J Behav Med DOI 10.1007/s10865-015-9634-5
Field experiment of a very brief worksite intervention to improve nutrition among health care workers Christopher J. Armitage1
Received: June 25, 2014 / Accepted: March 19, 2015 Ó Springer Science+Business Media New York 2015
Abstract Despite the potential of worksite interventions to boost productivity and save insurance costs, they tend to be costly and tested in nonrandomized trials. The aim of the present study was to test the ability of a very brief worksite intervention based on implementation intentions to improve nutrition among health care workers. Seventynine health care workers were randomly allocated to a control condition or to form implementation intentions using standard instructions or with a supporting tool. Fruit intake and metacognitive processing (operationalized as awareness of standards, self-monitoring and self-regulatory effort) were measured at baseline and follow-up. Participants who formed implementation intentions ate significantly more fruit and engaged in significantly more metacognitive processing at follow-up than did participants in the control condition (ds [ .70). The findings support the efficacy of implementation intentions for increasing fruit intake in health care workers and preliminary support for the utility of a tool to support implementation intention formation. Keywords Worksite intervention Metacognition Brief intervention Health behavior change Implementation intentions Fruit intake Volitional help sheet
& Christopher J. Armitage [email protected] 1
Manchester Centre for Health Psychology, School of Psychological Sciences, Manchester Academic Health Science Centre, University of Manchester, Coupland Street, Oxford Road, Manchester M13 9PL, UK
Introduction Worksite health interventions Worksite health interventions can confer considerable individual and organizational benefits, for example, by reducing personal risk of illness and thereby reducing absenteeism and increasing productivity. Despite these many advantages, a recent systematic review of worksitebased nutrition/physical activity interventions revealed mixed findings (e.g., a dearth of randomized controlled trials), and the more successful interventions tended to be the most costly in terms of both time and resources (van Dongen et al., 2012). This is potentially problematic because cost and disruption to the working day can represent major barriers to implementing programs in the first place (Barbeau et al., 2004). Thus, it is important to develop very brief, effective, evidence-based interventions. The present study tests the ability of a very brief psychological intervention based on implementation intentions (Gollwitzer, 1993) to improve nutrition among health care workers. Implementation intentions Implementation intentions are ‘‘if–then’’ plans that improve performance on a wide variety of cognitive tasks and have been shown to cause sustained changes in health behavior (Gollwitzer, 1993; Gollwitzer & Sheeran, 2006). Participants form implementation intentions by linking in memory a critical situation (‘‘if’’) with an appropriate response (‘‘then’’). Laboratory studies show that specifying the ‘‘if’’ component of an implementation intention enhances the accessibility of critical situations and that linking ‘‘if’’ with ‘‘then’’ automates the response specified in the ‘‘then’’ component (Gollwitzer & Sheeran, 2006). For example, one
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possible cue might be ‘‘because I am busy’’ and it could be linked to ‘‘then I will tell myself that if I try hard enough I can have an extra portion of fruit each day’’ as an appropriate response. The idea is that when the temptation not to eat fruit through being busy is encountered, the appropriate response (‘‘telling myself that if I try hard enough I can have an extra portion of fruit each day’’ in this example) is triggered automatically (Gollwitzer & Sheeran, 2006). Gollwitzer and Sheeran’s (2006) meta-analysis shows that implementation intentions are an effective means of changing behavior: Across 94 independent studies in laboratory and field settings, implementation intentions exerted an average effect size of d = 0.65. Gollwitzer and Sheeran (2006) also report medium-large effect sizes associated with the formation of implementation intentions and both the activation of the mental representation of the ‘‘if’’ component and automatic activation of the ‘‘then’’ component. Unfortunately, the means by which these processes are evaluated in the laboratory (e.g., accessibility of the critical situation; immediacy of the appropriate response) are not amenable to testing in the field, where many implementation intention studies have been conducted (Gollwitzer & Sheeran, 2006). Mechanisms that might explain the operation of implementation intentions in field settings over extended periods of time have not yet been found (Webb & Sheeran, 2008). Metacognitive processing The present research tests the idea that, in addition to enhancing the accessibility of critical situations and automatizing the appropriate response, implementation intentions operate through changes in metacognitive processing that persist beyond the experimental session. Metacognitive processes are defined as, ‘‘the self-regulatory activities of the cognitive system’’ (Reeve & Brown, 1985, p. 346) that, ‘‘include planning, monitoring, checking, and regulating problem-solving behavior’’ (Reeve & Brown, 1985, p. 346). A growing body of laboratory research shows that implementation intentions can improve people’s metacognitive processing by minimizing the demands that metacognitive processing makes on executive functioning. For example, the P300 response is an index of metacognitive processing assessed using electroencephalography that is associated with the anterior cingulate cortex, which in turn is an important route for frontal brain regions to regulate behavior, specifically around error detection, conflict monitoring, and behavioral adjustment after erroneous responses (Botvinick et al., 2004; Holroyd & Coles, 2002). Paul et al. (2007) showed that implementation intentions improved response inhibition and increased the P300 in children with attention deficit hyperactivity disorder (Gawrilow et al., 2011).
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However, this laboratory research has been limited to cognitive tasks such as the Wisconsin Card Sorting Test (Gawrilow et al., 2011), which are less complex than the sustained behavior change that has been demonstrated in field studies conducted over periods of time extending to weeks or even years. For example, Conner and Higgins (2010) showed that implementation intentions prevented adolescents from taking up smoking 2 years following the intervention. The question therefore arises as to whether such sustained effects of implementation intentions on complex behaviors such as not starting to smoke cigarettes or increasing fruit consumption can be explained solely by activation of the mental representation of an ‘‘if’’ component and automatic activation of a ‘‘then’’ component. The present research tests the hypothesis that forming implementation intentions per se (i.e., as opposed to targeting specific metacognitive processes, see Gawrilow et al., 2011; Paul et al., 2007) facilitates behavior change by freeing up executive resources and thereby augmenting the operation of metacognitive processes such as awareness of standards, self-monitoring and self-regulatory effort (Karoly, 1993). The principal idea is that specifying critical situations increases awareness of standards and selfmonitoring, and that the automatization of appropriate responses enhances self-regulatory effort. In addition to considering the impact of implementation intentions on metacognitive processing, the aim of the present research was to increase fruit intake, a behavior that is important in reducing cancer, heart disease and stroke (van Duyn & Pivonka, 2000). To date, two published studies have shown that implementation intentions significantly increase fruit intake independent of vegetable intake (Armitage, 2007; de Nooijer et al., 2006), meaning there are good grounds for assuming that the manipulations used in the present research will be effective and thereby allow for the appropriate tests of mediation. The research reviewed above provides the following rationale for the present research. First, implementation intentions have been shown to be effective in changing behaviour over extended periods of time, but field experiments have yet to identify a causal mechanism (Webb & Sheeran, 2008). Second, laboratory research shows that implementation intentions can augment specific metacognitive processes (Paul et al., 2007) and it is hypothesized that because implementation intentions minimize the impact of metacognitive processes on executive functioning, forming implementation intentions can augment the use of a range of metacognitive processes (Gawrilow et al., 2011). Third, it is hypothesized that using a tool to support the formation of implementation intentions (a ‘‘volitional help sheet’’, Armitage, 2008), as opposed to asking people to form their own (‘‘self-generated’’) implementation intentions, will further reduce the demands on executive func-
J Behav Med Fig. 1 Hypothesized relationships among study components
Implementation Intention Formation
tioning and thereby maximize metacognitive processing and hence behavior change. The present research is conducted in the field with the overarching goal of increasing fruit intake, an important health behavior (van Duyn & Pivonka, 2000). To summarize, it was hypothesized that: (a) forming implementation intentions would significantly increase fruit consumption; (b) the effects of forming implementation intentions on behavior change would be mediated by increases in metacognitive processing (Fig. 1); and (c) a tool that supported implementation intention formation (the ‘‘volitional help sheet’’) would exert the largest effects on metacognitive processing and behavior change.
Method Participants The University ethics committee gave approval to conduct the research: Participants were assured of their confidentiality and anonymity (personal codes were used to identify individuals), and were made aware of their right to withdraw from the study or have their data removed at any point. Informed consent was obtained before the study began. Seventy-nine participants were recruited from staff working at a private hospital in England. The sample consisted of 19 men and 60 women aged 19–63 years (M = 42.44 years, SD = 11.28). All participants were contacted again and were invited to complete follow-up questionnaires; however, 17 participants did not complete the follow-up questionnaire (Fig. 2), largely due to exercising their right to withdraw from the research. The data were analyzed according to intention to treat, with the last observation carried forward.1 Design A mixed measures design was employed with one betweenparticipants factor and one within-participants factor. Condition (control versus self-generated implementation 1
The analyses were rerun using per protocol and item imputation, and there were no substantive differences to the pattern of findings. Moreover, there were no significant differences in key variables at baseline between those who remained in the study and those who dropped out, F(5, 73) = 0.30, p = .91, g2p = .02, d = .29.
