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The role of action control and action planning on fruit and vegetable consumption
Appetite 91 (2015) 64–68
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Research report
The role of action control and action planning on fruit and vegetable consumption Guangyu Zhou a, Yiqun Gan b,*, Miao Miao b, Kyra Hamilton c,d, Nina Knoll a, Ralf Schwarzer e,f a
Department of Educational Science and Psychology, Freie Universität Berlin, Germany Department of Psychology, Peking University, Beijing, China c School of Applied Psychology, Griffith University, Brisbane, Queensland, Australia d School of Psychology and Speech Pathology, Curtin University, Perth, Western Australia, Australia e Institute for Positive Psychology and Education, Australian Catholic University, Strathfield, Australia f University of Social Sciences and Humanities, Warsaw, Poland b
A R T I C L E
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Article history: Received 31 January 2015 Received in revised form 10 March 2015 Accepted 19 March 2015 Available online 26 March 2015 Keywords: Intention Action control Action planning Fruit and vegetable intake
A B S T R A C T
Globally, fruit and vegetable intake is lower than recommended despite being an important component to a healthy diet. Adopting or maintaining a sufficient amount of fruit and vegetables in one’s diet may require not only motivation but also self-regulatory processes. Action control and action planning are two key volitional determinants that have been identified in the literature; however, it is not fully understood how these two factors operate between intention and behavior. Thus, the aim of the current study was to explore the roles of action control and action planning as mediators between intentions and dietary behavior. A longitudinal study with three points in time was conducted. Participants (N = 286) were undergraduate students and invited to participate in a health behavior survey. At baseline (Time 1), measures of intention and fruit and vegetable intake were assessed. Two weeks later (Time 2), action control and action planning were assessed as putative sequential mediators. At Time 3 (two weeks after Time 2), fruit and vegetable consumption was measured as the outcome. The results revealed action control and action planning to sequentially mediate between intention and subsequent fruit and vegetable intake, controlling for baseline behavior. Both self-regulatory constructs, action control and action planning, make a difference when moving from motivation to action. Our preliminary evidence, therefore, suggests that planning may be more proximal to fruit and vegetable intake than action control. Further research, however, needs to be undertaken to substantiate this conclusion. © 2015 Elsevier Ltd. All rights reserved.
Introduction Despite accumulating evidence suggesting that nutritionally balanced diets rich in fruit and vegetables are associated with a reduced risk of chronic diseases including cardiovascular diseases and certain cancers (Boeing et al., 2012), globally fruit and vegetable consumption is lower than recommended. There are manifold reasons for consuming less than the minimum five daily servings of fruit and vegetables recommended by the World Health Organization (WHO) (Guilbert, 2003; Hall, Moore, Harper, & Lynch, 2009; Nebeling, Yaroch, Seymour, & Kimmons, 2007). However, to a large extent, unhealthy eating behavior is due to psychological reasons which can be of a motivational or volitional nature (Schwarzer, 2008; Shaikh, Yaroch, Nebeling, Yeh, & Resnicow, 2008). It is pivotal, therefore, for any prevention effort to be effective at promoting fruit and vegetable
* Corresponding author. E-mail address: [email protected] (Y. Gan). http://dx.doi.org/10.1016/j.appet.2015.03.022 0195-6663/© 2015 Elsevier Ltd. All rights reserved.
consumption to identify the relevant psychological determinants underpinning people’s fruit and vegetable intake. Most studies that have explored such predictors of dietary behaviors have focused on motivational factors such as beliefs about capabilities and consequences, social influence, knowledge, habits, and goals (Guillaumie, Godin, & Vézina-Im, 2010). However, dietary behavior change requires not only motivation but also a volitional process that guides self-regulatory efforts (Adriaanse, Vinkers, De Ridder, Hox, & De Wit, 2011). Thus, there is a need for more theory-based research on volitional determinants of dietary behaviors. Motivation to eat fruit and vegetables and the intention–behavior gap Forming an intention has been regarded as a “watershed” between an initial goal setting phase (motivation) and a subsequent goal pursuit phase (volition), and the foundation of psychological theories of health behavior change such as the Health Action Process Approach (HAPA; Schwarzer, 2008). The terms
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motivation and goal setting refer to the preintentional phase, whereas the terms volition and goal pursuit pertain to the postintentional phase. Although the construct of intention is central in explaining dietary behavior change, its predictive value is limited (Sheeran, 2002). McEachan, Conner, Taylor, and Lawton (2011) reviewed 30 studies and found a mean correlation of r = .38 between intention and dietary behavior, accounting only for about 14% of the behavior variance. Thus, it is clear that people who are motivated to change their behavior often do not behave according to their intentions (Gollwitzer & Sheeran, 2006). When trying to translate intentions into behavior, individuals are faced with various obstacles such as distractions, forgetting, or conflicting bad habits. If individuals are not equipped with the means to meet these obstacles, then motivation alone will not be sufficient to change people’s dietary behaviors. To overcome this limitation, further constructs that operate in concert with intention are required to help close this intention–behavior gap (Gholami, Lange, Luszczynska, Knoll, & Schwarzer, 2013; Lhakhang, Godinho, Knoll, & Schwarzer, 2014). Action planning and action control are two volitional determinants that have been identified in the literature. It is not fully understood, however, how these two factors operate between intention and behavior.
