ann. behav. med. DOI 10.1007/s12160-014-9668-x
ORIGINAL ARTICLE
Randomized Test of an Implementation Intention-Based Tool to Reduce Stress-Induced Eating Daryl B. O’Connor, PhD & Christopher J. Armitage, PhD & Eamonn Ferguson, PhD
# The Society of Behavioral Medicine 2014
Abstract Background Stress may indirectly contribute to disease (e.g. cardiovascular disease, cancer) by producing deleterious changes to diet. Purpose The purpose of this study was to test the effectiveness of a stress management support (SMS) tool to reduce stress-related unhealthy snacking and to promote stressrelated healthy snacking. Methods Participants were randomized to complete a SMS tool with instruction to link stressful situations with healthy snack alternatives (experimental) or a SMS tool without a linking instruction (control). On-line daily reports of stressors and snacking were completed for 7 days. Results Daily stressors were associated with unhealthy snack consumption in the control condition but not in the experimental condition. Participants highly motivated towards healthy eating consumed a greater number of healthy snacks in the experimental condition on stressful days compared to participants in the experimental condition with low and mean levels of motivation. Conclusions This tool is an effective, theory driven, intervention that helps to protect against stress-induced high-calorie snack consumption. Keywords Eating . Stress . Intervention . Stress management . Obesity . Daily diary . Volitional help sheet D. B. O’Connor (*) School of Psychology, University of Leeds, Leeds, UK e-mail:
[email protected] C. J. Armitage Manchester Centre for Health Psychology, School of Psychological Sciences, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK E. Ferguson School of Psychology, University of Nottingham, Nottingham, UK
Introduction There is growing evidence that stress affects health directly, through autonomic and neuroendocrine responses, but also indirectly, through modifications to health behaviours [1]. Research shows that high levels of stress can be associated with increased food intake (e.g. saturated fat consumption), decreased food intake (e.g. overall calories) or no change in food intake [2–4]. However, it has been suggested that stress contributes to diseases like cardiovascular disease and obesity risk to the extent that it produces deleterious changes in diet and helps maintain unhealthy eating behaviours [5, 6]. Recent findings have confirmed that stress is frequently associated with increased unhealthy food intake in laboratorybased and naturalistic studies (e.g. [3, 7–10]). For example, in a 28-day diary study, O’Connor et al. [9] showed that daily stressors were associated with increased consumption of highfat and high-sugar between-meal snack foods and with a reduction in main meals and vegetable consumption. In a large-scale prospective investigation of employees, distressinduced eating (but not lifestyle behaviours) was found to predict weight gain at follow-up [11]. Taken together, these results are cause for concern as an overwhelming body of research has demonstrated the importance of maintaining a balanced diet, including eating a low-fat diet and five portions of fruit and vegetables a day, for good health (e.g. [12–14]). Therefore, the central aim of the current study was to test the effectiveness of a brief psychological intervention aimed at reducing stress-related unhealthy eating and promoting stressrelated healthy eating. Numerous stress management interventions have been tested with a view to preventing or reducing stress [15, 16]. Many of these approaches have involved emotion-focused techniques (e.g. progressive muscle relaxation) and/or problem-focused techniques (e.g. time management, assertiveness training) aimed at eliminating stressors from one’s environment or to
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minimize the psychological and physiological responses afterwards. However, to our knowledge, no stress management interventions have been developed that help individuals identify stressful situations that trigger maladaptive health behaviour changes and help facilitate a more adaptive behavioural response. In addition, existing stress management interventions are relatively time-consuming, expensive and yield mixed results [15]. Therefore, within this context, we developed a low cost, easy to administer stress management support (SMS) tool that was directly informed by two recent studies [17, 18]. The first was Armitage’s [17] Volitional Help Sheet (VHS), which is a tool that helps people to form implementation intentions [19, 20]. Implementation intentions promote health behaviour change by asking people to link in memory a critical situation (if) with an appropriate response (then). In Armitage’s [17] study, smokers in the VHS condition were asked to link temptations to smoke (from a list of options, e.g. “If I am tempted to smoke when things are not going the way I want and I am frustrated”) with appropriate responses (e.g. “then I will tell myself I can quit if I want to”) derived from Prochaska and DiClemente’s [21] transtheoretical model. The results of this study found significantly higher levels of quitting in the VHS condition compared to the control