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Breaking unhealthy snacking habits: the effectiveness of
implementation intentions and triggering cues.
Name: Maayke Dost, BSc Student ID: 6061001 Supervisor: A.A.C. Verhoeven
University of Amsterdam April 2016
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Table of contents
Introduction 4 Method 8 Results 13 Discussion 28 References 343
Abstract
Implementation intentions have been found to effectively change counter-intentional habits, including unhealthy snacking habits. The present study aimed to extend earlier findings by showing that implementation intentions are more effective than goal intentions in altering unhealthy snacking habits. Furthermore, it was investigated whether defining an internal rather
than an external cue might be more effective in interventions targeting unhealthy snacking behavior. Participants in the goal- as well as the implementation intention condition monitored their snacking behavior and corresponding internal and external cues in an online snack diary for
a total of five weeks. After the first week, participants formulated a goal intention or an implementation intention. Snacking frequency as well as the percentage of snacking behavior in
reaction to internal and external cues was examined using participants’ snack diary. Implementation intentions reduced unhealthy snacking behavior more than goal intentions, but
healthy snacking behavior also decreased in both conditions. The results further showed that snacking behavior did not change more in reaction to internal cues than to external cues. Taken
together, the results suggest that implementation intentions enable people to contemplate unhealthy snacking behavior before acting, and that internal cues are not in all cases more important to consider than external cues. Future research should examine whether the
effectiveness of internal cues might be moderated by factors such as self-reflection.
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Breaking unhealthy snacking habits: the effectiveness of implementation intentions and triggering cues.
Acting upon their good intentions is something people often fail to do (Webb & Sheeran, 2006): despite being highly motivated, it seems that efforts to change behavior usually only result in short-term success followed by relapse. For instance, Marlatt and Kaplan (1972) found that in a time span of 15 weeks, 25% of New Years’ resolutions had already been cast aside – with health-related resolutions being the most difficult to uphold. Resolutions concerning dieting are of particular interest, as many people intend to cut down on unhealthy snacks, but often fail to do so (Kumanyika et al., 2000). The present study therefore examines unhealthy snacking behavior and investigates the effectiveness of strategies that may reduce unhealthy snacking behavior.
Habits and snacking behavior
It appears that, for many health-related behaviors, having an intention to change behavior simply is not enough to actually ensure behavior change (e.g. Adriaanse, De Ridder, & De Wit, 2009; Orbell & Verplanken, 2010). Even though an intention may be strongly held, one may postpone the behavior necessary to achieve ones’ goal, or even counteract their intention. An explanation for this is that many of our behavioral patterns are relatively habitual (Ouellette & Wood, 1998).
The development of habitual behavior can be explained by the dual-process theory (De Wit & Dickinson, 2009), which distinguishes between a goal-directed system and a habitual system that dictate behavior. In the goal-directed system, behavior is performed consciously to achieve a particular goal, or outcome (O). Subsequently, cues (or stimuli, S) in our environment activate a memory of the desired outcome, which in turn activates the response (R) that was performed to acquire this outcome in the past (De Wit & Dickinson, 2009). In this way, reinforcing a
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behavioral response (R) with a desired outcome (O) in the presence of a cue (or stimulus, S), shapes a S O R associative chain (De Wit, Niry, Wariway, Aitken, & Dickinson, 2007). When a behavioral response is rewarding, the association between the cues that define the situation and the response is strengthened, resulting in a direct stimulus-response (S R) link (Thorndike, 1911). The more frequently such a response is performed in the same situation, the more likely it is to become a habit (Verplanken & Orbell, 2003): eventually, in the habitual
system, the response is elicited automatically by a specific cue, regardless of its outcomes (De Wit & Dickinson, 2009).
Since the habitual system is characterized by automaticity of behavior, habits are performed efficiently, unintentionally, unconsciously, and with little controllability (Bargh, 1994). Logically, precisely these characteristics are what make them inherently difficult to change. Indeed, research has pointed out that habits also play a large role in unhealthy snacking (e.g. De Bruijn, Kroeze, Oenema, & Brug, 2008; Verhoeven, Adriaanse, Evers, & De Ridder, 2012; Verplanken, 2006;), hereby providing an explanation why people have such a hard time reducing unhealthy snacking (e.g. Kumanyika et al., 2000) despite their intention to do so. To illustrate, even though someone with a habit of eating chips when watching TV may intend to eat more healthily, the association between the situation (or stimulus; watching TV) and the response (eating chips) remains. Since the habitual response is triggered automatically by the critical situation, one may very well find oneself guided by the old habit of eating chips while watching a movie, before the intention to eat more healthily even came to mind. Interventions aimed at reducing unhealthy snacking should therefore address habits in order to effectively help people act on their – counter-habitual – intentions. One such strategy is forming implementation intentions (e.g. Adriaanse et al., 2009; Armitage, 2004; Gollwitzer, 1999; Gollwitzer & Sheeran, 2006; Holland, Aarts, & Langendam, 2006; Verplanken & Faes, 1999).
6 Implementation intentions
Gollwitzer (1999) distinguishes between two types of intentions, specifically goal intentions and implementation intentions. Whereas goal intentions merely specify a desired outcome (for example, “I intend to eat more healthily!”), implementation intentions are specific plans that define the place and manner in which a desired outcome is to be procured (Gollwitzer, 1999). This specification takes the form of an if-then plan, for example: “If I’m watching TV, then I will eat more fruit!”. In this way, a specific cue (S) is directly linked to a desired response (R), hereby creating a mental stimulus-response (S R) association (e.g. Aarts & Dijksterhuis, 2000b). The working mechanism of implementation intentions hereby reveals important similarities between implementation intentions and habits. In both cases, associations between specific situational cues and particular behavioral responses have developed. They only differ in origin: whereas the strong mental associations that define habits are the result of frequent and consistent
performance of a behavior in a particular context, in the case of implementation intentions these associations are formed consciously and intentionally (Aarts & Dijksterhuis, 2000a).
