrumenten zonder veel aanpassingen direct weer worden uitgevoerd, zodat nieuwe inzichten kunnen worden vergaard over het mechanisme van feedback op fysieke activiteit en de invloed hiervan op bewegingsgedrag.

7 Preface Een jaar geleden ging ik op zoek naar een afstudeeropdracht. Ik wilde graag iets doen met gezondheidsgedrag van mensen, wat beweegd iemand om iets wel of juist niet te doen. Bij het RRD vond ik deze opdracht die precies bij mij paste, hier en daar had ik nog wel een uitdaging, want het gezochte profiel Een technisch geörienteerde masterstudent met affiniteit voor psychologie en met bij voorkeur enige programmeerervaring kwam niet helemaal overeen met mijn kennis en ervaring. Ik heb met heel veel plezier bijgeleerd over phsychologische aspecten van gezondheidsgedrag en hoe je hier wetenschappelijk onderzoek naar kunt doen. Aan de andere kant heb ik ook met heel veel plezier leren programmeren en ik ben best trots dat ik zelf een programma (de interface) heb geschreven, die bovenal nog goed werkte ook. Dankzij deze afstudeeropdracht ben ik vele ervaringen rijker. Ik wil iedereen bedanken die me heeft geholpen tijdens mijn afstuderen. Specifiek, wil ik Hermie Hermens, Thijs Tönis en Betsy van Dijk als leden van mijn afstudeercommissie bedanken voor hun adviezen, begeleiding en steun tijdens tegenslagen. En ik wil graag afsluiten met het bedanken van mijn ouders en mijn broer, voor alle liefde, kennis en ervaring die mij hier hebben gebracht. ENSCHEDE, AUGUST 26, 2009 SIMONE THERESA BOEREMA

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9 Contents 1 Introduction Motivation Goal Approach Structure Literature study: Measuring Physical Activity Physical activity Physical activity of the Dutch population Recommendations Campaign 30 minuten bewegen & PA at work Measuring Physical Activity & Interpreting data Physical activity monitors Physical activity feedback system Monitoring of physical activity Feedback on physical activity Integrating the physical activity recommendations Chapter summary

10 viii CONTENTS 3 Literature study: Theories and models on health behaviour Models Health Belief Model (HBM) - Rosenstock (1966) Social Cognitive Theory (SCT) - Bandura (1977) Theory of Planned Behaviour (TPB) - Ajzen (1988) Protection Motivation Theory (PMT) - Rogers (1975) The Transtheoretical (stages of change) Model (TTM) - Prochaska (1983) Remarks on the models Tailoring Physical activity interventions PA interventions using Pedometers User Interface Design Chapter summary Development Identification of outcomes, performance objectives and change objectives Design steps Development of the User Interface Focus group Prototype Evaluation Final design - Technical specifications Final design - Use specifications UI Evaluation Questionnaire Development of the TPB questionnaire Elicitating study: The behaviour of interest Focus group First draft - Number & type of questions final TPB questionnaire

11 CONTENTS ix 4.4 Chapter summary Method Study design Intervention Outcome measures Statistical analysis Results Subjects Physical activity data - IMA values Case 1: Two sensors having the same accelerations Case 2: Walking, though no physical activity period reported by the system Case 3: Walking test with all 10 sensors Case 4: walking together, though different physical activity feedback Case 5: activity reported while being in a meeting Summary cases Extra program Frequency & IMA values Loop count Overestimation by the system Time step effects in processed IMA values Sample frequency settings of the sensor TPB Questionnaire Time effects Internal consistency TPB Overview Motivation to Comply and Evaluation of Compliance User Interface usage

12 x CONTENTS 6.6 User Satisfaction Questionnaire Feedback system & Feedback at the User Interface Improving the feedback system & User Interface Personal gain from the intervention Summary Discussion Failing of the acceleration monitoring system Physical activity Proximal determinants of behaviour Methodological considerations Conclusion & Recommendations Type of information & presentation Measure of behaviour and motivation Correct feedback Effect of the intervention Methodological conclusions Recommendations Abbreviations & Behaviour Models 85

13 List of Figures 2.1 Activity Monitors. From left to right: Actigraph [1], Actical [2], PAM [3], and the RRD system Overview of the physical activity feedback system [4] Overview monitoring part of the system [4] Overview feedback generating part of the system [4] Example of the feedback given on the feedback monitor, with two employees near the monitor [4] The Health Belief Model [5] Social Cognitive Theory [5] Theory of Planned Behaviour [5] Protection Motivation Theory [5] The Transtheoretical (stages of change) model (TTM) [6] The star life cycle, key items in the User Interface design process [7] Outline of the design process for the interface (prototyping) and the development of the TPB questionnaire Overview of the different steps of the sensor and feedback system Overview of the different steps of the interface program Clock. First screen of the interface

14 xii LIST OF FIGURES 4.5 Timeline. Shows periods of activity, on a timeline of the current day Performance Indicator. Shows the performance level with respect to the target Week overview & Ranking. Shows the performance of the user, and the average of colleagues, for each day of last week. As well as the position in the ranking of all 10 colleagues of the past 6 days Study design Objective and subjective outcome measures The IMA values of sensors ID4 and ID6 are given, for an interval of about 400 seconds. The sensors were placed at the same rotating object, expecting the same IMA values The IMA values of sensor ID8 are given, for an interval of 191 seconds. Although the subject was walking during this interval, not all IMA samples are above the threshold of 0.14 m/s IMA values of all sensors against time (duration is 5.29 minutes = 329 seconds). 0 seconds is the start of the physical activity interval. Most of the IMA values are above the threshold of 0.14 m/s Two subjects of group B walked next to each other through the RRD building Above: Overview of the IMA values of sensor ID5 during a meeting. Below: Detailed view of figure above, of the IMA samples on the interval 1,700-2,100 seconds. Minor vertical gridlines are placed every 30 seconds, revealing TS variations from 5 up to 88 seconds on this interval. IMA 0.14 m/s 2 was on the interval 1,778-1,960 seconds (9:29:38 to 9:32:40 hours) The average frequency in Hz, per sensor ID. Frequency is determined by the number of received data samples, after removing duplicates (when received by multiple bridges). Theoretical maximum sample frequency is Hz (sample interval = 26 ms) Average IMA value from the 26ms logfile ( , 08:45:00-15:59:59 hours) with the standard deviation. IMA values are calculated as the summation of absolute acceleration in x, y, and z direction. Acceleration in each direction is calibrated with respect to the gravity, which is set to 1. The offset (due to gravity) is removed by lowering the calculated IMA value by Per sensor the time step length is shown against time in hours of time steps 60 seconds are indicated with a red box beneath each graph (on the vertical axis interval [-20 0]). The percentage of these untrusted interval with respect to the total measured time is given in each subtitle

15 LIST OF FIGURES xiii 6.9 time step length of sensor ID3. Case: subject was in a meeting. Period: 12:15-13:10 hours. During this period almost no data has been received, resulting in a gigantic time step length time step length of sensor ID5. Case: subject was in a meeting. Period: 13:30-14:00 hours. During this period time step length increases to far above 60 seconds, resulting in almost a full interval of untrusted time steps time step length of sensor ID6. Cases 1): subject was sitting behind his desk. Period: 12:00-13:25 hours, and 2) subject was in a meeting. Period: 14:00-16:00 hours. During both periods time steps are longer than 60 seconds, resulting in untrusted time steps Boxplots of the 4 Self Report questions, at T 0, T 1, and T 2. Details are given in Appendix H Overview of the alphas (internal consistency) for all TPB items. D = direct questions; I = indirect questions. PA = questions addressing being PA; MOVE = questions addressing the use of the feedback system for achieving physical activity. An alpha coefficient 0.70 concludes sufficiently reliable Overview of TPB construct averages, split to PA and MOVE, including all questions and moments. IN = Intention; AT = Attitude; SN = Subjective Norm; PBC = Perceived Behavioural Control Histograms of answers from all 20 subjects on the Compliance and Evaluation question at T 1 and T 2 respectively. Answer range was from [1-5] with 1 being definitely not and 5 being definitely planning on being / have been physically active, every workday, for at least 30 minutes Interface usage of group A (ID1-ID10). Above: feedback moments ( * ) and number of interactions ( o ), showing counts per day. Below: average feedback duration and standard deviation per day in seconds. Missing points are because no feedback was registered from that subject at that day. Not visible in the figure are: ID3, 05-28, std = 1278s; ID9, 05-28, std = 992s; ID10, 05-28, std = 956s, and ID6 had (compared to the other users) excessive feedback counts: [ ] Interface usage of group B (ID11-ID20). Above: feedback moments ( * ) and number of interactions ( o ), showing counts per day. Below: average feedback duration and standard deviation per day in seconds. Missing points are because no feedback was registered from that subject at that day. Not visible in the figure are: ID3, 05-28, std = 1278s; ID9, 05-28, std = 992s; ID10, 05-28, std = 956s, ID11, 06-10, std = 524s; ID13, 06-08, std = 466s; ID17, 06-12, std = 763s; ID18, 06-02, std = 389s, and 06-08, std = 352s Boxplots of the answers regarding the Feedback system per construct the questions were averaged per subject. Response scales are unipolar Likert scales (1 to 5), ranging from 1 totally not agree to 5 totally agree

16 xiv LIST OF FIGURES 6.19 Boxplots of the answers regarding the Feedback on the User Interface. Per construct the questions were averaged per subject, except for Compatibility and Overall, which had only 1 question. Response scales are unipolar Likert scales (1 to 5) ranging from 1 totally not agree to 5 totally agree Boxplots per question about the specific interface parts. 15 questions of the UI Perceived usefulness questions from figure All 20 subjects are included. 1) Useful = consider this visual feedback useful. ; 2) Awareness = This visual feedback has enhanced my awareness of daily physical activity. ; and 3) Motivation = This visual feedback has motivated me to adapt my physical activity practise.. Response scales are unipolar Likert scales (1 to 5) ranging from 1 totally not agree to 5 totally agree

17 List of Tables 1.1 Model PRECEDE/PROCEED Nederlandse Norm Gezond Bewegen (NNGB) (Dutch norm on physical activity) [8] Recommendations for sufficient physical activity at work [9] Percentages complying the 30 minutes recommendation, depending on the inclusion criteria of physical activity bouts. Adjusted from Hagströmer et al. (2007) [10] Overview of several commonly used activity monitors Values of specific parameters for calculating the IMA value and specific parameters for calculating the physical activity (PA) [4, 11] Explanation of tailoring goals and strategies. Adjusted from Hawkins et al. (2008) [12] Updated values of specific parameters for calculating the IMA value and specific parameters for calculating the physical activity (PA). Adjusted from table Overview of the status intervals, determining the feedback in colour and text Overview of the number of questions in the final evaluation questionnaire The items of the Theory of Planned Behaviour (TPB) and the constructs targeted in this study Summary of focus group results. Perceived barriers and levers on attitude and perceived behaviour control (PBC). Full report in appendix A

18 xvi LIST OF TABLES 4.6 Overview of the number of questions in the final TPB questionnaire, split on item and direct or indirect question. PA = Physical Activity. MOVE = The developed physical activity feedback system Study design: information and questionnaires, full text available in the appendices Chronological overview of the adjustments and cases during the experiment. The feedback system was used by group A from 5-15 till 5-29, and by group B from 6-09 till Items marked with an asterisk (*) are adjustments of the system Descriptive statistics of all 10 sensors during the walking test. IMA = Integral of the Modulus of body Acceleration output; TS = Time step. Std = standard deviation. # = number of samples Descriptive statistics of the IMA loop count. An indication of the average loop count maxima is approximately twice the mean or median. Extracted from the unprocessed data gathered at Overview of the tested sample frequencies. At the left the programmed frequencies of the sensor and at the right the frequencies of the corresponding received datasets Statistical analysis of time effects in the TPB constructs, given are alpha s resulting from each test. Significance level: alpha = * Significant differences between T 0, T 1, or T Overview of the questions which can be removed from the TPB item to increase internal consistency. D = direct; I = indirect. PA = questions addressing being PA; MOVE = questions addressing the use of the feedback system for achieving physical activity. An alpha coefficient 0.70 concludes sufficiently reliable Statistical analysis of the model Theory of Planned Behaviour. Given are alpa s resulting from each test. * The contribution of the construct to Intention is significant when alpha Cross table showing the Evaluations answers, given after a certain Compliance answer Short descriptions of the behaviour models described in Chapter 3.[5, 13] Overview of all abbreviations in this thesis

19 Chapter 1 Introduction This chapter is an introduction to the research which was carried out for this thesis assignment. First a short motivation is given, followed by the aim of the research. The approach and structure of this report are covered last. 1.1 Motivation Interest in the field of sports and physical activity has risen in general population as well as in politics in the Netherlands. Sports and physical activity play an important role in public health and labour productivity. Physically active employees are less absent and recover faster from illness. Moreover, physical inactivity is one of the most prominent independent health risk factors. It is seen as one of the most important factors in lower life expectancy due to heart and vascular diseases and other chronic conditions [8, 14]. Specialized institutes (e.g. TNO (organisatie voor Toegepast Natuurwetenschappelijk Onderzoek) and NISB (Nederlands Instituut Sport en Bewegen) in the Netherlands) provide recommendations on physical activity, based on which politics have started numerous campaigns to promote physical activity [9, 15, 16]. Employers also benefit from promoting physical activity and reducing the in-activity of their employees, due to its positive effects on the employees availability and productivity. Different ways of promoting physical activity have been developed, such as the 30 minuten bewegen (30 minutes physical active) campaign [17]. In line with these developments, Roessingh Research and Development (RRD) [18] is working on a feedback system that stimulates desk workers to be more physically active during office hours, as part of the European project Smart Surroundings [19]. Commercial available systems are mostly computer applications which act upon taking short breaks and perform muscle relaxing exercises. These applications are based on the periods the computer is being operated, and lack input from the physical activity of the employee, making them annoying

20 2 INTRODUCTION for desk workers, as they are interrupted during their work. The target of this RRD project is to provide the employee with feedback on his or her physical activity and whether or not it is sufficient. Additional feedback can be given to stimulate the employee to change his/her physical activity. The system, which is in development, operates in the following manner. Physical activity is measured by a 3D accelerometer which is worn on the hip. The accelerometer has a wireless connection to the receivers in the building of RRD. These receivers are positioned in such a way that the accelerometers will be detected throughout the whole building. The system is able to detect physical activity, save this data and give feedback to its users. The basic principle of this system is to give feedback at a central point in the building (in this case the coffee machine). Employees wearing an accelerometer get individual feedback on their level of physical activity on the central display, if they are in the proximity of the central point. 1.2 Goal The primary goal of this research is to develop a physical activity feedback system that encourages employees, working in an office setting, to be sufficiently physically active, using the monitoring system which has already been developed. This is the step from technical development towards health promotion intervention. RRD would like this to be accomplished by optimising the feedback itself (the interface, message, etc.) and conducting an evaluation using employees of RRD. Subquestions that arise from this goal are: Which information of physical activity is relevant for office employees? In which way should information on physical activity be presented in order to promote physical activity? What are the desired goals of the system and how can these be measured? Does the activity feedback system stimulate employees working in an office setting to change their behaviour when necessary? 1.3 Approach The approach used to reach the goal mentioned above, and answer the subquestions is given below: 1. Study physical activity monitors and algorithms 2. Study psychological models used in health behaviour 3. Study methods on feedback given on a public display 4. Study physical activity interventions 5. Define and implement the physical activity output measures of the system 6. Design and implement the user interface 7. Evaluate the feedback system and behavioural changes