Self-regulation: action planning and action control Good intentions are more likely to be translated into action when people make plans to perform the desired behavior. Intentions foster planning, which in turn, facilitates behavior change. Several longitudinal studies have found that planning served as a mediator between intention and dietary behavior (e.g., Richert et al., 2010; Zhou, Gan, Knoll, & Schwarzer, 2013). Action planning pertains to a mental simulation of when, where, and how to act in line with the intention. It aims at creating new contingencies between (external) situational cues and behavioral responses (e.g., eating at least five portions of fruit and vegetable). Reviews have documented the mediating role of planning in health behavior change, including fruit and vegetable consumption (for an overview see Hagger & Luszczynska, 2014). While action planning is a prospective strategy, that is, behavioral plans are made before the situation is encountered, action control is a concurrent self-regulatory strategy where the ongoing behavior is continuously evaluated with regard to a behavioral standard. Action control can comprise three facets: self-monitoring (“I consistently monitored when, where, and how to eat either fruit or vegetables”), awareness of standards (“I have always been aware of my prescribed intentions to consume enough fruit and vegetables”), and self-regulatory effort (“I took care to eat fruit and vegetables as much as I intended to”) (Carver & Scheier, 2002; Sniehotta, Scholz, & Schwarzer, 2005). An empirical study testing the effects of action planning and action control on physical activity found action control to have the strongest direct effect on behavior compared to action planning and maintenance self-efficacy (Scholz, Keller, & Perren, 2009). Other studies have observed a mediation effect. For example, in a longitudinal study, action control was observed to serve as a mediator between action planning and exercise behavior (Sniehotta, Scholz et al., 2005). Changes in self-monitoring, a key component of action control, was also found to operate as a mediator in a dental flossing experiment (Schwarzer, Antoniuk, & Gholami, 2015). More specifically, studies investigating fruit and vegetable consumption have demonstrated that action control mediates the relation between dietary intentions and fruit and vegetable intake (Godinho, Alvarez, Lima, & Schwarzer, 2013). In addition, action control has been found to mediate between intervention conditions and fruit and vegetable consumption at follow up (Lange et al., 2013).
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The current study Although action planning and action control are two key volitional determinants of behavior, few studies have examined them jointly. Action control implies not only the recall of behavior but also the recall of intentions and previously formulated plans. Selfmonitoring of the concurrent fruit and vegetable consumption makes people aware of their intentions, plans, and behaviors focusing on possible discrepancies between planning and action. Hence, action control can be specified either as a predictor or as an outcome of action planning or both. Accordingly, a parallel mediation effect could materialize or a mediator sequence may emerge with one of the two volitional constructs being first and the other second. The aim of the current study, therefore, was to identify the psychosocial determinants of fruit and vegetable intake in young adults and unpack the order of sequence of these factors. Given the dearth of literature investigating simultaneously the role of action planning and action control, the focus of the current study was on these post-intentional constructs. Based on the HAPA, it is assumed that a mediation model starts with the intention, leading to action control and action planning, and finally affecting fruit and vegetable consumption. Three time points were selected to assess the sequence of effect of these variables: baseline behavior and intention at Time 1, action control and action planning at Time 2, and fruit and vegetable intake at Time 3. The following hypotheses were formulated: 1. Action control and action planning predict behavior, serving as two parallel mediators between the intention and fruit and vegetable intake. 2. Action control and action planning predict behavior, serving as two sequential mediators between the intention and fruit and vegetable intake. Method Participants Three hundred and seven undergraduate students from a major university in China participated in the study. Participants who were vegan were excluded from the data analysis (n = 6), as were extreme outliers scoring above 3.29 SD of the grand mean (which equates to consuming 10 portions of fruit and vegetables per day; n = 15). In total, 286 participants completed the questionnaire at Time 1, with 156 participants completing all three assessment points. The age of participants ranged from 17 to 46 years (M = 23.64, SD = 4.44), with the majority of the sample comprising of women (n = 113, 72.4%). Procedure The study adopted a longitudinal design with three waves of data collection two weeks apart, and was approved by the University Human Research Ethics Committee. At baseline (Time 1), trained research assistants, prior to the commencement of class, invited undergraduate students from two courses to participate in a health behavior survey. Participants were provided with information about the purpose of the study and informed written consent was obtained. Consenting students were then asked to report on their intake of fruit and vegetables in the previous week as well as their intentions of future fruit and vegetable consumption. Two weeks later (Time 2), participants filled out a questionnaire on dietary action control and action planning. At Time 3 (two weeks after Time 2), participants completed a third questionnaire about their fruit and vegetable intake during the previous month. To thank participants for their efforts, those who completed all three questionnaires