These similarities seem to make implementation intentions a good strategy to create new habits. They have indeed been found to promote the initiation of several health-related
behaviors, such as attending cancer screenings (Sheeran & Orbell, 1999), promoting exercising (Milne, Orbell, & Sheeran, 2002), increasing vitamin C intake (Sheeran & Orbell, 1999), and promoting the consumption of more fruit and vegetables (Wiedemann, Lippke, & Schwarzer, 2011) . But implementation intentions can also be used to alter existing counter-intentional habits by using them to link a new, desired behavior to a cue that originally triggered an unwanted, habitual response (Gollwitzer & Sheeran, 2006). Along with promoting change in behavior such as recycling (Holland et al., 2006) and smoking (Webb, Sheeran, and Luszczynska, 2009),
implementation intentions have indeed also proven to be effective in altering eating patterns. For instance, implementation intentions were found to reduce dietary fat intake (Armitage, 2004), and several studies have shown that implementation intentions significantly reduced unhealthy
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snacking in general (e.g. Adriaanse et al., 2009; Adriaanse, Gollwitzer, De Ridder, De Wit & Kroese, 2011a; Verplanken & Faes, 1999).
The critical cue for unhealthy snacking
The dual-process theory (De Wit & Dickinson, 2009) typically assumes that the critical cue that elicits a habitual response is external. Correspondingly, Gollwitzer (1999) asserts that the if-part of an implementation intention should always be an external (situational) cue. However,
unhealthy snacking behavior doesn’t seem to be directly related to specific external cues (De Graaf, 2006). For instance, Jackson, Cooper, Mintz & Austin (2003) found that there are four motivational or emotional (internal) reasons to eat, namely to cope with negative affect, to be social, to comply with others, and to enhance pleasure. Since these more subjective internal cues can also trigger unhealthy snacking behavior, consequently, it might be more important to examine internal reasons rather than external cues when it comes to finding the critical
stimulus-response link for unhealthy snacking (Adriaanse et al., 2009). To exemplify, although a person often eats unhealthy snacks to cope with negative affect (internal) when watching TV (external), “watching TV” may not be the critical cue: this person might not eat unhealthy snacks when
watching TV with friends, whereas snacking may occur regularly in multiple situations in order to cope with negative affect – suggesting that the personally relevant cue is internal rather than external.
Research by Adriaanse et al. (2009) indeed shows that implementation intentions that specify internal cues were more effective in changing unhealthy snacking behavior than
implementation intentions in which the if-part represented an external cue. Moreover, the results revealed that only implementation intentions specifying internal cues led to a significant decrease
in the consumption of unhealthy snacks. However, this study has several limitations. First of all, snacking behavior was only reported for a short period of time (1 week). Secondly, snacking behavior was only measured after forming an implementation intention, so the reduction in
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unhealthy snack consumption could not be compared to past behavior. The present study therefore aims to examine whether the change in snacking behavior is lasting and stable, and if the link between past- and future behavior is consistently reduced.
Present study
The present study investigates two related hypotheses. First, it is tested whether forming implementation intentions is a more effective strategy to break unhealthy snacking habits than forming mere goal intentions. It is hypothesized that implementation intentions lead to (a) a greater decrease in unhealthy snacking behavior and (b) a stronger increase in healthy snacking behavior than goal intentions. Second, it is investigated whether internal cues are more important to consider than external cues when trying to alter unhealthy snacking behavior. It is expected that (a) unhealthy snacking behavior in reaction to internal cues decreases more than unhealthy snacking behavior in reaction to external cues and, correspondingly, that (b) healthy snacking behavior associated with internal cues will increase more when compared with healthy snacking behavior associated with external cues.
Method
Participants
Participants were recruited via flyers, the Healthyways website, and social media (i.e. Facebook). Participants could sign up for the Healthyways program via email. Previous research within Healthyways had resulted in a list of participants; individuals on this list that had not participated yet were also approached. Several inclusion criteria had to be met: only female subjects between 18 and 35 years old with a Body Mass Index (BMI: kg/m2) between 20 and 35 with no (history
of) eating disorders were selected. Furthermore, individuals had to experience at least three snacking moments a week and be motivated to reduce this, and have a smartphone with internet
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access. Initially, 57 participants aged 18-34 years old were recruited. Forty participants completed the study. Three participants were excluded from the program because they responded
affirmatively to the question “do you have (a history of) an eating disorder?”, and encouraging further monitoring of their eating behavior might have an adverse effect.Six participants dropped out before the experiment actually started. Two participants were excluded from the program because of lack of motivation (<2 SD below the mean score). One participant did not
have enough snacking moments (<3 per week). Three participants dropped out during baseline and three more participants dropped out after baseline due to personal reasons. This resulted in a final sample of 40 female subjects with an average age of 24.22 (SD = 3.45, range: 18-34) and an
average BMI of 25.17 (SD = 3.41, range: 19.84-33.28).
Design
The experiment had a 2 (time: baseline versus follow-up) by 2 (condition: implementation
intention versus goal intention) design. Considering the finding that mere monitoring of snacking behavior can change unhealthy snacking behavior in itself (i.e. Adriaanse et al., 2009; Verhoeven et al., 2015), an active control condition was adopted. The effectiveness of implementation intentions on unhealthy snacking behavior may otherwise be overstated (Adriaanse et al., 2011b). Therefore, to demonstrate the effectiveness of implementation intentions over forming goal intentions, participants in both conditions monitored the cues triggering snacking behavior. Motivational interviewing techniques were additionally employed in both conditions, in order to ensure that, besides planning manipulation, conditions were similar.
Procedure
Upon recruitment, participants were informed that Healthyways is a lifestyle intervention program
that tries to change unhealthy snacking habits using psychological insights. Consequently,
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intention condition. Participants were then asked to sign an informed consent form and fill out an entry questionnaire assessing demographic variables, motivation and inclusion criteria, as well as snacking habit strength. Next, height, weight and waist were measured. After this, participants were told to monitor their snacking behavior in an online snack diary for a total of five weeks. It was explained that participants were to snack ‘as usual’ during the first week, since this week was the baseline measurement and was supposed to provide an accurate overview of participants’ snacking behavior. After the first week, depending on their condition, participants were given instructions to formulate either an implementation intention or a goal intention (see Conditions) during the second appointment. Subsequently, participants were instructed to once again register their snacking behavior in their online snack diary for 28 days. After four weeks, participants filled out the exit questionnaire. The formulated goal- or implementation intention was then discussed. After this, participants were asked how they had experienced the coaching program, after which they were thanked and debriefed. The progress participants had made was then assessed using graphs illustrating the average daily number of healthy and unhealthy snacking moments per week. Finally, participants in the goal intention condition were provided the opportunity to form an implementation intention.