Boosts Metacognitive Processing
Increases Fruit Intake
intention versus volitional help sheet implementation intention) was the between-participants variable; and time (baseline versus follow-up) was the within-participants variable. The main outcome measure was fruit intake. The follow-up took place 1 month post-baseline. Procedure All participants received a copy of the baseline questionnaire through the internal mail system. The manipulations were placed at the end of identical-looking questionnaires, which were sorted into random order using a web-based randomizer prior to data collection. Once the baseline questionnaire and manipulations were completed, the participant returned it to the researcher in a sealed envelope via the internal mail system. Follow-up questionnaires were also administered via the internal mail system. Baseline and follow-up questionnaires were matched using personal codes. Manipulations Participants in all three conditions were presented with a brief statement designed to encourage them to plan to consume an extra portion of fruit each day (‘‘We want you to plan to have an extra portion of fruit each day because forming plans has been shown to increase fruit intake’’).2 Following this statement, participants randomized to the control condition were given space to write their plans. In addition to the brief statement, participants in the selfgenerated implementation intention condition were given standard (Armitage, 2006) implementation intention instructions: ‘‘You are free to choose how you will do this, 2
Participants in the control condition were asked to form plans because it controls for the possibility that being asked to plan exerts effects that are independent of being asked to form an implementation intention (it is not possible to ask people to form implementation intentions without asking them to plan, but it is possible to ask people simply to plan). Importantly, research shows that simply asking people to plan is equivalent to the passive/questionnaire-only control groups that have commonly been used in implementation intention research (Armitage, 2008). In addition to controlling for ‘‘generic planning,’’ the present control group may also be preferable to asking participants to form goal intentions (another control condition employed in implementation intention research) because prompting intention formation is a behavior change technique in its own right (Abraham & Michie, 2008) that brings about a deliberative mindset that works counter to implementation intentions (Brandsta¨tter & Frank, 2002; Heckhausen & Gollwitzer, 1987). Generic planning can therefore be considered an active but neutral control group.
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Pool of Potential Participants N = 118 Participants Randomized to Condition n = 79
Did not Participate n = 39
Active Control n = 23
VHS Implementation Intentions n = 33
Self-Generated Implementation Intentions n = 23
Lost to Follow-Up n=6
Lost to Follow-Up n=7
Lost to Follow-Up n=4
Follow-Up (Intention to Treat) n = 23
Follow-Up (Intention to Treat) n = 33
Follow-Up (Intention to Treat) n = 23
Fig. 2 Flow of participants through the experiment. Note: VHS volitional help sheet
but we want you to formulate your plans in as much detail as possible. Please pay particular attention to the situations in which you will implement these plans’’ and participants were left space in which to write their implementation intentions. Participants in the volitional help sheet implementation intention condition had a volitional help sheet appended to their questionnaires following the brief statement encouraging them to plan to consume an extra portion of fruit each day. The volitional help sheet was similar to those used to support implementation intention formation and successful health behavior change in previous research (Armitage, 2008). It consisted of a table with two columns each containing lists of ten critical situations and ten appropriate responses. The ten critical situations were derived from items used to measure temptations and the ten appropriate responses were derived from items used to measure the processes of change from Prochaska and DiClemente’s (1983) transtheoretical model. The temptation items were translated into ‘‘if’’ statements, for example: ‘‘If I’m tempted not to have an extra portion of fruit because I feel that I don’t have the time;’’ the processes of change items were translated into ‘‘then’’ statements, for example, ‘‘then I will tell myself that if I try hard enough I can have an extra portion of fruit each day.’’ There was one item for each of the ten processes of change. Participants in the volitional help sheet condition were told that identifying situations in which they were tempted not to have an extra portion of fruit each day and identifying ways to overcome those temptations had been shown to increase fruit intake. They were then asked to draw links between as many critical situations and appropriate responses as they wanted and thereby form implementation intentions.
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Measures Fruit intake was assessed at baseline and follow-up using the fruit section of Bogers et al. (2004) measure, which has been successfully validated against biomarkers. Bogers et al.’s (2004) measure consists of five pairs of items designed to measure fruit consumption in terms of both frequency and quantity. Frequency was measured with items such as, ‘‘In the last month, how often on average did you eat apples and pears?’’ which was measured on a 9-point scale with the response options: Never or less than 1 day a month, 1–3 days a month, 1 day a week, 2 days a week, 3 days a week, 4 days a week, 5 days a week, 6 days a week, and 7 days a week. Quantity was measured with items such as ‘‘On a day when you ate apples and pears in the last month, how many did you eat?’’ to which participants responded on 5-point scales ranging from ‘‘1’’ to ‘‘5 or more.’’ The means of the frequency and quantity items were used as scales in addition to the sum of the products of the frequency and quantity items (Bogers et al., 2004). Metacognitive processing was also measured at both baseline and follow-up. Adaptations of Sniehotta et al. (2006) six items were used to capture three facets of action control (Karoly, 1993) on 7-point (+1 to +7) strongly disagree-strongly agree scales. Awareness of standards was measured with two items, ‘‘During the last month I often had my intention to have an extra portion of fruit each day on my mind’’ and ‘‘During the last month I was always aware of my ideal levels of fruit intake’’, which had good internal reliability abaseline = .71; afollow-up = .66. Selfmonitoring was assessed with: ‘‘During the last month I constantly monitored my fruit intake to make sure it was enough’’ and ‘‘During the last month I watched myself to
J Behav Med
make sure I was having enough fruit,’’ which had adequate internal reliability, abaseline = .60; afollow-up = .66. Selfregulatory effort was measured with: ‘‘During the last month I tried my best to act in a way that was consistent with my personal standards regarding having fruit’’ and ‘‘During the last month I really tried to have an extra portion of fruit each day,’’ which had good internal reliability, abaseline = .85; afollow-up = .83. Data analysis Randomization was tested using MANOVA. This was designed to establish that the three groups of participants were similar in terms of their demographics and other descriptive characteristics. The effects of the manipulations were tested using ANCOVAs that controlled for baseline values (i.e., metacognitive processing and fruit intake). Simple planned contrasts were used to test whether the volitional help sheet was superior to the control condition and the self-generated implementation intention for each of the six dependent variables. Thus the alpha value for these tests was set at .004 [standard alpha = .05/(2 comparisons 9 6 dependent variables) = .004] in accordance with Bonferroni’s correction. Because the use of difference scores (i.e., subtracting follow-up measure of metacognitive processing from baseline measure of metacognitive processing) has been subject to criticism on statistical grounds (Cronbach & Furby, 1970), residualized change scores were used to capture changes in metacognitive processing over time (Cohen et al., 2003). Residualized change scores have the effect of partialing out the part of the follow-up data that is linearly predictable from baseline. Using residualized change scores thus eliminates the correlation between changes in metacognitive processing over time and baseline metacognitive processing. Mediation was tested formally using the bootstrapping procedures outlined in Preacher and Hayes (2008). The basis for these analyses is that the indirect effect of implementation intentions on the dependent variable (i.e., fruit intake) is the product of the paths between implementation intentions and mediator (i.e., changes in metacognitive processing), and between mediators and dependent variable. However, such indirect effects are not normally distributed, meaning that bootstrapping is necessary (Preacher & Hayes, 2008). Bootstrapping involves resampling random subsets of data in order to gain a nonparametric approximation of the sampling distribution of the product of the implementation intention—mediator and mediator-dependent variable paths. The analyses presented here are based on 10,000 resamples, although repeating the bootstrapping analyses with fewer and more resamples made no difference to the findings of the mediation analyses.
Results Randomization check Randomization was checked using MANOVA. The independent variable was condition with three levels: Control, self-generated implementation intentions and volitional help sheet implementation intentions. The dependent variables were age, gender, baseline metacognitive processes and baseline fruit intake (frequency of consumption, grams consumed and the multiplicative term). The multivariate test and all the univariate tests were nonsignificant, F(10, 144) = 0.26, p = .99, g2p = .02, meaning randomization was achieved (Table 1). Effects of implementation intentions on metacognitive processing The effect of implementation intention formation on metacognitive processing was tested using ANCOVAs, with each facet of action control as the dependent variables and baseline action control facets as the covariates. Condition was the between-participants factor. With follow-up awareness of standards as the dependent variable and baseline awareness of standards statistically controlled, there was a significant main effect of condition, F(2, 79) = 7.85, p = .001, g2p = .17, d = .90 (Table 2). The nature of this effect was decomposed using simple planned contrasts, with the volitional help sheet condition as the reference category. This showed that people in the volitional help sheet condition reported significantly greater awareness of standards than those in the control condition (contrast estimate = -1.32, 95 % CI = -2.00, -0.63, p \ .001) but only marginally more in the self-generated implementation intention condition (contrast estimate = -0.86, 95 % CI = -1.54, -.17, p = .01).3 Equivalent analyses were conducted with self-monitoring as the dependent variable. Again, there was a significant main effect of condition, F(2, 79) = 7.70, p = .001, g2p = .17, d = .90 (Table 2). Simple planned contrasts showed that people in the volitional help sheet condition reported significantly greater levels of selfmonitoring than people in the control condition (contrast 3
Note that one possible alternative explanation for this pattern of findings is that the nature of the implementation intentions differed between the volitional help sheet condition and the self-generated condition. However, consistent with previous research (Armitage, 2008), differences in the nature of implementation intentions formed within the self-generated condition were larger than the differences in the nature of implementation intentions formed between the selfgenerated and volitional help sheet conditions. Moreover, in the volitional help sheet condition, implementation intentions are formed by drawing lines; consequently, it is not possible to judge the quality of the implementation intention accurately.