21 1.4 STRUCTURE 3 The focus is twofold, first on the models used in health behaviour (2) and methods on feedback (3), and second on acquiring the right output measures (5), implementing these in a user interface (6) and evaluating the developed system (7). The first item will need less attention because previous research at the RRD has already resulted in a monitoring system with algorithms to determine physical activity level and desk work. Gather information on physical activity interventions (4) will be done to gain insight into the models and methods used in general. 1.4 Structure The system, which gives feedback on health behaviour, can be seen as a health intervention program. A method used for designing an intervention, is the PRECEDE/PROCEED model [20]. This model distinguishes 6 steps in the development of a health intervention program, and is given in table 1.1. The design process of the complete intervention starts with gathering requirements, going through steps 1-3 of the PRECEDE/PROCEED model by using literature, user interviews, and user-tests. And as part of the intervention the design of the interface will be done during steps 4 and 5. The following chapters will be according to the steps of the PRECEDE/PROCEED model. Theoretical foundations on measuring physical activity, psychological models and an overview of the system of the RRD as well as commercially available systems are given in Chapter 2. This is followed by theories and models on health behaviour in Chapter 3. The resulting requirements of the system and iterative design of the user interface will be described in Chapter 4 followed by the method of evaluation of the system, using employees of the RRD, in Chapter 5. Chapter 6 gives the results of this evaluation. Finally, an overall evaluation of this project will be given, as well as conclusions and recommendations. Model PRECEDE/PROCEED 1. Analysis of quality of life and health CH2 and CH3 2. Analysis of behaviour and environment CH3 and CH4 3. Analysis of behaviour determinants CH3 and CH4 4. Development of the intervention CH4 5. Implementation of the intervention CH5 6. Evaluation CH6, CH7, and CH8 Table 1.1: Model PRECEDE/PROCEED.

22 4 INTRODUCTION

23 Chapter 2 Literature study: Measuring Physical Activity This chapter focuses on the risks and recommendations of physical (in)activity and gives an overview of the physical activity monitoring system, developed at the RRD as well as some commercially available systems. 2.1 Physical activity Physical activity is defined as any bodily movement produced by skeletal muscles that results in energy expenditure [21]. Not being sufficiently physically active is regarded as one of the most important independent risk factors of reduced life expectancy. It increases the risk of obesity, coronary heart disease and stroke, type 2 diabetes, as well as colon and breast cancer. It is therefore recognised as one of the most important modifiable risk factors that is causing the rising global burden of chronic disease. Physical as well as mental health benefits have been linked to regular exercise. Physical health benefits include a reduced risk of the illnesses described above, and examples of mental health benefits are reduced levels of anxiety, reduced life stress, positive mood states, and enhanced satisfaction with physical shape [5]. About 40% of Dutch adults are not sufficiently physically active and over 40% are overweight or obese. Studies showed pooled prevalence of sedentary lifestyles for 15 European countries being 31%, whereas 17.7% of the population of 51 mainly low- and middle-income worldwide countries were physically inactive, indicating that inactivity may be more prevalent in wealthier countries [22]. Many contemporary work tasks are characterised by little or no physical activity. More than a quarter of all employees in the Netherlands have sedentary work and sit on average 4 hours while at work and traveling to and from work. In adition to the independent risk factors resulting from sedentary behaviour, low-intensity tasks of a static

24 6 LITERATURE STUDY: MEASURING PHYSICAL ACTIVITY nature are also regarded as one of the risk factors of work related musculoskeletal disorders of the neck and shoulders [14]. TNO Care and Prevention [16] states that, employees that sport are less frequently ill and if so, mostly for a shorter period then their non-sporting colleagues. This is predominantly the case for employees with sedentary work. Over a period of 4 years, sportsmen were 25 days less absent from work compared to non-sportsmen and even 50 days less then employees who had never done any sport [16]. Given the various health benefits of physical activity, and the high prevalence of physical inactivity, many health interventions focus on promoting physical activity Physical activity of the Dutch population Every 2 years TNO publishes Bewegen gemeten (physical activity quantified), a report about the physical activity and sporting behaviour of the Dutch society [8]. The physical activity level, is given as meeting or not meeting the national recommendations on physical activity (NNGB), given in table 2.1. In the Netherlands 56% of the adults and elderly met the requirements of the NNGB recommendation for physical activity in 2005, and 63% complied with the Combinorm (NNGB or the Fitnorm 1 ). The focus group employees with sedentary work 2, like VDU work (Video Display Unit), are less active and are more sedentary during work, about 4 hours a day, compared to little over 2 hours for the total working population. Moreover, these employees do not compensate their physical inactivity with a more active life-style in their free time, which makes them more vulnerable to aforementioned health risks. Promoting physical activity of employees with sedentary work therefore needs more attention and has to be twofold: focussed on physical activity (like the NNGB in table 2.1) and reducing sedentary activities during work, traveling, and free-time. Although promoting physical activity can lead to reducing physical inactivity, these two goals need different approaches. Stimulating fitness workouts results in more physically active employees, but does not necessarily influence sedentary behaviour, whereas promoting regular breaks, lunch-walks and walking to the printer does [14]. Since most adults spend approximately half their waking hours at the workplace, the workplace is believed to provide good opportunities to attempt to influence employee behaviour, and is consequently targeted by many health promotion programs [23] Recommendations The Dutch government has determined recommendations for the Dutch population, thereby defining the threshold for sufficient physical activity. These national recommendations on physical activity (NNGB), are based on international recommendations and are given in table 1 Fitnorm: at least 3 times a week, more than 20 minutes of heavy-intensity physical activity 2 Examples of sedentary work are: all sorts of VDU tasks; working with microscopes; assembly of small, light products; cashiers in a supermarket; musicians; dentists; surgeons; (bus-, truck-, tram-, train-) drivers; enginedrivers [9].

25 2.1 PHYSICAL ACTIVITY Supplemental to these recommendations, TNO has developed recommendations for sufficient physical activity at work. [9] These recommendations are applicable for all employees that have sedentary work at least 75% of their workday, and are given in table 2.2. Overestimation Too many people fail the NNGB and overestimate their own physical activity level. It seems intuitive that physical activity of routine, intermittent, nature moderate-intensity activities such as walking are less memorable and therefore more likely to be underestimated by self-report. TNO shows that 35% underestimates and 20% overestimates his/her physical activity. [14, 8] This makes it more difficult to draw conclusions from statements as 2/3 of the Dutch adults believe that they are sufficient physical active. The overestimation is one of the determinants that results in people not being sufficiently physically active. Feedback on the actual amount of physical activity may overcome this problem of poor self-evaluation and thereby positively affect the physical activity level of inactive persons [24, 25]. Dutch norm of physical activity (NNGB) Youth (<18 years old) Daily, 1 hour of moderate-intensity physical activity (5-8 MET) a, from which at least 2 days a week are focussed on improving or maintaining physical fitness (force, flexibility and coordination). Moderate-intensity physical activity is for youth for example aerobics, skateboarding or running (8 km/h). Adults (18-55 years old) Daily, at least half an hour moderate-intensity physical activity (4-6.5 MET) b, at least 5 days a week. Moderate-intensity physical activity is for adults for example walking (5 km/h) or bicycling (16 km/h). Elderly (>55 years old) Half an hour moderate-intensity physical activity (3-5 MET), at least 5 days a week, but everyday is preferred. Moderateintensity physical activity is for elderly for example walking (4 km/h) or bicycling (10 km/h). a 1 MET is equal to the amount of energy expended during 1 minute at rest, which is roughly 3.5 milliliters of oxygen per kilogram of bodyweight per minute (3.5 ml/kg/min) [14]. b 4 MET corresponds to 30% Heart Rate Reserve. Heart Rate Reserve (HRR) = HR max - HR rest. Indications of HR max and HR rest are: HR max = age; HR rest = bpm. Example of 40 years old: HRR = = 110 HR at 30% HRR = (0.30 * 110) + 70 = 103 bpm [9]. Table 2.1: Nederlandse Norm Gezond Bewegen (NNGB) (Dutch norm on physical activity) [8] Campaign 30 minuten bewegen & PA at work The campaign 30 minuten bewegen (30 minutes physical active) [17] is the nationwide intervention to the Dutch public to become more active, based on the NNGB. One has to be physically active for at least 30 minutes a day in bouts of at least 10 minutes, during which his heart is beating above the resting frequency and one is getting warm. Tips given for being more physical active at work are: walking during lunch break, bicycling to and from work, and being more physical active at work (e.g. taking the stairs instead of the elevator; walking

26 8 LITERATURE STUDY: MEASURING PHYSICAL ACTIVITY Recommendations for sufficient physical activity at work I On an 8-hours workday, an adult employee accumulates 30 minutes or more of moderateintensity physical activity, either during work, lunch break, or on his/her way to or from work (commuting). II On an 8-hours workday, continuous standing is limited to 1 hour, continuous sitting to 2 hours and the total standing duration does not exceed 4 hours. III On an 8-hours workday, an adult employee takes a recovering time-out of at least minutes in the morning and at least 10 minutes in the afternoon after each work shift of at most hours. IV Within each hours work shift, an adult employee takes a recovering time-out of at least 30 seconds after at most 20 minutes. Table 2.2: Recommendations for sufficient physical activity at work [9]. to the printer; active-sit on a specific ball; walking to colleagues in stead of sending an and regular short pauses to relax muscles and relieve tension). More general tips are: take one bus-stop early; park one block from work; start the day with stretching muscles and use a public-transfer bike ( OV-fiets ) instead of the bus. TNO Care and Prevention recommends employers to: stimulate sports in free time; facilitate a fitness room; change the menu of the in-house restaurant; installing RSI software for VDU workers; perform a health check for employees; promote bicycling to and from work; and promote walking during lunch break [16]. The proposed changes act on physical activity, as well as on reduction of sedentary behaviour, change food intake and awareness of health and behaviour. An example of a commercial product employers can invest in is Trappers [26] to promote bicycling to and from work. This system puts sensors on bikes of employees, and registered bike-trips are rewarded with points, which can be used to buy gifts. 2.2 Measuring Physical Activity & Interpreting data Physical activity (PA) has four principal characteristics: intensity, type, duration and frequency. Another important characteristic is its absolute intensity defined as Energy Expenditure resulting from activity (EE ACT in kcal min 1 ). Methods for measuring PA can be divided in four classes: subjective reports and observations, indirect calorimetry, double-labeled water (DLW), and portable monitors [2, 27]. Subjective reports are mostly acquired using specially designed physical activity questionnaires. Indirect calorimetry measures O 2 consumption and CO 2 production to determine the rate of EE using standard predictive models. The EE ACT (which is relating to the activity) can be obtained after removing the resting EE and summing the area under the curve. Double-labeled water (DLW) is the gold standard for measuring free-living EE, and DLW is based upon the difference in the turnover rates of H and O into body water. This method is achieved by measuring CO 2 production and the disappearance rates of the isotopes

27 2.2 MEASURING PHYSICAL ACTIVITY & INTERPRETING DATA 9 ( 2 H and 18 O) in urine, blood, or saliva. EE ACT can only be calculated as an average over time (of the entire study period) instead of per separate activity, using the following equation: EE ACT = EE total - EE resting - thermic effect of food. Portable monitors are often 1 or 3 dimensional (piezoelectric) accelerometers, worn at the hip. These devices are described in more detail below Physical activity monitors An overview of physical activity monitors most frequently used in published literature is given in table 2.4. These are all examples of first generation accelerometers 3 worn on the waist, close to the center of gravity of the body. The advantages of these monitors include their small size and the fact that they are wireless, non-invasive, and minimally intrusive to normal subject movement during daily activities. Limitations are the unmeasured activity of extremities, thus not being able to distinguish different PA types, and limited power for predicting EE ACT due to the predetermined time length of epochs [27]. Dimensions The majority of accelerometers are uniaxial (1D) and are sensitive to vertical acceleration, though some are biaxial (2D) or triaxial (3D) being also sensitive to anterioposterior and/or lateral accelerations. Omni-directional accelerometers assess acceleration in multiple directions, but are most sensitive to accelerations in the vertical plane, which make them approximately uniaxial. Compared to uniaxial monitors, triaxial accelerometer can provide a more comprehensive assessment of the body movements (e.g. the increased horizontal acceleration of running) [1, 28]. Data acquisistion The frequency of the center of mass in humans is in normal nonimpact physical activity, below 8 Hz (during running in the vertical direction). Therefore, currently used ranges by most PA monitors are between 0.25 and 7 Hz [27]. Counts PA monitors give the output of measured accelerations in counts. Counts are the result of preprocessed raw data. Different analytical approaches to calculate counts from raw data can be applied, like counting the number of threshold crossings, the maximum value of an epoch, or most common applied an integration algorithm. This uses full-wave rectified raw data to calculate the sum for each time window (integrating), mostly 1 minute (epoch or bout), resulting in the PA counts of the accelerometer [27]. Corder et al. (2007) [1] states that discrepancies regarding PA intensity thresholds are one of the main issues surrounding accelerometer data. The wide range used, hinders study comparability and it is unlikely that consensus will be reached on the best intensity thresholds to use. Corder et a. (2007) plead for public conversion algorithms for acceleration into counts, so data 3 Next generation monitors use multisensor arrays (placed at different body segments) or combine accelerometry with physiological sensors (e.g. heartrate) in a single-site device [27]. This generation of monitors will not be discussed in this report.