Conditions
Implementation intentions. In the implementation intention condition, participants were told that formulating an if-then plan would help them to eat fewer unhealthy snacks. The most important snacking situation was first identified. Participants were instructed to elaborate on the monitoring process from baseline in order to find their most important internal and external triggers for unhealthy snacking. After the identification of an important internal as well as an important external cue, participants would consequently think of ways to deal with their triggers. Participants were coached using motivational interviewing techniques: they were requested to
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think about how they could change their snacking behavior, what advantages there would be if they did, and how important this change would be for them. Then, participants were given a pre-printed format of an ‘if-then’ sentence, and were requested to complete this sentence with their own personal plan. The implementation intention had to be formulated as ‘if I [external cue] and I [internal cue], I will eat [fruit/vegetables]!’. Finally, participants were instructed to write down the implementation intention three times, to repeat it to themselves five times, and to imagine themselves acting out their plan.
Goal intentions. In the goal intention condition, participants were told that forming a goal would
help them to eat fewer unhealthy snacks. Subsequently, participants were coached using the same motivational interviewing techniques that were used in the implementation intention condition. After this, participants received a pre-printed format of a goal intention. Participants were instructed to complete this with their own goal intention, which had to be formulated as ‘I will snack less and eat more [fruit/vegetables]!’. The goal intention was written down three times and repeated to themselves five times by participants.
Measures
Entry Questionnaire
To examine the samples’ demographic variables and in order to do a randomization check, participants reported gender, age, weight and height (to calculate BMI), education level, and weight changes and information regarding possible eating disorders. Allergies and possible dietary restrictions were also addressed.
Motivation
To ensure that all participants were highly motivated to eat fewer unhealthy snacks, the entry questionnaire included a question regarding participants’ motivation to reduce unhealthy snacking. Additionally, participants’ motivation to eat more healthy snacks was assessed by a
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similar question. Participants rated their answers on a 7-point Likert-scale ranging from “not motivated at all” to “very motivated”.
Habit Strength
The SRHI (Self-Report Habit Index; Verplanken & Orbell, 2003) was included in the entry questionnaire to examine the samples’ snacking habit strength. The SRHI was modified to measure unhealthy snacking habits (Cronbach’s alpha = .80) and includes 12 items concerning repetition of behavior, automaticity (lack of control & awareness, efficiency) and expressed self-identity (e.g. ‘Eating unhealthy snacks is [something I do frequently], [something I do
automatically]’). Answers were rated on a 7-point Likert-scale ranging from 1 (“totally disagree”) to 7 (“totally agree”). Habit strength was measured by computing the mean score for the 12 items. A high score on the SRHI would indicate a strong unhealthy snacking habit.
Exit Questionnaire
The exit questionnaire consisted of several questions regarding participation, sick leave and whether participants had gone on holidays during the course of this study. Also, participants were asked to grade the coaching program and make suggestions for improvement.
Snacking Behavior
Snacking behavior was measured for five weeks using an online snack diary, which had been conceptualized for and used in previous research in collaboration with a dietician (e.g. Adriaanse, De Ridder & De Wit, 2009). Twice every day (9AM and 9PM), participants received a text message as well as an email with a link to the online snack diary. Participants were asked to report snack intake and to think about external and internal cues during a snacking situation. Participants were instructed to fill out the diary right after taking a snack and could fill out up to 8 snacking moments per day. A snack was defined as any type of food consumed between regular meals (c.f. De Graaf, 2006). For each snacking moment, participant were presented with one column with 12 options for healthy snacks (e.g. ‘fruit, namely…’) and one column with 13
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where participants could list what ‘other’ snack(s) they had consumed. For each snack, participants were asked to specify the quantity of the snack they had consumed in units (e.g. a ‘handful’ of nuts or ‘pieces’ of fruit). When multiple snacks were consumed in the same snacking situation (i.e. within an hour), participants filled this out in one snacking scheme. Participants monitored external and internal cues by, for each snacking moment, reporting (a) the situation they were in, including context (e.g. at school), activity (e.g. watching TV) and company (e.g. with friends), and (b) the most important internal reason (e.g. feeling bored) for eating the particular snack. The first week of the online diary was used as a baseline measure of snacking behavior. The internal and external cues reported during this first week were additionally used to identify the specific personal cues for participants in the implementation intention condition.
Results
Data-analyses
A power analysis using G*Power 3.1 showed that a total of 74 participants were required. Since there were not enough participants (n=40) to ensure enough power, obtained results must be interpreted carefully.
In order to investigate the initial effects of goal- and implementation intentions, separate repeated-measures ANOVAs will be employed comparing baseline with one week after the intervention. Additionally, repeated-measures ANOVAs comparing baseline with four weeks after the intervention are conducted in order to assess if there is still a direct effect of
14 Drop-out analysis and randomization check
A drop-out analysis was conducted using separate ANOVAs with study completion as
independent variable and age, BMI, motivation to eat fewer unhealthy snacks and motivation to eat more healthy snacks as dependent variables. No differences were found between drop-outs and participants that had completed the study (all p > .18). A separate Chi-squared test revealed
no significant differences between conditions regarding study completion, , χ2(1) = 1.53, p = .22.
To investigate whether randomization was successful, separate ANOVAs were employed with condition as independent variable and age, BMI, and motivation to eat fewer unhealthy snacks. The conditions did not differ in terms of mean age, BMI, or motivation to reduce unhealthy snacking (all p > .31). A separate Chi-squared test also revealed no significant difference between
conditions regarding education level, χ2(4) = 2.29, p = .68, indicating that randomization was
successful.
Descriptive statistics
After filtering out participants that did not meet the inclusion criteria, descriptives were calculated over the remaining 40 participants (17 in the implementation intention condition and 23 in the goal intention condition). At baseline, participants had a weekly average of 1.40 (SD = 0.65)
unhealthy snacking moments per day and 0.85 (SD = 0.61) healthy snacking moments per day.