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J Behav Med Table 1 Pre-randomization characteristics of the sample Variables
Age (years)
Control condition
Self-generated condition
Volitional help sheet condition
M
SD
M
M
SD
43.91
11.09
40.87
9.52
42.51
12.66
.66
0.22
0.42
0.26
0.45
0.24
0.43
.94
Gendera
SD
p
Awareness of standards
3.63
1.55
3.76
1.70
3.53
1.82
.88
Self-monitoring
4.09
0.97
3.63
1.57
3.80
1.78
.59
Self-regulatory effort
2.83
1.43
3.35
1.71
3.45
1.93
.39
Quantity of fruit (portions/day)
1.30
0.26
1.31
0.33
1.36
0.39
.75
Frequency of fruit consumption
3.41
1.18
3.76
1.29
3.56
1.66
.70
28.04
11.38
30.96
15.06
31.30
17.91
.72
Volitional help sheet condition
F(2, 79)
Sum of quantity 9 frequency a
Men = 0; women = 1
Table 2 Effects of implementation intentions on metacognitive processing at follow-up Dependent variables
Control condition
Self-generated condition
Awareness of standards
7.85**
M
3.56a
4.09b
4.83c
SD
1.46
1.70
1.37
M
4.02a
4.41b
5.09b
SD
1.69
1.40
1.24
Self-monitoring
7.70**
Self-regulatory effort
1.13
M
4.13
3.76
4.64
SD
3.48
1.74
1.66
Figures are ‘‘raw’’ scores unadjusted for baseline. Values with different subscripts in a row differ significantly, p B .05 ** p \ .01
estimate = -1.23, 95 % CI = -1.85, -0.60, p \ .001) but the higher levels of self-monitoring in the volitional help sheet condition proved not to be statistically significant compared with the self-generated condition (contrast estimate = -0.58, 95 % CI = -1.20, 0.04, p = .07). In contrast, analyses with self-regulatory effort as the dependent variable showed no significant differences between conditions, F(2, 79) = 1.13, p = .33, g2p = .03. Effects of implementation intentions on health behavior change The effect of implementation intention formation on health behavior change was tested using ANCOVAs, with followup fruit intake as the dependent variable and baseline fruit intake as the covariate. Condition was the experimental between-participants factor. With quantity of fruit consumed as the dependent variable and baseline quantity of fruit consumed statistically controlled, there was a significant main effect of condition, F(2, 79) = 5.85, p = .004, g2p = .13, d = .77 (Table 2). The nature of this effect was decomposed using simple planned contrasts,
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with the volitional help sheet condition as the reference category. This showed that people in the volitional help sheet condition ate no more fruit than people in the selfgenerated condition (contrast estimate = -0.06, 95 % CI = -0.16, 0.04, p = .25) but a significantly greater quantity of fruit than those in the control (contrast estimate = -0.18, 95 % CI = -0.28, -0.07, p = .001) condition. Similar analyses were conducted using frequency of fruit consumption as the dependent variable. This also showed a significant effect of condition, F(2, 79) = 5.79, p = .005, g2p = .13, d = .77 (Table 3). Simple planned contrasts showed that people in the volitional help sheet condition ate fruit significantly more often than those in the control condition (contrast estimate = -0.67, 95 % CI = -1.08, -0.26, p = .002), but only marginally more than those in the self-generated condition (contrast estimate = -0.47, 95 % CI = -0.89, -0.06, p = .02). A similar pattern of findings emerged when the summed multiplicative scale was used as the dependent variable. There was a significant effect of condition, F(2, 79) = 6.49, p = .003, g2p = .15, d = .84 (Table 3). Sim-
J Behav Med Table 3 Effects of implementation intentions on follow-up fruit intake Dependent variables
Control condition
Self-generated condition
Volitional help sheet condition
Quantity of fruit (portions/day)
F(2, 79) 5.85**
M
1.25a
1.38b
1.46c
SD
0.19
0.26
0.27
M
3.54a
4.04b
4.34c
SD
1.35
1.25
1.55
M
28.43a
33.96b
41.09c
SD
13.28
14.06
19.63
Frequency of fruit consumption
5.79**
Sum of quantity 9 frequency
6.49**
Figures are ‘‘raw’’ scores unadjusted for baseline. Values with different subscripts in a row differ significantly, p B .05 ** p \ .01
ple planned contrasts showed that people in the volitional help sheet condition ate significantly more fruit than those in the control condition (contrast estimate = -9.99, 95 % CI = -15.77, -4.26, p = .001), but only marginally more than in the self-generated condition (contrast estimate = -6.85, 95 % CI = -12.61, -1.09, p = .02). Does metacognitive processing mediate the effects of implementation intentions? In order to test whether the changes in metacognitive processing mediated the effect of implementation intentions on changes in fruit intake, bootstrapping procedures for testing multiple potential mediators outlined in Preacher and Hayes (2008) were used. Thus, the independent variable was condition (dummy-coded as 1 = volitional help sheet and self-generated implementation intention conditions, 0 = control condition); the mediators were changes in awareness of standards, self-monitoring and self-regulatory effort; the dependent variable was fruit intake at follow-up; and baseline fruit intake was entered as a covariate. The analyses were repeated three times, with each index of fruit consumption serving as covariate (baseline measure) and dependent variable (follow-up measure) in the analysis. With frequency of fruit consumption as the dependent variable, the confidence intervals associated with changes in metacognitive processing all contained zero. Thus, metacognitive processing did not significantly mediate the effects of implementation intentions on changes in the frequency of fruit consumption. When the quantity of fruit consumed was entered as the covariate/dependent variable, the confidence intervals associated with changes in both awareness of standards and self-regulatory effort contained zero, but increases in self-monitoring did significantly
(p \ .05) mediate the effects of implementation intentions on the quantity of fruit consumed (95 % CI 0.001, 0.14). Consistent with the preceding analyses, when the multiplicative scale was used as the covariate/dependent variable, the confidence intervals of the indirect effect associated with changes in awareness of standards and selfregulatory effort contained zero. However, the confidence intervals associated with the indirect effect of selfmonitoring did not contain zero (95 % CI 0.34, 8.42). Thus, the effect of implementation intention formation on the multiplicative index of change in fruit intake was significantly (p \ .05) mediated by increases in participants’ levels of self-monitoring. Does metacognitive processing mediate the effects of the volitional help sheet? Similar procedures were used to test whether the effect of the volitional help sheet on changes in fruit intake was mediated by the changes in metacognitive processing. For these analyses, the independent variable was condition (dummy-coded as 1 = volitional help sheet implementation intention condition, 0 = self-generated implementation intention condition); the mediators were changes in awareness of standards, self-monitoring and self-regulatory effort; the dependent variable was fruit intake at follow-up; and baseline fruit intake was entered as a covariate. Consistent with the approach described above, the analyses were repeated three times, with each index of fruit consumption serving as covariate/dependent variable in the analysis. The results were consistent across each index of fruit consumption: All the confidence intervals associated with changes in awareness of standards and self-regulatory effort contained zero, but increases in self-monitoring significantly (p \ .05) mediated the effects of the volitional
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help sheet on the frequency of fruit consumption (95 % CI 0.02, 0.65), quantity of fruit consumed (95 % CI 0.01, 0.17) and the multiplicative index (95 % CI 0.64, 9.65).4
Discussion Summary The present study set out to test the hypothesis that implementation intentions bring about changes in behavior by increasing metacognitive processing in a field setting. The key findings were that forming implementation intentions both increased metacognitive processing and fruit consumption, and that the effects of implementation intentions on behavior change were mediated via increases in selfmonitoring. In addition, this is the first study to have tested a volitional help sheet in a health care setting with the aim of increasing fruit intake. The following discussion considers the practical and theoretical implications of the findings. Fruit intake and implementation intentions Consistent with a growing body of research on the impact of implementation intention-based interventions on health behavior change, the present study showed that implementation intentions were effective in increasing self-reported fruit intake (Armitage, 2007; de Nooijer et al., 2006) with marginally superior effects when a volitional help sheet supported implementation intention formation. Although the intervention was deployed on a minimal basis by incorporating it into a questionnaire, there is much potential for employing the strategy in clinical practice. Alongside other treatment techniques, implementation intentions could be developed on a one-to-one basis, with the health professional and client identifying critical situations and appropriate behavioral responses using the volitional help sheet. Nevertheless, the volitional help sheet offers an efficient and cost-effective means of encouraging people to form theory-based implementation intentions, which has recently been adapted into an online format (Soureti et al., 2011). What mediates the effects of implementation intentions? To date, implementation intentions have been shown consistently to work by increasing the salience of critical 4 Note that the direct paths between independent variable and dependent variable were statistically significantly reduced. However, because none of the direct paths was reduced to zero, technically, this is best described as partial (as opposed to full) mediation.
