ann. behav. med. (2013) 46:260 272 DOI 10.1007/s12160-013-9503-9 ORIGINAL ARTICLE Using Mental Imagery to Deliver Self-Regulation Techniques to Improve Sleep Behaviors Marisa H. Loft, PhD & Linda D. Cameron, PhD Published online: 3 May 2013 # The Society of Behavioral Medicine 2013 Abstract Background Poor sleep habits and insufficient sleep represent significant workplace health issues. Purpose Applying self-regulation theory, we conducted a randomized, controlled trial testing the efficacy of mental imagery techniques promoting arousal reduction and implementation intentions to improve sleep behavior. Method We randomly assigned 104 business employees to four imagery-based interventions: arousal reduction, implementation intentions, combined arousal reduction and implementation intentions, or control imagery. Participants practiced their techniques daily for 21 days. They completed online measures of sleep quality, behaviors, and self-efficacy at baseline and Day 21; and daily measures of sleep behaviors. Results Participants using implementation intention imagery exhibited greater improvements in self-efficacy, sleep behaviors, sleep quality, and time to sleep relative to participants using arousal reduction and control imagery. Conclusions Implementation intention imagery can improve sleep behavior for daytime employees. Use of arousal reduction imagery was unsupported. Self-regulation imagery techniques show promise for improving sleep behaviors. Neither author has any conflicts of interest in that no organization has financial interest in the subject matter of this paper. This research was conducted in Auckland, New Zealand Electronic supplementary material The online version of this article (doi:10.1007/s12160-013-9503-9) contains supplementary material, which is available to authorized users. M. H. Loft (*) Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Sunway Campus, Jalan Lagoon Selatan, 46150 Bandar Sunway, Selangor, Malaysia e-mail: marisa.loft@monash.edu L. D. Cameron University of California, Merced, CA, USA Keywords Sleep. Self-regulation. Implementation intentions. Intervention. Mental simulation Introduction Poor sleep habits leading to insufficient sleep present a significant risk to health and work productivity for many adults [1]. An estimated 10 to 40 % of the general adult population are chronically sleep deprived, due in large part to lifestyle and sleep behavior choices that undermine sleep quality and quantity [2]. To date, much of the research on sleep problems has concentrated on treatments for individuals with clinical insomnia, a condition characterized by chronic states of hyper-arousal and difficulty initiating or maintaining sleep despite ample opportunity [3]; or on insufficient sleep among shift-workers [4]. Relatively few studies have focused on developing interventions for daytime workers who get insufficient sleep primarily due to poor lifestyle and sleep habits [5]. Many working adults have the intentions and the means to get sufficient sleep, but they fail to execute the necessary actions to do so. Recent evidence suggests that both pre-sleep arousal and failure to translate sleep intentions into sleep preparation behaviors are partially responsible for insufficient sleep in daytime workers [6]. Self-regulation theory, with its emphasis on emotion regulation and goal-directed behavior, provides a framework for developing interventions that can target these emotional and planning processes to promote adaptive sleep behavior [7 10]. In this randomized, controlled trial, we tested the efficacy of behavioral interventions aimed at improving sleep behavior for working adults. These interventions involve the use of mental imagery tasks designed using principles delineated by self-regulatory theory.
ann. behav. med. (2013) 46:260 272 261 Self-Regulation and Sleep Behavior Self-regulation refers to the processes by which individuals direct their thoughts, emotions, and behaviors to achieve their goals [7, 8]. Sleep self-regulation, which typically requires effortful planning and behavioral control, can be difficult to achieve when confronted with cognitive and emotional demands arising from the workplace and other life domains [9]. These demands can exacerbate problems associated with both emotional arousal at night and the implementation of sleep preparation routines that enable one to get to bed, fall asleep, and wake up at the desired times. Emotional arousal induced by work demands can undermine sleep quality through at least two routes. First, this emotional arousal can interfere with efforts to disengage from work tasks and attend to sleep preparations. Second, it can exacerbate sympathetic arousal at bedtime, which can delay sleep onset and trigger early awakenings [11]. Relaxation strategies can directly reduce arousal and, in turn, promote sleep onset, sleep quality, and appropriate times of awakening. Relaxation strategies have been used frequently for sleep promotion, although evidence of their efficacy remains mixed [12]. Working adults also report difficulties in developing and implementing sleep preparation routines that enable them to get to bed, fall asleep, and wake up at the desired times [6, 9]. Although they typically value getting enough sleep, they often fail to engage in sleep hygiene behaviors (i.e., taking appropriate steps to prepare for bed and avoiding behaviors such as exercise that interfere with sleep onset). As delineated by the Health Action Process Approach [13] and other self-regulation models [7], implementing a behavior such as sleep hygiene involves two phases: a motivation phase and a volitional phase. The motivation phase involves the development of behavioral intentions (e.g., to get a good night s sleep). Factors shaping intentions include behavioral goals (e.g., desires for sufficient quality sleep) and self-efficacy, or beliefs in one s ability to enact the behaviors (e.g., appropriate sleep hygiene). Considerable research implicates low self-efficacy beliefs in discouraging motivation and leading to failures in the self-regulation of health behavior [14]. The volitional phase involves the planning, implementation, and maintenance of the behavior over time. Implementation intentions can promote success