All participants were highly motivated to eat fewer unhealthy snacks (M = 5.87, SD = 0.46) and
more healthy snacks (M = 5.85, SD = 0.74). Participants had a mean BMI of 25.17 (SD = 3.41)
15 Main Analyses
Reducing Unhealthy Snacking Moments. To examine whether implementation intentions were more
effective in reducing unhealthy snacking moments than goal intentions, a repeated-measures analysis of variance (ANOVA) was conducted with time (five weeks) as a within-subjects variable, condition (implementation intention versus goal intention) as a between-subjects variable, and average daily number of unhealthy snacking moments per week as the dependent variable. No main effect of condition was observed, F(1, 34) = 0.03, p = .57. A significant main
effect of time was found, F(4, 136) = 27.2, p < .001, ηp2= 0.45, illustrating that all participants
reported fewer unhealthy snacking moments after having formulated a goal intention or an implementation intention compared to before the manipulation. The results showed a marginal time-by-condition interaction effect, F(4, 136) = 2.26, p = .09, ηp2 = 0.06, which suggests that the
reduction in the number of snacking moments varied between conditions (Figure 1).
Figure 1. The mean number of unhealthy snacking moments per day in the implementation intention condition (II) and the goal intention condition (GI) over a course of five weeks.
0 0,2 0,4 0,6 0,8 1 1,2 1,4 1,6 1,8 1 2 3 4 5 Num be r o f unhe al thy s na ck ing m om ent s Time II GI
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Initial Effect. To examine the initial effect of the experimental manipulation – that is,
one week after the intervention – a repeated-measures ANOVA was conducted with time (baseline versus week 2) as within-subjects variable, condition (goal intentions versus
implementation intentions) as between-subjects variable and average daily number of unhealthy snacking moments per week as the dependent variable. Again, a main effect of time was
observed, F(1, 38) = 43.04, p < .001, ηp2 = 0.53, indicating that, for both conditions, the mean
daily number of unhealthy snacking moments had indeed decreased one week after the
manipulation compared to baseline. There was no significant main effect for condition, F(1, 38)
= 0.77, p = .39. The time-by-condition interaction effect, F(1, 38) = 3.58, p = .07, approached
significance, suggesting that a reduction in mean daily unhealthy snacking moments one week after the experimental manipulation differed between conditions. Indeed, the average daily number of unhealthy snacking moments per week reduced more in the implementation intention condition than in the goal intention condition. To explore how this reduction varied between conditions, simple main effects of time were calculated within each condition separately.
Simple Main Effects. A repeated-measures ANOVA with time (baseline versus week 2) as within-subjects variable and average daily number of unhealthy snacking moments per week as the dependent variable was conducted for each condition separately. There was a significant main effect of time in both conditions, respectively F(1, 22) = 25.15, p < .001, ηp2 = 0.61 in the
implementation intention condition and F(1, 22) = 15.47, p = .001, ηp2 = 0.41 in the goal
intention condition. Participants in the implementation intention condition significantly reduced their number of unhealthy snacking moments from on average 1.58 (SD = 0.67) per day at
baseline, to 0.75 (SD = 0.40) per day at week 2. Participants in the goal intention condition
reduced their average number of unhealthy snacking moments from 1.27 (SD = 0.61) a day at
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Longer-Term Effect. With a repeated-measures ANOVA with time (baseline versus week 5) as within-subjects variable, condition as between-subjects variable and average daily number of unhealthy snacking moments per week as the dependent variable, the longer-term effects of the manipulation were also investigated. The results showed no main effect for
condition, F(1, 36) = 0.61, p = .44. However, a significant main effect of time was observed, F(1,
36) = 73.23, p < .001, ηp2 = 0.67, indicating that, in both conditions, the average daily number of
unhealthy snacking moments had decreased at week 5 compared with before the manipulation. Moreover, this effect was qualified by a significant time-by-condition interaction effect, F(1, 36)
= 5.82, p = .02, ηp2 = 0.14, illustrating that the reduction in the average daily number of unhealthy
snacking moments differed between conditions. In order to examine how the reduction in unhealthy snacking moments varied between conditions, simple main effects of time were calculated within each condition separately.
Simple Main Effects. For each condition, a repeated-measures ANOVA with time (baseline versus week 5) as a within-subjects variable and average daily number of unhealthy snacking moments per week as the dependent variable was conducted. A main effect of time was found in both the implementation intention condition, F(1, 14) = 31.23, p <.001, ηp2 = 0.69. and
in the goal intention condition, F (1, 22) = 38.35, p < .001, ηp2 = 0.64. In the implementation
intention condition, participants’ average daily number of unhealthy snacking moments
significantly reduced from on average 1.58 per day at baseline (SD = 0.67) to 0.66 per day at week
5 (SD = 0.49). Participants in the goal intention condition significantly reduced their average daily
number of unhealthy snacking moments from on average 1.27 (SD = 0.61) per day at baseline to
0.74 (SD = 0.38) per day at week 5.
Healthy Snacking Moments. A repeated-measures ANOVA with time (five weeks) as a within-subjects variable, condition (implementation intention versus goal intention) as a between-subjects variable, and average daily number of healthy snacking moments per week as the
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dependent variable was conducted to examine whether healthy snacking moments would increase more after forming an implementation intention than after forming a goal intention. There was a main effect of time, F(4, 136) = 5.30, p = .002, ηp2 = 0.14. Contrary to the expectation that
healthy snacking moments would increase over time, however, the analysis indicated that all participants reported fewer healthy snacking moments after the manipulation compared with before (Figure 2). No main effect was found for condition, F(1, 34) = 1.94, p = .17, nor a
significant time-by-condition interaction effect, F(4, 136) = 1.75, p = .14, suggesting that the
reduction in the average daily number of healthy snacking moments did not differ between conditions over time.
Figure 2. The mean number of healthy snacking moments per day in the implementation intention condition (II) and the goal intention condition (GI) at week 1 (=baseline), and weeks two to five.