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were awarded 20 yuan (equivalent to 3 US$). The data were collected between November and December 2014 in Beijing, China. Measures All measures were adapted from Schwarzer (2008), except the action control scale which was taken from Sniehotta, Schwarzer, Scholz, and Schüz (2005). All measures were translated from English into Chinese by two bilingual psychologists and two others who approved of the translations and back-translations. A prior qualitative pilot study assured that all scales could be well understood. Responses were given on a 5-point Likert scale ranging from 1 (totally disagree) to 5 (totally agree), except for fruit and vegetable intake which was an account of the number of consumed portions. Intention Intention was measured at Time 1 with the item: “I intend to eat at least five portions of fruit and vegetables a day from today on”. Action control Action control was measured at Time 2 by three items each of which addressed a different component of action control: “I have consistently monitored when, where and how to eat either fruit or vegetables”, for self-monitoring; “I have often had my fruit and vegetable intake intentions on my mind”, for awareness of standards; and “I have really tried hard to eat the required amount of fruit and vegetables regularly”, for self-regulatory effort. Cronbach’s α was .86. Action planning Action planning was measured at Time 2 with the stem “I have already made a concrete and detailed plan regarding…” which was followed by three items such as “when and where to eat fruit or vegetables (at which occasion)”. Cronbach’s α was .79. Fruit and vegetable intake The consumption of fruit and vegetables was measured at Time 1 and Time 3 with two items to assess fruit and vegetable intake: “During the (last week [T1]/last month [T3]), how many portions of fruit/vegetables did you eat per day?” To assist participants, examples of the portion size of fruit and vegetables were provided. Similar items had been validated against a food frequency questionnaire and dietary biomarkers (Steptoe et al., 2003). Data analysis First, to assess for any attrition effects, χ2 tests were performed on the categorical variables (gender and ethnicity), and independentsample t-tests and multivariate analyses of variance (MANOVA) were performed on the continuous variables (age, intention, and fruit and vegetable intake). Second, to explore the mediational mechanism of action control and action planning at Time 2 between intentions at Time 1 and fruit and vegetable intake at Time 3, two nested models were estimated by path analysis with AMOS 22.0. One is a constrained model to test the first hypothesis, where action control and action planning (measured at Time 2) were specified as two parallel mediators between Time 1 intention and Time 3 fruit and vegetable intake, controlling for Time 1 fruit and vegetable intake (Model 1). The other is an unconstrained model to test the second hypothesis, where two volitional predictors were specified as sequential mediators (Model 2). There is only a minimal difference between the two models: in Model 1, the path from action control to action planning is constrained to 0, whereas it is freely estimated in Model 2. As missing values were at least missing at random (MAR), that is, the attrition was associated with variables under study
(see below), full information maximum likelihood (FIML) estimates with the initial full sample of 286 participants was applied. Goodness-of-fit indices indicating an adequate fit were χ2/df, with a value below 2.0 (Arbuckle, 2008); comparative fit index (CFI) and Tucker–Lewis Index (TLI), with values larger than 0.90 (Bentler, 1990); as well as the root mean square error of approximation (RMSEA), with values lower than .08 (Hu & Bentler, 1999). Third, to obtain the direct and indirect effects of Time 1 intention on Time 3 fruit and vegetable intake in Model 2, the PROCESS macro (Hayes, 2013) was applied to the longitudinal sample of 156 participants. Bias-corrected bootstrapping with 5000 resamples was chosen to establish 95% confidence intervals for all parameter estimates. Results Attrition analysis Of the initial sample, 45.45% were lost at Time 3 one month later. Attrition analyses revealed that there were no differences with regard to most variables between participants who completed all three measurement points in time and those who did not. Gender was the only exception with more men than women dropping out (60.55% vs. 36.16%, χ2 (df = 1) = 16.19, p < .01). Descriptive analysis Means, standard deviations, and correlations of all the variables investigated in the current study are reported in Table 1. The average fruit and vegetable intake per day was 4.29 portions (SD = 2.75) at Time 1 and 4.59 portions (SD = 2.16) at Time 3, with 67.48% (57.05% at Time 3) of the students not attaining the recommended level of five portions of fruit and vegetables per day. There were no significant changes on fruit and vegetable consumption from Time 1 to Time 3 (t(165) = 0.14, p > .05). All variables measured at Time 1 and Time 2 were significantly correlated with fruit and vegetable intake at Time 3, with intention having the highest association. Intention at Time 1 was related to both action control and action planning measured at Time 2. Action control and action planning were moderately related to each other (r = .47, p < .01). Besides the association between gender and action control (r = −.33, p < .05), gender and age were not related to other variables (all ps > .05). Thus, they are not included in the models. Model 1: action control and action planning as parallel mediators of the relationship between intention and fruit and vegetable consumption The first estimated model specified action control and action planning (measured at Time 2) as two independent mediators between Time 1 intention and Time 3 fruit and vegetable consumption, controlling for Time 1 baseline behavior. The parallel mediation model fit was poor: χ2(3) = 30.71, p < .05, χ2/df = 10.24, CFI = 0.70, and RMSEA = 0.18, 90% CI [0.13; 0.24]. Thus, the results did not support the first hypothesis that the two volitional predictors served as two parallel mediators between intention and behavior.
Table 1 Descriptive statistics and correlations of study variables. 1 1. Intention (Time1) 2. Action control (Time2) 3. Action planning (Time2) 4. Fruit and vegetable intake (Time1) 5. Fruit and vegetable intake (Time3) Note: *p < .05; **p < .01.
.36** .31** .28** .22**
2
.47** .17* .16*
3
.03 .21**
4
M
SD
.21*
3.71 3.80 3.27 4.29 4.59
1.10 0.87 0.80 2.75 2.16
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Table 2 Fit indices and contrasting results of the two nested models.