condition. Subsequently, Armitage and colleagues have demonstrated the effectiveness of the VHS for other health behaviours (e.g. physical activity, alcohol consumption and weight loss [22–24]). In parallel, Adriaanse et al. [18] have been using implementation intentions to promote healthy eating and to reduce unhealthy food intake. In a series of experiments, these authors tested whether implementation intentions can be used to replace unhealthy snacks with healthy snack alternatives by linking critical cues for unhealthy snacking (if) to healthy snacks (then). They also explored the effects of different types of critical cues in relation to the undesirable behaviour and differentiated between situational (where/when) and motivational (why) cues. Moreover, in order to make the intervention more personally relevant, the participants were permitted to choose the critical cue (from a list of six motivational or six situational cues, depending on condition) that was most often associated with their unhealthy snacking. The results showed that only implementation intentions that specified a motivational cue (e.g. feeling bored, enjoyment, to be social) and not a situational cue (e.g. watching television, at work) were effective in decreasing unhealthy snacking behaviour. These findings are important because they indicate that specifying traditional situational cues, which are often used in implementation intentions research, are not necessarily effective in breaking habits in relation to complex behaviours such as unhealthy snacking. Instead, for snacking behaviour, these data suggest that it may be more important to understand why the behaviour is being performed rather than just when and where it is performed [18].
It has been shown that stress-induced or emotional eating is an automatic response to negative emotions (e.g. stress) that leads to overconsumption [25] irrespective of feelings of hunger [26]. In particular, evidence suggests that stress promotes glucocorticoid-induced and insulin-delineated palatable food intake that leads to the formation of strong associations between “feeling stressed” and “feeling better” following consumption of “comfort foods” [3]. In other words, people who are stress-induced eaters learn to cope with stress by unhealthy snacking, which alleviates the negative emotions associated with stressful encounters and do not necessarily directly deal with the stressor [3]. Moreover, Dallman [3] has argued that reinforced associations become automatic habits with little conscious recognition. In addition, work by Schwabe and Wolf [27] has demonstrated that stress promotes habits at the expense of other goal-directed performance. Therefore, consistent with this work, as outlined earlier, stress has been shown to exert an automatic-like effect on unhealthy eating behaviour, in particular in triggering the consumption of high-fat/sugar between-meal snacks (e.g. [2, 3, 7, 28]). Given that implementation intentions can be used strategically to overcome such automatic influences on behaviour [18], they represent a promising tool with which to help people to overcome these responses. Surprisingly, stress as a trigger for unhealthy eating has not been examined in the context of implementation intentions. To our mind, stress is best conceptualized as a motivational cue (similar to feeling bored for example) for action. That is, individuals are motivated to reduce the negative feelings such as annoyance, irritation, worry or frustration associated with stress. Coping is a mechanism to do this, and engaging in unhealthy snacking is one such coping option. Within this context, stress helps explain why a large number of people turn to highly palatable, energy dense comfort foods at different times. In the current study, we tested the effectiveness of a SMS tool for eating behaviour that combined and extended the approaches adopted by Armitage [17] and Adriaanse et al. [18]. The most important difference related to allowing participants to generate their own personally relevant stressful triggers for unhealthy eating. Adriaanse et al. [18] permitted participants to choose their critical cue from a predefined list of motivational and/or situational cues (depending on condition). However, given the vast number of potential stressful triggers and individual differences in stress appraisals, the participants were best placed to generate their own stress-related motivational cues. In addition, the quality of control groups has also been recently identified as an important issue in implementation intentions research in this context, with active control groups, in which the instructions, feedback and time spent related to the behaviour is held constant across the conditions, being considered the gold standard [17, 29, 30]. Therefore, we randomized participants to complete a SMS tool with explicit