28 10 LITERATURE STUDY: MEASURING PHYSICAL ACTIVITY may be expressed in SI units (International System [of Units]) [1]. Hagströmer et al. (2007) [10] reported physical activity of a sample of the Swedisch population, determined with the ActiGraph. Depending on the criteria for determining PA more or less subjects complied the 30 minutes/day recommendation. Table 2.3 shows that the results depend strongly on the chosen method. Although 52% of the sample accumulated 30 minutes of at least moderate activity per day, only 1% accumulated the 30 minutes by three or more 10- minute bouts per day. Which is in sharp contrast to studies that are based on questionnaires, although activity monitors may, to a certain extent, underestimate physical activity for specific activities, such as carrying heavy loads, walking on stairs, or riding a bike. Esliger (2007) [28] confirms that varying the PA criteria results in contrasting numbers of people who meet various physical activity guidelines, ranging from 1% - 100%. Physical activity calculation variation Method of determining physical activity bouts Complying 30-min recomm. Including separate minutes or bouts of 2 minutes 52% Including separate minutes, and at least one 10-min bout 37% Including bouts of 2 minutes, and at least one 10-min bout 7% Including only 10-min bouts 1% Table 2.3: Percentages complying the 30 minutes recommendation, depending on the inclusion criteria of physical activity bouts. Adjusted from Hagströmer et al. (2007) [10]. Energy Expenditure Many studies have validated activity counts with EE measures using indirect calorimeter or DLW. The common method used to fit these two measures (PA counts and EE) is linear regression. Studies on indirect calorimeter as well as on DLW showed the highest correlation when using triaxial accelerometers, compared to uniaxial sensors. Therefore, a triaxial accelerometer provides a more comprehensive assessment of body movements. Research on nonlinear approaches showed even better predictions of EE ACT. The advantage of nonlinear modeling is its improved precision to predict EE ACT, on the other hand, the linear regression approach has the advantage of being simple, making it easy to apply [27]. Activity mode Analysing the variability in accelerometer counts makes it possible to detect the activity mode (distinguish walking from other lifestyle activities such as gardening, ironing and tennis). This can also improve EE estimation. Crouter et al. (2006) [29] did this using two regression lines on 1D accelerometer (Actigraph) data. Other advanced methods have been used by Pober et al. (2006) [30] and Paul et al. (2008) [31]. Pober et al. studied classification algorithms (Quadratic discriminant analysis and hidden Markov model) based on the mean, (co)variances and autocorrelation in time series of ActiGraph data. They distinguished computerwork and walking and determined time spend in a certain PA-level more precisely than with traditional count step-levels [30]. Resulting in only a small effect on EE, Paul et al. (2008) studied the coefficient of variation (CV) of free-living adults using the Actical and Actigraph, though the predominant factor determining EE in this study appeared to be body mass [31]. The universal method used in the past to detect activity mode is count step-levels. Mcclain et al. (2007) [32] researched Actigraph output during free-living using step levels of Freedson et

29 2.2 MEASURING PHYSICAL ACTIVITY & INTERPRETING DATA 11 al. (1998) [33] being: sedentary (0-499 counts per minute (cpm)), light ( cpm), moderate ( cpm), and vigorous PA ( 5724 cpm). Mcclain et al. (2007) found that combining moderate and vigorous PA into one level, improved the inter-instrument reliability without effecting outcomes compared with health recommendations (at least the 30 minutes of moderate intensity level [8]). Feedback Of the accelerometers given in table 2.4 only the PAM gives direct feedback to the user, via a display on the device. Data from the other accelerometers can only be extracted using a computer and specific software and is done by specialists, not ordinary users, to draw conclusions on physical activity behaviour afterwards. Because the interest of this research is feedback, the PAM is concisely described below. PAM [3] is a Dutch commercial available physical activity monitor using a 1D accelerometer. It produces a cumulative score, which is a proxy measure of total daily (24h) physical activity. The PAM score is displayed continuously to the user giving direct feedback. Via a docking station the PAM can be connected to a computer (on which specific software has to be installed). A special designed website, Pam Coach, uses the PAM data to coach the user in reaching an physical activity goal. At first use, the user fills in personal data (i.e. height, weight) and answers 10 dichotomous questions (e.g. Do you think you are able to walk to your work when it is raining?). The user also formulates an activity goal guided by the Coach, based on his or her PAM score and age. On every subsequent login the Coach website presents all the collected PAM scores in orderly graphs per day which is supported by practical information on user preferred activities (e.g. daily an extra 60 minutes walking, or 25 minutes running, or 20 minutes playing squash). In addition to this, the user receives tailored feedback on determinants of physical activity based on the answers of the one-time questionnaire. Using the Coach website, the user can easily monitor its own progress of its personal activity goal. Finally, also general (not-tailored) practical advise om implementing physical activity in daily life is given on each subsequent login [24, 3]. Overview of Activity Monitors Activity monitor Acc. dimension Output Epoch Sources RRD system 3D IMA [m/s 2 ] 10 sec [4, 11] Actical a Omnidirectional Counts 1 min [2, 34] ActiGraph (CSA/MTI) a 1D Counts 1 min [1, 2, 31, 35, 36, 27] Biotrainer Pro 1D b Counts 1 min [36] PAM 1D Counts? [24, 3] Tracmor 3D Counts c 1 min [37] Tracmor2 3D Counts 1 min [38] Tritrac-R3D / RT3 3D 3x Counts d? [1, 36] a Waterproof; also applicable for swimming. b Accelerometer is positioned 45 degrees to vertical in the sagital plane. c Calibration: 1000 counts/min = 1G. d Output is given per dimension (X, Y, Z) Table 2.4: Overview of several commonly used activity monitors.

30 12 LITERATURE STUDY: MEASURING PHYSICAL ACTIVITY Figure 2.1: Activity Monitors. From left to right: Actigraph [1], Actical [2], PAM [3], and the RRD system. 2.3 Physical activity feedback system The system developed by the RRD consists of several personal wireless transmitting units (sensors) that transmit their data to the receiving units in its range (bridges), see figure 2.2. Each transmitting unit contains two biaxial accelerometers forming a triaxial accelerometer, and is worn by an employee at its hip. The receiving units are spread in the building in order to detect the transmitted data everywhere within the building. All receiving units are connected via a local area network to a server that collects the data and leads it through its algorithms. Feedback based on the physical activity of an employee is in the current version displayed on a monitor at a central point in the building (in the RRD building: the coffee corner). The feedback information will only appear on the monitor if the transmitting unit is in range of the receiving unit near the monitor. This receiving unit has a smaller range, resulting in only displaying feedback of employees being in its proximity [4]. The details of the systems algorithms will be explained in the next paragraphs, as developed by Siemerink [11] and Schooneman [4]. Figure 2.2: Overview of the physical activity feedback system [4]

31 2.3 PHYSICAL ACTIVITY FEEDBACK SYSTEM Monitoring of physical activity Physical activity is measured by a triaxial accelerometer. The Integral of the Modulus of body Acceleration output (IMA) is used as an estimate for energy expenditure when the accelerometer is body-fixed. The IMA value is calculated using equation 2.1, with a x, a y, a z the accelerometer output components in three dimensions and T the time interval of integration. Important system parameters are summarized in table 2.5. More details and background information of the system can be found in reports the of Siemerink [11] and Schooneman [4]. To simplify the implementation, the calculation of the IMA samples is divided into two separate operations. a) The monitoring part of the system creates temporary IMA samples (IMA ) as the sum of the averages of the acceleration samples in the last 10 seconds ( t) of all three acceleration axes, see equation (2.2). This algorithm runs continuously. b) The feedback generating part processes at once all the IMA samples stored so far and only runs when a transmitting unit is within range of the receiving unit near the feedback monitor. To complete the IMA calculation from the monitoring part, the signal is filtered using a moving averaging window with width T = 30s that is moved t per iteration (average of the last three samples), as given in equation (2.3). When this feedback is generated, it is saved in a log file on the server, so that it can be assessed for further research. A scheme of the monitoring system is given in figure 2.3 [4] Feedback on physical activity The feedback system calculates if the employee is doing well, based on the calculated IMA values and the values of specific parameters given in table 2.5, using equation (2.4). The left part of the statement is the ratio of the performed activity until t act (actual time) to the total recommended activity per workday. The right part is the ratio of the time elapsed until t act to the time per workday. If the statement is true, the employee will be encouraged to start exercising: You might do so exercising. If the statement is false, the employee gets a positive feedback message: Doing very well. Finally, if the employee is more active in the morning than necessary, the feedback system will try to slow him down: Cool down. This is done when the left part of the equation is at least two times greater than the right part [4]. A scheme of the feedback system is given in figure 2.4. The positive physical activity feedback message changes in time towards less positive messages if the employee has not reached 30 minutes of moderate physical activity yet, and has not been active for a long time. Other quantities that are given as feedback are: the number of periods of activity (P act ); the start time of the workday (t 0 ); the actual time (t act ); the total period of activity (ACT total ); the recommended activity (ACT rec ) and the percentage of ACT rec that is completed. An example of the feedback, as it is given on the feedback monitor is given in figure 2.5. Schooneman [4] stated that the feedback system is able to correctly monitor and calculate the physical activity performed by office employees, and the next step in the development is the feedback interface and the feedback message itself. Which type of information of physical activity is relevant and in which way information should be presented to office employees to give effective feedback will be discussed in the next chapter.

32 14 LITERATURE STUDY: MEASURING PHYSICAL ACTIVITY Integrating the physical activity recommendations Siemerink [11] suggests the following on the ability of the system to measure physical (in)activity to determine compliance with the recommendations for sufficient physical activity at work, table 2.2: Using this feedback system, employees can be encouraged to meet two of the recommendations, no. I and III, although the development of the system was focused on recommendation no. I. no.i Siemerink [11] has researched the ability of the system to distinct low and moderate intensity physical activity. This distinction was rather difficult to make, so she proposed to combine low and moderate physical activity, to meet the 30 minutes recommendation. The system therefore only detects: desk work versus (low and moderate) physical activity. no.iii Siemerink [11] states that it is possible to detect the recovering time-out of minutes in the morning and of 10 minutes in the afternoon. The threshold for activity regarded as worktime is: peaks have a shorter duration than 30 seconds or lower than 0.28 m/s 2 are regarded as work-time. The given values were based on her dataset of 8 subjects. To detect and assign the recovering time-out properly it is necessary that the employee is more physical active, than during work-time. For example, by walking to the coffee corner. System parameters Monitoring system parameters Sample frequency Bandpass filter Time between 2 IMA samples Optimal T value f s 38.5 Hz Hz t = 10 s T = 30 s Decision parameters for calculation of PA Threshold office work versus higher levels of physical activity IMA T R = 0.14 m/s 2 Minimal duration of physical activity bout to account as PA T A 5 min Number of IMA values allowed < IMA T R to account as PA R ACT = 1/29 Duration of normal workday at RRD (08:30-17:00 hour) T T OT AL = 8.5 hours Total recommended activity per workday (according to NNGB) ACT REC = 30 min Table 2.5: Values of specific parameters for calculating the IMA value and specific parameters for calculating the physical activity (PA) [4, 11].

33 2.3 PHYSICAL ACTIVITY FEEDBACK SYSTEM 15 Figure 2.3: Overview monitoring part of the system [4]. Figure 2.4: Overview feedback generating part of the system [4]. Figure 2.5: Example of the feedback given on the feedback monitor, with two employees near the monitor [4].

34 16 LITERATURE STUDY: MEASURING PHYSICAL ACTIVITY IMA(t) = 1 T IMA (k) = 1 t ( IMA(n) = 1 T ( t t T ( k t fs t ) t a x dt + a y dt + a z dt t T t T k t f s k t f s a x (i) + a y (i) + a z (i) i=(k t t)f s i=(k t t)f s i=(k t t)f s ) n IMA (k) k= (n t T ) t ) (2.1) (2.2) (2.3) ACT T OT AL ACT REC < t act t 0 T T OT AL (2.4) 2.4 Chapter summary More than a quarter of all employees in the Netherlands have sedentary work and sit on average 4 hours during work and traveling to and from work, and about 40% of the Dutch adults are not sufficient physical active. Being not sufficiently physical active is regarded one of the most important independent risk factors of reduced life expectancy, indicating the need of physical activity interventions, especially focussed on sedentary workers. The physical activity monitoring system of the RRD is able to monitor physical activity from sedentary workers within the RRD building. Compared to commercial available products which are mostly 1D, this 3D accelerometer system can provide a more comprehensive assessment of the body movements and it can distinguish the activity mode desk work from low and moderate physical activity. Resulting data is suitable for implementing feedback for employees based on the physical activity recommendations as well as the recommendations on physical inactivity.

35 Chapter 3 Literature study: Theories and models on health behaviour This chapter focuses on the theories and models used in health communication. First, the most common used theories will be explained. Then, insight is given in how models have been used and which (physical activity) measures are often used in physical activity interventions. Finally, the design process of a User Interface will be described. The study of health behaviours is based on two assumptions. First, that in industrialised countries the leading causes of death is due to particular behaviour patterns and second, that these patterns are modifiable. Social cognition models (SCMs) describe how social behaviours are produced by cognitive factors, and have been widely used by health psychologists. Many health promotion models come from the behavioural and social sciences like psychology, sociology, management, consumer behaviour, and marketing. The commonly used SCMs to predict health behaviours include the Health Belief Model (HBM); Social Cognitive Theory (SCT); Theory of Planned Behaviour (TPB); and Protection Motivation Theory (PMT). Finally, the Trans- Theoretical Model of change (TTM) focuses on the idea that behaviour change occurs through a series of qualitatively different stages. These models are all described briefly in the next section and are summarised in the Abbreviations & Behaviour Models list at the end of this thesis. 3.1 Models Health Belief Model (HBM) - Rosenstock (1966) The Health Belief Model (HBM) is a psychological model that attempts to explain and predict health behaviours by focusing on the attitudes and beliefs of individuals [39]. The Health Belief Model (HBM) uses two aspects of individuals representations of health behaviour in response

36 18 LITERATURE STUDY: THEORIES AND MODELS ON HEALTH BEHAVIOUR to threat of illness: perceptions of illness threat and evaluation of behaviours to counteract this threat. Threat perceptions depend upon two beliefs: the perceived susceptibility to the illness and the perceived severity of the consequences of the illness. These two variables determine the likelihood of the individual following a health-related action, although their effect is modified by individual differences in demographic variables, social pressure and personality. The particular action taken is determined by the evaluation of the available alternatives, focusing on the benefits or efficacy of the health behaviour and the perceived costs or barriers to performing the behaviour. Other variables often included in the model are cues to action and health motivation. Cues to action are a diverse range of triggers including individual perceptions of symptoms, social influence and health education campaigns. The value individuals place on their health influences the response to such cues, affecting health motivation and level of concern about health matters. The interaction of all elements of the model is given in figure 3.1. The results of quantitative reviews of the susceptibility, severity, benefits and barriers constructs, suggest that these variables are often found to be significant predictors of health-related behaviours but that their effects are small [5]. DEMOGRAPHIC VARIABLES Perceived susceptibility class, gender, age, etc. Perceived severity Health motivation Action DEMOGRAPHIC CHARACTERISTICS Perceived benefits personality, peer group pressure, etc. Perceived barriers Cues to action Figure 3.1: The Health Belief Model [5] Social Cognitive Theory (SCT) - Bandura (1977) Social Cognitive Theory (SCT) is based on the belief that human motivation and action are extensively regulated by forethought. This anticipatory control mechanism involves expectations related to possible outcomes of undertaking a specific action. SCT is based upon three types of expectancies: situation-outcome, action-outcome, and perceived self-efficacy. Situation-outcome expectancies are beliefs about consequences occurring without interference of personal action. Action-outcome expectancy is the belief that a given behaviour will or will not lead to a given outcome. Self-efficacy expectancy is the belief that a behaviour is or is not within an individual s control or ability performing it successfully. Situation-outcome expectancies are assumed to influence behaviour via their impact on action-outcome expectancies. Action-outcome expectancies in turn are assumed to impact on behaviour via their influence on goals or intentions