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situations and automatizing appropriate responses (Gollwitzer & Sheeran, 2006). However, this does not fully explain why implementation intentions are capable of changing behavior over sustained periods of time (Conner & Higgins, 2010). It was therefore hypothesized that implementation intentions work to reduce demands on executive functioning, increase metacognitive processing and thereby enhance self-regulation (Gawrilow et al., 2011; Paul et al., 2007). There was support for this hypothesis: Regardless of the means by which they were formed (selfgenerated or with volitional help sheet), implementation intentions significantly boosted awareness of standards, self-monitoring and self-regulatory effort (Karoly, 1993) in addition to changing behavior. Marginally superior effects were observed for the effects of the volitional help sheet on awareness of standards, which implies that providing participants with examples of critical situations and appropriate behavioral responses might help raise awareness of appropriate actions in specific contexts. Further work is required to test this hypothesis. Despite significant increases in metacognitive processing in general, mediational analyses revealed that the effects of implementation intentions were mediated by selfmonitoring specifically, and the question arises as to why. Implementation intentions are part of a broader theory, the Rubicon model (Gollwitzer, 1990; Heckhausen & Gollwitzer, 1987) that distinguishes between a predecisional phase when a deliberative mindset facilitates decisionmaking and a postdecisional phase when an implemental mindset facilitates goal achievement. Implemental mindsets are activated by asking participants to plan when, where and how they will achieve their goal (Brandsta¨tter & Frank, 2002), just like the implementation intention manipulations described in the present study. A key characteristic of the implemental mindset is that ‘‘it allows a flexible response to situational demands’’ (Brandsta¨tter & Frank, 2002, p. 1377) and Armor and Taylor (2003) have shown that increases in performance assessments mediated the effects of an implemental mindset on task performance. Given that performance assessments are contingent on selfmonitoring (Carver & Scheier, 1982), it is plausible that this is the mechanism through which implementation intentions facilitate self-monitoring.5 Further work is required to test this proposition directly. 5 Note that it could be argued that the present intervention improves self-monitoring per se, by drawing participants’ attention to critical situations. However, this explanation is ruled out by research that has tested volitional help sheets against control conditions in which participants are asked to identify critical situations. Specifically, participants in these control conditions are presented with the volitional help sheet and are given identical instructions to the experimental condition, but are asked to tick critical situations/appropriate responses that might work for them (as opposed to linking critical situations/appropriate responses in the experimental condition and in
J Behav Med
Given that increased self-monitoring alone was found to mediate the relationship between implementation intention formation and behavior change significantly yet did not reduce the direct path between implementation intention formation and behavior change to zero, it raises the question of whether there are other metacognitive processes, such as task-switching and resilience to distraction (Gawrilow et al., 2011) that might improve on this explanation of the relationship between implementation intentions and behavior change. Interestingly, though, the finding that self-monitoring was the only significant mediator is consistent with Michie et al.’s (2009) metaregression. In their analysis of 26 behavior change techniques across 122 empirical tests, Michie et al. (2009) were able to demonstrate that self-monitoring was the most powerful ingredient of interventions designed to increase healthy eating and physical activity. It would therefore be valuable to investigate whether direct prompting of selfmonitoring via implementation intentions would be an effective intervention in its own right. It is also worth reflecting on the fact that the present research was conducted over the period of a month, meaning that increases in self-monitoring might only mediate the effects of implementation intentions on behavior change in the medium-term. It is therefore plausible that other processes, such as changes in identity (Charng et al., 1988) or mere exposure (Pliner, 1982) might explain the longer-term effects of implementation intentions on behavior change (Conner & Higgins, 2010). Further research is required to explore these alternative explanations of implementation intention effects and contrast them with the mediating effects of increased self-monitoring found in the present research. Limitations Although the present research takes the literature on implementation intentions forward in a number of important respects, it is important to acknowledge some potential limitations of the study. First, the sample was recruited from a group of health care workers that included more women than men, and so it may be that the findings are not generalizable to the population at large. However, the present research represents a promising start to the investigation of the impact of implementation intentions on metacognitive processes. Second, it was not feasible to measure whether implementation intentions enhanced the salience of the critical cues and caused the appropriate responses to be Footnote 5 continued the present study). Thus the operation of implementation intentions cannot be ascribed solely to increasing self-monitoring of critical situations per se (see Arden & Armitage, 2012; Armitage, 2008; Armitage et al., in press; Armitage & Arden, 2010, 2012; Armitage et al., 2014).
linked automatically in this field study, but confidence in this part of the causal chain can be gleaned from numerous laboratory studies (Gollwitzer & Sheeran, 2006). Third, the main outcome measure has not yet been validated in an English sample of health care workers and does not show perfect correspondence with biomarkers (Bogers et al., 2004). As with most self-report measures of food intake, caution is therefore warranted in interpreting the findings. Fourth, the Cronbach’s alpha values associated with the self-monitoring scales did not breach the 0.70 criterion that is commonly used to denote ‘‘good’’ internal reliability. However, the main effect of a lack of reliability in the measures would be to decrease the likelihood of any differences reaching statistical significance, which was not the case in the present study. Nevertheless, it would be valuable to develop more reliable measures of self-monitoring for use in future work. Fifth, although the control group received instructions that were not completely passive, it is possible that more complex instructions could have boosted the fruit intake of participants in the control group.6
Conclusion The present study shows that implementation intentions at least partially operate via increases in self-monitoring. Further research is required to replicate the present findings in alternative behavioral domains and to explore what other metacognitive processes might be influenced by implementation intention formation. Nevertheless, the present approach might be an effective but relatively inexpensive way of promoting health behavior. Acknowledgments I would like to thank Tasos Droulias for his help with collecting and entering the data. Conflict of interest interest.
Christopher J. Armitage declares no conflict of
Human and Animal Rights and Informed Consent The procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all participants for being included in the study.
References Abraham, C., & Michie, S. (2008). A taxonomy of behavior change techniques used in interventions. Health Psychology, 27, 379–387. doi:10.1037/0279-6133.27.3.379
6 Thanks are due to the anonymous reviewer who noted this potential limitation.
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J Behav Med Arden, M. A., & Armitage, C. J. (2012). A volitional help sheet to reduce binge drinking in students: A randomized exploratory trial. Alcohol and Alcoholism, 47, 156–159. Armitage, C. J. (2006). Evidence that implementation intentions promote transitions through the stages of change. Journal of Consulting and Clinical Psychology, 74, 141–151. Armitage, C. J. (2007). Effects of an implementation intention-based intervention on fruit consumption. Psychology and Health, 22, 917–928. Armitage, C. J. (2008). A volitional help sheet to encourage smoking cessation: A randomized exploratory trial. Health Psychology, 27, 557–566. Armitage, C. J., Abdul Rahim, W., Rowe, R., & O’Connor, R. C. (in press). An exploratory randomized trial of a simple, brief psychological intervention to reduce subsequent suicidal ideation and behaviour in patients hospitalized for self-harm. British Journal of Psychiatry. Armitage, C. J., & Arden, M. A. (2010). A volitional help sheet to increase physical activity in people with low socioeconomic status: A randomized exploratory trial. Psychology and Health, 25, 1129–1145. Armitage, C. J., & Arden, M. A. (2012). A volitional help sheet to reduce alcohol consumption in the general population: A field experiment. Prevention Science, 13, 635–643. Armitage, C. J., Norman, P., Noor, M., Alganem, S., & Arden, M. A. (2014). Evidence that a very brief psychological intervention boosts weight loss in a weight loss program. Behavior Therapy, 45, 700–707. Armor, D. A., & Taylor, S. E. (2003). The effects of mindset on behavior: Self-regulation in deliberative and implemental frames of mind. Personality and Social Psychology Bulletin, 29, 86–95. Barbeau, E. M., Wallace, L., Lederman, R., Lightman, N., Stoddard, A., & Sorensen, G. (2004). Recruiting small manufacturing worksites that employ multiethnic, low-wage workforces into a cancer prevention research trial. Preventing Chronic Disease, 1(3). Retrieved October 18, 2013 from http://www.cdc.gov/pcd/ issues/2004/jul/03_0020.htm Bogers, R. P., van Assema, P., Kester, A. D. M., Westerterp, K. R., & Dagnelie, P. C. (2004). Reproducibility, validity, and responsiveness to change of a short questionnaire for measuring fruit and vegetable intake. American Journal of Epidemiology, 159, 900–909. Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: An update. Trends in Cognitive Sciences, 8, 539–546. Brandsta¨tter, V., & Frank, V. (2002). Effects of deliberative and implemental mindsets on persistence in goal-directed behavior. Personality and Social Psychology Bulletin, 28, 1366–1378. Carver, C. S., & Scheier, M. F. (1982). Control theory: A useful conceptual framework for personality-social, clinical, and health psychology. Psychological Bulletin, 92, 111–135. doi:10.1037// 0033-2909.92.1.111 Charng, H. W., Piliavin, J. A., & Callero, P. L. (1988). Role-identity and reasoned action in the prediction of repeated behavior. Social Psychology Quarterly, 51, 303–317. doi:10.2307/2786758 Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. London: Erlbaum. Conner, M., & Higgins, A. R. (2010). Long-term effects of implementation intentions on prevention of smoking uptake among adolescents: A cluster randomized controlled trial. Health Psychology, 29, 529–538. doi:10.1037/a0020317 Cronbach, L. J., & Furby, L. (1970). How we should measure ‘‘change’’: Or should we? Psychological Bulletin, 74, 68–80. de Nooijer, J., de Vet, E., Brug, J., & de Vries, N. K. (2006). Do implementation intentions help to turn good intentions into