in the volition phase [15]. Implementation intentions are plans that link key situational cues to the intended actions. They conceptually have an if then structure: They specify that if one is in a particular situation, then one will engage in a particular behavior [15]. They can enhance the precision and accessibility of mental plans (e.g., to brush teeth and read prior to bed) when encountering situational cues (e.g., one s bedroom, reading book, toothbrush, etc.). Implementation intentions for sleep hygiene behaviors can directly foster relaxing behaviors and timely sleep preparations that, in turn, reduce pre-sleep arousal and enhance sleep quality and related features such as an early time of turning off the lights, less time taken to get to sleep, more hours of sleep, reduced sleep disturbance, and appropriate time of waking. Success in implementing sleep hygiene behaviors and getting quality sleep in turn promotes sleep selfefficacy, thereby fueling a positive motivational and volitional self-regulation process. Fostering Behavior Change through Mental Imagery Techniques Mental imagery techniques have been used to promote desired behaviors in a variety of health settings, including exercise [16, 17], reduction of alcohol use [18], and relaxation during cancer treatment [19]. Mental simulations of experiences engage the individual in vivid, perceptual experiences that can induce affect and motivation, enhance the salience of goals, and specify aspects of the behavior process [20]. Mental imagery techniques hold considerable promise as low-cost, time-efficient interventions for fostering arousal reduction and implementation strategies. Imagery techniques for arousal reduction are commonly used in a variety of health domains, although empirical evidence of their efficacy in promoting sleep quality is mixed [12]. Imagery interventions for promoting implementation intentions are relatively new, but emerging evidence suggests that repeated use of imagery tasks in which one visualizes the behavioral steps leading to a desired goal can enhance behavioral adherence and goal attainment [16, 18, 21]. Study Aims and Hypotheses This randomized, controlled trial tested the individual and combined effects of arousal reduction imagery and implementation intention imagery on sleep behaviors and experiences in a sample of adults with daytime employment in business settings who reported sleep difficulties. We hypothesized that, over the 21-day trial, daily use of the arousal reduction imagery and implementation intentions imagery would have additive, positive effects on sleep self-efficacy, sleep motivation, sleep-related planning, sleep-related behavior (encompassing positive behaviors that promote sleep and negative behaviors that interfere with sleep), pre-sleep arousal reduction, and sleep quality. We also hypothesized that the arousal reduction technique would have a relatively greater impact on pre-sleep arousal whereas the implementation intentions technique would have a relatively greater impact on sleep-related planning and behavior.
262 ann. behav. med. (2013) 46:260 272 Method Study Design The study utilized a 2 (Arousal Reduction: imagery present versus absent) 2 (Implementation Intentions: imagery present versus absent) 2 (Time: baseline versus Day 21 follow-up) mixed design, with additional 21 daily assessments of sleep behavior from baseline to follow-up. The University of Auckland Human Participants Ethics Committee approved the study, which is a registered clinical trial; www.clinicaltrials.gov; Identifier: NCT01648062. Participants We recruited employees from ten large corporations and one small firm in the Auckland region. Notices inviting study participation were distributed by email and attached in prominent locations around the workplace. These notices invited any daytime employees who were interested in improving night-time sleep quality to follow a link to an online baseline survey that included screening measures. Eligibility criteria included: (1) ability to read and write in English; (2) full-time employment; (3) work shifts during daytime hours (participants were excluded if they worked night shifts through the organization or a secondary job); (4) a job role that provided daily access to email; (5) a score of four or greater on the Pittsburgh Sleep Quality Inventory (PSQI) [22], which indicates at least moderate difficulties in two or more areas (e.g., sleep quality and daytime dysfunction); (6) no identified, biological cause of sleep problems (e.g., restless leg syndrome, sleep apnea, narcolepsy, periodic limb movement disorder, or pregnancy); (7) no current diagnosis of insomnia; (8) no depression; (9) no child under the age of 5; and (10) no commitment outside of work that caused them to regularly lack sleep. Participants who met the eligibility criteria were then invited to attend an initial training session. Recruitment lasted for 12 months. Of the 204 individuals who completed the online baseline and screening questionnaire (see Fig. 1), 100 did not meet inclusion criteria due to low PSQI scores (n=10), children under 5 (n=24), incomplete screening data with PSQI scores and demographic information missing (n=36; most also failed to provide email addresses so that follow-up was not possible), working less than full-time (n=12), or high depression scores (n=3). Fifteen individuals who were eligible and invited to the training session were too busy to participate further. Excluded participants who presented data were comparable on baseline characteristics to those included. Table 1 presents the demographic characteristics of the 104 participants. About two thirds were women, and most identified their ethnicity as New Zealand European or European. Most were married or in de facto relationships; about one third had children (all over the age of 5). Average age was 37 years (SD=10.56; Range=21 to 61). Power analyses using G-Power3 [23] indicated that a sample size of 104 was sufficient to detect moderate group effects on changes from baseline to follow-up in pre-sleep arousal and sleep habits; partial n 2 ¼ 0:28. η 2 p Procedure and Interventions Prior to the present RCT, we conducted two pilot studies to evaluate the acceptability, participant engagement, and