Initial Effect. In order to investigate what the initial effect of the manipulation was on
the amount of healthy snacking occasions, a repeated-measures ANOVA was conducted with time (baseline versus week 2) as a within-subjects variable, condition as a between-subjects variable, and average daily number of healthy snacking moments per week as the dependent
0 0,2 0,4 0,6 0,8 1 1,2 1 2 3 4 5 Num be r o f he al thy s na ck ing m om ent s Time II GI
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variable. There was no main effect of time, F(1, 38) = 0.39, p = .53. The main effect of condition
was significant, F(1, 38) = 4.20, p = .047, ηp2 = 0.10, indicating that the mean daily number of
healthy snacking moments per week was higher in the implementation intention condition when compared with the goal intention condition (Figure 2). There was no significant
time-by-condition interaction effect, F(1, 38) = 0.01, p = .93.
Longer-Term Effect. The longer-term effects of the intervention were explored by conducting a similar repeated-measures ANOVA, with time (baseline versus week 5) as within-subjects variable, condition as between-within-subjects variable and average daily number of healthy snacking moments per week as the dependent variable. The main effect for condition approached significance, F(1, 36) = 4.03, p = .052, ηp2 = 0.10, illustrating that the average daily number of
healthy snacking moments varied between conditions. As Figure 2 shows, the average daily number of healthy snacking moments was higher in the implementation intention condition than in the goal intention condition. The analysis showed a significant main effect for time, F(1, 36) =
12.35, p = .001, ηp2 = 0.26, indicating that, in both conditions, the average daily number of
healthy snacking moments had decreased after the manipulation compared with before.
However, no significant time-by-condition interaction effect was observed, F(1, 36) = 0.73, p =
.38.
Type of Cue for Unhealthy Snacking Behavior. To test whether unhealthy snacking behavior in reaction to an internal cue decreases more compared with unhealthy snacking in reaction to an external cue, a repeated-measures ANOVA was conducted with time (five weeks) and type of cue (internal versus external) as within-subjects variables, and condition (implementation intention versus goal intention) as between-subjects variable. The dependent variable was the percentage of unhealthy snacking moments in reaction to internal and external cues. No main effect of
condition was found, F(1, 31) = 0.14, p = .71. No main effect of time was observed, F(4, 124) =
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showed a significant effect for type of cue, F(1, 31) = 6.64, p = .015, ηp2 = 0.18, indicating that
the percentage of unhealthy snacking moments in reaction to internal cues was lower than it was for external cues (Figure 3). There was no condition-by-type of cue interaction effect, F(1, 31) =
0.12, p = .73. No time-by-type of cue interaction effect was found, F(4, 124) = 0.19, p = .89, nor
a three-way interaction between time, type of cue, and condition, F(4, 124) = 0.92, p = .45.
Figure 3. The percentage of unhealthy snacking moments per day in reaction to internal and external cues in the implementation intention condition and the goal intention condition at week one (= baseline) and weeks two to five.
Initial Effect. To examine whether unhealthy snacking behavior in reaction to internal
cues decreased more than unhealthy snacking behavior in reaction to external cues one week after the manipulation, a similar repeated-measures ANOVA was conducted with time (baseline versus week 2) and type of cue (internal versus external) as within-subjects variables, condition (implementation intention versus goal intention) as between-subjects variable and percentage of unhealthy snacking moments in reaction to internal and external cues as the dependent variable. There was no main effect for condition, F(1, 36) = 0.19, p = .66. No main effect for time was
0 5 10 15 20 25 30 35 40 1 2 3 4 5 Pe rc ent ag e o f unhe al thy sna ck ing m om ent s Time Internal cue -implementation intention condition External cue -implementation intention condition
Internal cue - goal intention condition External cue - goal intention condition
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found, F(1, 36) = 0.50, p = .48, nor a time-by-condition interaction effect, F(1, 36) = 0.44, p =
.51. The main effect for type of cue was significant, F(1, 36) = 8.91, p = .005, ηp2 = 0.19,
indicating that the percentage of unhealthy snacking moments was lower in reaction to internal cues as opposed to external cues (Figure 3). However, no type of cue-by-condition interaction effect was observed, F(1, 36) = 0.03, p = .86. There was no time-by-type of cue interaction effect, F(1, 36) = 0.27, p = .61, and no significant three-way interaction between time, type of cue, and
condition, F(1, 36) = 0.56, p = .46.
Longer-Term Effect. In order to investigate whether the longer-term reduction in unhealthy snacking behavior related to internal cues is stronger when compared with unhealthy snacking behavior related to external cues, another repeated-measures ANOVA was conducted, with time (baseline versus week 5) and type of cue (internal versus external) as within-subjects variables, condition (implementation intention versus goal intention) as between-subjects variable and percentage of unhealthy snacking moments in reaction to internal and external cues as the dependent variable. No main effect of condition was found, F(1, 33) = 0.005, p = .95. A main
effect for type of cue was observed, F(1, 33) = 6.05, p = .02, ηp2 = 0.16, showing that the
percentage of unhealthy snacking moments in reaction to internal cues was lower than the percentage of unhealthy snacking moments in reaction to external cues (Figure 4). The main effect of time was significant, F(1, 33) = 4.98, p = .03, ηp2 = 0.13, illustrating that, in both
conditions, the percentage of unhealthy snacking moments in reaction to both internal and external cues had significantly changed at week 5 compared to baseline (Figure 4). Moreover, there was a marginal time-by-condition interaction effect, F(1, 33) = 3.2, p = .08, ηp2 = 0.09,
suggesting that the percentage of unhealthy snacking moments in reaction to internal and external cues decreased more in the implementation intention condition than in the goal
intention condition. Interestingly, the three-way interaction between condition, time and type of cue also approached significance, F(1, 33) = 3.21, p = .08, ηp2 = 0.09. This implies that the
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compared to baseline, but that unhealthy snacking in reaction to internal cues had reduced more than unhealthy snacking in reaction to external cues. Moreover, it implies that this decline was stronger for participants that had formed an implementation intention as opposed to a goal intention. In order to analyze how unhealthy snacking behavior in reaction to internal cues differed from unhealthy snacking behavior in reaction to external cues over time per condition, another repeated-measures ANOVA was conducted for internal and external cues separately.