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higher fruit intakes? Journal of Nutrition Education and Behavior, 38, 25–29. Gawrilow, C., Gollwitzer, P. M., & Oettingen, G. (2011). If-then plans benefit executive functions in children with ADHD. Journal of Social and Clinical Psychology, 30, 616–646. Gollwitzer, P. M. (1990). Action phases and mind-sets. In E. T. Higgins & R. M. Sorrentino (Eds.), The handbook of motivation and cognition: Foundations of social behavior (pp. 53–92). New York: Guilford Press. Gollwitzer, P. M. (1993). Goal achievement: The role of intentions. European Review of Social Psychology, 4, 141–185. Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta-analysis of effects and processes. Advances in Experimental Social Psychology, 38, 69–119. Heckhausen, H., & Gollwitzer, P. M. (1987). Thought contents and cognitive functioning in motivational versus volitional states of mind. Motivation and Emotion, 11, 101–120. Holroyd, C. B., & Coles, M. G. H. (2002). The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679–709. doi:10.1037//0033-295X.109.4.679 Karoly, P. (1993). Mechanisms of self-regulation: A systems view. Annual Review of Psychology, 44, 23–52. doi:10.1146/annurev. psych.44.1.23 Michie, S., Abraham, C., Whittington, C., McAteer, J., & Gupta, S. (2009). Effective techniques in healthy eating and physical activity interventions: A meta-regression. Health Psychology, 28, 690–701. doi:10.1037/a0016136 Paul, I., Gawrilow, C., Zech, F., Gollwitzer, P., Rockstroh, B., Odenthal, G., et al. (2007). If-then planning modulates the P300 in children with attention deficit hyperactivity disorder. NeuroReport, 18, 653–657. doi:10.1097/WNR.0b013e3280bef966 Pliner, P. (1982). The effects of mere exposure on liking for edible substances. Appetite, 3, 283–290. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891. Prochaska, J. O., & DiClemente, C. C. (1983). Stages and processes of self-change in smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51, 390–395. Reeve, R. A., & Brown, A. L. (1985). Metacognition reconsidered: Implications for intervention research. Journal of Abnormal Child Psychology, 13, 343–356. Sniehotta, F. F., Nagy, G., & Scholz, U. (2006). The role of action control in implementing intentions during the first weeks of behaviour change. British Journal of Social Psychology, 45, 87–106. doi:10.1348/014466605X62460 Soureti, A., Murray, P., Cobain, M., van Mechelen, W., & Hurling, R. (2011). Web-based risk communication and planning in an obese population: Exploratory study. Journal of Medical Internet Research, 13, e100. doi:10.2196/jmir.1579 van Dongen, J. M., Proper, K. I., van Wier, M. F., van der Beek, A. J., Bongers, P. M., van Mechelen, W., & van Tulder, M. W. (2012). A systematic review of the cost-effectiveness of worksite physical activity and/or nutrition programs. Scandinavian Journal of Work, Environment & Health, 38, 393–408. doi:10.5271/ sjweh.3275 van Duyn, M. A. S., & Pivonka, E. (2000). Overview of the health benefits of fruit and vegetable consumption for the dietetics professional: Selected literature. Journal of the American Dietetic Association, 100, 1511–1521. Webb, T. L., & Sheeran, P. (2008). Mechanisms of implementation intention effects: The role of goal intentions, self-efficacy, and accessibility of plan components. British Journal of Social Psychology, 47, 373–395. doi:10.1348/014466607X267010
Field experiment of a very brief worksite intervention to improve nutrition among health care workers Christopher J. Armitage1
Received: June 25, 2014 / Accepted: March 19, 2015 Ó Springer Science+Business Media New York 2015
Abstract Despite the potential of worksite interventions to boost productivity and save insurance costs, they tend to be costly and tested in nonrandomized trials. The aim of the present study was to test the ability of a very brief worksite intervention based on implementation intentions to improve nutrition among health care workers. Seventynine health care workers were randomly allocated to a control condition or to form implementation intentions using standard instructions or with a supporting tool. Fruit intake and metacognitive processing (operationalized as awareness of standards, self-monitoring and self-regulatory effort) were measured at baseline and follow-up. Participants who formed implementation intentions ate significantly more fruit and engaged in significantly more metacognitive processing at follow-up than did participants in the control condition (ds [ .70). The findings support the efficacy of implementation intentions for increasing fruit intake in health care workers and preliminary support for the utility of a tool to support implementation intention formation. Keywords Worksite intervention Metacognition Brief intervention Health behavior change Implementation intentions Fruit intake Volitional help sheet
& Christopher J. Armitage [email protected] 1
Manchester Centre for Health Psychology, School of Psychological Sciences, Manchester Academic Health Science Centre, University of Manchester, Coupland Street, Oxford Road, Manchester M13 9PL, UK
Introduction Worksite health interventions Worksite health interventions can confer considerable individual and organizational benefits, for example, by reducing personal risk of illness and thereby reducing absenteeism and increasing productivity. Despite these many advantages, a recent systematic review of worksitebased nutrition/physical activity interventions revealed mixed findings (e.g., a dearth of randomized controlled trials), and the more successful interventions tended to be the most costly in terms of both time and resources (van Dongen et al., 2012). This is potentially problematic because cost and disruption to the working day can represent major barriers to implementing programs in the first place (Barbeau et al., 2004). Thus, it is important to develop very brief, effective, evidence-based interventions. The present study tests the ability of a very brief psychological intervention based on implementation intentions (Gollwitzer, 1993) to improve nutrition among health care workers. Implementation intentions Implementation intentions are ‘‘if–then’’ plans that improve performance on a wide variety of cognitive tasks and have been shown to cause sustained changes in health behavior (Gollwitzer, 1993; Gollwitzer & Sheeran, 2006). Participants form implementation intentions by linking in memory a critical situation (‘‘if’’) with an appropriate response (‘‘then’’). Laboratory studies show that specifying the ‘‘if’’ component of an implementation intention enhances the accessibility of critical situations and that linking ‘‘if’’ with ‘‘then’’ automates the response specified in the ‘‘then’’ component (Gollwitzer & Sheeran, 2006). For example, one
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possible cue might be ‘‘because I am busy’’ and it could be linked to ‘‘then I will tell myself that if I try hard enough I can have an extra portion of fruit each day’’ as an appropriate response. The idea is that when the temptation not to eat fruit through being busy is encountered, the appropriate response (‘‘telling myself that if I try hard enough I can have an extra portion of fruit each day’’ in this example) is triggered automatically (Gollwitzer & Sheeran, 2006). Gollwitzer and Sheeran’s (2006) meta-analysis shows that implementation intentions are an effective means of changing behavior: Across 94 independent studies in laboratory and field settings, implementation intentions exerted an average effect size of d = 0.65. Gollwitzer and Sheeran (2006) also report medium-large effect sizes associated with the formation of implementation intentions and both the activation of the mental representation of the ‘‘if’’ component and automatic activation of the ‘‘then’’ component. Unfortunately, the means by which these processes are evaluated in the laboratory (e.g., accessibility of the critical situation; immediacy of the appropriate response) are not amenable to testing in the field, where many implementation intention studies have been conducted (Gollwitzer & Sheeran, 2006). Mechanisms that might explain the operation of implementation intentions in field settings over extended periods of time have not yet been found (Webb & Sheeran, 2008). Metacognitive processing The present research tests the idea that, in addition to enhancing the accessibility of critical situations and automatizing the appropriate response, implementation intentions operate through changes in metacognitive processing that persist beyond the experimental session. Metacognitive processes are defined as, ‘‘the self-regulatory activities of the cognitive system’’ (Reeve & Brown, 1985, p. 346) that, ‘‘include planning, monitoring, checking, and regulating problem-solving behavior’’ (Reeve & Brown, 1985, p. 346). A growing body of laboratory research shows that implementation intentions can improve people’s metacognitive processing by minimizing the demands that metacognitive processing makes on executive functioning. For example, the P300 response is an index of metacognitive processing assessed using electroencephalography that is associated with the anterior cingulate cortex, which in turn is an important route for frontal brain regions to regulate behavior, specifically around error detection, conflict monitoring, and behavioral adjustment after erroneous responses (Botvinick et al., 2004; Holroyd & Coles, 2002). Paul et al. (2007) showed that implementation intentions improved response inhibition and increased the P300 in children with attention deficit hyperactivity disorder (Gawrilow et al., 2011).