short-term effects of the interventions on relaxation (by the arousal reduction imagery) and behavior visualization (by the implementation intentions imagery). The first pilot test was conducted with a sample of 40 university students and the second pilot test was conducted with a sample of 40 academic staff members. These pilot RCTs, which utilized methods that were highly similar to those of the present RCT, led to refinements in the interventions and study measures and supported the acceptability and participant engagement with the intervention tasks. Employees who responded to study notices received study information and, after providing informed consent, completed screening questionnaires. Eligible participants were randomized into the four imagery task conditions using the website tool, www.randomiser.com. Participants were blinded to their condition assignment and were only informed about the condition allocations after the study ended. They were instructed to not discuss the imagery tasks with colleagues. Participants attended a 30-min group session held at their workplace, during which they completed the pre-session questionnaires and then received training in their imagery tasks. They listened to audiotaped instructions for visualizing the intervention scenario; these instructions were of comparable length across the four conditions, averaging 2 min and 10 s. Full descriptions of the instructions are included in the Electronic supplementary material. Participants in the Implementation Intentions condition received instructions to visualize a specific plan for obtaining quality sleep each night through the practice of established sleep hygiene practices [24]. They visualized the process of changing into comfortable clothes and relaxing prior to going to bed, the time they planned to go to sleep, where they planned to sleep, and the bedtime routine they follow to help them to get to sleep. These instructions were framed according to a modified structure for implementation intentions, with statements to imagine that when it is a particular time (e.g., a half-hour before bedtime) and one is in a particular place (e.g., at home), then one engages in a set of behaviors (e.g., sitting down and relaxing quietly). We used this statement structure rather than the typical if then structure used for implementation intentions (often for
ann. behav. med. (2013) 46:260 272 263 Fig. 1 Flow of participants through the study Completed screening survey=204 Met screening criteria randomized and confirmed for initial practice session=104 Excluded=100 Reasons included PSQI scores < 4 (10), child under 5 (24), incomplete data (36), not employed full-time (12), High depression (3), too busy (15) Attended First Session Control Group Baseline=28 Arousal Reduction Group Baseline=27 Implementation Intentions Group Baseline=26 Combined Group Baseline=23 Completed over 50% daily emails= 28 Completed over 50% daily emails= 26 Completed over 50% daily emails= 26 Completed over 50% daily emails= 23 Completed final 3- week follow-up= 25 Completed final 3- week follow-up= 26 Completed final 3- week follow-up= 26 Completed final 3- week follow-up= 22 Table 1 Demographic characteristics of the four intervention groups Total sample (n=104) n (%) Control (n=28) n (%) Arousal reduction (n=27) n (%) Implementation intentions (n=26) n (%) Arousal reduction/implementation intentions (n=23) n (%) Gender Male 38 (36.5) 9 (36.0) 5 (18.5) 16 (61.5) 7 (31.8) Female 66 (63.5) 16 (64.0) 22 (81.5) 10 (38.5) 15 (68.2) Ethnicity NZ European 80 (76.9) 23 (82.2) 20 (74.1) 19 (73) 18 (78.3) Asian 8 (7.7) 0 2 (7.4) 3 (11.5) 2 (8.7) Other a 12 (11.5) 5 (17.8) 5 (18.5) 4 (15.3) 3 (13.0) Marital Status Single 28 (26.9) 8 (28.6) 9 (33.3) 6 (23.1) 5 (21.7) Divorced/Separated 6 (5.8) 2 (7.1) 2 (7.4) 0 2 (8.7) Married/De facto 70 (67.3) 18 (64.3) 16 (59.3) 20 (76.9) 16 (69.6) Position Graduate/Lower level 76 (73.1) 19 (76.0) 21 (77.7) 17 (65.4) 19 (76.0) Mid-level management 21 (20.2) 5 (20.0) 4 (14.8) 7 (26.9) 5 (20.0) Senior management 6 (5.8) 1 (4.0) 2 (7.4) 2 (7.7) 1 (4.0) Income <$69,999 32 (30.7) 10 (38.4) 11 (42.3) 5 (19.1) 6 (26.0) $70,000 $89,999 15 (14.4) 3 (11.5) 6 (23.1) 3 (11.5) 3 (13) $90,000+ 54 (51.9) 13 (50) 9 (34.6) 18 (69.2) 14 (60.9) a Ethnicity; Others include European [3], Maori [5], Pacific Islander [2], South African [2], South American [2], and Canadian/American [2]
264 ann. behav. med. (2013) 46:260 272 impulsive behaviors) because pilot test participants reported that it made relatively more sense given the habitual nature of their evening schedules. At bedtime, they were instructed to mentally run through a checklist of these behaviors and then do any behaviors that they had not yet completed. These actions only related to positive sleep-related behaviors due to the possibility that instructions to avoid negative behaviors (e.g., Do not use alcohol four hours before bedtime ) could trigger ironic effects of participants being more tempted to engage in these behaviors [25, 26]. Participants in the Arousal Reduction condition received instructions to imagine a scenario of wearing a backpack loaded with their worries, then putting the heavy backpack down, and then experiencing the relief and freedom from tension. Participants in the Combined Arousal Reduction and Implementation Intentions condition engaged in both arousal reduction and implementation intention imagery. Participants in the Control condition were instructed to practice neutral imagery in which they visualized what they usually did between finishing work and going to bed with no other specific instructions. Participants in all conditions received a set of laminated, written instructions of their imagery task as well as audiotaped recordings of the instructions, and they were asked to complete the imagery tasks twice daily, at the end of work and just prior to going to bed, for the following 20 workdays (weekends were excluded). Participants were told to put these instructions next to their beds and practice the imagery task within half an hour of going to bed. Participants were thanked and debriefed upon study completion. Measures Participants completed online questionnaires at baseline and Day 21 (follow-up). The baseline questionnaire included