Figure 4. The percentage of unhealthy snacking moments per day in reaction to internal and external cues in the implementation intention condition and the goal intention condition at week one (= baseline) and at week five.
Unhealthy Snacking in Reaction to Internal Cues. To assess the longer-term change in
unhealthy snacking in reaction to internal cues, a repeated-measures ANOVA was conducted with time (baseline versus week 5) as within-subjects variable, condition (implementation intention versus goal intention) as between-subjects variable and percentage of unhealthy snacking moments in reaction to internal cues as the dependent variable. There was no main effect for condition, F(1, 33) = 0.02, p = .88. No main effect of time was observed, F(1, 33) =
2.59, p = .12. However, there was a significant time-by-condition interaction effect, F(1, 33) =
0 5 10 15 20 25 30 35 40 1 5 Pe rc ent ag e o f unhe al thy sna ck ing mo men ts Time Internal cue -implementation intention External cue -implementation intenion
Internal cue - goal intention
External cue - goal intention
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7.08, p = .012, ηp2 = 0.18, illustrating that the change in the percentage of unhealthy snacking
moments in reaction to internal cues differed between conditions (Figure 4). To explore how the conditions varied with regard to the percentage of unhealthy snacking moments in reaction to internal cues, simple main effects of time were calculated for each condition.
Simple Main Effects. A repeated-measures ANOVA with time (baseline versus week 5) as within-subjects variable and percentage of unhealthy snacking moments in reaction to internal cues as dependent variable was conducted for each condition separately. A main effect of time was found in the implementation intention condition, F(1, 12) = 5.84, p = .03, ηp2 = 0.33, but not
in the goal intention condition, F(1, 21) = 0.86, p = .36. In the implementation intention
condition, the percentage of unhealthy snacking moments in reaction to internal cues had significantly reduced from on average 30.51% (SD = 24.66) a day at baseline to 12.24% (SD =
19.27) a day at week 5. Interestingly, in the goal intention condition, the percentage of unhealthy snacking moments in reaction to internal cues increased from an average of 20.20% (SD = 18.53)
a day at baseline to 24.70% (SD = 28.02) a day at week 5.
Unhealthy Snacking in Reaction to External Cues. In order to investigate how unhealthy
snacking behavior related to external cues changed on the longer-term, another
repeated-measures ANOVA was employed with time (baseline versus week 5) as a within-subjects variable, condition (implementation intention versus goal intention) as a between-subjects variable and the percentage of unhealthy snacking moments in reaction to external cues as the dependent variable. The analysis showed no significant main effect for condition, F(1, 33) = 0.001, p = .97. There was
a marginal effect of time, F(1, 33) = 3.09, p = .088, ηp2 = .09, illustrating that the percentage of
unhealthy snacking moments in reaction to external cues had lowered for participants in the implementation intention condition (M = 37.96%, SD = 23.54 at baseline, M = 28.85%, SD =
24
M = 29.40%, SD = 28.51 at week 5). However, there was no time-by-condition interaction effect, F(1, 33) = 0.03, p = .87.
Type of Cue for Healthy Snacking Behavior. A repeated-measures ANOVA with time (five weeks) and type of cue (internal versus external) as within-subjects variables, condition
(implementation intention versus goal intention) as between-subjects variable and percentage of healthy snacking moments in reaction to internal and external cues as dependent variable was then conducted to examine whether healthy snacking behavior related to internal cues would increase more than healthy snacking behavior related to external cues. The main effect for condition approached significance, F(1, 27) = 3.70, p = .065, ηp2 = 0.12, indicating that the
percentage of healthy snacking moments in relation to both internal and external cues was higher in the implementation intention condition when compared with the goal intention condition (Figure 5). The analysis revealed no main effect of time, F(4, 108) = 0.47, p = .76 , nor a
time-by-condition interaction effect, F(4, 108) = 0.68, p = .61. The main effect for type of cue was
significant, F(1, 27) = 13.33, p = .001, illustrating that there was a lower percentage of healthy
snacking moments in reaction to internal cues than to external cues (Figure 5). Yet, no condition-by-type of cue interaction effect was observed, F(1, 27) = 0.34, p = .56. Results did not show a
time-by-type of cue interaction effect, F(4, 108) = 0.32, p = .86, nor a three-way interaction
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Figure 5. The percentage of healthy snacking moments per day in reaction to internal and external cues in the implementation intention condition and the goal intention condition at week one (= baseline) and weeks two to five.
Initial Effect. To examine whether healthy snacking behavior associated with internal
cues increased more than healthy snacking behavior associated with external cues one week after the manipulation, a repeated-measures ANOVA was employed with time (baseline versus week 2) and type of cue (internal versus external) as within-subjects variables, condition
(implementation intention versus goal intention) as between-subjects variable and percentage of healthy snacking moments in reaction to internal and external cues as the dependent variables. The results showed no main effect for condition, F(1, 36) = 0.20, p = .66. There was a main
effect for type of cue, F(1, 36) = 11.35, p = .002, ηp2 = 0.24, indicating that the percentage of
healthy snacking in reaction to internal cues was lower compared to external cues in both
conditions. No main effect of time was observed, F(1, 36) = 0.06, p = .81. No condition-by-type
of cue interaction effect was found, F(1, 36) = 1.48, p = .23, nor a time-by-type of cue interaction
effect, F(1, 36) = 1.20, p = .28. 0 10 20 30 40 50 60 1 2 3 4 5 Pe rce nt age o f h ea lth y sn ack in g mo men ts Time Internal cue -implementation intention External cue -implementation intention Internal cue - goal intention
External cue - goal intention
26
Longer-Term Effect. A similar repeated measures ANOVA with time (baseline versus week 5) and type of cue (internal versus external) as within-subjects variables, and condition (implementation intention versus goal intention) as between-subjects variable was then conducted, to assess whether healthy snacking behavior in reaction to internal cues increased more than healthy snacking behavior in reaction to external cues on the longer-term. No main effect for condition was found F(1, 33) = 0.005, p = .95. However, the main effect of time was
significant, F(1, 33) = 4.975, p = .03, ηp2 = 0.13, indicating that the percentage of healthy
snacking moments had significantly changed 4 weeks after the experimental manipulation
compared with baseline (Figure 6). There was no condition-by-type of cue interaction effect, F(1,
33) = 0.02, p = .89, nor a time-by-type of cue interaction effect, F(1, 33) = 0.06, p = .81. There
was a marginal time-by-condition interaction effect, F(1, 33) = 3.2, p = .08, ηp2 = 0.09, which
implies that the change in percentage of healthy snacking moments in reaction to internal and external cues differed between conditions. Moreover, the three-way interaction effect between condition, time, and type of cue was marginal, F(1, 33) = 3.2, p = .08, ηp2 = 0.09. This suggests
that at week 5 compared to baseline, the percentage of healthy snacking moments had increased in both conditions, but that this increase was stronger in the implementation intention condition than in the goal intention condition. It also implies that the percentage of healthy snacking moments in reaction to internal cues had increased more when compared with the percentage of healthy snacking moments in reaction to external cues (Figure 6). In order to examine how the percentage of healthy snacking moments in reaction to internal cues varied from the percentage of healthy snacking moments in reaction to external cues, a repeated-measures ANOVA was employed for internal and external cues separately.