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However, this laboratory research has been limited to cognitive tasks such as the Wisconsin Card Sorting Test (Gawrilow et al., 2011), which are less complex than the sustained behavior change that has been demonstrated in field studies conducted over periods of time extending to weeks or even years. For example, Conner and Higgins (2010) showed that implementation intentions prevented adolescents from taking up smoking 2 years following the intervention. The question therefore arises as to whether such sustained effects of implementation intentions on complex behaviors such as not starting to smoke cigarettes or increasing fruit consumption can be explained solely by activation of the mental representation of an ‘‘if’’ component and automatic activation of a ‘‘then’’ component. The present research tests the hypothesis that forming implementation intentions per se (i.e., as opposed to targeting specific metacognitive processes, see Gawrilow et al., 2011; Paul et al., 2007) facilitates behavior change by freeing up executive resources and thereby augmenting the operation of metacognitive processes such as awareness of standards, self-monitoring and self-regulatory effort (Karoly, 1993). The principal idea is that specifying critical situations increases awareness of standards and selfmonitoring, and that the automatization of appropriate responses enhances self-regulatory effort. In addition to considering the impact of implementation intentions on metacognitive processing, the aim of the present research was to increase fruit intake, a behavior that is important in reducing cancer, heart disease and stroke (van Duyn & Pivonka, 2000). To date, two published studies have shown that implementation intentions significantly increase fruit intake independent of vegetable intake (Armitage, 2007; de Nooijer et al., 2006), meaning there are good grounds for assuming that the manipulations used in the present research will be effective and thereby allow for the appropriate tests of mediation. The research reviewed above provides the following rationale for the present research. First, implementation intentions have been shown to be effective in changing behaviour over extended periods of time, but field experiments have yet to identify a causal mechanism (Webb & Sheeran, 2008). Second, laboratory research shows that implementation intentions can augment specific metacognitive processes (Paul et al., 2007) and it is hypothesized that because implementation intentions minimize the impact of metacognitive processes on executive functioning, forming implementation intentions can augment the use of a range of metacognitive processes (Gawrilow et al., 2011). Third, it is hypothesized that using a tool to support the formation of implementation intentions (a ‘‘volitional help sheet’’, Armitage, 2008), as opposed to asking people to form their own (‘‘self-generated’’) implementation intentions, will further reduce the demands on executive func-
J Behav Med Fig. 1 Hypothesized relationships among study components
Implementation Intention Formation
tioning and thereby maximize metacognitive processing and hence behavior change. The present research is conducted in the field with the overarching goal of increasing fruit intake, an important health behavior (van Duyn & Pivonka, 2000). To summarize, it was hypothesized that: (a) forming implementation intentions would significantly increase fruit consumption; (b) the effects of forming implementation intentions on behavior change would be mediated by increases in metacognitive processing (Fig. 1); and (c) a tool that supported implementation intention formation (the ‘‘volitional help sheet’’) would exert the largest effects on metacognitive processing and behavior change.
Method Participants The University ethics committee gave approval to conduct the research: Participants were assured of their confidentiality and anonymity (personal codes were used to identify individuals), and were made aware of their right to withdraw from the study or have their data removed at any point. Informed consent was obtained before the study began. Seventy-nine participants were recruited from staff working at a private hospital in England. The sample consisted of 19 men and 60 women aged 19–63 years (M = 42.44 years, SD = 11.28). All participants were contacted again and were invited to complete follow-up questionnaires; however, 17 participants did not complete the follow-up questionnaire (Fig. 2), largely due to exercising their right to withdraw from the research. The data were analyzed according to intention to treat, with the last observation carried forward.1 Design A mixed measures design was employed with one betweenparticipants factor and one within-participants factor. Condition (control versus self-generated implementation 1
The analyses were rerun using per protocol and item imputation, and there were no substantive differences to the pattern of findings. Moreover, there were no significant differences in key variables at baseline between those who remained in the study and those who dropped out, F(5, 73) = 0.30, p = .91, g2p = .02, d = .29.
Boosts Metacognitive Processing
Increases Fruit Intake
intention versus volitional help sheet implementation intention) was the between-participants variable; and time (baseline versus follow-up) was the within-participants variable. The main outcome measure was fruit intake. The follow-up took place 1 month post-baseline. Procedure All participants received a copy of the baseline questionnaire through the internal mail system. The manipulations were placed at the end of identical-looking questionnaires, which were sorted into random order using a web-based randomizer prior to data collection. Once the baseline questionnaire and manipulations were completed, the participant returned it to the researcher in a sealed envelope via the internal mail system. Follow-up questionnaires were also administered via the internal mail system. Baseline and follow-up questionnaires were matched using personal codes. Manipulations Participants in all three conditions were presented with a brief statement designed to encourage them to plan to consume an extra portion of fruit each day (‘‘We want you to plan to have an extra portion of fruit each day because forming plans has been shown to increase fruit intake’’).2 Following this statement, participants randomized to the control condition were given space to write their plans. In addition to the brief statement, participants in the selfgenerated implementation intention condition were given standard (Armitage, 2006) implementation intention instructions: ‘‘You are free to choose how you will do this, 2
Participants in the control condition were asked to form plans because it controls for the possibility that being asked to plan exerts effects that are independent of being asked to form an implementation intention (it is not possible to ask people to form implementation intentions without asking them to plan, but it is possible to ask people simply to plan). Importantly, research shows that simply asking people to plan is equivalent to the passive/questionnaire-only control groups that have commonly been used in implementation intention research (Armitage, 2008). In addition to controlling for ‘‘generic planning,’’ the present control group may also be preferable to asking participants to form goal intentions (another control condition employed in implementation intention research) because prompting intention formation is a behavior change technique in its own right (Abraham & Michie, 2008) that brings about a deliberative mindset that works counter to implementation intentions (Brandsta¨tter & Frank, 2002; Heckhausen & Gollwitzer, 1987). Generic planning can therefore be considered an active but neutral control group.
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Pool of Potential Participants N = 118 Participants Randomized to Condition n = 79
Did not Participate n = 39
Active Control n = 23
VHS Implementation Intentions n = 33
Self-Generated Implementation Intentions n = 23
Lost to Follow-Up n=6
Lost to Follow-Up n=7
Lost to Follow-Up n=4
Follow-Up (Intention to Treat) n = 23
Follow-Up (Intention to Treat) n = 33
Follow-Up (Intention to Treat) n = 23
Fig. 2 Flow of participants through the experiment. Note: VHS volitional help sheet
but we want you to formulate your plans in as much detail as possible. Please pay particular attention to the situations in which you will implement these plans’’ and participants were left space in which to write their implementation intentions. Participants in the volitional help sheet implementation intention condition had a volitional help sheet appended to their questionnaires following the brief statement encouraging them to plan to consume an extra portion of fruit each day. The volitional help sheet was similar to those used to support implementation intention formation and successful health behavior change in previous research (Armitage, 2008). It consisted of a table with two columns each containing lists of ten critical situations and ten appropriate responses. The ten critical situations were derived from items used to measure temptations and the ten appropriate responses were derived from items used to measure the processes of change from Prochaska and DiClemente’s (1983) transtheoretical model. The temptation items were translated into ‘‘if’’ statements, for example: ‘‘If I’m tempted not to have an extra portion of fruit because I feel that I don’t have the time;’’ the processes of change items were translated into ‘‘then’’ statements, for example, ‘‘then I will tell myself that if I try hard enough I can have an extra portion of fruit each day.’’ There was one item for each of the ten processes of change. Participants in the volitional help sheet condition were told that identifying situations in which they were tempted not to have an extra portion of fruit each day and identifying ways to overcome those temptations had been shown to increase fruit intake. They were then asked to draw links between as many critical situations and appropriate responses as they wanted and thereby form implementation intentions.
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Measures Fruit intake was assessed at baseline and follow-up using the fruit section of Bogers et al. (2004) measure, which has been successfully validated against biomarkers. Bogers et al.’s (2004) measure consists of five pairs of items designed to measure fruit consumption in terms of both frequency and quantity. Frequency was measured with items such as, ‘‘In the last month, how often on average did you eat apples and pears?’’ which was measured on a 9-point scale with the response options: Never or less than 1 day a month, 1–3 days a month, 1 day a week, 2 days a week, 3 days a week, 4 days a week, 5 days a week, 6 days a week, and 7 days a week. Quantity was measured with items such as ‘‘On a day when you ate apples and pears in the last month, how many did you eat?’’ to which participants responded on 5-point scales ranging from ‘‘1’’ to ‘‘5 or more.’’ The means of the frequency and quantity items were used as scales in addition to the sum of the products of the frequency and quantity items (Bogers et al., 2004). Metacognitive processing was also measured at both baseline and follow-up. Adaptations of Sniehotta et al. (2006) six items were used to capture three facets of action control (Karoly, 1993) on 7-point (+1 to +7) strongly disagree-strongly agree scales. Awareness of standards was measured with two items, ‘‘During the last month I often had my intention to have an extra portion of fruit each day on my mind’’ and ‘‘During the last month I was always aware of my ideal levels of fruit intake’’, which had good internal reliability abaseline = .71; afollow-up = .66. Selfmonitoring was assessed with: ‘‘During the last month I constantly monitored my fruit intake to make sure it was enough’’ and ‘‘During the last month I watched myself to
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make sure I was having enough fruit,’’ which had adequate internal reliability, abaseline = .60; afollow-up = .66. Selfregulatory effort was measured with: ‘‘During the last month I tried my best to act in a way that was consistent with my personal standards regarding having fruit’’ and ‘‘During the last month I really tried to have an extra portion of fruit each day,’’ which had good internal reliability, abaseline = .85; afollow-up = .83. Data analysis Randomization was tested using MANOVA. This was designed to establish that the three groups of participants were similar in terms of their demographics and other descriptive characteristics. The effects of the manipulations were tested using ANCOVAs that controlled for baseline values (i.e., metacognitive processing and fruit intake). Simple planned contrasts were used to test whether the volitional help sheet was superior to the control condition and the self-generated implementation intention for each of the six dependent variables. Thus the alpha value for these tests was set at .004 [standard alpha = .05/(2 comparisons 9 6 dependent variables) = .004] in accordance with Bonferroni’s correction. Because the use of difference scores (i.e., subtracting follow-up measure of metacognitive processing from baseline measure of metacognitive processing) has been subject to criticism on statistical grounds (Cronbach & Furby, 1970), residualized change scores were used to capture changes in metacognitive processing over time (Cohen et al., 2003). Residualized change scores have the effect of partialing out the part of the follow-up data that is linearly predictable from baseline. Using residualized change scores thus eliminates the correlation between changes in metacognitive processing over time and baseline metacognitive processing. Mediation was tested formally using the bootstrapping procedures outlined in Preacher and Hayes (2008). The basis for these analyses is that the indirect effect of implementation intentions on the dependent variable (i.e., fruit intake) is the product of the paths between implementation intentions and mediator (i.e., changes in metacognitive processing), and between mediators and dependent variable. However, such indirect effects are not normally distributed, meaning that bootstrapping is necessary (Preacher & Hayes, 2008). Bootstrapping involves resampling random subsets of data in order to gain a nonparametric approximation of the sampling distribution of the product of the implementation intention—mediator and mediator-dependent variable paths. The analyses presented here are based on 10,000 resamples, although repeating the bootstrapping analyses with fewer and more resamples made no difference to the findings of the mediation analyses.