37 3.1 MODELS 19 to engage in the behaviour, and on self-efficacy expectancies. And social-outcome expectancies impact on self-efficacy expectancies [20, 5]. The construct of the SCT is given in figure 3.2. According to the SCT, personal sense of control makes it possible to change behaviour. If people believe that they can take action to accomplish a certain goal, they become more inclined to do so and feel more committed to the decision. Self-efficacy influences how people feel, think and act. A strong sense of self-efficacy is related to better social integration. A low sense of self-efficacy is associated with depression, anxiety and helplessness. Conner et al. (2005) [5] mentions that SCT has been applied to diverse areas as school achievement, emotional disorders, mental and physical health, career choice, and sociopolitical change, making SCT a fundamental resource in clinical, educational, social, developmental, health, and personality psychology [5, 6]. Outcome expectations: Physical Social Self-evaluative Self-efficacy Goals Behaviour Sociostructural factors: Facilitators Impediments Figure 3.2: Social Cognitive Theory [5] Theory of Planned Behaviour (TPB) - Ajzen (1988) The Theory of planned behaviour (TPB) is an extension of the theory of reasoned action, and outlines the factors that determine an individual s decision to follow a particular behaviour. The proximal determinants of behaviour are intention and control perception. Intentions represent a person s motivation in the sense of his/her conscious plan or decision to exert effort to perform the behaviour. Perceived behavioural control is a person s expectancy that performance of the behaviour is within his/her control [5]. The construct of the TPB is given in figure 3.3. Intention is determined by three sets of factors: (1) attitudes, which are the overall evaluations of the behaviour by the individual, subcategorised into affective attitude (enjoyment) and instrumental attitude (benefit); (2) subjective norms, which consist of a person s beliefs about whether significant others think he/she should engage in the behaviour; and (3) perceived behavioural control (PBC), which is the individual s perception of the extent to which performance of the behaviour is easy or difficult. Each of these components have prior determinants, as attitudes are a function of beliefs about the perceived consequences of the behaviour; subjective norm is

38 20 LITERATURE STUDY: THEORIES AND MODELS ON HEALTH BEHAVIOUR a function of normative beliefs, which represent perceptions of specific significant others preferences about whether one should or should not engage in a behaviour; and perceived behaviour control is a function of beliefs concerning whether one has access to the necessary resources and opportunities to perform the behaviour successfully. By influencing beliefs and exerting social pressure, behaviours can be changed or maintained [6, 40]. Alexandris et al. (2007) [41] investigated the degree to which the elements of the Theory of Planned Behaviour mediated the relationship between constraints and intention to continuing participation in physical activities among older individuals using questionnaires. The results showed that both attitudes and perceived behavioural control partially mediated the relationship between constraints and intention, with perceived behavioural control being the strongest mediator. This suggests that constraints influence intention both directly and indirectly through their negative effects on attitudes and perceived behavioural control [41]. External variables Demographic variables (e.g., age, sex, occupation, socioeconomic status, religion, education) Behavioural beliefs Attitude Personal traits (e.g., extraversion, agreeableness, conscientiousness, neuroticism, openness) Environmental influences (e.g., access, physical environment) Normative beliefs Control beliefs Subjective norm PBC Intention Behaviour Actual behavioural control Figure 3.3: Theory of Planned Behaviour [5] Protection Motivation Theory (PMT) - Rogers (1975) The Protection Motivation Theory (PMT) describes adaptive and maladaptive responses to a health threat as the result of two appraisal processes: threat appraisal and coping appraisal. Threat appraisal focuses on the source of the threat and factors that increase or decrease the probability of maladaptive responses (e.g. avoidance, denial, wishful thinking). Individuals perceptions of the severity of, and their vulnerability to, the threat are seen to inhibit maladaptive responses. Coping appraisal focuses on available coping responses to deal with the threat and factors that increase or decrease the probability of an adaptive response, such as following behavioural advice. Beliefs of the response efficacy (the effectiveness of the recommended behaviour) and self-efficacy (capability of performing the recommended behaviour) increase the probability of an adaptive response. Protection motivation results from the two appraisal processes and is a positive function of perceptions of severity, vulnerability, response efficacy and self-efficacy,

39 3.1 MODELS 21 and a negative function of perceptions of the rewards associated with maladaptive responses and the response costs of the adaptive behaviour. Protection motivation is typically equated with behavioural intention and operates as a mediating variable between the threat and coping appraisal processes and protective behaviour [5]. The interaction of all elements of the model is given in figure 3.4. Maladaptive Response Intrinsic and Extrinsic Rewards Severity Vulnerability Threat Appraisal Protection Motivation Behaviour Adaptive Response Response Efficacy Self-Efficacy Response Costs Coping Appraisal Figure 3.4: Protection Motivation Theory [5]. Research has shown that coping appraisal variables, especially self-efficacy, provide the strongest predictions of protection motivation (i.e. intention) and behaviour. Only a small number of studies have employed PMT in relation to exercise. Conner et al. (2005) [5] concluded that in PA studies, self-efficacy was the only PMT variable to emerge as a significant predictor of exercise intentions, although fear also had a weak effect on intention, and intention was the only significant predictor of exercise behaviour The Transtheoretical (stages of change) Model (TTM) - Prochaska (1983) Different cognitions may be important at different stages of initiation and maintenance of health behaviour [5]. The TransTheoretical stages of change Model (TTM) identifies five stages of change: pre-contemplation, contemplation, preparation, action, and maintenance as shown in figure 3.5. Individuals are seen to process through each stage to achieve successful maintenance of a new behaviour. The model is best considered circular rather than linear, as people can enter or exit at any point, and it is applicable to people who self-initiate change as well as those who are responding to external stimuli such as advice from health professionals or health campaigns. Relapse from a certain state is also possible. Often several periods of contemplation, preparation and action are necessary to accomplish permanent health behaviour change [20, 6]. The stage model has become an important reference point in health interventions. Apart from the advantage in health promotion of focusing on the change process, the model is important in emphasizing the range of needs for intervention in any given population, the changing needs of different populations, and the need for sequencing of interventions to match different stages of change. It underlines the importance of tailoring to the real needs and circumstances

40 22 LITERATURE STUDY: THEORIES AND MODELS ON HEALTH BEHAVIOUR of individuals rather than assuming an intervention will be equally applicable to all. Although the model is widely applied, the evidence in support of the model is relative weak. Stagetailored-interventions are often as effective as unmatched interventions [5, 6]. Stage of change precontemplation No intent to change behaviour contemplation Intent to change within the next 6 months determination or preparation Intent to take steps to change within the next month action Overt, perceptible lifestyle modification for fewer than 6 months maintenance Working to prevent relapse and consolidate gains Figure 3.5: The Transtheoretical (stages of change) model (TTM) [6] Remarks on the models There is considerable overlap between constructs contained in the main social cognition models of health behaviour. Behavioural intention typically emerges as the strongest predictor of behaviour as a mediating variable between other social cognitive variables and behaviour. It also marks the end of a motivational phase of decision making that many social cognitive models focus upon [5]. Self-efficacy has become a very important determinant of behaviour. As a result it has been incorporated into most health behaviour theories (HBM as barriers, TPB, and PMT) making self-efficacy an essential component in all major models, instead of only present in the SCT [5]. Finally, the models imply a central role for health education and the preference of individual knowledge about health. The models emphasise the importance of personalising health information, such that it is more immediately relevant to an individual, and the short-term consequences of behaviours [20]. Personalising or tailoring health information is discussed in the next section. Practical guides on the development of health interventions mention that using models to gather determinants must be done with limited expectancy. They state that it is unrealistic that an extensive determinant analysis solves all the problems involved in designing an intervention. The design process still needs creativity, in which the determinant analysis can function as a source of inspiration [20]. Gathering determinants is essential in preventing blunders and is extensively described in the next chapter.

41 3.1 MODELS Tailoring Hawkins et al. [12] gives the following description of tailoring: Tailoring means creating communications in which information about a given individual is used to determine what specific content he or she will receive, the context or frames surrounding the content, by whom it will be presented and even through which channels it will be delivered. Overall, tailoring aims to enhance the relevance of the information presented and thus to produce greater desired changes in response to the communications [12]. Tailored communications implicitly present two primary goals, which can be achieved by three basic tailoring strategies, resulting in a 2 3 goals-by-strategies matrix, from which the elements are given in table 3.1. The table clarifies a wide range of specific tailoring tactics and psychological mechanisms by which strategies might affect goals. The items in the table can easily be recognised as the determinants of aforementioned health behaviour models. For example, the element personalisation mainly seeks to promote attention and processing, whereas feedback also targets psychosocial determinants of health behaviours. As with the health behaviour models, different tailoring strategies are almost always combined in practice. Feedback with evaluation seems to have at least three potential effects. First, the factual feedback links the information to the individual, thereby increasing involvement. Second, the evaluation contains content information in that it gives a meaning to the factual psychological or behavioural state, thereby changing relevant beliefs. Finally the whole feedback text may increase the sense of being acknowledged [12]. Noar et al. (2007) [42] report that interventions with several contact points were more effective in stimulating change in health behaviour than those that did not. They reason that messages based on a comparison of one s current responses with their responses at the previous intervention time point (i.e. feedback), might account for this effect. Noar et al. (2007) [42] report that attractive health information materials are significantly more likely to get attention, be liked, and understood. They refer to a statement of Kreuter, Ferrel et al. (2000) with regard to tailored materials, Good visual design can be as important to the success of a tailored communication piece as the message content itself. Not only tailoring on text, but also on images and other visual elements might be promising, as well as length of print materials, because those being too lengthy may not be read. Although evidence suggests that tailored messages are likely to be viewed as more relevant than more generic communications (more likely to be read, understood, recalled, rated highly, and perceived as credible), it is questioned whether such messages can eventually result in greater health behaviour change as compared to generic or targeted messages [42].

42 24 LITERATURE STUDY: THEORIES AND MODELS ON HEALTH BEHAVIOUR Tailoring goals and strategies Goals Strategies Message processing mechanisms Enhance cognitive preconditions for message processing or acceptance. Tactics: (1) increase attention, (2) enhance central processing a, (3) enhance peripheral/emotional processing a, and (4) encourage self-referential thinking. Immediate determinants of goal outcomes Enhance message impact by selectively modifying initial behavioural determinants of desired outcomes. Target determinants: being informed, decision making, behavioural intention, skills, self-efficacy, attitudes/outcome expectations, and normative perceptions. Personalisation Increase attention or motivation to process messages by conveying, explicitly or implicitly, that the communication is designed specially for you. Tactics: (1) identification, (2) raising expectation of customisation, and (3) contextualisation. Feedback Present individuals with information about themselves, obtained during assessment or elsewhere. Tactics: (1) descriptive feedback (report individuals summaries of their attitudes, beliefs or behaviours), (2) comparative feedback (compare with others or compare to progress over time self-comparison), and (3) evaluative feedback (add a level of interpretation, judgment and/or interference about an individual s attitudes, beliefs or behaviours). Content matching Direct messages to individuals status on key theoretical determinants (e.g. knowledge, outcome expectations, normative beliefs, efficacy and/or skills) of the behaviour of interest. a Referring to the central and peripheral routes of the Elaboration Likelihood Model Table 3.1: Explanation of tailoring goals and strategies. Adjusted from Hawkins et al. (2008) [12].

43 3.2 PHYSICAL ACTIVITY INTERVENTIONS Physical activity interventions Many physical activity interventions (PA interventions) have been described in literature. A short overview is given of commonly applied health behaviour models, feedback systems and feedback items. Then, some more detailed examples of PA interventions are given. Norman et al. (2007) [43] reviews ehealth interventions on physical activity, giving a broad definition of ehealth as well as a more precise description of ehealth used for their review: Broad: ehealth is the use of emerging information and communication technology, especially the Internet, to improve or enable health and health care. Narrow: ehealth is any form of interactive technology (e.g., , Internet, CD-ROM program, handheld computer, kiosk) used by program participants to facilitate behavior change. Reviews on PA interventions show that the majority of the studies were based on SCT, TTM and TPB, or a combination of these. Concepts most frequently encountered in studies include stage of change, self-efficacy (or perceived behavioural control), behavioural intentions, social norms, attitudes (including decisional balance, benefits and barriers, outcome expectancies, behavioural beliefs), perceived susceptibility, processes of change, and social support. Strategies often applied are goal setting (and braking them down into smaller ones using tailoring) and behavioural self-monitoring. Feedback was often given using letters, brochures, or interactive feedback, to provide awareness of own performance, intentions, and self-efficacy of PA [20, 42, 43, 44, 45]. Tailoring on 4 or 5 theoretical concepts had significantly larger effect sizes than tailoring on 0 to 3 concepts, and tailoring on attitudes, self-efficacy, stage of change, social support, and processes of change had significantly larger effect sizes than those that did not tailor on these concepts [42]. Though, Norman et al. (2007) 1 [43] determined the effect size of ehealth technology on PA being small to medium. In line with that finding, Portnoy et al. (2008) [46] concluded in a meta-analysis on 75 RCTs (Randomized controlled trials) on computer-interventions for health promotion and behavioural risk reduction, that no improvements were observed for physical activity, weight loss, or weight gain/maintenance, though many studies on other outcome measures did show improvement like dietary intake, tobacco use, sexual behaviour, and general health maintenance. 1 Most interventions reviewed by Norman et al. (2007) [43] assessed physical activity using physical activity questionnaires like the PAR-Q, IPAC (short and long), 7-day PA Recall questionnaire, inactivity questionnaire and the Modified 7-day activity recall. Also more objective measures from pedometers, walking tests, fitness tests, BMI and VO 2 max were assessed in determining effects of the PA interventions.

44 26 LITERATURE STUDY: THEORIES AND MODELS ON HEALTH BEHAVIOUR PA interventions using Pedometers Chan et al. (2004) [47] carried out a physical activity intervention in sedentary workers. Pedometers (step-counters) were used for feedback on PA, as motivational devices, and to objectively evaluate changes in PA. First 4 weeks, participants received the pedometer and coaching on behavioral strategies (goal-setting, learning strategies for overcoming relapse and benefits of PA, and self-monitoring (daily self-rapport). The following 8 weeks, participants only had a pedometer and self-report (no coaching). The increase of mean steps a day was 3,451 steps 2 from baseline: 7,029 steps/day to the plateau: 10,480 steps/day, taking about 4 weeks to reach a plateau. Though, this increase in PA was mainly achieved by changing habits during leisure time [47]. Dinger et al. (2007) [48] researched a 6 weeks pedometer-based intervention using the Transtheoretical model (TTM). Participants had to log their daily step count and got weekly reminders (tailored versus non-tailored). In both groups step count increased from 6,419 steps/day to 7,984 steps/day, concluding that the use of pedometers, step logs, goal setting, and reminders among insufficiently active people is useful for increasing PA. Though, the inclusion of TTM strategies had no influence on step count [48]. De Cocker et al. (2008) [49] reported comparable findings in healthy subjects. Wearing a pedometer increased PA from 9,291 steps/day in the first week to 10,010 steps/day in the third week. A brochure based on Social Cognitive Theory (SCT) determinants (importance of PA on health, 10,000 steps/day recommendation 3, and tips on how to increase PA), goal setting and daily step logs, had only influence on attitudes towards pedometer use, not on PA itself [49]. Faghri et al. (2008) [50] studied a 10 weeks pedometer-based intervention on sedentary workers during worktime, supported with and a website both TTM tailored (though, not tailored on stage of change). It contained weekly logs of steps and minutes walked and had a buddy system, by using teams of participants to increase motivation by making individuals responsible for others as well. The average steps per day during the working hours increased from baseline of 4,185 steps, to the plateau reached in week 8 being 5,300 steps. This is half of the recommended 10,000 steps/day, acquired during half of the waking hours spent at work. Finally, even though tailoring was not based on stage of change, there was a significant shift in stage of change for physical activity [50]. 3.3 User Interface Design Knowledge of models on health behaviour, knowledge from literature on physical activity interventions, and the output of the monitoring system have to merge in the User Interface (UI) of the system. The UI is the aggregate of means by which users interact with the system, allowing the users to manipulate a system (input) and allowing the system to produce the effects of the users manipulation (output). The UI is a vital part of the computer system in making it to function as desired. 2 3,500 steps/day corresponds to about 30 minutes of moderate intensity walking [47]. 3 With the increasing use of pedometers in interventions on PA, the step count goal of 10,000 steps/day has become a popular target [49].