measures of demographic and personal characteristics, the PSQI, pre-sleep arousal, frequency of negative sleep habits, and sleep planning. The Day 21 follow-up questionnaire included these measures (except demographic and personal characteristics) as well as measures of positive sleep actions, self-efficacy, sleep motivation, imagery vividness, and perceived quality of imagery instructions. Positive sleep actions were assessed only at follow-up because the measure targeted the implementation intentions behaviors and completing it at baseline could itself have triggered intentions to engage in these behaviors. At the beginning and end of the training session, participants completed measures of selfefficacy, sleep motivation, sleep planning and imagery vividness (post-session only). On Days 2 through 21, participants received email questionnaires each day at 8 a.m., in which they reported the times they completed the imagery exercise and imagery vividness the previous day, and they completed brief measures of sleep quality the previous night. Manipulation checks indicating the type of imagery instructions they used to assist with their practice (CD versus written) and vividness of the imagery were also completed daily. Unless otherwise noted, we generated measure scores by summing item ratings and assessed internal consistency using baseline data. Demographic and Personal Characteristics The baseline and screening questionnaire included items assessing age, gender, ethnicity, marital status, position within the organization, income, and presence of children as well as items assessing existing medical conditions, insomnia, restless legs syndrome, sleep apnea, and narcolepsy. It also included the Center for Epidemiological Studies in Depression Scale [27] to screen for high levels of depression. The short forms of the State Trait Anxiety Inventory [28] the Perceived Stress Scale [29] was used to evaluate equivalence in anxiety and perceived stress across the intervention conditions. Perceived Quality of Imagery Instructions To assess equivalence in perceived clarity and effectiveness of the imagery instructions across conditions, we adapted an imagery quality measure used in prior research [16]. Participants rated their agreement to eight statements (e.g., the instructions were easy to follow ) from 1 (strongly disagree) to 5 (strongly agree); α=0.78. Imagery Vividness and Use As manipulation checks, we assessed imagery contents and vividness with a measure adapted from the Vividness of Imagery Questionnaire [30] and used in prior research [16]. Participants rated the vividness of the following images: (1) putting things into a bag, (2) releasing a bag, (3) getting home from work, (4) relaxing at home, (5) their night-time routine, (6) the time of going to bed, (7) the environment of their bedroom, (8) the details of the bed they are sleeping in. Response options were: 1 (no image at all) 2(vague and dim), 3 (somewhat vivid), 4 (reasonably clear), and 5 (perfectly clear and vivid). The first two items comprised the arousal reduction imagery subscale (r=0.82) and the other six items comprised the implementation intentions imagery subscale (α=0.79). Each day, participants completed an imagery vividness item ( Please rate the vividness of the imagery you have experienced by highlighting the appropriate response below ), using the same scale with the additional response option of I did not do it, when using the recording and the written imagery instructions. The follow-up (Day 21) questionnaire included an item, How often did you practice the imagery without any instructions? (not at all to every day).
ann. behav. med. (2013) 46:260 272 265 Self-efficacy Participants rated their self-efficacy with two items [14]: How confident are you that you can take the actions necessary to get a good sleep tonight? and How confident are you that you will actually get a good sleep tonight? Ratings ranged from 1 (not at all confident) to 10(very confident); r=0.69. Sleep Motivation Participants rated their sleep motivation with the item [14]: How motivated are you to ensure that you get a good sleep tonight? Ratings ranged from 1 (not at all motivated) to 10 (very motivated). Sleep Planning We adapted the action planning measure developed by Luszczynska and Schwarzer [31] to assess sleep planning. The items included: I have made a detailed plan for: (1) how I am going to wind down before going to sleep; (2) how I am going to prepare for bed; (3) how I am going to prepare the place where I will sleep; and (4) the time when I go to sleep. Ratings ranged from 1 (not at all) to 7(very much); α =0.92. Positive Sleep Actions We used a purpose-built measure of positive sleep actions based on a measure used in an exercise intervention study [32] and recommendations on sleep hygiene [24]. The items assess actions targeted by the implementation intentions imagery: Each evening, how often did you: (1) change into comfortable clothes upon arriving home; (2) sit down and relax quietly for at least half an hour before bed; (3) carry out a bedtime routine; (4) have a set bedtime each night; (5) ensure that the bedroom was comfortable; and (6) make the bed as comfortable as possible. Ratings ranged from 1 (notatall)to7(every night); α=0.72. Negative Sleep Habits The Sleep Hygiene Index [24] was used to assess negative sleep habits. Respondents rate from 1 (never) to 5(all the time) how frequently they engage in 13 behaviors (e.g., I use my bed for things other than sleeping or sex like watching TV, reading, eating, or study ). Pre-sleep Arousal The Pre-Sleep Arousal Scale [33] includes an eight-item cognitive subscale (e.g., how often in the last week before bed have you reviewed or pondered events of the day? ) and an eight-item somatic subscale (e.g., how often in the last week before bed have you had a tight, tense feeling in your muscles? ). Ratings range from 1 (not at all)to 5(extremely); α=0.87. Sleep Quality The PSQI [22], administered at baseline and follow-up, includes 19 items assessing seven components: sleep quality, hours of sleep, sleep onset length, sleep efficiency, sleep disturbances, medication use, and daytime dysfunction. Item ratings are recoded and combined into component scores, each ranging from 