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Figure 6. The percentage of healthy snacking moments per day in reaction to internal and external cues in the implementation intention condition and the goal intention condition at week one (= baseline) and at week five.
Healthy Snacking in Reaction to Internal Cues. In order to examine how healthy snacking in reaction to internal cues had changed at week 5 compared to baseline, a repeated-measures ANOVA was conducted with time (baseline versus week 5) as within-subjects variable, condition (implementation intention versus goal intention) as between-subjects variable, and the percentage of healthy snacking moments in reaction to internal cues as the dependent variable. No main effect of time was found, F(1, 31) = 0.56, p = .47. The effect for condition was not significant, F(1, 31) = 1.59, p = .22, nor was there a significant time-by-condition interaction effect, F(1, 31)
= 0.05, p = .82.
Healthy Snacking in Reaction to External Cues. To investigate how healthy snacking in
reaction to external cues at week 5 differed compared to baseline, another repeated-measures ANOVA was performed with time (baseline versus week 5) as within-subjects variable, condition (implementation intention versus goal intention) as between-subjects variable, and the percentage of healthy snacking moments in reaction to external cues as the dependent variable. There was no main effect of time, F(1, 31) = 0.001, p = .97. No main effect for condition was found, F(1, 31) =
0.93, p = .34. The time-by-condition interaction effect, however, was significant, F(1, 31) = 5.08,
0 5 10 15 20 25 30 35 40 45 50 1 5 Pe rce nt age o f h ea lth y sn ack in g mo men ts Time
Internal cue - goal intention
External cue - goal intention Internal cue -implementation intention External cue -implementation intention
28
p = .03, ηp2 = .14. This indicates that healthy snacking behavior in reaction to external cues had
increased in the implementation intention condition, whereas it had decreased in the goal
intention condition (Figure 6). Simple main effects of time were calculated within each condition separately, in order to examine how the conditions differed.
Simple Main Effects. For each condition, a repeated-measures ANOVA was conducted, with time (baseline versus week 5) as within-subjects variable and percentage of healthy snacking moments in reaction to external cues as the dependent variable. No main effect of time was found in the implementation intention condition, F(1, 13) = 2.81, p = .12, nor in the goal
intention condition, F(1, 18) = 2.63, p = .12. As Figure 6 shows, for participants in the
implementation intention condition, the percentage of healthy snacking moments in reaction to external cues had increased from an average of 35.29% (SD = 36.24) at baseline to 46.58% (SD =
34.24) at week 5. In the goal intention condition, the percentage of healthy snacking moments in reaction to external cues had decreased from, on average, 35.11% (SD = 41.3) at baseline to
23.46% (SD = 35.44) at week 5 (Figure 6).
Discussion
This study aimed to investigate whether implementation intentions are more effective than goal intentions in altering unhealthy snacking habits. Additionally, it was examined whether it is more important to focus on internal cues rather than external cues when trying to change unhealthy snacking habits. Unhealthy snacking behavior was found to reduce more after forming implementation intentions than after forming goal intentions, although healthy snacking behavior did not increase. Furthermore, contrary to the expectations, it was shown that unhealthy snacking behavior associated with internal cues did not decrease more than unhealthy snacking behavior
29
that was associated with external cues in either condition. When comparing baseline with four weeks after the intervention, however, unhealthy snacking in reaction to internal cues did occur significantly less often for participants who had formed implementation intentions. Healthy snacking behavior in reaction to internal cues did not increase more than healthy snacking behavior in reaction to external cues, neither initially nor on the longer-term, in either condition. These findings suggest that although specifying internal cues is not in all cases more important than defining external cues, implementation intentions are an effective tool in reducing unhealthy snacking.
Implementation intentions already marginally reduced unhealthy snacking behavior more than goal intentions one week after the intervention. Moreover, regarding longer-term effects, participants who had formed implementation intentions displayed significantly less unhealthy snacking behavior compared with participants who had formed goal intentions. Interestingly however, although not as strongly as implementation intentions, goal intentions also significantly reduced unhealthy snacking behavior on the longer-term, suggesting that the additional effects of implementation intentions on reducing unhealthy snacking behavior may be negligible. It is important to note, however, that this reduction might very well have been facilitated by the strict control conditions applied in this study. That is to say, all participants were highly motivated to
eat fewer snacks and instructed to form a strong intention to do so, and all participants
monitored their snacking behavior including corresponding cues. The reduction in unhealthy snacking behavior could consequently have been caused by these conditions. Mere monitoring of snack consumption, for instance, can reduce unhealthy snacking in itself (e.g. Adriaanse et al., 2009; Verhoeven et al., 2015). Furthermore, since all participants identified internal and external cues for each snacking moment, participants who had formed a goal intention could also have gained insight into the cues triggering their unhealthy snacking behavior. This might have subsequently lead to plan formulation, limiting the possibility to observe additional effects of implementation intentions. Nevertheless, the additional effects of implementation intentions
30
appeared to exceed the effects of all the control-strategies in this study, implying that forming implementation intentions can indeed be used in interventions aimed at reducing unhealthy snacking behavior. Moreover, this study extends earlier findings (e.g. Adriaanse et al., 2009) by showing a longer-term reduction in unhealthy snacking behavior that is also measured in relation to past behavior.