45 3.3 USER INTERFACE DESIGN 27 The User Interface design will be an important part of this project. Realising a system that provides information of the physical activity of employees in such a way that the users change their behaviour to comply with recommendations on sufficient physical activity at work. The models on health behaviour give clues about which behaviour determinants have to be influenced to change behaviour, but the way in which this information has to be presented is not predetermined. These design choices are made during the development of the User Interface. The design of a User Interface affects the amount of effort the user experiences, providing input for the system and interpreting output of the system. The user convenience is the focus of usability, considering user human psychology and physiology as well as system effectiveness, efficiency, and satisfaction [51]. UI design is an iterative process of gathering requirements, using design principles, testing the User Interface and evaluating the UI. This process will be done according to the method used in Stone et al. (2005) [7] that is adapted from the star cycle of Hix and Hartson (1993) given in figure 3.6. That model encourages iteration and at the central point of the star evaluation. Evaluation is viewed as relevant at all stages in the life cycle instead of at the end of product development, as classical life cycles tend to suggest. Implementation Task analysis / Functional analysis Prototyping Evaluation Requirements specification Conceptual design / Formal design Figure 3.6: The star life cycle, key items in the User Interface design process [7].

46 28 LITERATURE STUDY: THEORIES AND MODELS ON HEALTH BEHAVIOUR 3.4 Chapter summary Health behaviour models are based on the assumptions that in industrialised countries the leading causes of death are due to particular behaviour patterns, and that these patterns are modifiable. The models have several constructs in common, e.g. intention as the strongest predictor of behaviour and self-efficacy has become an essential component in all major models. The models emphasise the importance of personalising health information which can be done by tailoring the health messages. This is often done using ehealth, which is the use of merging information and communication technology, to improve or enable health and health care. Physical activity interventions are often based on Social Cognitive Theory, the Transtheoretical (stage of change) Model and the Theory of Planned Behaviour, or a combination of these. Tailoring to attitudes, self-efficacy, stage of change, social support, and processes of change had significantly larger effect sizes than those that did not tailor to these concepts, though the effect size of ehealth technology on PA was small to medium. Interventions using a pedometer and extra intervention methods do not report which determinant was the most significant factor for step count increase. Effect size of physical activity behaviour in these studies is small to medium and in a short intervention behavioural change might not be seen. It is therefore important to measure changes in factors that determine an individual s decision to follow a particular behaviour. Implementation of the Theory of Planned Behaviour into a questionnaire, will provide valuable proximal determinants of behaviour. Knowledge of models on health behaviour, physical activity interventions, and the output of the monitoring system have to merge in the User Interface of the system to develop. User Interface design will be an important part of this project. Realising a system that provides information on the physical activity of employees in such a way that the users change their behaviour to comply with recommendations on sufficient physical activity at work.

47 Chapter 4 Development This chapter describes the development of both the feedback interface, and the questionnaires needed for the intervention and evaluation of the intervention. The development of the feedback system will be described in the first section, followed by the development of the questionnaires. 4.1 Identification of outcomes, performance objectives and change objectives The conclusions of the literature study on physical activity and sedentary workers (Chapter 2) and behaviour models and their implementation in physical activity interventions (Chapter 3) are the justification for the focus on physical activity at the workplace, using tailored (personal) physical activity feedback. The overall desired outcome of the intervention is to change behaviour at work, resulting in sufficiently physically active sedentary employees. Performance objectives are create intention to be physically active at work and sufficient actual physical activity at work. Appropriate theoretical determinants for intention are self-efficacy, attitudes and subjective norms, according to the Theory of Planned Behaviour (TPB) [52]. An appropriate determinant of actual physical activity, is the feedback based on IMA values, gathered using 3D accelerometers Design steps An overview of the design and evaluation iterations is given in figure 4.1. The top row of this figure shows the development of the TPB questionnaire, beginning with an elicitation study, then an evaluation step via the focus group session, resulting in the first draft. This becomes the final version, after being evaluated by individuals. The interface design follows a likewise process, resulting in prototype 0, 1 and the final version. The focus group meeting therefor

48 30 DEVELOPMENT considered both the Elicitation study as well as prototype 0. TPB Questionnaire Elicitation study assessing attitude subjective norm and PBC items concerning PA. First draft Questionnaire including most questions. Final version Including all questions and remarks of evaluation User Interface Design Prototype 0 (Concept design) Set of posters describing different possibilities Prototype 1 (High fidelity prototype) Second and improved implementation Final version Including all questions and remarks of evaluation Review Focus group session User review on key aspects; Informative vs. persuasive interface, use of rewards... Evaluation with individuals User feedback on possible usability problems and improvements. Final evaluation the experiment Figure 4.1: Outline of the design process for the interface (prototyping) and the development of the TPB questionnaire. 4.2 Development of the User Interface The already developed sensor system has to be extended with a new interface. In chapter 2.3 the working principle of the system has already been described and is simplified in figure 4.2. The design process of the graphical User Interface (UI) will be described, as well as some adjustments to the current system settings, concluding with an overview of the data processing steps of the system and a brief description of the working mechanism of the UI. Sensor Bridge Logger Pre-processor Interface Figure 4.2: Overview of the different steps of the sensor and feedback system Focus group One focus group is created, consisting of five employees of the department of Human Media Interaction from the University of Twente, having sedentary work (VDU-work). This focus group is brought together to discuss about perceived barriers and levers of physical activity at the workplace (for the TPB questionnaire) and to determine what will be feasible to implement in terms of a workplace intervention, based on accelerometers and a multi-user-feedback interface (using prototype 0 as a discussion guide). From this discussion, there occurred to be three major types of motivation: 1) Individual motivation, driven by interest in numbers and individual progress; 2) Group motivation, driven by doing something together with significant others, like friends or colleagues, and having group competition; and 3) Motivation by argument, driven by effort versus health effect.

49 4.2 DEVELOPMENT OF THE USER INTERFACE 31 The focus group concluded that the details of the interface (prototype 0) were not that important as long as it contained methods to fulfil all three motivation types, and the possibility to deal with the arguments in favour and against physical activity (described in the TPB questionnaire section, paragraph Focus group). The complete report of the focus group meeting is in appendix A Prototype 1 Prototype 1 had been programmed in C # (C-sharp) and.net using Microsoft Visual Studio C # is a object-oriented (class-based) programming language with possibilities for events. The prototype is created intuitively. The prototype has a main screen and extra personal feedback screens. To research subject preference for type of feedback, the extra personal feedback can be chosen via three buttons thereby trying to pitch to individual and group motivation. The group motivation feedback can be personalised by self choosing the colleagues to compare PA with. A schematic overview of all steps within the interface is given in figure Evaluation This prototype was evaluated by means of a usability test, carried out by 4 subjects of the target population. A short report of the test outcomes are given in appendix B. All recommendations emerging from the evaluation considered interpretation of feedback figures, the structure of the interface itself, as given in figure 4.3 was found intuitive and therefore not changed. The Clock had 12 dots, resembling the 12 hours of a normal clock, though users found it confusing and thought 8 dots would be easier, thereby corresponding to 8 hours of work. The icons on the buttons were examples of the feedback item they linked to. These icons were sometimes mistaken for their actual feedback, therefore these images should be replaced with screen shots only containing the feedback frames without any PA information. The Week Overview missed a legend and the Ranking should be clarified with a new title like Group Ranking. Users also double clicked while 1 click was sufficient. This could be clarified in the manual. These recommendations were processed into the final design Final design - Technical specifications 1 minute bouts of physical activity The guideline of TNO [9] serves as the foundation for this feedback system. They recommend that the 30 minutes of activity may be accumulated during the day, without a lower limit to the duration of one activity bout. They argue that, activity bouts of short duration will help to lose weight or change ones attitude towards healthy physical activity. Moreover, short duration bouts are more easily put into daily work practice than 5-minutes bouts. Thereby not focusing on the required unbroken physical activity bouts to affect the cardio-vascular system [9]. Because of technical restrictions a lower limit to the duration of one activity bout has to beset. Comparing results with other experiments done

50 32 DEVELOPMENT Main Displays the users within range. Functions - general personal PA level & status Links - Enter personal page Timeline Overview of today, showing blocks of PA on a timeline. Functions - insight in when PA - insight in how long PA Links Personal page Displays personal information. Functions - show options (buttons) & - show personal PA information Links - Timeline - Status Indicator - WeekOverview & Ranking - Log off (return to Main) Status Indicator Indicator of personal status of PA with respect to the target of the day. Functions - more detailed status indication - option to show status of chosen colleagues Links - Choose colleagues to compare with WeekOverview & Ranking Displays status of the last 5 days. Functions - of the last week, per day the PA status - over the last week, average status resulting in a ranking position Links - Choose colleagues to compare with Colleagues selection List with all colleagues wearing sensors. Functions - selection of colleagues Links - Update Status Indicator or WeekOverview & Ranking Figure 4.3: Overview of the different steps of the interface program.

51 4.2 DEVELOPMENT OF THE USER INTERFACE 33 with frequently used Activity Monitors (see table 2.4 (Chapter 2)) is useful and therefore the lower limit of an activity bout is set to 1 minute, equal to most Activity Monitors. Feedback calculation In section Feedback on physical activity a detailed description is given, on the feedback calculation method of the feedback system. Because of the 1 minute bout consideration from the previous paragraph, an other approach of feedback calculation is chosen to implement in the feedback system. Physical activity is defined as an interval of at least one minute of IMA values 0.14 m/s 2, thereby applying the threshold defined by Siemerink (2007) [11]. The duration of a normal workday at RRD is 8.5 hours (T total ), and the total recommended activity per workday is 30 minutes, during work or lunch break. An update of the system parameters is given in table 4.1. This feedback system has two important indicators by which the status of the user is calculated. The first one is a Target Physical Activity (TPA) indicator, which will run from 0 to 30 minutes (ACT rec ) in 8.5 hours (T total ), resulting in a TPA of about 3.5 minutes of activity per hour. This TPA indicator starts the moment the sensor is switched on. The second indicator is the Actual Physical Activity (APA) indicator, which is the cumulative physical activity of the day, including only bouts of at least one minute (T A ), as given in equation (4.1). The total time the sensor is on up to that moment (t act ), is equal to number of hours worked. Over the day the TPA will increase, reaching 30 minutes at the end of the workday, as given in equation (4.2). Feedback on the Status of the user is calculated as the ratio between APA and TPA according to equation (4.3). Depending on the Status, textual and visual feedback will change, according to table 4.2. The feedback Status is based on the ability, to be sufficiently physical active during the day, following a linear increasing target (TPA). APA = ( Activity bouts > T A ) (4.1) TPA = t act ACT rec T total (4.2) Status = APA 100% TPA (4.3) Final design - Use specifications The final design has the product name MOVE. It shows preprocessed 3D-accelerometer data of the person standing close to the monitor (above the coffee machine). As a first screen the user gets a clock with information of his actual activity (APA) up to that moment as well as the length of his workday up to that moment (t act ). When the user clicks on the clock, he gets access to more detailed feedback. The next screen contains the avatar and three buttons, each button-click showing consecutively Timeline, Performance Indicator, and Week Overview & Ranking. These are different representations of the physical activity, each with its own focus. Every interaction of the user with the feedback interface (being within range and clicking on items), is logged for evaluation purposes. Detailed information is given in the (Dutch) manual of MOVE in appendix C, which also includes a manual of the sensor.

52 34 DEVELOPMENT Updated system parameters Monitoring system parameters Sample frequency Bandpass filter Time between 2 IMA samples Optimal T value f s 38.5 Hz Hz t = 10 s T = 30 s Decision parameters for calculation of PA Threshold office work versus higher levels of physical activity IMA T R = 0.14 m/s 2 Minimal duration of physical activity bout to account as PA T A 1 min Duration of normal workday at RRD (08:30-17:00 hour) T total = 8.5 hours Total recommended activity per workday (according to NNGB) ACT rec = 30 min Table 4.1: Updated values of specific parameters for calculating the IMA value and specific parameters for calculating the physical activity (PA). Adjusted from table 2.5. Feedback based on status Status percentage Feedback text (English & Dutch) Feedback colour 0.0% status 12.5% Very poor Slecht Red 12.5% < status 37.5% Poor Zeer Matig Gold 37.5% < status 62.5% Average Matig Yellow 62.5% < status 87.5% Good Goed PaleGreen 87.5% < status 100.0% On Goal Op Schema! Lime (green) 100.0% < status Too much Te Veel Blue Table 4.2: Overview of the status intervals, determining the feedback in colour and text.

53 4.2 DEVELOPMENT OF THE USER INTERFACE 35 Clock The level in which the user has been able to be sufficient physical active (status or performance), determines the color of the activity filling and bullets of past time. This performance indication, converts from 0% (red) to 100% (green), with > 100% being blue, as given in table 4.2. The interval > 100% has a neutral color, giving no possitive nor negative feedback. When multiple users are at the coffee machine, one can recognise his own feedback by the self chosen avatar next to the clock, shown in figure 4.4. MOVE uses an avatar instead of names, to make the feedback more anonymous. This results in a feedback screen being more difficult to memorise and link to the current user, by a uninvolved passer-by. Figure 4.4: Clock. First screen of the interface. Timeline Timeline gives an overview of the current day, showing the period in which the sensor is on and the periods the user has been active. The on period is given by a change of color on the horizontal timeline, and activity is given as vertical rectangles, as shown in figure 4.5. The color in which these items are given depends on the performance level of the user, using the same color as on the Clock. Additionally the actual and target duration of physical activity are given in numbers. Figure 4.5: Timeline. Shows periods of activity, on a timeline of the current day.