0 (no difficulty) to 3(severe difficulty). Items addressed experiences for the past week. Component scores are summed into global scores ranging from 0 (no difficulty) to21(severe difficulties in all areas); α=0.87. Daily sleep quality was assessed with six PSQI items tailored to tap experiences on the previous night: time of lights out, time to sleep, hours of sleep, and time of waking (respondents recorded the actual times); awakenings during the night (yes/no); and sleep quality (the PSQI item is How was your sleep quality last night? with ratings ranging from 1=very restless to 10=very sound). Statistical Analyses We used SPSS version 15.0 for analysis of the baseline, post-session, and Day 21 data. Preliminary analyses confirmed that data met statistical assumptions. We used oneway ANOVAs and χ 2 analyses to determine a priori, intervention group differences in demographic, personal, and sleep variables. For manipulation checks, we tested Arousal Reduction and Implementation Intentions effects with between-subjects ANOVAs for instruction quality, arousal reduction imagery vividness, and implementation intentions imagery vividness; and repeated measures ANOVA for sleep planning. Repeated measures ANOVAs were used to assess the effects of Arousal Reduction and Implementation Intentions imagery on changes from baseline to Day 21 in self-efficacy, sleep motivation, sleep-related behavior, and sleep quality. We examined significant Intervention Time interactions with simple effects analyses testing changes over time within relevant intervention groups. We used SAS (version 9.1.3) and the PROC MIXED procedure [34] to conduct mixed-model analyses of the daily measures: sleep quality, time of lights out, time to sleep, total hours of sleep, time of waking, and waking during the night. Arousal Reduction, Implementation Intentions, and Arousal Reduction Implementation Intentions were treated as fixed effects, with Type III (Htype=3) as the hypothesis test. The model of time-specific effects
266 ann. behav. med. (2013) 46:260 272 assumed a first-order autoregressive process; compared with the alternative, unstructured method, the autoregressive process provided a better fit. Significant Arousal Reduction Implementation Intentions effects were further evaluated through mixed-model analyses with dummy variables comparing each of the three intervention groups with the control group. We also used mixed-model analyses to test whether Monday assessments (Days 4, 9, 15, and 20) showed any variation from the other day assessments given there had been a 2-day gap in practice over the weekend. Participants with more than 70 % of their daily data missing (n=7) were excluded from the mixed-model analyses. We conducted intention-to-treat analyses on the analyses of changes in sleep-related measures from baseline to the Day 21 follow-up to examine the effects of attrition on the patterns of findings, using the last observation carried forward technique for the five participants lost to follow-up. Results Randomization Success, Completion Rates, and Variations in Daily Responses Preliminary analyses confirmed that the four intervention groups did not differ in demographic characteristics or baseline levels of anxiety, depression, perceived stress, sleep efficacy, sleep motivation, negative sleep habits, pre-sleep arousal, or sleep difficulty, suggesting that randomization was successful. Of the 104 participants, 99 (95 %) completed the final assessments on Day 21. Five participants dropped out, four from the Arousal Reduction- Implementation Intentions condition and one from the Control condition. Responses to the daily email surveys indicated that 84.0 % of participants completed the imagery practice using the audio-recorded instructions on five or more days, with 70.1 % using the recording for over 50 % of the intervention period. Of the 30 % who did not use the recording for 50 % of the intervention period, 76 % reported on the follow-up survey that they typically did the practice from memory. Participants used the written instructions slightly more frequently, with 86.0 % doing so on five or more days and 76.4 % using them for over 50 % of the intervention period. Practice levels remained constant across the 21-days, with the exception that rates on Day 4 (46 %, a Monday) were lower than Day 1(78 %, a Wednesday; χ 2 =0.34, p<0.05), and Day 7 (a Thursday; 63 %, χ 2 =0.26, p<0.05). Mixed model analyses revealed that Monday assessments (Days 4, 9, 14 and 19) of sleep patterns, negative sleep habits, and imagery vividness were not significantly different from those on other days. Manipulation Checks The four intervention conditions reported comparably high levels of perceived quality of the imagery instructions (see Table 2). Analyses of the imagery vividness scores indicated that participants in the four intervention conditions used the intended imagery contents. At post-session, Arousal Reduction groups (compared to the other two groups) reported greater vividness of arousal reduction imagery, F(3,94) = 144.62, p<0.001, η 2 p ¼ 0:59; and lower vividness of implementation intentions imagery, F(1,100)=36.90, p<0.001, η 2 p ¼ 0:27. Conversely, the Implementation Intentions groups (compared to the other two groups) reported greater vividness of implementation intentions imagery, F(1,100) = 103.08, p<0.001, η 2 p ¼ 0:51 ; and the Implementation Intentions task had no effect on the vividness of arousal reduction imagery; F(1,100) =0.21, ns. At follow-up, the Arousal Reduction groups (compared to the other two groups) also reported greater vividness of arousal reduction imagery; Welch s F(3,94)=121.30, p<0.001, η 2 p ¼ 0:59 and lower vividness of implementation intentions imagery; F(1,94)=9.74, p<0.01, η 2 p ¼ 0:09. Conversely, the Implementation Intentions groups reported greater vividness of implementation intentions imagery, F(1,94)=50.90, p< 0.001, η 2 p ¼ 0:35, but not arousal reduction imagery; F(1,94) =2.87, ns. Finally, repeated measures ANOVAs revealed that the Implementation Intentions imagery induced greater increases in sleep planning as intended. Sleep planning generally increased for all groups from baseline (M=11.62, SD=5.29) to post-session (M=14.81, SD=6.34) and follow-up (M=14.28, SD=6.79); Time