Results Randomization check Randomization was checked using MANOVA. The independent variable was condition with three levels: Control, self-generated implementation intentions and volitional help sheet implementation intentions. The dependent variables were age, gender, baseline metacognitive processes and baseline fruit intake (frequency of consumption, grams consumed and the multiplicative term). The multivariate test and all the univariate tests were nonsignificant, F(10, 144) = 0.26, p = .99, g2p = .02, meaning randomization was achieved (Table 1). Effects of implementation intentions on metacognitive processing The effect of implementation intention formation on metacognitive processing was tested using ANCOVAs, with each facet of action control as the dependent variables and baseline action control facets as the covariates. Condition was the between-participants factor. With follow-up awareness of standards as the dependent variable and baseline awareness of standards statistically controlled, there was a significant main effect of condition, F(2, 79) = 7.85, p = .001, g2p = .17, d = .90 (Table 2). The nature of this effect was decomposed using simple planned contrasts, with the volitional help sheet condition as the reference category. This showed that people in the volitional help sheet condition reported significantly greater awareness of standards than those in the control condition (contrast estimate = -1.32, 95 % CI = -2.00, -0.63, p \ .001) but only marginally more in the self-generated implementation intention condition (contrast estimate = -0.86, 95 % CI = -1.54, -.17, p = .01).3 Equivalent analyses were conducted with self-monitoring as the dependent variable. Again, there was a significant main effect of condition, F(2, 79) = 7.70, p = .001, g2p = .17, d = .90 (Table 2). Simple planned contrasts showed that people in the volitional help sheet condition reported significantly greater levels of selfmonitoring than people in the control condition (contrast 3
Note that one possible alternative explanation for this pattern of findings is that the nature of the implementation intentions differed between the volitional help sheet condition and the self-generated condition. However, consistent with previous research (Armitage, 2008), differences in the nature of implementation intentions formed within the self-generated condition were larger than the differences in the nature of implementation intentions formed between the selfgenerated and volitional help sheet conditions. Moreover, in the volitional help sheet condition, implementation intentions are formed by drawing lines; consequently, it is not possible to judge the quality of the implementation intention accurately.
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J Behav Med Table 1 Pre-randomization characteristics of the sample Variables
Age (years)
Control condition
Self-generated condition
Volitional help sheet condition
M
SD
M
M
SD
43.91
11.09
40.87
9.52
42.51
12.66
.66
0.22
0.42
0.26
0.45
0.24
0.43
.94
Gendera
SD
p
Awareness of standards
3.63
1.55
3.76
1.70
3.53
1.82
.88
Self-monitoring
4.09
0.97
3.63
1.57
3.80
1.78
.59
Self-regulatory effort
2.83
1.43
3.35
1.71
3.45
1.93
.39
Quantity of fruit (portions/day)
1.30
0.26
1.31
0.33
1.36
0.39
.75
Frequency of fruit consumption
3.41
1.18
3.76
1.29
3.56
1.66
.70
28.04
11.38
30.96
15.06
31.30
17.91
.72
Volitional help sheet condition
F(2, 79)
Sum of quantity 9 frequency a
Men = 0; women = 1
Table 2 Effects of implementation intentions on metacognitive processing at follow-up Dependent variables
Control condition
Self-generated condition
Awareness of standards
7.85**
M
3.56a
4.09b
4.83c
SD
1.46
1.70
1.37
M
4.02a
4.41b
5.09b
SD
1.69
1.40
1.24
Self-monitoring
7.70**
Self-regulatory effort
1.13
M
4.13
3.76
4.64
SD
3.48
1.74
1.66
Figures are ‘‘raw’’ scores unadjusted for baseline. Values with different subscripts in a row differ significantly, p B .05 ** p \ .01
estimate = -1.23, 95 % CI = -1.85, -0.60, p \ .001) but the higher levels of self-monitoring in the volitional help sheet condition proved not to be statistically significant compared with the self-generated condition (contrast estimate = -0.58, 95 % CI = -1.20, 0.04, p = .07). In contrast, analyses with self-regulatory effort as the dependent variable showed no significant differences between conditions, F(2, 79) = 1.13, p = .33, g2p = .03. Effects of implementation intentions on health behavior change The effect of implementation intention formation on health behavior change was tested using ANCOVAs, with followup fruit intake as the dependent variable and baseline fruit intake as the covariate. Condition was the experimental between-participants factor. With quantity of fruit consumed as the dependent variable and baseline quantity of fruit consumed statistically controlled, there was a significant main effect of condition, F(2, 79) = 5.85, p = .004, g2p = .13, d = .77 (Table 2). The nature of this effect was decomposed using simple planned contrasts,
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with the volitional help sheet condition as the reference category. This showed that people in the volitional help sheet condition ate no more fruit than people in the selfgenerated condition (contrast estimate = -0.06, 95 % CI = -0.16, 0.04, p = .25) but a significantly greater quantity of fruit than those in the control (contrast estimate = -0.18, 95 % CI = -0.28, -0.07, p = .001) condition. Similar analyses were conducted using frequency of fruit consumption as the dependent variable. This also showed a significant effect of condition, F(2, 79) = 5.79, p = .005, g2p = .13, d = .77 (Table 3). Simple planned contrasts showed that people in the volitional help sheet condition ate fruit significantly more often than those in the control condition (contrast estimate = -0.67, 95 % CI = -1.08, -0.26, p = .002), but only marginally more than those in the self-generated condition (contrast estimate = -0.47, 95 % CI = -0.89, -0.06, p = .02). A similar pattern of findings emerged when the summed multiplicative scale was used as the dependent variable. There was a significant effect of condition, F(2, 79) = 6.49, p = .003, g2p = .15, d = .84 (Table 3). Sim-
J Behav Med Table 3 Effects of implementation intentions on follow-up fruit intake Dependent variables
Control condition
Self-generated condition
Volitional help sheet condition
Quantity of fruit (portions/day)
F(2, 79) 5.85**
M
1.25a
1.38b
1.46c
SD
0.19
0.26
0.27
M
3.54a
4.04b
4.34c
SD
1.35
1.25
1.55
M
28.43a
33.96b
41.09c
SD
13.28
14.06
19.63
Frequency of fruit consumption
5.79**
Sum of quantity 9 frequency
6.49**
Figures are ‘‘raw’’ scores unadjusted for baseline. Values with different subscripts in a row differ significantly, p B .05 ** p \ .01
ple planned contrasts showed that people in the volitional help sheet condition ate significantly more fruit than those in the control condition (contrast estimate = -9.99, 95 % CI = -15.77, -4.26, p = .001), but only marginally more than in the self-generated condition (contrast estimate = -6.85, 95 % CI = -12.61, -1.09, p = .02). Does metacognitive processing mediate the effects of implementation intentions? In order to test whether the changes in metacognitive processing mediated the effect of implementation intentions on changes in fruit intake, bootstrapping procedures for testing multiple potential mediators outlined in Preacher and Hayes (2008) were used. Thus, the independent variable was condition (dummy-coded as 1 = volitional help sheet and self-generated implementation intention conditions, 0 = control condition); the mediators were changes in awareness of standards, self-monitoring and self-regulatory effort; the dependent variable was fruit intake at follow-up; and baseline fruit intake was entered as a covariate. The analyses were repeated three times, with each index of fruit consumption serving as covariate (baseline measure) and dependent variable (follow-up measure) in the analysis. With frequency of fruit consumption as the dependent variable, the confidence intervals associated with changes in metacognitive processing all contained zero. Thus, metacognitive processing did not significantly mediate the effects of implementation intentions on changes in the frequency of fruit consumption. When the quantity of fruit consumed was entered as the covariate/dependent variable, the confidence intervals associated with changes in both awareness of standards and self-regulatory effort contained zero, but increases in self-monitoring did significantly
(p \ .05) mediate the effects of implementation intentions on the quantity of fruit consumed (95 % CI 0.001, 0.14). Consistent with the preceding analyses, when the multiplicative scale was used as the covariate/dependent variable, the confidence intervals of the indirect effect associated with changes in awareness of standards and selfregulatory effort contained zero. However, the confidence intervals associated with the indirect effect of selfmonitoring did not contain zero (95 % CI 0.34, 8.42). Thus, the effect of implementation intention formation on the multiplicative index of change in fruit intake was significantly (p \ .05) mediated by increases in participants’ levels of self-monitoring. Does metacognitive processing mediate the effects of the volitional help sheet? Similar procedures were used to test whether the effect of the volitional help sheet on changes in fruit intake was mediated by the changes in metacognitive processing. For these analyses, the independent variable was condition (dummy-coded as 1 = volitional help sheet implementation intention condition, 0 = self-generated implementation intention condition); the mediators were changes in awareness of standards, self-monitoring and self-regulatory effort; the dependent variable was fruit intake at follow-up; and baseline fruit intake was entered as a covariate. Consistent with the approach described above, the analyses were repeated three times, with each index of fruit consumption serving as covariate/dependent variable in the analysis. The results were consistent across each index of fruit consumption: All the confidence intervals associated with changes in awareness of standards and self-regulatory effort contained zero, but increases in self-monitoring significantly (p \ .05) mediated the effects of the volitional