Model 1: Parallel model Model 2: Sequential model Model 1 vs Model 2
χ2
df
CFI
TLI
RMSEA
Δχ
30.71 2.59 –
3 2 –
.70 .99 –
.52 .95 –
.18 .03 –
28.12
Model 2: action control and action planning as sequential mediators of the relationship between intention and fruit and vegetable consumption The hypothesized sequential mediation model fit the data satisfactorily, χ2(2) = 2.59, p > .05, χ2/df = 1.30, TLI = 0.95, CFI = 0.99, and RMSEA = 0.03, 90% CI [0.01; 0.06]. When contrasting the sequential model with the parallel model, there was a significant increase in the model fit, Δχ2(1) = 28.12, p < .01 (see Table 2). Thus, the sequential model was the best among the tested models. Intention measured at baseline (Time 1) significantly predicted action control at Time 2, β = .36, p < .01. Action control and action planning had positive and significant associations at Time 2, β = .41, p < .01. Fruit and vegetable intake at Time 1 and action planning at Time 2 predicted fruit and vegetable consumption at Time 3, β = .20, p < .01 and β = .18, p < .05, respectively. However, the association between Time 2 action control and Time 3 fruit and vegetable intake was not significant, β = .02, p > .05. The model accounted for 11.1% of variance in fruit and vegetable intake at Time 3. The results supported the second hypothesis that the two volitional predictors served as sequential mediators between intention and fruit
Table 3 Decomposition of the effect of intention at Time 1 on fruit and vegetable intake at Time 3. Fruit and vegetable intake
Total effect Indirect effects through Action control Action planning AC and AP Direct effect
Estimate
95% CI
.15
−0.09, 0.42
.01 .03 .02 .10
−0.05, 0.07 0.01, 0.09 0.01,0.06 −0.09, 0.27
Note: CI = confidence interval. Estimates are standardized coefficients; AC = Action Control; AP = Action Planning.
2
Δdf
p
1
<.01
and vegetable intake. The FIML standardized parameter estimates based on the full sample are displayed in Fig. 1. The decomposition of the effect of Time 1 intention on Time 3 fruit and vegetable intake is presented in Table 3. The direct path from intention to fruit and vegetable intake at Time 3 was not significant, β = .10, 95% CI [−0.09, 0.27]; whereas the indirect effect through action planning as well as through action control and action planning were significant, β = .03, 95% CI [0.01, 0.09] and β = .02, 95% CI [0.01, 0.06], respectively. The indirect effect of Time 1 intention through Time 2 action control on Time 3 fruit and vegetable intake was not significant, β = .01, 95% CI [−0.05, 0.07]. Discussion The aim of the current study was to identify the psychosocial determinants of fruit and vegetable intake in young adults, specifically focusing on the role of volitional mechanisms, namely action control and action planning, and unpacking the order of sequence of these factors. To gain this understanding, the current study adopted a longitudinal research design with three waves of data collection and examined these post-intentional processes of action control and action planning as mediators between intentions and dietary behavior. Previous research has demonstrated that action planning mediates the intention–behavior relation (Richert et al., 2010; Zhou et al., 2013; for an overview see Hagger & Luszczynska, 2014). The findings of the current study support this hypothesized mediational pathway, which in turn, has important implications for health-behavior promotion. Planning is a modifiable behavior change strategy and can be easily communicated to individuals with self-regulatory deficits. A number of studies that have included a planning component to their intervention design have shown this strategy to be effective in improving fruit and vegetable intake (e.g., Stadler, Oettingen, & Gollwitzer, 2010). Action control has also been shown to serve as a mediator between intention and dietary behaviors (Godinho et al., 2013; Lange et al., 2013). In contrast to previous studies, the present study did not confirm the
Fig. 1. Action control and action planning as sequential mediators of the relationship between intention and fruit and vegetable intake. Standardized solution (N = 301) based on full information maximum likelihood estimation (FIML). Note: *p < .05; **p < .01.
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independent mediating role of action control, as there was a substantial correlation between action control and action planning (r = .47, p < .01). However, and unique to the current study, the co-existence of action control and action planning within the same data set has raised the question of which of the two factors may be more proximal to the behavioral outcome. The findings in the current study revealed action planning compared to action control to be the more proximal determinant of fruit and vegetable consumption. This result is contrary to other studies where action control was found to be more proximal to fruit and vegetable consumption (Godinho et al., 2013) and physical activity engagement (Sniehotta, Scholz et al., 2005). In these two studies, action control mediated between planning and behavior. The latter authors argued that planning may result in closer self-monitoring of exercise, which implied that planning comes first and action control comes later. However, action control might not be a time-specific variable. People may self-monitor their behaviors at any point in time, even before goal setting. Actions can be controlled before making plans, while doing so, or afterwards. Thus, the current findings about the sequence of factors in which a mediation between intention and dietary behavior occurred in a sequential manner with action planning following action control are intuitively meaningful, although not supported by previous research. Given the paucity of studies exploring the sequential operation of action control and action planning, caution should be taken in generalizing any conclusion about the temporal or causal order of the two mediators. Future studies need to examine such mediating mechanisms in terms of diverse behavioral domains, sample characteristics, and study context to substantiate these preliminary findings. Despite the current study being novel in trying to unpack the roles of action control and action planning in people’s health behavior decision making, in the context of the current study their fruit and vegetable consumption, some limitations need to be mentioned. First, the longitudinal, non-experimental research design does not allow for causal inferences; although the temporal order in this three-wave study may somewhat justify the mediation hypotheses. To further elucidate the mechanisms of behavior change, intervention designs that manipulate volitional mediators are recommended (Michie, Rothman, & Sheeran, 2007). Second, the assessment of fruit and vegetable intake was self-reported which can generate bias as people may forget to recall consumed food types or portions during follow-up period. In spite of such potential bias, the scale we used had been validated against objective methods such as dietary biomarkers (Steptoe et al., 2003). Third, there was a high attrition rate. However, except for gender, dropout analyses, revealed no other differences on main variables between those participants who completed all time points and those who dropped out. Finally, action control and action planning were assessed at the same measurement point in time (Time 2) which violated the assumption of temporal order. Furthermore, their intercorrelation was substantial as is the case in other studies (Godinho et al., 2013); thus, further research needs to be undertaken to disentangle such volitional factors. Nevertheless, this research contributes to the investigation of selfregulatory processes that occur between intention formation and fruit and vegetable consumption, and points to the possible sequential mediator by action control and action planning.