effect F(1,92)= 24.63, η 2 p ¼ 0:35, p<0.001. An Implementation Intentions Time effect confirmed that the increase in sleep planning was greater for the two Implementation Intentions groups than for the other two groups; F(1,92)=4.46,, p< 0.05, η 2 p ¼ 0:09. The Implementation Intentions groups showed relatively greater increases in sleep planning from baseline to post-session (p<0.01), and they maintained high sleep planning levels from post-session to follow-up (p= 0.71, ns). In contrast, the Control group showed an increase from baseline to post-session (p<0.05) with a slight decrease in sleep planning from post-session to follow-up (p=0.21, ns) and the Arousal Reduction group showed no changes across the assessment points. Imagery Intervention Effects on Self-Efficacy and Sleep Motivations Analyses of self-efficacy scores (see Table 3) revealed that, as predicted, both the Implementation Intentions imagery and the Arousal Reduction imagery induced greater increases in self-efficacy over time; Implementation Intentions Time
ann. behav. med. (2013) 46:260 272 267 Table 2 Imagery intervention group means (SDs) for manipulation check measures of instruction quality, imagery vividness, and sleep planning Measure Range Control Arousal reduction Implementation intentions Arousal reduction/ implementation intentions M (SD) M (SD) M (SD) M (SD) Quality of instructions 31 60 38.54 (5.12) 39.21 (4.79) 40.96 (4.24) 39.45 (5.54) Arousal reduction imagery vividness Session a,b 3 15 3.25 (1.32) 9.30 (3.29) 3.69 (2.51) 9.35 (3.58) Follow-up a,b 2 10 2.00 (0.00) 5.50 (2.08) 2.31 (0.93) 6.29 (2.33) Implementation Intentions Imagery Vividness Session a,b 7 35 22.11 (5.55) 8.59 (5.69) 24.00 (3.91) 25.43 (3.49) Follow-up a,b 7 35 16.68 (5.06) 9.12 (5.76) 20.96 (5.56) 21.00 (5.75) Sleep planning c Baseline 4 24 12.12 (5.28) 10.24 (4.59) 12.76 (6.13) 11.91 (5.15) Session 4 28 14.72 (6.18) 11.64 (5.42) 16.52 (5.04) 17.82 (7.21) Follow-up 4 28 12.96 (5.97) 11.76 (6.09) 16.96 (6.28) 15.59 (7.91) The Welch s test is reported for Arousal Reduction imagery vividness at follow-up based on a significant Levene s test (p<0.001) a Arousal Reduction main effect b Implementation Intentions main effect c Implementations Intentions Time effect Table 3 Imagery intervention group means (SDs) for sleep variables Measure Range Control Arousal reduction Implementation intentions Arousal reduction/ implementation intentions M (SD) M (SD) M (SD) M (SD) Sleep self-efficacy a,b Pre-session 2 20 9.92 (3.56) 11.81 (3.89) 9.92 (3.97) 11.68 (3.80) Post-session 2 20 11.32 (3.33) 12.81 (3.93) 11.62 (3.84) 14.68 (3.48) Follow-up 4 20 12.04 (3.96) 11.42 (4.55) 13.27 (3.24) 13.91 (3.79) Sleep motivation c Pre-session 1 10 6.56 (2.65) 7.17 (1.59) 7.00 (2.65) 7.38 (2.06) Post-session 1 10 7.24 (2.31) 8.13 (1.49) 8.31 (1.52) 8.14 (1.88) Follow-up 5 10 7.64 (1.22) 7.83 (1.61) 7.92 (1.26) 8.43 (1.36) Positive sleep actions Follow-up d 6 42 28.32 (6.90) 24.27 (7.29) 30.81 (6.37) 31.82 (6.38) Negative sleep habits b Baseline 14 46 31.70 (7.59) 31.85 (6.48) 31.69 (6.98) 33.67 (4.61) Follow-up 14 50 32.57 (6.08) 32.27 (5.73) 29.96 (7.51) 30.05 (5.13) Pre-sleep arousal c Baseline 16 61 34.00 (11.19) 36.03 (8.91) 32.96 (8.24) 35.18 (11.40) Follow-up 16 52 30.25 (9.89) 30.15 (10.35) 26.96 (7.80) 27.23 (8.20) PSQI total c Baseline 5 15 7.71 (2.65) 8.19 (3.45) 7.69 (2.45) 8.18 (2.70) Follow-up 1 15 5.88 (2.17) 6.42 (3.29) 6.15 (2.89) 5.91 (3.53) a Arousal Reduction Time effect b Implementation Intentions Time effect c Time effect d Implementation Intentions main effect
268 ann. behav. med. (2013) 46:260 272 effect F(1,95)=3.16, η 2 p ¼ 0:06, p<0.05; Arousal Reduction Time effect F(1,94)=3.95, η 2 p ¼ 0:08, p<0.05. Simple effects analyses revealed that the Implementation Intentions imagery led to increases in sleep self-efficacy from presession to post-session (p<0.05) and from post-session to follow-up (p<0.05). The Arousal Reduction imagery induced increases in self-efficacy from pre-session to postsession (p<0.05) that exhibited a trend towards decreasing at follow-up (p=0.08, ns). All four intervention groups exhibited increases in sleep motivation from pre-session to post-session and follow-up; Time F(1,91)=14.16, η 2 p ¼ 0:14, p<0.001. Contrary to predictions, neither Implementation Intentions nor Arousal Reduction imagery led to greater increases in motivation relative to the Control imagery. Imagery Intervention Effects on Sleep Behaviors For positive sleep actions at follow-up (see Table 3), the Implementation Intentions groups reported more positive actions than did the Arousal Reduction and Control groups; F(1,95)=13.58, η 2 p ¼ 0:13, p<0.001. The Arousal Reduction main and interaction effects were not significant (F s<1.63, ns). For negative sleep habit frequency, analyses revealed an Implementation Intentions Time effect; F(1,92)=4.89, η 2 p ¼ 0:05, p<0.05 such that the Implementation Intentions groups reduced their negative sleep habit frequency from baseline to follow-up assessment. Arousal Reduction imagery did not affect negative sleep habit frequency over time. Taken together, these analyses indicate that the Implementation Intentions imagery increased the use of positive sleep actions and reduced the frequency of negative sleep habits whereas the Arousal Reduction imagery did not influence these sleep behaviors. Imagery Intervention Effects on Pre-Sleep Arousal and Sleep Quality at Follow-up We predicted that both Arousal Reduction and Implementation Intentions imagery would lead to decreases in pre-sleep arousal over time, with Arousal Reduction having the greatest impact. Analyses revealed overall decreases in pre-sleep arousal from baseline to follow-up (see Table 3); Time main effect F(1,94)=36.13, η 2 p ¼ 0:28, p<0.001. Contrary to predictions, these decreases did not vary by Arousal Reduction or Implementation Intentions. Analyses of changes in total PSQI scores over time revealed a general decrease in scores from baseline to follow-up, indicating that all groups experienced reductions in sleep difficulty over time; Time main effect F(1,94)= 30.73, η 2 p ¼ 0:25, p<0.001. Contrary to predictions, no Arousal Reduction or Implementation Intentions effects emerged for these scores. Further analyses of the PSQI component