Predicting Physical Activity Outcomes During Episodes of Academic Goal Conflict

Abstract

The moderating role of academic goal conflict in the relations between action planning (AP) and coping planning (CP) with physical activity was tested using samples of university students concurrently pursuing an academic and a physical activity goal. In Study 1 (N = 317), AP was found to positively relate to physical activity goal progress at low, but not at high, levels of goal conflict. CP trended toward being positively related to goal progress at high, but not at low levels of goal conflict. Study 2 (N = 97), using a 1-week daily diary design and measures of self-reported physical activity behavior and goal progress, showed that daily AP positively related to daily physical activity outcomes on days when students experienced lower, but not higher, levels of goal conflict relative to their average. Conversely, CP positively related to daily physical activity outcomes on days when students experienced higher, but not lower, levels of goal conflict.

Keywords

action planning coping planning dual goal pursuit physical activity goal conflict

Most of us juggle multiple goals at one time. We aim to maintain a healthy lifestyle, succeed at school or work, enjoy hobbies, and spend time with significant others. When life gets busy, physical activity goals are often shifted to the backburner. Recent meta-analyses have confirmed that planning is generally a useful strategy to increase physical activity behavior (Bélanger-Gravel, Godin, & Amireault, 2013; Carraro & Gaudreau, 2013), but the majority of studies included in these reviews exclusively focused on physical activity goals. Given that individuals regularly pursue multiple goals (e.g., Austin & Vancouver, 1996), it is important to examine whether planning helps people reach their physical activity goals in a real-world context, where goal striving is threatened by the pursuit of other goals (Presseau, Tait, Johnston, Francis, & Sniehotta, 2013; Scholz, Keller, & Perren, 2009). Hence, the aim of the present research was to examine, at both the between- and within-person levels of analysis, whether action planning (AP) and coping planning (CP) can help individuals move toward their physical activity goals, even at times of higher goal conflict.

Goal Conflict

Multiple goal pursuit is the norm rather than the exception (Austin & Vancouver, 1996). Although pursuing multiple goals allows people to strive toward numerous desired ends in various areas of their lives, it also means their many goals may conflict with one another (Riediger, 2001; Riediger & Freund, 2004) in such a way that progress on a focal goal is somehow impaired by the pursuit of another goal. Riediger and Freund (2004) have outlined two primary sources of goal conflict: resource limitations in time and energy (e.g., feeling too fatigued from working to exercise) and logical incompatibility between goals (e.g., exercising self-care by resting an injured ankle vs. playing a softball game). Goal conflict is likely a key barrier to regular engagement in physical activity (Gebhardt, 2007; Gebhardt & Maes, 2001). Studies have shown significant negative associations between goal conflict and both objective measures of physical activity (e.g., Riediger & Freund, 2004) and subjective ratings of progress on physical activity goals (e.g., Gebhardt & Maes, 1998). Conversely, goal facilitation refers to both the degree of instrumentality between goals (e.g., the self-discipline acquired in pursuing a physical activity goal transfers in the pursuit of one’s academic goal) and the overlap of means used to attain the goals (e.g., cycling to school is simultaneously beneficial for one’s academic and physical activity goals). The latter shares features with the broader concept of multifinality (Kruglanski et al., 2012; Kruglanski et al., 2002), which denotes the fact that a single mean can serve multiple goals. Studies have shown the positive impact of goal facilitation on physical activity (Presseau, Sniehotta, Francis, & Gebhardt, 2010; Riediger & Freund, 2004), making it an important factor to consider in multiple goal pursuit.

Goal conflict, as a barrier to physical activity behavior, appears critical when considering the university student population. Developmental studies showed that the sharpest decline in physical activity occurs between late adolescence and young adulthood (Stone, McKenzie, Welk, & Booth, 1998). Time constraints associated with school duties are primary exercise barriers for university students (Gyurcsik, Bray, & Brittain, 2004), who also exercise less than their usual amount during busy periods such as examinations (Steptoe, Wardle, Pollard, Canaan, & Davies, 1996). Hence, research is needed to elucidate what happens to the effectiveness of self-regulation strategies to promote physical activity when students experience goal conflict stemming from school.