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Research report
The role of action control and action planning on fruit and vegetable consumption Guangyu Zhou a, Yiqun Gan b,*, Miao Miao b, Kyra Hamilton c,d, Nina Knoll a, Ralf Schwarzer e,f a
Department of Educational Science and Psychology, Freie Universität Berlin, Germany Department of Psychology, Peking University, Beijing, China c School of Applied Psychology, Griffith University, Brisbane, Queensland, Australia d School of Psychology and Speech Pathology, Curtin University, Perth, Western Australia, Australia e Institute for Positive Psychology and Education, Australian Catholic University, Strathfield, Australia f University of Social Sciences and Humanities, Warsaw, Poland b
A R T I C L E
I N F O
Article history: Received 31 January 2015 Received in revised form 10 March 2015 Accepted 19 March 2015 Available online 26 March 2015 Keywords: Intention Action control Action planning Fruit and vegetable intake
A B S T R A C T
Globally, fruit and vegetable intake is lower than recommended despite being an important component to a healthy diet. Adopting or maintaining a sufficient amount of fruit and vegetables in one’s diet may require not only motivation but also self-regulatory processes. Action control and action planning are two key volitional determinants that have been identified in the literature; however, it is not fully understood how these two factors operate between intention and behavior. Thus, the aim of the current study was to explore the roles of action control and action planning as mediators between intentions and dietary behavior. A longitudinal study with three points in time was conducted. Participants (N = 286) were undergraduate students and invited to participate in a health behavior survey. At baseline (Time 1), measures of intention and fruit and vegetable intake were assessed. Two weeks later (Time 2), action control and action planning were assessed as putative sequential mediators. At Time 3 (two weeks after Time 2), fruit and vegetable consumption was measured as the outcome. The results revealed action control and action planning to sequentially mediate between intention and subsequent fruit and vegetable intake, controlling for baseline behavior. Both self-regulatory constructs, action control and action planning, make a difference when moving from motivation to action. Our preliminary evidence, therefore, suggests that planning may be more proximal to fruit and vegetable intake than action control. Further research, however, needs to be undertaken to substantiate this conclusion. © 2015 Elsevier Ltd. All rights reserved.
Introduction Despite accumulating evidence suggesting that nutritionally balanced diets rich in fruit and vegetables are associated with a reduced risk of chronic diseases including cardiovascular diseases and certain cancers (Boeing et al., 2012), globally fruit and vegetable consumption is lower than recommended. There are manifold reasons for consuming less than the minimum five daily servings of fruit and vegetables recommended by the World Health Organization (WHO) (Guilbert, 2003; Hall, Moore, Harper, & Lynch, 2009; Nebeling, Yaroch, Seymour, & Kimmons, 2007). However, to a large extent, unhealthy eating behavior is due to psychological reasons which can be of a motivational or volitional nature (Schwarzer, 2008; Shaikh, Yaroch, Nebeling, Yeh, & Resnicow, 2008). It is pivotal, therefore, for any prevention effort to be effective at promoting fruit and vegetable
* Corresponding author. E-mail address: [email protected] (Y. Gan). http://dx.doi.org/10.1016/j.appet.2015.03.022 0195-6663/© 2015 Elsevier Ltd. All rights reserved.
consumption to identify the relevant psychological determinants underpinning people’s fruit and vegetable intake. Most studies that have explored such predictors of dietary behaviors have focused on motivational factors such as beliefs about capabilities and consequences, social influence, knowledge, habits, and goals (Guillaumie, Godin, & Vézina-Im, 2010). However, dietary behavior change requires not only motivation but also a volitional process that guides self-regulatory efforts (Adriaanse, Vinkers, De Ridder, Hox, & De Wit, 2011). Thus, there is a need for more theory-based research on volitional determinants of dietary behaviors. Motivation to eat fruit and vegetables and the intention–behavior gap Forming an intention has been regarded as a “watershed” between an initial goal setting phase (motivation) and a subsequent goal pursuit phase (volition), and the foundation of psychological theories of health behavior change such as the Health Action Process Approach (HAPA; Schwarzer, 2008). The terms
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motivation and goal setting refer to the preintentional phase, whereas the terms volition and goal pursuit pertain to the postintentional phase. Although the construct of intention is central in explaining dietary behavior change, its predictive value is limited (Sheeran, 2002). McEachan, Conner, Taylor, and Lawton (2011) reviewed 30 studies and found a mean correlation of r = .38 between intention and dietary behavior, accounting only for about 14% of the behavior variance. Thus, it is clear that people who are motivated to change their behavior often do not behave according to their intentions (Gollwitzer & Sheeran, 2006). When trying to translate intentions into behavior, individuals are faced with various obstacles such as distractions, forgetting, or conflicting bad habits. If individuals are not equipped with the means to meet these obstacles, then motivation alone will not be sufficient to change people’s dietary behaviors. To overcome this limitation, further constructs that operate in concert with intention are required to help close this intention–behavior gap (Gholami, Lange, Luszczynska, Knoll, & Schwarzer, 2013; Lhakhang, Godinho, Knoll, & Schwarzer, 2014). Action planning and action control are two volitional determinants that have been identified in the literature. It is not fully understood, however, how these two factors operate between intention and behavior.