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help sheet on the frequency of fruit consumption (95 % CI 0.02, 0.65), quantity of fruit consumed (95 % CI 0.01, 0.17) and the multiplicative index (95 % CI 0.64, 9.65).4
Discussion Summary The present study set out to test the hypothesis that implementation intentions bring about changes in behavior by increasing metacognitive processing in a field setting. The key findings were that forming implementation intentions both increased metacognitive processing and fruit consumption, and that the effects of implementation intentions on behavior change were mediated via increases in selfmonitoring. In addition, this is the first study to have tested a volitional help sheet in a health care setting with the aim of increasing fruit intake. The following discussion considers the practical and theoretical implications of the findings. Fruit intake and implementation intentions Consistent with a growing body of research on the impact of implementation intention-based interventions on health behavior change, the present study showed that implementation intentions were effective in increasing self-reported fruit intake (Armitage, 2007; de Nooijer et al., 2006) with marginally superior effects when a volitional help sheet supported implementation intention formation. Although the intervention was deployed on a minimal basis by incorporating it into a questionnaire, there is much potential for employing the strategy in clinical practice. Alongside other treatment techniques, implementation intentions could be developed on a one-to-one basis, with the health professional and client identifying critical situations and appropriate behavioral responses using the volitional help sheet. Nevertheless, the volitional help sheet offers an efficient and cost-effective means of encouraging people to form theory-based implementation intentions, which has recently been adapted into an online format (Soureti et al., 2011). What mediates the effects of implementation intentions? To date, implementation intentions have been shown consistently to work by increasing the salience of critical 4 Note that the direct paths between independent variable and dependent variable were statistically significantly reduced. However, because none of the direct paths was reduced to zero, technically, this is best described as partial (as opposed to full) mediation.
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situations and automatizing appropriate responses (Gollwitzer & Sheeran, 2006). However, this does not fully explain why implementation intentions are capable of changing behavior over sustained periods of time (Conner & Higgins, 2010). It was therefore hypothesized that implementation intentions work to reduce demands on executive functioning, increase metacognitive processing and thereby enhance self-regulation (Gawrilow et al., 2011; Paul et al., 2007). There was support for this hypothesis: Regardless of the means by which they were formed (selfgenerated or with volitional help sheet), implementation intentions significantly boosted awareness of standards, self-monitoring and self-regulatory effort (Karoly, 1993) in addition to changing behavior. Marginally superior effects were observed for the effects of the volitional help sheet on awareness of standards, which implies that providing participants with examples of critical situations and appropriate behavioral responses might help raise awareness of appropriate actions in specific contexts. Further work is required to test this hypothesis. Despite significant increases in metacognitive processing in general, mediational analyses revealed that the effects of implementation intentions were mediated by selfmonitoring specifically, and the question arises as to why. Implementation intentions are part of a broader theory, the Rubicon model (Gollwitzer, 1990; Heckhausen & Gollwitzer, 1987) that distinguishes between a predecisional phase when a deliberative mindset facilitates decisionmaking and a postdecisional phase when an implemental mindset facilitates goal achievement. Implemental mindsets are activated by asking participants to plan when, where and how they will achieve their goal (Brandsta¨tter & Frank, 2002), just like the implementation intention manipulations described in the present study. A key characteristic of the implemental mindset is that ‘‘it allows a flexible response to situational demands’’ (Brandsta¨tter & Frank, 2002, p. 1377) and Armor and Taylor (2003) have shown that increases in performance assessments mediated the effects of an implemental mindset on task performance. Given that performance assessments are contingent on selfmonitoring (Carver & Scheier, 1982), it is plausible that this is the mechanism through which implementation intentions facilitate self-monitoring.5 Further work is required to test this proposition directly. 5 Note that it could be argued that the present intervention improves self-monitoring per se, by drawing participants’ attention to critical situations. However, this explanation is ruled out by research that has tested volitional help sheets against control conditions in which participants are asked to identify critical situations. Specifically, participants in these control conditions are presented with the volitional help sheet and are given identical instructions to the experimental condition, but are asked to tick critical situations/appropriate responses that might work for them (as opposed to linking critical situations/appropriate responses in the experimental condition and in
J Behav Med
Given that increased self-monitoring alone was found to mediate the relationship between implementation intention formation and behavior change significantly yet did not reduce the direct path between implementation intention formation and behavior change to zero, it raises the question of whether there are other metacognitive processes, such as task-switching and resilience to distraction (Gawrilow et al., 2011) that might improve on this explanation of the relationship between implementation intentions and behavior change. Interestingly, though, the finding that self-monitoring was the only significant mediator is consistent with Michie et al.’s (2009) metaregression. In their analysis of 26 behavior change techniques across 122 empirical tests, Michie et al. (2009) were able to demonstrate that self-monitoring was the most powerful ingredient of interventions designed to increase healthy eating and physical activity. It would therefore be valuable to investigate whether direct prompting of selfmonitoring via implementation intentions would be an effective intervention in its own right. It is also worth reflecting on the fact that the present research was conducted over the period of a month, meaning that increases in self-monitoring might only mediate the effects of implementation intentions on behavior change in the medium-term. It is therefore plausible that other processes, such as changes in identity (Charng et al., 1988) or mere exposure (Pliner, 1982) might explain the longer-term effects of implementation intentions on behavior change (Conner & Higgins, 2010). Further research is required to explore these alternative explanations of implementation intention effects and contrast them with the mediating effects of increased self-monitoring found in the present research. Limitations Although the present research takes the literature on implementation intentions forward in a number of important respects, it is important to acknowledge some potential limitations of the study. First, the sample was recruited from a group of health care workers that included more women than men, and so it may be that the findings are not generalizable to the population at large. However, the present research represents a promising start to the investigation of the impact of implementation intentions on metacognitive processes. Second, it was not feasible to measure whether implementation intentions enhanced the salience of the critical cues and caused the appropriate responses to be Footnote 5 continued the present study). Thus the operation of implementation intentions cannot be ascribed solely to increasing self-monitoring of critical situations per se (see Arden & Armitage, 2012; Armitage, 2008; Armitage et al., in press; Armitage & Arden, 2010, 2012; Armitage et al., 2014).
linked automatically in this field study, but confidence in this part of the causal chain can be gleaned from numerous laboratory studies (Gollwitzer & Sheeran, 2006). Third, the main outcome measure has not yet been validated in an English sample of health care workers and does not show perfect correspondence with biomarkers (Bogers et al., 2004). As with most self-report measures of food intake, caution is therefore warranted in interpreting the findings. Fourth, the Cronbach’s alpha values associated with the self-monitoring scales did not breach the 0.70 criterion that is commonly used to denote ‘‘good’’ internal reliability. However, the main effect of a lack of reliability in the measures would be to decrease the likelihood of any differences reaching statistical significance, which was not the case in the present study. Nevertheless, it would be valuable to develop more reliable measures of self-monitoring for use in future work. Fifth, although the control group received instructions that were not completely passive, it is possible that more complex instructions could have boosted the fruit intake of participants in the control group.6
Conclusion The present study shows that implementation intentions at least partially operate via increases in self-monitoring. Further research is required to replicate the present findings in alternative behavioral domains and to explore what other metacognitive processes might be influenced by implementation intention formation. Nevertheless, the present approach might be an effective but relatively inexpensive way of promoting health behavior. Acknowledgments I would like to thank Tasos Droulias for his help with collecting and entering the data. Conflict of interest interest.
Christopher J. Armitage declares no conflict of
Human and Animal Rights and Informed Consent The procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all participants for being included in the study.
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6 Thanks are due to the anonymous reviewer who noted this potential limitation.
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