The Role of Planning in Multiple Goal Pursuit

AP and CP are self-regulatory strategies that may provide students with a way of managing their goal pursuit. AP specifies when, where, and how goals will be pursued (Sniehotta, Schwarzer, Scholz, & Schüz, 2005). AP has been shown to help goal strivers notice opportunities to enact goal-directed behaviors (Parks-Stamm, Gollwitzer, & Oettingen, 2007) and to do so more swiftly and efficiently when opportunities are encountered (Brandstätter, Lengfelder, & Gollwitzer, 2001). AP may help preserve self-regulatory resources that can then be used for the successful pursuit of multiple goals because it renders goal striving more efficient and less taxing on conscious self-regulatory capacities (Gollwitzer & Schaal, 1998). CP is defined as anticipating obstacles to goal striving and pairing them with coping responses. In the same way that CP shields goal pursuit from temptations, obstacles, and distractions (Sniehotta et al., 2005), it may also facilitate the pursuit of physical activity goals even in a context in which students must concurrently contend with academic goals.

Two recent meta-analyses highlighted the positive impact of planning on physical activity. Carraro and Gaudreau (2013) reported a medium-to-large summary effect of both spontaneous AP and CP among the correlational studies included for review. Among the experimental studies, results pointed to a small summary effect for planning on physical activity. Similarly, Bélanger-Gravel et al. (2013) revealed small-to-medium effects of AP for physical activity at post-intervention and follow-up time points in experimental studies. Both meta-analyses pointed to the significant heterogeneity of the effect size, indicating the need for further research examining moderators of the association of AP and CP with physical activity. Several calls have been made (Austin & Vancouver, 1996; Emmons & King, 1988; Locke, Smith, Erez, Chah, & Schaffer, 1994; Shah, Friedman, & Kruglanski, 2002; Vancouver, Weinhardt, & Schmidt, 2010) to enhance our understanding of goal pursuit in real-world conditions by studying its processes as they exist within each of us: as part of a larger and interconnected system. Consequently, the present studies examined the moderating impact of goal conflict in the relationships of AP and CP with physical activity outcomes of university students operating in a multiple goal context.

Intra-Individual Planning and Goal Conflict

Research has mainly focused on the relation between planning and physical activity at the between-person level, and we can conclude that individuals using more AP and CP are more likely to be more physically active than those individuals using less AP and CP. Relationships analyzed at the between- and within-person levels can be different (Nezlek, 2001). Alternatively, relationships observed at one level can generalize to the other level. So far, limited evidence at the within-person level indicates that individuals are more active than their own average on occasions (e.g., days, weeks) during which they spontaneously use more AP and CP than their own personal average (Scholz, Nagy, Schüz, & Ziegelmann, 2008). However, inconsistent results on the link between planning and physical activity outcomes at the within- and between-person levels have also been found (Scholz et al., 2009; Scholz et al., 2008). These inconsistencies could be caused by random error and/or bona fide differences between self-regulation processes at the two levels of analysis.

There are theoretical and conceptual reasons to test the generalizability of effects across levels of analysis. Conventional health behavior theories and the associated methods to study them are focused on relations between the typical or aggregated self-regulation of individuals and their typical behavior. Such an approach can be limiting because aggregated self-regulation or behavior over a period of time is a statistical construction that may or may not resemble self-regulation or behavior at a given point in time (Conroy, Elavsky, Doerksen, & Maher, 2013). Moreover, an important amount of the variability in physical activity behavior and AP occurs at the within-person level (Conroy, Elavsky, Hyde, & Doerksen, 2011; Conroy et al., 2013; Hekler et al., 2012). Studying daily fluctuations in AP and CP offers a complementary level of analysis to understand why some people are more active than others (i.e., between-person differences), and why people are more active at certain times than others (i.e., within-person differences).

From an empirical perspective, at least three prospective studies have failed to show a relationship between perceived goal conflict and physical activity at the between-person level (Li & Chan, 2008; Presseau et al., 2010; Riediger & Freund, 2004). However, constructs conceptually similar to goal conflict (i.e., daily hassles and job demands) have been negatively related to health behaviors, including physical activity (e.g., O’Connor, Jones, Conner, McMillan, & Ferguson, 2008; Payne, Jones, & Harris, 2010), at the within-person level. Presseau and his colleagues (2010), in a 1-week daily dairy study, found that perceived goal conflict did not predict accelerometer assessed physical activity, whereas perceived goal facilitation did positively predict physical activity. However, they found that the behaviorally assessed pursuit of conflicting (but not facilitating) goals negatively predicted physical activity over and above intention and perceived behavioral control, which attenuated the effect of perceived goal facilitation on physical activity. Ultimately, the implication is that the more participants engaged in conflicting goals on a given day, the less they performed physical activity on that day. Presseau and his colleagues concluded that goal conflict may be a particularly important predictor of within-person variation in physical activity. Overall, these findings highlight the value of testing the associations between planning and conflict constructs with physical activity at both the between- and within-person levels.