Self-regulation: action planning and action control Good intentions are more likely to be translated into action when people make plans to perform the desired behavior. Intentions foster planning, which in turn, facilitates behavior change. Several longitudinal studies have found that planning served as a mediator between intention and dietary behavior (e.g., Richert et al., 2010; Zhou, Gan, Knoll, & Schwarzer, 2013). Action planning pertains to a mental simulation of when, where, and how to act in line with the intention. It aims at creating new contingencies between (external) situational cues and behavioral responses (e.g., eating at least five portions of fruit and vegetable). Reviews have documented the mediating role of planning in health behavior change, including fruit and vegetable consumption (for an overview see Hagger & Luszczynska, 2014). While action planning is a prospective strategy, that is, behavioral plans are made before the situation is encountered, action control is a concurrent self-regulatory strategy where the ongoing behavior is continuously evaluated with regard to a behavioral standard. Action control can comprise three facets: self-monitoring (“I consistently monitored when, where, and how to eat either fruit or vegetables”), awareness of standards (“I have always been aware of my prescribed intentions to consume enough fruit and vegetables”), and self-regulatory effort (“I took care to eat fruit and vegetables as much as I intended to”) (Carver & Scheier, 2002; Sniehotta, Scholz, & Schwarzer, 2005). An empirical study testing the effects of action planning and action control on physical activity found action control to have the strongest direct effect on behavior compared to action planning and maintenance self-efficacy (Scholz, Keller, & Perren, 2009). Other studies have observed a mediation effect. For example, in a longitudinal study, action control was observed to serve as a mediator between action planning and exercise behavior (Sniehotta, Scholz et al., 2005). Changes in self-monitoring, a key component of action control, was also found to operate as a mediator in a dental flossing experiment (Schwarzer, Antoniuk, & Gholami, 2015). More specifically, studies investigating fruit and vegetable consumption have demonstrated that action control mediates the relation between dietary intentions and fruit and vegetable intake (Godinho, Alvarez, Lima, & Schwarzer, 2013). In addition, action control has been found to mediate between intervention conditions and fruit and vegetable consumption at follow up (Lange et al., 2013).
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The current study Although action planning and action control are two key volitional determinants of behavior, few studies have examined them jointly. Action control implies not only the recall of behavior but also the recall of intentions and previously formulated plans. Selfmonitoring of the concurrent fruit and vegetable consumption makes people aware of their intentions, plans, and behaviors focusing on possible discrepancies between planning and action. Hence, action control can be specified either as a predictor or as an outcome of action planning or both. Accordingly, a parallel mediation effect could materialize or a mediator sequence may emerge with one of the two volitional constructs being first and the other second. The aim of the current study, therefore, was to identify the psychosocial determinants of fruit and vegetable intake in young adults and unpack the order of sequence of these factors. Given the dearth of literature investigating simultaneously the role of action planning and action control, the focus of the current study was on these post-intentional constructs. Based on the HAPA, it is assumed that a mediation model starts with the intention, leading to action control and action planning, and finally affecting fruit and vegetable consumption. Three time points were selected to assess the sequence of effect of these variables: baseline behavior and intention at Time 1, action control and action planning at Time 2, and fruit and vegetable intake at Time 3. The following hypotheses were formulated: 1. Action control and action planning predict behavior, serving as two parallel mediators between the intention and fruit and vegetable intake. 2. Action control and action planning predict behavior, serving as two sequential mediators between the intention and fruit and vegetable intake. Method Participants Three hundred and seven undergraduate students from a major university in China participated in the study. Participants who were vegan were excluded from the data analysis (n = 6), as were extreme outliers scoring above 3.29 SD of the grand mean (which equates to consuming 10 portions of fruit and vegetables per day; n = 15). In total, 286 participants completed the questionnaire at Time 1, with 156 participants completing all three assessment points. The age of participants ranged from 17 to 46 years (M = 23.64, SD = 4.44), with the majority of the sample comprising of women (n = 113, 72.4%). Procedure The study adopted a longitudinal design with three waves of data collection two weeks apart, and was approved by the University Human Research Ethics Committee. At baseline (Time 1), trained research assistants, prior to the commencement of class, invited undergraduate students from two courses to participate in a health behavior survey. Participants were provided with information about the purpose of the study and informed written consent was obtained. Consenting students were then asked to report on their intake of fruit and vegetables in the previous week as well as their intentions of future fruit and vegetable consumption. Two weeks later (Time 2), participants filled out a questionnaire on dietary action control and action planning. At Time 3 (two weeks after Time 2), participants completed a third questionnaire about their fruit and vegetable intake during the previous month. To thank participants for their efforts, those who completed all three questionnaires