54 36 DEVELOPMENT Performance Indicator The Performance Indicator gives more detailed information on the level of performance by means of an arrow pointing at the actual status on the performance indicator arch. The actual and target duration of physical activity are given in numbers (equal to Timeline) extended with current time and number of working hours. The Status is given as a percentage of what should have been achieved (100% = on target). For group motivation feedback the user can choose to get the status of colleagues (selecting at least three to secure privacy). Resulting in a diamond on the performance indicator arch and a percentage of the average status of the chosen colleagues, as shown in figure 4.6. Figure 4.6: Performance Indicator. Shows the performance level with respect to the target. Week overview & Ranking When the user clicks the Week overview & Ranking button, he gets a graph showing his performance of the last 5 workdays and today, with respect to the target set for each day (in this intervention 30 minutes). The average performance, including standard deviations, can be given after selecting colleagues as in Performance Indicator. On the right side of the graph, a Ranking is given on the average of the last 5 workdays and today of each user, only showing the position of the current user in this by means of the avatar. This feedback screen is shown in figure 4.7.

55 4.2 DEVELOPMENT OF THE USER INTERFACE 37 Figure 4.7: Week overview & Ranking. Shows the performance of the user, and the average of colleagues, for each day of last week. As well as the position in the ranking of all 10 colleagues of the past 6 days.

56 38 DEVELOPMENT UI Evaluation Questionnaire The User Interface will be evaluated in the experiment, by means of the usage of the UI itself as well as by a user satisfaction questionnaire. Many user satisfaction evaluation theories and methods have been developed resulting in even more different types of questionnaires and analyse methods. In this experiment is chosen to adapt four questionnaires, to build a custom User Satisfaction Questionnaire. These 4 questionnaires are: the Unified Theory of Acceptance and Use of Technology (UTAUT) of Venkatesh et al. (2003) [53], the Software Usability Measurement Inventory (SUMI) [54, 55], Scholtalbers L. [56], and Smit S. (2009) [57]. UTAUT is a questionnaire based on several theories focussing on behavioural intention and usage behaviour. The SUMI is a well known questionnaire to measure usability. Scholtalbers and Smit are students who have adapted the SUMI questionnaire for evaluating feedback system similar to the present feedback system. From each questionnaire, questions have been chosen and adapted to meet the experiment conditions. Resulting in a questionnaire, having two sections, one addressing the feedback system in general and one measuring specific the feedback and interface. Question types The usability of the feedback system is measured by 22 questions in 7 items, and the impact of the feedback itself is measured by 39 questions in 5 items. An overview is given in table 4.3. The full questionnaire can be found in appendix F. Response scales are unipolar Likert scales (1 to 5), ranging from Absolutely not agree absolutely agree (Dutch: Helemaal niet mee eens helemaal mee eens ). The questionnaire concludes with 5 open questions, asking the respondent for missing information, tips, aggravating items and positive experiences. Number of questions of of the Evaluation questionnaire Item Feedback system Feedback Perceived Usefulness 4 20 Compatibility 2 1 Perceived ease of use 6 15 Normative structures 3 - Perceived behavioral control 2 - Facilitating conditions 3 - Intention 2 2 Overall - 1 Table 4.3: Overview of the number of questions in the final evaluation questionnaire. 4.3 Development of the TPB questionnaire The TPB questionnaire is constructed using manuals from Francis et al. (2004) [58] and Azjen I. [59, 60, 61], and publications of Alexandris et al. (2007) [41] and Parrott et al. (2008) [40]. The development strategy has already been illustrated in figure 4.1, starting with an elicitation

57 4.3 DEVELOPMENT OF THE TPB QUESTIONNAIRE 39 study, defining the target behaviour to prepare for assessment of TPB items. These accessible beliefs are gathered during a discussion with the focus group, resulting in the first draft of the questionnaire. This draft is discussed with several individuals on usability, problems and improvements, which are taking into account for the final design Elicitating study: The behaviour of interest The behaviour of interest is defined in terms of its Target, Action, Context, and Time (TACT), as given below. Elaborating on this behaviour, constructs can be defined for each TPB item, as given in table 4.4. [EN] Being physical active at work or during lunch break, at least 30 minutes each day in the forthcoming weeks. [NL] Fysiek actief zijn tijdens werk of de lunchpauze, tenminste 30 minuten per dag, elke dag, tijdens de komende weken. TACT Target Action Context Time Description 30 minutes a day physical activity (e.g. walking or sports) at work or during lunch break (e.g. through the building) every day, in the forthcoming weeks All constructs are defined in terms of exactly the same elements TPB item Construct Attitude Attitude toward being physical active at work or during lunch break, at least 30 minutes each day in the forthcoming weeks Subjective Perceived social pressure to being physical active at work or during norm lunch break, at least 30 minutes each day in the forthcoming week PBC Control over being physical active at work or during lunch break, at least 30 minutes each day in the forthcoming week Intention Perceived intention to perform being physical active at work or during lunch break, at least 30 minutes each day in the forthcoming week Table 4.4: The items of the Theory of Planned Behaviour (TPB) and the constructs targeted in this study Focus group The focus group can help identifing accessible (salient) behavioural, normative, and control beliefs. Due to the small number of subjects, the responses are personal accessible beliefs, and in some cases these are also modal accessible beliefs (most commonly held beliefs in the research population). The focus group was asked to answer the following questions: Eliciting Salient Behavioural Outcomes To elicit behavior outcomes, participants in the pilot study are given a few minutes to list their thoughts in response to the following questions. a) What do you believe are the advantages of your physical activity at work or

58 40 DEVELOPMENT during lunch break, at least 30 minutes each day in the forthcoming weeks? b) What do you believe are the disadvantages of your physical activity at work or during lunch break, at least 30 minutes each day in the forthcoming weeks? c) Is there anything else you associate with your physical activity at work or during lunch break, at least 30 minutes each day in the forthcoming weeks? Eliciting Salient Normative Referents The following questions were asked to elicit the identity of relevant referent individuals and groups that are readily accessible in memory. a) Are there any individuals or groups who would approve of your physical activity at work or during lunch break, at least 30 minutes each day in the forthcoming weeks? b) Are there any individuals or groups who would disapprove of your physical activity at work or during lunch break, at least 30 minutes each day in the forthcoming weeks? c) Are there any other individuals or groups who come to mind when you think about physical activity at work or during lunch break, at least 30 minutes each day in the forthcoming weeks? Elicitation of Salient Control Factors To generate a list of accessible factors that may facilitate or impede performance of the behavior, the following questions were asked. a) What factors or circumstances would enable you to be physical active at work or during lunch break, at least 30 minutes each day in the forthcoming weeks? b) What factors or circumstances would make it difficult or impossible for you to be physical active at work or during lunch break, at least 30 minutes each day in the forthcoming weeks? c) Are there any other issues that come to mind when you think about the difficulty of physical activity at work or during lunch break, at least 30 minutes each day in the forthcoming weeks? Specific arguments in favour and against physical activity at work resulted from the focus group are given in table 4.5. The perceived barriers are in line with findings published by TNO. They reported reasons given for being not sufficiently physically active, being: not enough time; not in the mood; physical condition is insufficient (not capable) and too busy [14]. TNO also reported that productivity is not unfavourable affected by extra breaks. Active breaks (i.e. exercise) do not have more value, nor less compared to passive breaks (i.e. rest) with respect to limitation of fatigue and discomfort during work, and regarding disorder reduction in employees with musculoskeletal disorders [9]. Though, active breaks are useful to meet the recommendation of 30 minutes physical active a day. The identified accessible beliefs are used to construct a standard TPB questionnaire. This includes direct and indirect measures of attitudes, subjective norms, perceptions of behavioural control, intentions, and actual behaviour. Indirect measures (belief composites) are based on the results of the pilot study and are constructed by asking two questions with respect to each referent. Azjen [59] mentions that it is often easier to produce change of beliefs by introducing information designed to lead to the formation of new beliefs than it is to change existing beliefs. As new beliefs, the beliefs mentioned by only a small number of respondents in the pilot study can be used. Also, Azjen states that it is reasonable to target an intervention at any one of the three major predictors in the theory of planned behaviour (so long as there is room for change), but that it may be safer to target predictors that account for significant variance in intention and behaviour [59].

59 4.3 DEVELOPMENT OF THE TPB QUESTIONNAIRE 41 Attitude PBC Levers Improving health Flexible working hours Feel more energetic Better mood Possibility to relax Barriers Takes time Meetings Busy before deadlines Bad weather Concentration loss Table 4.5: Summary of focus group results. Perceived barriers and levers on attitude and perceived behaviour control (PBC). Full report in appendix A First draft - Number & type of questions Each construct of the TPB model (Attitude, Subjective Norm, PBC, and Intention) can be addressed using several direct and indirect questions. Alexandris et al. (2007) [41] and Parrott et al. (2008) [40] use one till six questions per TPB construct. Francis et al. [58] advise that each construct should be measured using a minimum of three items. Including more items will almost certainly improve the validity of the study but should be weighed against the issues of questionnaire length and its consequences in terms of participant fatigue and response rates. Extra tips to improve question and questionnaire quality given by Francis et al. (2008) [58]: Instead of extremely unimportant - extremely important, use extremely undesirable - extremely desirable, thereby enabling the respondent to indicate positive and negative items. instead of only using: extremely... - extremely... ; or strongly... - strongly...; also use: unlikely - likely. When a direct measure of Subjective norm or PBC is a complete sentence, and the responses range from strongly disagree to strongly agree, endpoints should not be mixed. (in an incomplete sentence the scales should be mixed). Direct measures of Attitude should have mixed positive and negative endpoints. And instrumental and experiential items as well as good versus bad. Self report of physical activity To obtain a reliable self-report measure of behaviour, it is desirable to use more than one question, because then an estimate of internal consistency can be computed. Azjen [60] proposes three types of questions: exact numerical report, rough numerical estimate, and rating scale. Direct measure of Attitude toward a behaviour Attitude towards a behaviour is defined as a person s overall evaluation of performing the behaviour in question. Azjen [60] states that overall evaluation (the direct measure) often contains two separable components: instrumental and experiential. Instrumental is represented by adjective pairs as valuable worthless, and harmful beneficial. Experiential quality is reflected in scales as pleasant unpleasant and

60 42 DEVELOPMENT enjoyable unenjoyable. Azjen [60] recommends selecting adjective pairs should include both types as well as the good bad scale, which tends to capture overall evaluation very well. Direct measure of Perceived behavioural control Two types of items have to be included to address people s beliefs that they have control over the behaviour. The first type captures respondent s perceived capability, which has to do with the difficulty of performing the behaviour, or with the likelihood that the participant could do it. The second type refers to the behaviours controllability. Question order In the final questionnaire, the different items assessing a given construct are separated and presented in non-systematic order, interspersed with items from the other constructs. Also the positive and negative framed items are presented in a non-systematic order, although endpoints of complete sentences will not be mixed. Analyses of the TPB questionnaire response The direct measures need to ensure high internal consistency. Each item is, by itself, designed to be a direct measure of the theoretical construct, and the different items used to assess the same construct should correlate with each other and exhibit high internal consistency. The belief composites (indirect questions) do not have this requirement. Salient beliefs do not have to be internally consistent. To estimate reliability of belief composites one can examine a measure s temporal stability (test retest reliability). Which can also be done to the direct measures of the theory s three major components. When different methods (direct and indirect measures) are tapping the same construct, scores are expected to be positively correlated. Calculation of the direct and indirect measures The direct measure will be calculated as the average of the answers using an unipolar (1 to 7) response scale. For the indirect measure the belief composites have to be calculated. Francis et al. (2004) [58] recommend using response scales being unipolar (1 to 7) and bipolar (-3 to +3), depending on whether the concept to be measured is unidirectional (e.g. probability) or bidirectional (e.g. evaluation), with the 7 and +3 endpoints being the positive answer (e.g. agree, positive, greater social pressure, favour, and approve ). An expectancy-value formulation is used to describe the relation between each of the theory s three major predictors and their underlying beliefs. Denoting belief strength as b, and the associated scale value of the belief (the outcome evaluation, motivation to comply, or control power) as s. Resulting in a aggregated set of beliefs: b i s i. Although this double question method is the recommended for the indirect measures, it is not commonly applied. The questions seem a bit artificial and often the scale value is already taken into account when answering the belief strength. Therefore only the belief part of the indirect questions will be assimilated in this questionnaire, having a unipolar scale (1 to 7).

61 4.4 CHAPTER SUMMARY final TPB questionnaire The numbers of questions per TPB item in the final questionnaire, are given in table 4.6. About half of the 48 questions addresses the behaviour of interest (being physically active (PA)) and the other half addresses the use of a feedback system (MOVE) for achieving the target behaviour. The barriers and levers for the indirect measures of Attitude and PBC resulted from the focus group, table 4.5. Direct subjective norm measures include: 2 injunctive quality and 2 descriptive norms, and direct PBC measures include: 3 direct capability questions and 1 direct controllability question. The full questionnaire is given in appendix E. Number of questions per item and type of question item Direct Indirect PA MOVE PA MOVE Self Report Attitude Subjective norm PBC Intention Table 4.6: Overview of the number of questions in the final TPB questionnaire, split on item and direct or indirect question. PA = Physical Activity. MOVE = The developed physical activity feedback system. 4.4 Chapter summary The User Interface is developed via an iterative design process. The final design contains different types of PA feedback to measure subject preferences. Additionally a user satisfaction questionnaire is developed to evaluate the feedback system during the experiment. To evaluate the effects of the Intervention (the PA feedback system) on behaviour and intention, a questionnaire is developed based on the Theory of Planned Behaviour. It will give insight in the aspects affecting behaviour and its weights in this.

62 44 DEVELOPMENT

63 Chapter 5 Method 5.1 Study design The effect of PA feedback (the intervention) will be studied in relation to the baseline behaviour and motivation. The study includes 20 healthy VDU-workers (at least 75% of tasks is with a Video Display Unit or sitting at a desk), aged 18 to 65 years old. Preferably with an man-woman ratio of 56:44, similar to the Dutch labour force distribution calculated by the CBS (Dutch Centre for National Statistics) [62]. Fulltime employees are preferred over parttime employees with respect to the intensity of the intervention and the amount of data gathered. Inclusion criteria are ability to walk (no complications) and speaking and reading Dutch. The exclusion criterion is smoking, due to the extra walks for smoking outside the building, and the bias this addiction can have over motivation to be physically active. Baseline (1 week) Intervention (2 weeks) T 0 T 1 T 2 Figure 5.1: Study design. Prior to participation, subjects provide written informed consent. The experiment exists of two parts, a one week baseline measurement to determine the physical activity of the VDUworkers, followed by two consecutive weeks of intervention, as illustrated in figure 5.1. Due to having maximal 10 sensors available, the experiment will be done 2 times, for 3 consecutive weeks, with 10 subjects each time (respectively group A and B). At T 0, T 1, and T 2, subjects get information and questionnaires as listed in table 5.1. There are various estimates of the number of days of wear required for a reliable estimate of habitual physical activity. For accelerometers it is recommended to measure between 3.5 and 7 days for adults. For many people, behaviour follows a weekly cycle, and measuring less than

64 46 METHOD 7 days can complicate the assessment of guideline compliance [28]. Vandelanotte et al. (2007) [45] found that positive results from physical activity interventions via internet an/or decreased in efficacy when time to follow-up increased. They recommend that to capture true maintenance of behaviour change, physical activity has to be measured at least 6 month after the end of the intervention. And they state that it is unclear what the optimal duration of an intervention should be, given the decrease in engagement and retention as the intervention progresses (decline of website visits). Due to time restraints the 6 month advise can not be integrated into this experiment, and evaluation will be done directly after the intervention. Information and questionnaires at moments T 0, T 1, and T 2 T 0 - Pre baseline T 1 - End of baseline T 2 - End of intervention Information brochure Informed consent Manual of the sensor General questionnaire TPB questionnaire Manual of the interface TPB questionnaire Motivation to comply question TPB questionnaire User Satisfaction Questionnaire Evaluation of motivation to comply question Table 5.1: Study design: information and questionnaires, full text available in the appendices. 5.2 Intervention During the two intervention weeks, feedback will be given on the physical activity level with respect to the length of the workday up to that moment. Physical activity will be registered in bouts of at least 1 minute above threshold (IMA = 0.14 m/s 2 ). The target duration of physical activity is set on 30 minutes a day, accumulated during work or lunch break. Feedback will be given when the subject is within reach of the feedback monitor (above the coffee machine). 5.3 Outcome measures The effect of the physical activity feedback system on behaviour, determinants of physical activity, and the attitude towards the system will be evaluated. Several measures will be extracted from the different measuring methods and correlation between various measures is hypothesized. All these measures (except for interaction) are determined at baseline and after the intervention, and will be studied on effects of the intervention. An overview of these measures is given in figure 5.2.