scores revealed significant Time effects for several components. Overall, sleep quality ratings increased from baseline (M=1.18, SD=0.52) to follow-up (M=1.64, SD=0.65),F(1,94)=46.42, η 2 p ¼ 0:33, p<0.001. Total hours of sleep also increased from baseline (M=6.40, SD=1.01) to follow-up (M=6.78, SD=0.95); F(1,94)=14.84, η 2 p ¼ 0:14.14, p<0.001. Reports of time to sleep generally decreased from baseline (M= 31.59, SD=25.56) to follow-up (M=22.23, SD=20.83); F(1,94)=13.71, η 2 p ¼ 0:08, p<0.001. No Arousal Reduction Time or Implementation Intentions Time interaction effects emerged in any of these PSQI component analyses, with one exception: For time of lights out, an Implementation Intentions Time interaction revealed that the Implementation Intentions imagery led to earlier times of lights out at follow-up (M=10.54, SD=0.90) relative to baseline (M=11.11, SD=1.37) whereas the Arousal Reduction and Control imagery did not (at baseline, M=10.74, SD=1.03; at follow-up, M=10.79, SD=0.74); F(1,96)=7.30, η 2 p ¼ 0:07, p<0.01. Arousal Reduction and Implementation Intentions Effects on Daily Reports of Sleep Patterns Table 4 presents the results of the mixed-model analyses evaluating the Arousal Reduction and Implementation Intentions imagery effects on daily reports of sleep patterns. For sleep quality, an Implementation Intentions effect indicated that the two implementation intentions groups reported higher sleep quality relative to the arousal reduction and control groups over the 21-day period. For time to sleep, significant Implementation Intentions and Arousal Reduction Implementation Intentions effects emerged. Further mixed-model analyses evaluating these effects revealed that the Arousal Reduction group reported longer times to sleep relative to the Control group (t=3.42, p<0.001). In contrast, the Arousal Reduction/Implementation Intentions group reported shorter times to sleep relative to the Control group (t= 2.70, p<0.01). The Implementation Intentions group did not differ from the Control Group (t=1.18, ns). These results suggest that the interaction was generated by the Arousal Reduction only group having a longer time to sleep whereas the Implementation Intentions imagery mitigated these effects. Finally, an Implementation Intentions effect on frequency of waking during the night indicated that the two Implementation Intentions groups reported lower frequencies relative to the Arousal Reduction and Control groups. No Arousal Reduction or Implementation Intentions effects for daily measures of time of lights out and time of waking emerged. In summary, analyses of the daily data provide support that the implementation intentions imagery improves sleep quality, reduces the time taken to get to sleep when combined
ann. behav. med. (2013) 46:260 272 269 Table 4 Mixed model analyses assessing daily intervention effects on daily sleep measures Sleep outcome Arousal reduction Implementation intentions Arousal reduction/implementation intentions Est. (SE) t Est. (SE) t Est. (SE) t [CI] [CI] [CI] Sleep quality a 0.18 (0.19) 0.93 0.43 (0.21) 2.09* 0.27 (0.29) 0.93 [ 0.20, 0.55] [ 0.03, 0.84] [ 0.83, 0.30] Time of lights out b 0.05 (0.10) 0.54 0.14 (0.11) 1.31 0.15 (0.15) 1.05 [ 25, 0.14] [ 0.07, 0.35] [ 0.44, 0.14] Time to sleep c 2.46 (2.77) 0.89 7.38 (2.99) 2.47* 10.59 (4.16) 2.55* [ 2.99, 7.91] [ 13.26, 1.51] [2.41, 18.77] Hours of sleep a 0.25 (0.36) 0.70 0.41 (0.38) 1.09 0.06 (0.53) 0.12 [ 0.96, 0.46] [ 1.16, 0.33] [ 0.98, 1.11] Time of waking d 0.04 (0.11) 0.38 0.09 (0.12) 0.70 0.22 (0.17) 1.27 [ 0.18, 0.26] [ 0.33, 0.16] [ 0.55, 0.12] Waking during night e 0.01 (0.04) 0.23 0.10 (0.05) 2.32* 0.06 (0.06) 1.01 [ 0.09, 0.07] [ 0.19, 0.02] [ 0.06, 0.19] Analyses include observations from 93 employees over 20 days with 10 22 observations missing a Observations=1,379 b Observations=1,386 c Observations=1,380 d Observations=1,377 e Observations=1,389 *p<0.05 with arousal reduction imagery, and reduces the frequency of waking during the night. In contrast, these analyses did not provide evidence that the arousal reduction imagery on its own has beneficial effects on sleep experiences. Intention-to-Treat Analyses Intention-to-treat analyses on changes over time in the baseline, post-session, and follow-up (Day 21) measures revealed comparable patterns of effects in all analyses. Discussion This randomized, controlled trial provides evidence that an implementation intentions imagery technique can improve sleep-related behavior and, in turn, promote better sleep patterns in a population of daytime employees reporting sleep difficulties. Use of the implementation intentions imagery (compared to arousal reduction or neutral imagery) led to greater improvements in self-efficacy, sleep planning, and positive sleep actions as well as greater reductions in negative sleep habits over the four weeks (encompassing 21 workdays). Moreover, use of the implementation intentions imagery led to relatively greater improvements in sleep quality, shorter times to fall asleep, and fewer awakenings at night as reported daily during the trial. These findings add further support to self-regulation theory and research that implementation intentions play a critical role in instilling health habits [35, 36] and they extend the current literature by demonstrating their role in changing sleep habits. To our knowledge, this trial is the first to test the use of this form of mental simulation technique to alter sleeprelated behavior. The study extends prior research demonstrating that daily use of an implementation intentions imagery task can facilitate self-regulation, such as by enabling individuals to improve exercise habits and fruit consumption [36] and reduce the intensity of food cravings [37]. This trial also provided a further demonstration of the potential in harnessing the effect of implementation intentions through imagery [38]. The implementation intentions imagery emphasized visualizing the appropriate time to go to bed and on organizing real sleep-related behaviors accordingly. Given that all four imagery tasks increased sleep motivations and both the implementations and arousal reduction imagery enhanced sleep self-efficacy, the implementation intentions imagery s relatively greater effects on sleep selfregulation appear to be primarily attributable to its impact on volitional stage processes rather than on motivational stage processes. It is notable that the implementation intentions technique reduced the frequency of negative sleep habits relative to the arousal reduction and control techniques even though it did not directly target these behaviors. The technique was designed to focus on positive behaviors