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were awarded 20 yuan (equivalent to 3 US$). The data were collected between November and December 2014 in Beijing, China. Measures All measures were adapted from Schwarzer (2008), except the action control scale which was taken from Sniehotta, Schwarzer, Scholz, and Schüz (2005). All measures were translated from English into Chinese by two bilingual psychologists and two others who approved of the translations and back-translations. A prior qualitative pilot study assured that all scales could be well understood. Responses were given on a 5-point Likert scale ranging from 1 (totally disagree) to 5 (totally agree), except for fruit and vegetable intake which was an account of the number of consumed portions. Intention Intention was measured at Time 1 with the item: “I intend to eat at least five portions of fruit and vegetables a day from today on”. Action control Action control was measured at Time 2 by three items each of which addressed a different component of action control: “I have consistently monitored when, where and how to eat either fruit or vegetables”, for self-monitoring; “I have often had my fruit and vegetable intake intentions on my mind”, for awareness of standards; and “I have really tried hard to eat the required amount of fruit and vegetables regularly”, for self-regulatory effort. Cronbach’s α was .86. Action planning Action planning was measured at Time 2 with the stem “I have already made a concrete and detailed plan regarding…” which was followed by three items such as “when and where to eat fruit or vegetables (at which occasion)”. Cronbach’s α was .79. Fruit and vegetable intake The consumption of fruit and vegetables was measured at Time 1 and Time 3 with two items to assess fruit and vegetable intake: “During the (last week [T1]/last month [T3]), how many portions of fruit/vegetables did you eat per day?” To assist participants, examples of the portion size of fruit and vegetables were provided. Similar items had been validated against a food frequency questionnaire and dietary biomarkers (Steptoe et al., 2003). Data analysis First, to assess for any attrition effects, χ2 tests were performed on the categorical variables (gender and ethnicity), and independentsample t-tests and multivariate analyses of variance (MANOVA) were performed on the continuous variables (age, intention, and fruit and vegetable intake). Second, to explore the mediational mechanism of action control and action planning at Time 2 between intentions at Time 1 and fruit and vegetable intake at Time 3, two nested models were estimated by path analysis with AMOS 22.0. One is a constrained model to test the first hypothesis, where action control and action planning (measured at Time 2) were specified as two parallel mediators between Time 1 intention and Time 3 fruit and vegetable intake, controlling for Time 1 fruit and vegetable intake (Model 1). The other is an unconstrained model to test the second hypothesis, where two volitional predictors were specified as sequential mediators (Model 2). There is only a minimal difference between the two models: in Model 1, the path from action control to action planning is constrained to 0, whereas it is freely estimated in Model 2. As missing values were at least missing at random (MAR), that is, the attrition was associated with variables under study
(see below), full information maximum likelihood (FIML) estimates with the initial full sample of 286 participants was applied. Goodness-of-fit indices indicating an adequate fit were χ2/df, with a value below 2.0 (Arbuckle, 2008); comparative fit index (CFI) and Tucker–Lewis Index (TLI), with values larger than 0.90 (Bentler, 1990); as well as the root mean square error of approximation (RMSEA), with values lower than .08 (Hu & Bentler, 1999). Third, to obtain the direct and indirect effects of Time 1 intention on Time 3 fruit and vegetable intake in Model 2, the PROCESS macro (Hayes, 2013) was applied to the longitudinal sample of 156 participants. Bias-corrected bootstrapping with 5000 resamples was chosen to establish 95% confidence intervals for all parameter estimates. Results Attrition analysis Of the initial sample, 45.45% were lost at Time 3 one month later. Attrition analyses revealed that there were no differences with regard to most variables between participants who completed all three measurement points in time and those who did not. Gender was the only exception with more men than women dropping out (60.55% vs. 36.16%, χ2 (df = 1) = 16.19, p < .01). Descriptive analysis Means, standard deviations, and correlations of all the variables investigated in the current study are reported in Table 1. The average fruit and vegetable intake per day was 4.29 portions (SD = 2.75) at Time 1 and 4.59 portions (SD = 2.16) at Time 3, with 67.48% (57.05% at Time 3) of the students not attaining the recommended level of five portions of fruit and vegetables per day. There were no significant changes on fruit and vegetable consumption from Time 1 to Time 3 (t(165) = 0.14, p > .05). All variables measured at Time 1 and Time 2 were significantly correlated with fruit and vegetable intake at Time 3, with intention having the highest association. Intention at Time 1 was related to both action control and action planning measured at Time 2. Action control and action planning were moderately related to each other (r = .47, p < .01). Besides the association between gender and action control (r = −.33, p < .05), gender and age were not related to other variables (all ps > .05). Thus, they are not included in the models. Model 1: action control and action planning as parallel mediators of the relationship between intention and fruit and vegetable consumption The first estimated model specified action control and action planning (measured at Time 2) as two independent mediators between Time 1 intention and Time 3 fruit and vegetable consumption, controlling for Time 1 baseline behavior. The parallel mediation model fit was poor: χ2(3) = 30.71, p < .05, χ2/df = 10.24, CFI = 0.70, and RMSEA = 0.18, 90% CI [0.13; 0.24]. Thus, the results did not support the first hypothesis that the two volitional predictors served as two parallel mediators between intention and behavior.
Table 1 Descriptive statistics and correlations of study variables. 1 1. Intention (Time1) 2. Action control (Time2) 3. Action planning (Time2) 4. Fruit and vegetable intake (Time1) 5. Fruit and vegetable intake (Time3) Note: *p < .05; **p < .01.
.36** .31** .28** .22**
2
.47** .17* .16*
3
.03 .21**
4
M
SD
.21*
3.71 3.80 3.27 4.29 4.59
1.10 0.87 0.80 2.75 2.16
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Table 2 Fit indices and contrasting results of the two nested models.