65 5.4 STATISTICAL ANALYSIS 47 Figure 5.2: Objective and subjective outcome measures. 5.4 Statistical analysis Descriptive statistics of all outcome measures will be given. Due to the small number of subjects and short duration of the experiment, statistical significant changes in behavioural measures are not expected. The TPB Questionnaire will be tested on internal consistency within the TPB items. Using a mixed model, effects of time (baseline versus intervention) on the TPB will be evaluated as well as the contribution of Attitude, Subjective Norm, and Perceived Behaviour Control to Intention, and possible time effects. Finally correlation between outcome measures from the TPB questionnaire and the feedback system will be tested.

66 48 METHOD

67 Chapter 6 Results This chapter will have an atypical setup. First some case studies describe misrepresentations by the system, giving some insight in the malfunctioning and resulting effects on the IMA values. Then the use of the interface will be described concluding with the results of the TPB questionnaire and the evaluation questionnaire. The reason for this atypical setup is given below. During the experiment, subjects reported that the feedback was sometimes incorrect, resulting in over- and underestimation of the actual physical activity. These problems had not been encountered in previous studies using this system, possibly because it has not been tested on this timescale with this many sensors simultaneously. Thorough analysis of all data processing steps of the system, revealed some errors, which were corrected during the experiment, though the reports of incorrect feedback remained. Up till now, the exact cause of the malfunctioning of the system has not been pinpointed. These findings have some serious implications on the data analyses. The IMA values themselves are not reliable as a measure for physical activity, which makes it impossible to determine baseline physical activity and possible changes in behaviour due to the intervention. Subsequently measures of the use of the interface are biased because of absents of trust in the system. The interface got a different purpose, instead of giving feedback on physical activity and thereby motivating users, it was used for testing the reliability of the system. The system being unreliable, resulted also in an aversion towards the system, less motivation to comply, and decreases of intention to change, making the results of the TPB questionnaire less valuable as well. Still the evaluation questionnaire resulted in valuable comments on the system itself, as well as usability and impact on daily work.

68 50 RESULTS 6.1 Subjects The experiment is done with 20 subjects (50% male), split into two groups of each 10 subjects. Their average age is 31 ± 5 1 years, and their professions are: 12 researchers; 4 students; 3 technicians and ICT; and 1 management assistant. There were no significant differences between group A and B. They work on average 37 ± 4 hours per week, from which about 84 ± 10% desk work. 19 subjects report having lunch breaks, with a duration of 33 ± 5 minutes. The one reporting not having a lunch break, indicated that he keeps on working at noon time. Lunch breaks were mostly used to do one or several of the following occupations: sit and lunch; walk; and talk to colleagues. 12 subjects report having coffee breaks, though the subjects reporting having no coffee breaks, indicate that they go for a coffee several times a day, and drink at their desks, while continuing work. Subjects have 3.3 ± 1.9 times a coffee break a day, with a duration of 7 ± 5 minutes per break. Finally, the subjects were asked about their level compared to their colleagues. 14 subjects beliefs they are more active and 16 belief that they are more fit than their colleagues. A full report of the introduction questionnaire is in Appendix D. 6.2 Physical activity data - IMA values The following system has been applied to refer to subjects and sensors: sensors have ID numbers from 1 to 10, for example ID5. The users from group A and B have the same ID number as their sensor, though when specific referring to the subjects from group B, the numbers will be used, which correspond to sensorids Table 6.1 gives a chronological overview of the adjustments and cases. All system adjustments were done during group A Case 1: Two sensors having the same accelerations At , during the time interval 16:08:09 till 16:14:00 hours, ID4 and ID6 were attached to the rotating axis of an electric drill. The electric drill elicited rotation accelerations to be measured by the sensors. Motive for this experiment was the large difference between feedback of ID4 and ID6, while the subjects reported being about equal physically active. A pre-test, with one subject wearing both sensors at the same time resulted in different IMA values (0.07 versus 0.05 m/s 2 ) and a different number of total IMA values (531 versus 635 samples), indicating that the characteristics of the sensors might be different. The test with the electric drill resulted in different characteristics as well. Although the sensors and their antennas had the same orientation and distance from the centre of rotation of the electric drill, significant differences were found. The number of received IMA values per sensor were for ID4 29 and for ID6 36 samples. The IMA values of ID4 ranged from 0.01 to 0.52 m/s 2, with an average of 0.19 ± 0.13 m/s 2 (median = 0.18). The IMA values of ID6 ranged from 0.02 to 0.27, with an average of 0.09 ± 0.04 m/s 2 (median = 0.08). In figure 6.1 the IMA values of 1 This is the possible error, instead of the standard deviation. The possible error presumes the full width of the intervals of age-categories, without applying a normal distribution over this width.

69 6.2 PHYSICAL ACTIVITY DATA - IMA VALUES 51 both sensors are given against time. The low number of datapoints of ID4 compared to ID6 might be correlated to its higher IMA values. Though, because the IMA values are processed values, the underlying cause cannot be determined. System adjustments & cases Date Report of malfunctioning / description of fix 5-15 Internal adjustment of the UI, to log more interaction information.* 5-18 Extra program to monitor the online database (monitor.php).* 5-18 Strong differences in number of datapoints per subject. Replacement of one bridge to room no. 28.* 5-20 Time indication of the Clock is too slow and the Status Indicator overshoots. Errors were fixed in 1) the preprocessor (with respect to the calculation of the start of the workday) and 2) the feedback application (with respect to misinterpreted and wrongly implemented parameters e.g. the time-base-definition).* 5-26 The ranking feedback was not in accordance with Week Overview. The error in the calculation was fixed and extra information, giving percentages was programmed.* 5-28 Case1 : 2 sensors attached to an electric drill Case2: Walking, though no physical activity reported Case3: Walking test, with all 10 sensors Case4: Walking together, though different Physical Activity Feedback Case5: Activity reported while being in a meeting Logger application on the server extended with extra file storage method, to gain insight in unprocessed activity data IMAcounter reprogrammed. It increased counts wrongfully when data was received by more than one bridge simultaneously, resulting in decreased IMA values.* 6-12 Extra program: to get detailed insight in received sensor data Table 6.1: Chronological overview of the adjustments and cases during the experiment. The feedback system was used by group A from 5-15 till 5-29, and by group B from 6-09 till Items marked with an asterisk (*) are adjustments of the system. Figure 6.1: The IMA values of sensors ID4 and ID6 are given, for an interval of about 400 seconds. The sensors were placed at the same rotating object, expecting the same IMA values.

70 52 RESULTS Case 2: Walking, though no physical activity period reported by the system At ID8 walked at least 3:20 minutes through the hallway of the RRD building, from 16:54 till 16:57 hours. Descriptive measures are given first for the whole working day (10:37-16:58 hours) and then for the specific walking interval (16:54-16:57 hours). IMA values range from 0.00 to 0.72, with an average of 0.05 ± 0.07 m/s 2 (median = 0.03), having about 8% of the IMA samples above the threshold of 0.14 m/s 2 (159 of a total of 2018 samples). During the interval of activity, IMA values range from 0.00 to 0.72, with an average of 0.17 ± 0.18 m/s 2, having 45% of the IMA samples above the threshold. The physical activity interval is given in figure 6.2. The IMA samples during the activity block have a higher percentage of IMA samples above threshold than during the whole day, and although the average IMA value during activity is above threshold, the standard deviation has a large value, indicating strong variations, which is not expected during a constant walking pace. The cause of these fluctuations is unknown. Figure 6.2: The IMA values of sensor ID8 are given, for an interval of 191 seconds. Although the subject was walking during this interval, not all IMA samples are above the threshold of 0.14 m/s Case 3: Walking test with all 10 sensors To rule out sensor specific effects, all sensors were tested during physical activity. Five students performed a walking exercise through the hallways of the RRD building, wearing 2 sensors each, during the interval 11:54:30-11:59:59 hours at In figure 6.3 the IMA values are plotted against time. The data received from all sensors, is mainly above 0.14 m/s 2, as expected during walking. Though, the total number of IMA samples per sensor and the average IMA value vary widely. In table 6.2 an overview is given of al sensor IDs. The average IMA value is 0.45 ± 0.05 m/s 2 and the percentage of sample 0.14 m/s 2 ranges from % per sensor. The number of samples range from 13 to 32, having time steps (TS) of 3 to 45 seconds, with an average of 16 ± 3 seconds between consecutive IMA samples. The descriptive statistics show no correlation between IMA values and TS length, nor clues for the IMA values below threshold.

71 6.2 PHYSICAL ACTIVITY DATA - IMA VALUES 53 Figure 6.3: IMA values of all sensors against time (duration is 5.29 minutes = 329 seconds). 0 seconds is the start of the physical activity interval. Most of the IMA values are above the threshold of 0.14 m/s 2. Descriptive statistics of IMA values and TS lengths during walking Measure ID1 ID2 ID3 ID4 ID5 ID6 ID7 ID8 ID9 ID10 IMA Average [m/s 2 ] Std Min Max Median # # TS Average [s] Std Min Max Median Table 6.2: Descriptive statistics of all 10 sensors during the walking test. IMA = Integral of the Modulus of body Acceleration output; TS = Time step. Std = standard deviation. # = number of samples

72 54 RESULTS Case 4: walking together, though different physical activity feedback During the experiment ( ) two subjects from group B walked together, next to each other through the RRD building to have sufficient physical activity registered by the system preceding the lunch break, to have a physical activity period during the lunchwalk as well. Subject 15 his feedback contains the activity period, while subject 13 has no activity period in his feedback. First an overview is given of the full dataset of that day, regarding ID13 and ID15, using the interval 8:33:56-15:39:31 hours, discussing IMA values and TS length separately, to see if the sensors produce structurally different data. IMA: ID13 has IMA values ranging from 0.00 to 1.18 m/s 2, with an average of 0.04 ± 0.05 m/s 2 (median 0.02 m/s 2 ). During this whole day 3.6% if the IMA samples are above the threshold. ID15 had IMA values ranging from 0.00 to 1.39 m/s 2 with an average of 0.07 ± 0.09 m/s 2 (median 0.04 m/s 2 ). During this whole day 10.3% if the IMA samples are above the threshold. TS: TS length of ID13 and ID 15 varied from 1 to 2,313 s and 2,256 s respectively (the maxima were lunch breaks). When disregarding the lunch break, average TS length of ID 13 is 11 ± 8 s (median 10 s) and 12 ± 10 s (median 10 s) of ID15. This full day dataset shows that the IMA values of ID15 are higher than ID13 and naturally resulting in a higher percentage above the threshold. The time step length is approximately the same for ID13 and ID15. The specific datasets during walking show large difference of IMA values, which are shown in figure 6.4. IMA values of ID13 range from 0.02 to 0.25, with an average of 0.11 ± 0.07 m/s 2. Although active, only 38% of the IMA samples are above the threshold. The IMA values of ID15 range from 0.08 to 0.39, with an average of 0.20 ± 0.08 m/s 2, having 87% of the IMA samples above the threshold. The average IMA value during walking is for both sensors about 2.8 times the average IMA value of the whole day. Figure 6.4 indicates possibly multiple effects influencing the IMA values. For example at the interval 2,500-2,540 seconds, both sensors show remarkable similar IMA values, having a depression both around 2,520 seconds. Because the subjects were walking next to each other this depression might be related to their position, or specific characteristics of their environment. Another remarkable effect is visible around 2,480 and 2,560 seconds, where the IMA values seem to be the result of an opposite effect. Causes for both effects are not clear Case 5: activity reported while being in a meeting Previous cases are reports of underestimation of physical activity by the system, though also overestimation is reported. On , subject 15 had a meeting in the grote vergaderzaal (meeting room), from 9:00 till 10:00 hours. The subject reported sitting on his chair during the meeting. The IMA values of the complete workday have already been described in the previous case (Case 4), showing no abnormalities. Figure top 6.5 shows the IMA values during the meeting. Figure bottom 6.5 gives a detailed view of activity period, showing IMA samples above 0.14 m/s 2 from 9:29:38 to 9:32:40 hours (1,778-1,960 seconds in figure top 6.5). The first 7 minutes of the meeting has elevated IMA values that could be explained by getting coffee and taking a seat. The cause of the activity period halfway the meeting is unclear.

73 6.2 PHYSICAL ACTIVITY DATA - IMA VALUES 55 Figure 6.4: Two subjects of group B walked next to each other through the RRD building. Figure 6.5: Above: Overview of the IMA values of sensor ID5 during a meeting. Below: Detailed view of figure above, of the IMA samples on the interval 1,700-2,100 seconds. Minor vertical gridlines are placed every 30 seconds, revealing TS variations from 5 up to 88 seconds on this interval. IMA 0.14 m/s 2 was on the interval 1,778-1,960 seconds (9:29:38 to 9:32:40 hours).