Although forming implementation intentions was effective in reducing unhealthy snacking behavior, it did not seem to increase healthy snacking behavior. What is more, healthy snacking behavior decreased. Earlier research on the cognitive effects of implementation intentions
designed to replace an unhealthy habitual response with a healthy alternative shows that linking the critical cue for a habitual response to a new alternative causes the habitual and the alternative response to become equally accessible (Adriaanse et al., 2011b). As a result, the habitual response no longer has an advantage in the “horse race” with the alternative response, making both responses equally likely to win the race for early activation again. Subsequently, repeatedly choosing the alternative response strengthens the stimulus-response link, eventually leading to automatic activation of the desired, healthy response upon encountering the critical cue
(Adriaanse et al., 2011). Following this so-called “horse-race model”, a reduction of the habitual response (unhealthy snacking) would imply that the alternative response (healthy snacking) won the race for activation and should therefore increase. In this study, however, the simultaneous
decrease in healthy snacking behavior proves that automatic initiation of the desired response failed
to occur. This contradicts Gollwitzer’s (1999) notion that implementation intentions create instant habits by operating through the same automatic processes as habits. Indeed, research suggests that rather than replacing an unhealthy snacking habit with a new, desirable one (e.g. Verhoeven et al., 2013), it might be more likely that activation of two conflicting responses leads to the conflict being brought into consciousness, so that behavior is subsequently contemplated before it is executed (e.g. Botvinick, Braver, Barch, Carter, & Cohen, 2001; Yeung, Botvinick, & Cohen, 2004). In line with a suggestion made by Lally & Gardner (2013), implementation
31
intentions may therefore support initiation of healthy snacking behavior and inhibition of unhealthy snacking behavior by bringing the decision to consciousness upon encountering the specified critical cue.
Interestingly, when defining the critical cue for unhealthy snacking behavior, internal cues might not be much more important to consider than external cues after all, since unhealthy snacking behavior associated with internal cues did not decrease more than unhealthy snacking behavior that was associated with external cues. Other research indeed suggests that it might be worthwhile to reconsider the results by Adriaanse et al. (2009). For example, Achtziger,
Gollwitzer & Sheeran (2008) postulated that whereas people with a high capability of self-reflection might benefit more from specifying internal states, people with low self-self-reflection might benefit more from specifying external cues. Possibly, participants in this study were not capable enough self-reflection, making external cues more relevant for them. Future research could account for this effect by adding a measure of self-reflection. Nevertheless, the finding that – on the longer-term – unhealthy snacking in reaction to internal cues reduced significantly for participants who had formed implementation intentions, supports the notion that when targeting a complex behavior such as snacking, the critical cue may very well be internal.
Some limitations of the present study should be noted. First of all, this study included only women, making it difficult to generalize the findings since the results are likely to be different for men: earlier research has indicated that gender is an important contributing factor concerning differences in eating behavior (Dietrich, Grellmann, Villringer, & Horstmann, 2014). For instance, fewer men than women are aware of the links between eating fruits and vegetables and disease prevention, and men are less like to be dieting to lose weight (Baker & Wardle, 2003). Men may therefore be less motivated to change unhealthy snacking habits. Future research should reveal whether the obtained results will be similar for men. Secondly, the majority of the participants were students, once again obstructing generalization of the findings. Future research
32
should analyze a community sample consisting of participants with different levels of education and/or occupation.
Secondly, the longer-term effects of implementation intentions were assessed using only two levels of time (the first and fifth week). As a result, only participants who had filled out the first and fifth week were analyzed, resulting in loss of data: snack consumption during weeks 2, 3 and 4 was not accounted for. Data-analyses in future research with a similar design could create a sum-score of the four weeks following baseline, hereby creating two representative levels of time
(baseline versus follow-up).
Furthermore, the results are based on a self-report method, the validity of which is determined by the accuracy with which participants report their dietary intake (e.g. Schoeller, 1995). Since many people have a tendency to underreport caloric intake (Rennie, Coward & Jebb, 2007), the use of a more objective measure of snacking behavior is suggested in order to increase the validity of the results. However, self-reporting dietary intake in a food diary is one of the most sophisticated naturalistic ways of measuring eating behavior (De Castro, 2000). Moreover, the present study used a computerized food diary which obligated participants to report their snacking moments daily, hereby preventing participants from forgetting and later reporting
possibly incorrect intake. Furthermore, participants in this study were urged to be honest when reporting their caloric intake, since underreporting would affect the coaching process negatively. These factors combined are thought to make the food diary used reasonably valid.
In addition it should be noted that the snacking diary provided participants with three categories of external cues (company, location and activity) as opposed to only one internal cue. This likely resulted in a higher probability of participants linking snacking behavior to an external cue than to an internal cue, so that (un)healthy snacking in reaction to an external cue seemed to occur more often than in reaction to an internal cue. Future research could overcome this limitation by including an equal amount of categories for external and internal cues in the snacking diary.
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This study has only evaluated the longer-term effectiveness of implementation intentions over a relatively short follow-up period. Since other research on dietary intake showed no lasting effect of implementation intentions on behavior change at a 12 month follow-up (Scholz, Ochsner, & Luszczynska, 2013), the sustainability of the effects of implementation intentions on altering unhealthy snacking behavior across a period of one year should be investigated.
To conclude, implementation intentions indeed appear to be a helpful strategy to facilitate people in eating fewer unhealthy snacks. Moreover, implementation intentions appear to
effectively reduce unhealthy snacking in the long run. Additionally, although forming
implementation intentions did not seem to replace unhealthy snacking behavior with healthy snacking, it still can enable people to contemplate unhealthy snacking behavior before one acts upon encountering the critical cue. Although snacking behavior in reaction to internal cues was not found to change more when compared with external cues, whether an internal or an external cue is more important in defining the critical cue-response link for snacking behavior may be moderated by peoples’ introspection. Further research is needed to examine the effectiveness of the use of internal and external cues in implementation intentions targeting unhealthy snacking behavior.
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