Model 1: Parallel model Model 2: Sequential model Model 1 vs Model 2
χ2
df
CFI
TLI
RMSEA
Δχ
30.71 2.59 –
3 2 –
.70 .99 –
.52 .95 –
.18 .03 –
28.12
Model 2: action control and action planning as sequential mediators of the relationship between intention and fruit and vegetable consumption The hypothesized sequential mediation model fit the data satisfactorily, χ2(2) = 2.59, p > .05, χ2/df = 1.30, TLI = 0.95, CFI = 0.99, and RMSEA = 0.03, 90% CI [0.01; 0.06]. When contrasting the sequential model with the parallel model, there was a significant increase in the model fit, Δχ2(1) = 28.12, p < .01 (see Table 2). Thus, the sequential model was the best among the tested models. Intention measured at baseline (Time 1) significantly predicted action control at Time 2, β = .36, p < .01. Action control and action planning had positive and significant associations at Time 2, β = .41, p < .01. Fruit and vegetable intake at Time 1 and action planning at Time 2 predicted fruit and vegetable consumption at Time 3, β = .20, p < .01 and β = .18, p < .05, respectively. However, the association between Time 2 action control and Time 3 fruit and vegetable intake was not significant, β = .02, p > .05. The model accounted for 11.1% of variance in fruit and vegetable intake at Time 3. The results supported the second hypothesis that the two volitional predictors served as sequential mediators between intention and fruit
Table 3 Decomposition of the effect of intention at Time 1 on fruit and vegetable intake at Time 3. Fruit and vegetable intake
Total effect Indirect effects through Action control Action planning AC and AP Direct effect
Estimate
95% CI
.15
−0.09, 0.42
.01 .03 .02 .10
−0.05, 0.07 0.01, 0.09 0.01,0.06 −0.09, 0.27
Note: CI = confidence interval. Estimates are standardized coefficients; AC = Action Control; AP = Action Planning.
2
Δdf
p
1
<.01
and vegetable intake. The FIML standardized parameter estimates based on the full sample are displayed in Fig. 1. The decomposition of the effect of Time 1 intention on Time 3 fruit and vegetable intake is presented in Table 3. The direct path from intention to fruit and vegetable intake at Time 3 was not significant, β = .10, 95% CI [−0.09, 0.27]; whereas the indirect effect through action planning as well as through action control and action planning were significant, β = .03, 95% CI [0.01, 0.09] and β = .02, 95% CI [0.01, 0.06], respectively. The indirect effect of Time 1 intention through Time 2 action control on Time 3 fruit and vegetable intake was not significant, β = .01, 95% CI [−0.05, 0.07]. Discussion The aim of the current study was to identify the psychosocial determinants of fruit and vegetable intake in young adults, specifically focusing on the role of volitional mechanisms, namely action control and action planning, and unpacking the order of sequence of these factors. To gain this understanding, the current study adopted a longitudinal research design with three waves of data collection and examined these post-intentional processes of action control and action planning as mediators between intentions and dietary behavior. Previous research has demonstrated that action planning mediates the intention–behavior relation (Richert et al., 2010; Zhou et al., 2013; for an overview see Hagger & Luszczynska, 2014). The findings of the current study support this hypothesized mediational pathway, which in turn, has important implications for health-behavior promotion. Planning is a modifiable behavior change strategy and can be easily communicated to individuals with self-regulatory deficits. A number of studies that have included a planning component to their intervention design have shown this strategy to be effective in improving fruit and vegetable intake (e.g., Stadler, Oettingen, & Gollwitzer, 2010). Action control has also been shown to serve as a mediator between intention and dietary behaviors (Godinho et al., 2013; Lange et al., 2013). In contrast to previous studies, the present study did not confirm the
Fig. 1. Action control and action planning as sequential mediators of the relationship between intention and fruit and vegetable intake. Standardized solution (N = 301) based on full information maximum likelihood estimation (FIML). Note: *p < .05; **p < .01.
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independent mediating role of action control, as there was a substantial correlation between action control and action planning (r = .47, p < .01). However, and unique to the current study, the co-existence of action control and action planning within the same data set has raised the question of which of the two factors may be more proximal to the behavioral outcome. The findings in the current study revealed action planning compared to action control to be the more proximal determinant of fruit and vegetable consumption. This result is contrary to other studies where action control was found to be more proximal to fruit and vegetable consumption (Godinho et al., 2013) and physical activity engagement (Sniehotta, Scholz et al., 2005). In these two studies, action control mediated between planning and behavior. The latter authors argued that planning may result in closer self-monitoring of exercise, which implied that planning comes first and action control comes later. However, action control might not be a time-specific variable. People may self-monitor their behaviors at any point in time, even before goal setting. Actions can be controlled before making plans, while doing so, or afterwards. Thus, the current findings about the sequence of factors in which a mediation between intention and dietary behavior occurred in a sequential manner with action planning following action control are intuitively meaningful, although not supported by previous research. Given the paucity of studies exploring the sequential operation of action control and action planning, caution should be taken in generalizing any conclusion about the temporal or causal order of the two mediators. Future studies need to examine such mediating mechanisms in terms of diverse behavioral domains, sample characteristics, and study context to substantiate these preliminary findings. Despite the current study being novel in trying to unpack the roles of action control and action planning in people’s health behavior decision making, in the context of the current study their fruit and vegetable consumption, some limitations need to be mentioned. First, the longitudinal, non-experimental research design does not allow for causal inferences; although the temporal order in this three-wave study may somewhat justify the mediation hypotheses. To further elucidate the mechanisms of behavior change, intervention designs that manipulate volitional mediators are recommended (Michie, Rothman, & Sheeran, 2007). Second, the assessment of fruit and vegetable intake was self-reported which can generate bias as people may forget to recall consumed food types or portions during follow-up period. In spite of such potential bias, the scale we used had been validated against objective methods such as dietary biomarkers (Steptoe et al., 2003). Third, there was a high attrition rate. However, except for gender, dropout analyses, revealed no other differences on main variables between those participants who completed all time points and those who dropped out. Finally, action control and action planning were assessed at the same measurement point in time (Time 2) which violated the assumption of temporal order. Furthermore, their intercorrelation was substantial as is the case in other studies (Godinho et al., 2013); thus, further research needs to be undertaken to disentangle such volitional factors. Nevertheless, this research contributes to the investigation of selfregulatory processes that occur between intention formation and fruit and vegetable consumption, and points to the possible sequential mediator by action control and action planning.
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