74 56 RESULTS Summary cases 1-5 Below is a summary of the phenomena seen in the 5 cases. Physical activity is under- and overestimated by the system. Time step (TS) length varies strongly, within and between subjects. IMA values fluctuate rather much during a constant walking pace. IMA value fluctuations might have a relation with TS length. Phenomena do not seem to be sensor specific. Some effects seem to be related to position, or environmental characteristics. There might be a simultaneously opposite effect on IMA values. 6.3 Extra program To gain more insight in the mechanisms resulting in the malfunctioning of the system, a special program was written, to gather the raw accelerometer data as received by the bridges (as unprocessed as possible). This program logs the data and executes only 2 pre-processes steps: 1. Correction of the accelerations in x, y and z direction for their calibration values so that one unit in each direction is equal to gravity (1G = 9.81 m/s 2 ). 2. Removal of duplicate entries, when a data sample is received by multiple bridges. The output of the extra program is: a) SensorID; b) Nr. of bridges; c) Last bridge; d) IMA loop count; e) Acceleration in x, y, and z direction, and f) a time stamp every so many data samples. This output is referred to as 26ms logfile. During the last day of the intervention ( ), subjects were asked to report overestimation of the system, so specific intervals could be investigated, using the output of the extra program. The focus was on overestimation, because the day before ( ) an error in the IMAcounter was found and solved, see table 6.1, which could be the cause of all underestimations of the system Frequency & IMA values At all sensors except ID2 were worn. Because of the size of the data file, a limited interval was analysed from 08:45:00 till 15:59:59 hours. The average frequency of data samples received by the bridges is given in figure 6.6. The frequency varies strongly from 3.4 to 18.7 Hz with an average of 11 ± 5 Hz. The maximum average frequency is about half the theoretical maximum frequency of Hz, possible with the current sensor settings, sending each 26 ms a data sample. The average IMA values per sensor and its standard deviation are given in figure 6.7. The average IMA values are calculated according to formula (2.1) and varies between 0.2 and 0.9 m/s 2 with an average of 0.4 ± 0.2 m/s 2.

75 6.3 EXTRA PROGRAM Loop count The low sample frequency indicates possible errors in the number of samples per IMA value. This measure is the loop counter. It registers the number of data samples collected to calculate the 10 second average IMA value (10 seconds = 1 loop). Descriptive statistics of the loop counter are given in table 6.3. The average samples per loop is 80 ± 2*10 samples. With each new datasample the loop counter increases by 1, reaching maxima of 259, 296, and 333, before reset for the next loop. These loop count maxima seem to be discrete steps, what is not expected, since the loop counter resets after 10 seconds, independently of the number of samples. All loop count maxima seen in the complete dataset were one of the following sequence: 37; 74; 111; 148; 185; 222; 259; 296; and 333. The cause of these discrete steps might be an earlier implementation of loop count and interval determination, which was coupled to the theoretical sample frequency of 38.46Hz (sample time = 26ms), instead of the real 10 seconds. In this old implementation the loop count increases from 0 to 37 (38 samples), corresponding to approximately 1 second at sample frequency is Hz. Figure 6.6: The average frequency in Hz, per sensor ID. Frequency is determined by the number of received data samples, after removing duplicates (when received by multiple bridges). Theoretical maximum sample frequency is Hz (sample interval = 26 ms). Descriptive measures of the IMA loop count ID1 ID2 ID3 ID4 ID5 ID6 ID7 ID8 ID9 ID10 mean median std max Table 6.3: Descriptive statistics of the IMA loop count. An indication of the average loop count maxima is approximately twice the mean or median. Extracted from the unprocessed data gathered at

76 58 RESULTS Figure 6.7: Average IMA value from the 26ms logfile ( , 08:45:00-15:59:59 hours) with the standard deviation. IMA values are calculated as the summation of absolute acceleration in x, y, and z direction. Acceleration in each direction is calibrated with respect to the gravity, which is set to 1. The offset (due to gravity) is removed by lowering the calculated IMA value by Overestimation by the system Four cases of overestimation by the system were reported by subjects, corresponding to 3 different sensors: ID3, ID5, and ID6. The 4 cases are summarised below: 1. ID3. Period: 12:15-13:10 hours. Subject was in a meeting. 2. ID5. Period: 13:30-14:00 hours. Subject was in a meeting. 3. ID6. Period: 12:00-13:25 hours. Subject was sitting at his desk. 4. ID6. Period: 14:00-16:00 hours. Subject was in a meeting. The data was examined for outliers and abnormalities in IMA value, IMA loop count, number of bridges, and time step. During the 4 intervals (cases) no clues for the cause of overestimation were found, except for the time step length. The time step length (between consecutive data samples) revealed a factor that could cause over- as well as underestimation of the true physical activity. Time steps (TS) longer than 60 seconds between consecutive samples of one sensor were found. This sending-receiving characteristic was not foreseen and has serious implications on the data analyses and feedback calculation. In the feedback calculation, physical activity is defined by two factors: 1) IMA 0.14, and 2) during at least 1 minute. The second restriction should exclude very short activities and artifacts caused by other factors than physical activity. TS longer than 60 seconds can result in physical activity feedback solely on the IMA value preceding the long time step. An impression of the extent of this effect, is given in figure 6.8. Time steps 60 seconds are indicated by a red box underneath the TS graph. These untrusted intervals take up a significant part of the total measurement time, varying from 4 till 31% of the workday.

77 6.3 EXTRA PROGRAM 59 Figure 6.8: Per sensor the time step length is shown against time in hours of time steps 60 seconds are indicated with a red box beneath each graph (on the vertical axis interval [-20 0]). The percentage of these untrusted interval with respect to the total measured time is given in each subtitle.

78 60 RESULTS Time step effects in processed IMA values The input of the feedback system is the online database containing 10 seconds average IMA samples. Availability of the raw datasamples as well as the IMA samples, makes it possible to examine the influence of long time steps on the IMA values. In figure 6.9, 6.10, and 6.11 both the raw datasamples (26mslog) and the IMA samples (IMAfile) are given for the same time intervals. In these figures can be seen that the ratio of TS 60 seconds increases in the most dramatically case from 13% (minimally preprocessed) to 25% (fully preprocessed) of the total time. These untrusted time intervals correspond to the reported cases of overestimation. Figure 6.9: time step length of sensor ID3. Case: subject was in a meeting. Period: 12:15-13:10 hours. During this period almost no data has been received, resulting in a gigantic time step length Sample frequency settings of the sensor The dataset collected with the extra program revealed an average sample frequency that approximates half of the theoretical sample frequency of Hz, indicating a possible error in the settings of the sensors, sending data at a lower frequency. This was tested with only one sensor and one bridge, close to each other to exclude environmental influences on data loss. Two different sample frequencies were tested: Hz and 0.60 Hz. The standard setting Hz (TS = 26 ms) was tested twice. The first test was done with the same (unchanged) settings as during the experiments, the second test was done with Hz programmed to the sensor. Finally the sensor was programmed to a sample frequency of 0.60 Hz. The sample frequencies of the resulting datasets are given in table 6.6. The approximately equal results of the Hz tests, indicate that the sensors have been programmed correctly during the experiments. Though, in all cases the received dataset seems to have half the expected (set) sample frequency. It is unclear if this is caused by the sensor, the bridges, or the first processing steps.

79 6.3 EXTRA PROGRAM 61 Figure 6.10: time step length of sensor ID5. Case: subject was in a meeting. Period: 13:30-14:00 hours. During this period time step length increases to far above 60 seconds, resulting in almost a full interval of untrusted time steps. Figure 6.11: time step length of sensor ID6. Cases 1): subject was sitting behind his desk. Period: 12:00-13:25 hours, and 2) subject was in a meeting. Period: 14:00-16:00 hours. During both periods time steps are longer than 60 seconds, resulting in untrusted time steps.

80 62 RESULTS Sample frequency settings of the sensor Sensor Received dataset Action Hz Hz unchanged Hz Hz programmed 0.60 Hz 0.33 Hz programmed Table 6.4: Overview of the tested sample frequencies. At the left the programmed frequencies of the sensor and at the right the frequencies of the corresponding received datasets. 6.4 TPB Questionnaire Subjects were asked to fill in the TPB questionnaire at moments (T 0, T 1, and T 2 ). The TPB questionnaire has 48 questions, from which about half addresses the behaviour of interest (being physically active (PA)) and the other half addresses the use of a feedback system in achieving physical activity (MOVE). The outcomes of the questionnaire will be described in the following paragraphs, differentiating between the TPB constructs and between questions addressing PA and MOVE. An overview of all questions and the answers per moment (T 0, T 1, and T 2 ) is given in Appendix H. All outliers have been checked, and were not caused by specific subjects. Per TPB item there are two important considerations, before general conclusions can be drawn: 1) Are time effects apparent. And 2) Have the questions addressing the same TPB constructs sufficient internal consistency Time effects TPB constructs were tested for changes during the experiment (effect of Moment), using a linear mixed model, with each Construct as the dependent variable, and Moment (T 0, T 1, and T 2 ) as factor. Constructs were defined as the average of all questions assigned to that construct, done for PA+MOVE, PA and MOVE, resulting in a total of 12 tests. Significant differences between T 0, T 1, and T 2, are further examined by post hoc analysis, via pair wise comparison. Results are summarised in table 6.5. Intention changed significant over time, when regarding all questions or only regarding the questions addressing MOVE. Post hoc analysis showed significant changes between T 1 and T 2, not between T 0 and T 2. For PA+MOVE alpha was 0.47 and 0.03, for T 0 - T 2 and T 1 - T 2 respectively. For MOVE alpha was 0.22 and 0.01 for T 0 - T 2 and T 1 - T 2 respectively. Intention was at T 2 not significant different from T 0, though at T 1 Intention was lower. Analysis of time effects in Theory of Planned Behaviour constructs TPB construct PA+MOVE PA MOVE Intention 0.05* * Attitude Subjective Norm Perceived Behavioural Control Table 6.5: Statistical analysis of time effects in the TPB constructs, given are alpha s resulting from each test. Significance level: alpha = * Significant differences between T 0, T 1, or T 2.

81 6.4 TPB QUESTIONNAIRE 63 The questionnaire contained four Self Report measures. Three were about the number of days per workweek being sufficient active, and one about the average number of minutes active per workday. The number of workdays being sufficient physical active per week was for T 0, T 1, and T 2 respectively 3.9 ± 1.0; 3.5 ± 1.1; and 3.2 ± 1.5 days per week. About how frequently subjects were sufficiently physically active per week, subjects answered at T 0, T 1, and T 2 respectively 5.1 ± 1.2; 5.0 ± 1.9; and 4.1 ± 2.0 on the scale of [1-7], with 1 being never and 7 being every workday. Finally the average number of minutes of physical activity per workday for T 0, T 1, and T 2 was respectively 30 ± 9; 31 ± 13; and 27 ± 17 minutes per day. Boxplots of these outcomes are given in figure Figure 6.12: Boxplots of the 4 Self Report questions, at T 0, T 1, and T 2. Details are given in Appendix H Internal consistency Per TPB item, split for PA and MOVE, the alpha coefficient (α) has been calculated. The alpha coefficient is a measure for internal consistency of the questions addressing the same TPB item, and is therefore a measure of questionnaire reliability. An alpha coefficient of 0.70 or higher is regarded sufficient to conclude sufficiently reliable [63]. For Attitude and Perceived Behaviour Control alpha is also calculated when combining direct and indirect measures. An overview of all alpha coefficients is given in figure In some cases internal consistency can increase by removing a question from that item, an overview of this is given in table 6.6. Only Intention and Subjective Norm show sufficiently high internal consistency for PA as well as MOVE questions. Implications of this internal consistency analysis is given per TPB item below. Intention Intention has high internal consistency for PA as well as MOVE. Items that were questioned are for PA as well as MOVE: I plan, I will try, and I will do my best to be physically active for at least 30 minutes a day, during the forthcoming weeks.

82 64 RESULTS Attitude When considering direct, indirect and direct+indirect questions, than the alpha coefficient can be increased by either removing question Q4, Q15c or Q10. This indicates that questions regarding 1) PA and concentration, 2) PA and acting wise and 3) MOVE and health influence are valued differently than all other Attitude questions regarding a) PA: stress, important, good, good feeling, and more energy ; and b) MOVE: helpful, enjoyable, useful, good, concentration, and less time for work. Q4: At least 30 minutes physically active per day, during the forthcoming weeks, makes it hard to get back to work afterwards [agree - disagree] 2, Although the average is about the same as for the other indirect Attitude questions regarding PA, the range of answers is larger. Q15c: I think that, being physically active for at least 30 minutes a day, during the forthcoming weeks is [not wise - wise] 3. An higher percentage beliefs that it is notwise to be PA during work, for at least 30 minutes a day, during the forthcoming weeks, with respect to the other direct Attitude PA questions. Q10: Using MOVE, during the forthcoming weeks, is good for my health [disagree - agree] 4. This question has been answered less positive than the other indirect Attitude questions regarding MOVE. Subjective Norm Subjective Norm has high internal consistency for PA as well as MOVE. Items that were questioned are for a) PA: opinions of family, friends, and acquaintances regarding PA, and friends being PA; and b) MOVE: opinions of executive and colleagues regarding MOVE, and roommates at work and the chef using MOVE. Perceived Behaviour Control When considering PA, internal reliability is rather low of direct as well as indirect questions, though when considered together (D+I) the internal reliability increases to a sufficient level. This is not the case for the questions addressing MOVE. Internal consistency is only above 0.70 for the direct questions when removing Q40. This indicates that Perceived Behaviour Control questions regarding the following list, are each valued differently: 1) PA is difficult; 2) faith that I can be PA; 3) I can be PA, if I want; 4) I can decide myself to be PA; 5) I can decide myself to use MOVE; 6) PA even when I am tired; 7) PA even when bad weather; 8) PA because flexible working hours; 9) PA because nice sightseeing; 10) MOVE even when busy; 11) easy MOVE, lots of coffee/thee; and 12) MOVE is difficult because of meetings. Only these three direct questions are valued approximately the same: 1) MOVE is difficult to use; 2) faith in going to use MOVE; and 3) I can use MOVE, if I want. Q40: I can choose to use MOVE the forthcoming weeks [disagree - agree] 5. This question was answered most positively (6 or 7), with a small range of answers. 2 NL: Tenminste 30 minuten per dag fysiek actief zijn, tijdens de komende weken, maakt het moeilijk om daarna weer aan de slag te gaan. [eens - oneens] 3 NL: Tenminste 30 minuten per dag fysiek actief zijn, tijdens de komende weken, vind ik [onverstandig - verstandig] 4 NL: MOVE gebruiken, tijdens de komende weken, is goed voor mijn gezondheid [oneens - eens] 5 NL: Ik kan zelf bepalen of ik MOVE gebruik de komende weken [oneens - eens]

83 6.4 TPB QUESTIONNAIRE 65 Figure 6.13: Overview of the alphas (internal consistency) for all TPB items. D = direct questions; I = indirect questions. PA = questions addressing being PA; MOVE = questions addressing the use of the feedback system for achieving physical activity. An alpha coefficient 0.70 concludes sufficiently reliable. Internal consistency of constructs with low alpha Item D / I Behaviour Alpha Remove New alpha Attitude D+I PA 0.69 Q4 or Q15c 0.75 or 0.73 D PA 0.60 Q15c 0.66 I PA 0.46 Q I MOVE 0.08 Q PBC D+I MOVE 0.54 Q D MOVE 0.60 Q I MOVE 0.36 Q Table 6.6: Overview of the questions which can be removed from the TPB item to increase internal consistency. D = direct; I = indirect. PA = questions addressing being PA; MOVE = questions addressing the use of the feedback system for achieving physical activity. An alpha coefficient 0.70 concludes sufficiently reliable.