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instruction to link self-generated (personally relevant) stressful situations that trigger unhealthy snacking (if) with healthy snack alternatives (then) or to complete a SMS tool without the linking instruction (active control condition). Recent findings in the stress and eating field have highlighted the importance of incorporating daily diary approaches because (i) a great deal of stress stems from recurrent day-to-day problems known as daily stressors or hassles (e.g. [2, 31]) and (ii) stress is a process that emerges and changes over time [32]. Indeed, important early work by Kanner and colleagues [32] argued that it is “day-to-day events that ultimately have proximal significance for health outcomes and whose accumulative impact…. should be assessed” (p. 3). Therefore, with this in mind, we used (on-line) open-ended diaries as they allow respondents to record day-to-day minor life events or hassles that are part of everyday life. Daily stressors/hassles are events, thoughts or situations which, when they occur, produce negative feelings such as annoyance, irritation, worry or frustration and/or make you aware that your goals and plans will be more difficult or impossible to achieve (see [9, 33]). Similarly, we employed a free response format to measure daily consumption of between-meal snacks. This approach has been utilized successfully in previous research and allowed participants to list the snacks they had consumed at the end of each day together with their perceptions of snacking behaviour [9, 33]. Moreover, the assessment of stressors and between-meal snacking repeatedly in this way, using a daily diary format, allowed us to use multilevel modelling techniques to model the day-to-day withinperson effects together with the impact of the intervention. Finally, the current study was also interested in exploring the extent to which participants were motivated to eat more healthily might moderate the effectiveness of the SMS tool intervention. The main reasons for this were (i) levels of motivation to eat healthily are likely to vary considerably in the general population, (ii) there is evidence that implementation intentions are more successful when accompanied by strong intentions (motivation) to engage in the behaviour of interest [19, 20, 34] and (iii) the importance of individual differences in helping us to understand when interventions work and for whom has recently been highlighted [35]. To summarize, this study, using a randomized controlled, daily diary design, investigated the effectiveness of a stress management support tool to reduce stress-related unhealthy snacking and to promote stress-related healthy snacking. Specifically, the following hypotheses were tested: H1
H2
The association between daily stressors and unhealthy snacking will be weaker in the experimental condition than in the active control condition Daily stressors will show stronger associations with healthy snacking in participants in the experimental condition than the active control condition
H3
Motivation to eat healthily will moderate the effectiveness of the intervention on the daily stressors, between-meal snacking relationship, such that those in the experimental condition who are highly motivated will be more likely to eat (a) more healthy snacks and (b) less unhealthy snacks when stressed.
Method Design and Participants Two hundred and nineteen participants were randomized to complete a SMS tool with explicit instruction to link stressful situations with healthy snack alternatives (experimental condition) or a SMS tool without a linking instruction (active control condition). Participants were recruited from a large university in the UK via poster, flyer and emails. Eligible participants were required to be at least 18 years old and to understand English. On-line daily diary reports of stressors and unhealthy and healthy snacks were completed for 7 days post-intervention. An interval-contingent method was employed, where the participants completed their diary at the end of each day. To increase protocol adherence, participants received a reminder email each afternoon with a link to their on-line diary. Diary entries could only be made between 9 pm and 2 am in order to reduce backfilling, and participants were informed that all entries were date and time stamped. Thirteen participants failed to provide adequate baseline information to allow for the matching of their level 1 and level 2 data files (i.e. inputted an incorrect identification code), and four participants failed to complete any daily diary entries. The dropout/discontinuation rates were similar in both conditions (see Fig. 1). The final sample consisted of 107 (78.5 % female) and 95 (77.9 % female) participants in the experimental and control conditions, respectively (mean age = 22.96 years [18–60 years], mean BMI=22.88, 85 % college students and 94 % Caucasian) and yielded 1060 person days. Following procedures utilized previously [9], days within diaries that contained missing data were removed from the dataset. Complete data was missing for 354 person days because participants failed to record a response or because they were ‘locked out’ of the time window for completing each daily diary. Importantly, the proportion of missing data was similar across conditions (n=185 [24.7 %] in the experimental condition and n=172 [25.9 %] in the active control condition). In addition, Little’s MCAR test confirmed that these data were missing at random, χ2 (23, n=1414)=30.68, ns. Students received course ‘participation credits’ for taking part, and the study received ethical approval from the University Department Research Ethics Committee.