270 ann. behav. med. (2013) 46:260 272 rather than on their opposing negative behaviors, as giving instructions to not engage in a behavior can elicit ironic processes that can motivate impulses to engage in that very action [25, 26]. The positive sleep habits are likely to have replaced and thus reduced the negative sleep habits. Use of the arousal reduction imagery was unsuccessful in reducing pre-sleep arousal and it did not lead to greater improvements in sleep quality or behaviors relative to the use of neutral imagery about one s typical sleep behaviors. Guided imagery for inducing relaxation is often used to improve sleep among people with insomnia [39], but it may be less successful in improving sleep for individuals getting insufficient sleep due to behavioral choices and activities. All imagery conditions reported improvements in sleep motivation, pre-sleep arousal, sleep planning, and sleep difficulties over the trial. These improvements could be due to attention effects and increased attentiveness to sleep behavior due to study participation. Alternatively, they could reflect regression to the mean, since all participants were selected on the basis of having sleep difficulties. They also may be due to the daily reporting of sleep quality given that self-monitoring can be a powerful instigator of behavior change [40]. The relatively greater benefits provided by the implementation intentions imagery supports its efficacy relative to comparison treatments [41] and the promise of this technique over self-monitoring alone. The implementation intentions technique used a modified format of the typical if then statements used in other studies, in that the statements specified that when it is a certain time and one is in a specific situation, then one is engaging in a specific behavior. It was found through pilottesting that this format (relative to if then statements) made more sense to participants while maintaining focus on enhancing the precision and accessibility of mental plans when encountering situational cues. The implementation intentions imagery technique is conceptually similar to process simulations (mental simulations of action plans) used to promote behaviors [42], including health behaviors such as physical activity [16]. Both in turn are conceptually similar to the concept of action plan formulation as delineated by the Common-Sense Model of Self-Regulation [16]. These research areas converge to provide theoretical and empirical support for the use of mental simulations of desired actions in specific situations. The analyses revealed significant effects of the implementation intentions imagery on the daily sleep measures but not on changes in reports of sleep quality over the past week from baseline to follow-up on Day 21. It is likely that the weekly reports are more susceptible to biases in recall, and that the daily reports are more accurate and sensitive to the intervention effects [43]. The high retention rate (95 %) may be attributed to several factors. First, potential participants were required to attend an initial training session. The effort required to attend this initial session was such that only those who were motivated and likely to commit to study participation would be included at the study s onset. Second, the daily assessments, which were conducted via a short email questionnaire, were designed to be highly convenient and require minimal time and interference with daily activities. Third, the interventions required minimal time and could be implemented easily into the course of one s daily routine. Some study limitations warrant comment. The relatively small sample of New Zealanders, two thirds of whom were women, limits the potential generalizability of the findings to men and groups in other cultural settings. The promising effects of the implementation intentions imagery on sleeprelated behavior supports further research efforts testing this technique within a wider population. Further studies utilizing alternative designs and larger samples can directly test for mediational relationships delineated by the sleep selfregulation theoretical framework. Specifically, the implementation intentions technique is expected to have direct effects on sleep behaviors which, in turn, are expected to improve sleep quality and sleep self-efficacy. The multiple analyses increased the risk of Type 1 error; however, the consistent patterns of findings across the analyses increase confidence in the observed intervention effects. Daily use of the imagery tasks was also variable. Adherence issues have been noted in studies testing sleep interventions [44, 45], although the problem in this study was reduced due to the use of daily prompts and take-home instructions. In future research, booster sessions might help individuals to maintain improvements in sleep over the longer term. The sample consisted of individuals with behaviorally induced insufficient sleep, which can be considered a pre-cursor to insomnia (although insomnia also involves difficulty initiating or maintaining sleep despite ample opportunity). Although diagnosed insomnia was a criterion for exclusion, it is possible that some participants had undiagnosed insomnia, particularly since insomnia is largely undiagnosed in the general population [46]. Participants with insomnia may have been less responsive to the intervention manipulations than individuals who were sleep deprived through lifestyle choices [3]. Further research should compare the intervention effects for individuals with and without insomnia. To conclude, this study contributes to self-regulation theory and research through the development and testing of theory-guided interventions targeting sleep-related behavior. Continuing research in this area will address the need to improve sleep in the general population and advance