ann. behav. med. Fig. 1 Flow diagram of participant progress throughout the phases of the study
Enquired about study, assessed for eligibility (n= 235)
Declined participation (n= 16) - Lack of time (n= 4) - No reason offered (n= 12)
Joined study, randomized (n= 219)
Experimental Condition (n= 116)
Completed baseline (n= 107) Incorrect ID (n = 7) Completed no diaries (n= 2)
Completed study (n= 107)
Measures Initial Questionnaire Participants were asked to complete an initial demographics questionnaire (age, gender, height, weight, occupation and ethnicity). They were also asked to rate “How motivated are you to eat more healthily?” on a scale extending from 1 (not at all) to 7 (extremely). This measure of motivation has been used successfully by Adriaanse et al. [18]. On-line Daily Diary In each daily diary, participants were requested—using free responses—to report each stressor or hassle experienced. Daily stressors and between-meal snacks were defined and examples provided at the start of each on-line diary. Daily stressors were defined as events, thoughts or situations which, when they occur, produce negative feelings such as annoyance,
Active Control Condition (n= 103)
Completed baseline (n= 95) Incorrect ID (n =6) Completed no diaries (n= 2)
Completed study (n= 95)
irritation, worry or frustration and/or make you aware that your goals and plans will be more difficult or impossible to achieve [9, 33]. Similar procedures have been used successfully by O’Connor and colleagues [9, 36]. Daily between-meal snack consumption was measured in two ways. First, using free responses, participants were asked to list each food eaten between meals on each day. Each between-meal snack was categorized as being an unhealthy snack (i.e. high in fat and/or high in sugar) or a healthy snack (i.e. apple, banana, carrot) based upon validated food composition tables [37]. The coding was conducted by two trained individuals and resulted in very good inter-rater reliability with all Kappas above 0.80 [38]. The second means by which daily between-meal snack consumption was measured involved eliciting participants’ perceptions of snack intake by asking them to rate: “To what extent have you eaten HEALTHY snacks today? (e.g., apple, banana, dried fruit)” and then “To what extent have you eaten UNHEALTHY
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snacks today? (e.g. chocolate, crisps, cakes)” on a seven-point scale extending from 1 (not at all) to 7 (very much). This more general measure of perceived snack intake was included to provide an additional test of the effectiveness of the intervention and to allow us to corroborate any observed effects for reported healthy and unhealthy snack intake. Stress Management Support (SMS) Tool The development of the SMS tool was informed by the approaches adopted by Armitage [17] and Adriaanse et al. [18] and was pen and paper based. Participants were provided with background information about the relationship between stress, unhealthy between-meal snacking and health followed by general information on the usefulness of planning. Definitions and examples of both daily stressors and between-meal snacks were also provided. Next, all participants were required to complete steps 1 and 2 of the SMS tool. In step 1, participants received the following instruction “In the box below (left hand column), please briefly describe up to 5 stressful situations that make you experience negative emotions (e.g. feeling sad, frustrated, irritated, worried) and cause you to eat unhealthy snacks (such as chocolate, crisps, cakes). In step 2, “For each of your stressful situations, please choose a healthy snack alternative you could eat. Remember to pick a snack that you really like and that would be usually available in each particular stressful situation. Once chosen please enter it in the right hand column”. Finally, only participants in the experimental condition completed step 3. These participants received the following instruction “Research has shown that plans will be most effective when you picture the specific stressful situation in your mind and LINK each stressful situation with your healthy snack choice.1 Therefore, please (i) DRAW a line linking each ‘stressful situation’ and ‘healthy snack choice’; (ii) VISUALIZE yourself acting out each of your plans to eat healthier when you are stressed.”
Healthier When Stressed”. Allocation concealment was achieved by stapling information sheets to the SMS tool of each condition in advance, then shuffling them before placing them face down on the computer desks. This was done to guarantee blindness of conditions to both researchers and participants. The demographics questionnaire was completed on-line after the participants signed informed consent forms. Upon completion, participants turned over, read their randomly allocated SMS tool covering sheet and then completed steps 1 and 2 (both conditions) and step 3 (experimental condition only). Starting the next day, participants were instructed to complete their online diary shortly before going to bed for the following 7 days. Data Analysis The data were analyzed utilizing multi-level modelling [39] and contained a two-level hierarchical structure, level 1 being the within-person variation (i.e. daily stressors, between-meal snacking) and level 2 being the between-person variability (i.e. SMS condition, motivation to eat healthily). The level 1 variables were group mean centred, the level 2 motivation to eat healthily was grand mean centred and the dichotomous experimental condition variable was uncentered [40, 41]. The data were analysed in two blocks. First, to test the cross-level effects of experimental condition, we modelled the day-to-day within-person effects of daily stressors on eating behaviours together with the impact of condition. Second, statistically significant cross-level effects were decomposed using simple slopes as recommended by Preacher, Curran and Bauer [42]. The general form of the cross-level models is expressed by the following equation: Eating behavior ¼ β 00 þ β 01 ðexperimental conditionÞ þ β 10 daily stressors þ β 11 experimental condition* daily stressors þ r0 þ r1 daily stressors þ ε
Procedure Baseline testing was conducted in group sessions in a controlled laboratory setting where each participant had their own computer. Privacy was established with the use of partitions. Participants were told the study was related to “Eating 1
Research to date suggests that the means by which implementation intentions are formed are less important than the if-then formulation. For example, Armitage (2009) showed that although implementation intentions were effective in reducing alcohol consumption, there was no difference between implementation intentions formed by participants with no support versus participants who recited implementation intentions that had been provided by researchers. Similarly, Armitage and Arden [23] found that implementation intentions formed using a volitional help sheet (i.e. drawing lines between critical situations and appropriate responses) were equally as effective at reducing alcohol consumption as were self-generated implementation intentions.
The models fitted are Poisson (see below), so β 00 (intercept) is the log of the event (eating behaviour) rate, β01 indicates the extent to which this is influenced by experimental condition; β10 indicates the average size of the relationship between daily stressors and eating behaviour, β11 the extent to which that relationship is moderated by (or conditional on) experimental condition, r0 =is the error term associated with the intercept and accounts for variation around the mean, r1 = is the error term associated with the slope and accounts for variation around the slope and ε=the error term. As the outcome variable represents a count (number of healthy or unhealthy snacks), these data were analysed using a Poisson link function [43]. The model uses a log link
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function when the level-1 sampling model is Poisson. Thus, an event rate (average snacks consumed) of one has a log of zero; when event rate is less than one, the log is negative and greater than one, the log is positive. Thus, negative intercepts reflect a daily average of snacking that is less than 1 snack per day, 0 reflects an average of 1 snack per day and positive values, a snack rate of more than 1 snack per day on average. Furthermore, as we are interested in processes that operate at the group and individual level and have longitudinal data, the results from the unit-specific models are reported [44, 45]. Note that the perceptions of snack intake were analysed as continuous variables.
Results Descriptive Statistics and Initial Models Table 1 shows descriptive statistics for the main level 1 (within participant) and level 2 (between participant) study variables including the number of daily stressors, between-meal snacks consumed as well as perceptions of snacking behaviour in the total sample and in each condition. Scrutiny of Table 1 shows that across the entire sample, participants reported experiencing approximately two stressors per day and consuming less than one unhealthy snack per day and just over one healthy snack per day. Initial level 1 models demonstrated that daily stressors were significantly positively associated with unhealthy snacking (β=0.08, p