The Current Studies

Examining the potential moderating role of goal conflict at both the between-person and within-person levels is a relevant avenue to determine the circumstances under which AP and CP are more effective in promoting physical activity. Relative to experimental studies, which involve explicit instructions from the experimenter on how to create implementation plans, we know less about how people generate spontaneous AP and CP (Sniehotta et al., 2005). It is conceivable that people fail to sufficiently consider their alternative goals when creating plans for a focal goal, thereby leaving them vulnerable to goal conflict. For example, the time and energy allocated to schoolwork on a particular day might interfere with the plan to attend an exercise class after school. In this scenario, the person experiencing conflict from the academic domain would not be able to enact the plans as intended, which could hinder the progress on his or her physical activity goal.

In this research, we explored the extent to which AP and CP are associated with physical activity when students are experiencing conflict between their academic and physical activity goals, both at the between- (Study 1) and within-person (Study 2) levels. In Study 1, we used a prospective design to examine whether academic goal conflict¹ during the midterm exam period moderates the relationship of AP and CP with progress made in the pursuit of physical activity goal 6 weeks later. In Study 2, we examined the moderating role of daily academic goal conflict on the concurrent within-day relation of daily AP and CP with two indicators of daily physical activity behavior at the within-person level. The physical activity goal progress measure used in Study 1 was supplemented with a physical activity measure capturing daily self-reported energy expenditure.

Study 1

In Study 1, we examined the extent to which academic goal conflict influenced the strength and direction of the relations of AP and CP with physical activity goal progress. We controlled for goal facilitation because it is a consistent positive predictor of subjective (Gebhardt & Maes, 1998; Presseau et al., 2010) and objective physical activity behavior (Presseau et al., 2013; Riediger & Freund, 2004). In line with bourgeoning research on the boundary conditions of AP (e.g., Dalton & Spiller, 2012) and recent meta-analyses showing heterogeneous effects, it was hypothesized that the positive association between Time 1 AP and Time 2 physical activity goal progress would be significantly stronger for individuals experiencing lower, compared with higher, levels of academic goal conflict (Hypothesis 1). Coping becomes even more important when obstacles arise, rendering coping plans more salient. Therefore, we expected the positive association between Time 1 CP and Time 2 physical activity goal progress to be significantly stronger for individuals experiencing higher, compared with lower, levels of academic goal conflict (Hypothesis 2).

Method

Participants

Participants were undergraduate students recruited in classrooms and via a psychology participant pool at a large Canadian university. Only participants providing relevant goals were included in the study. Two independent coders assessed goal relevance with high agreement (kappa physical activity goals = .93; kappa academic goals = .97). Of the initial sample (n = 371), 51 participants were excluded because they failed to report a goal or reported an irrelevant (e.g., “Get my life in order”) or unrealistic goal (e.g., “I want to lose 30 pounds”). Of the participants with relevant goals (n = 320), 92 did not complete the Time 2 questionnaires, yielding a 71% retention rate. Three participants failed to complete any items of Time 1 planning measures and were excluded. Attrition analyses indicated no significant differences between the sample with complete data (n = 226) and the attrition sample (n = 91) on gender, χ²(1) = 2.0, p = .16; age, F(1, 315) = .21, p = .65; Time 1 AP, F(1, 316) = 3.38, p = .28; and Time 1 CP, F(1, 316) = 0.77, p = .57.

The final sample was comprised of 318 participants (73% female) ranging from 17 to 38 years (M = 19.0, SD = 2.23). Participants were ethnically diverse (69% Caucasian, 4% African Canadian, 14% Asian Canadian, and 13% “Other”). Ninety-nine percent of students were enrolled in their studies full-time. Approximately 35% of students received full financial support from their parents during their studies, while 42% received partial support, and 23% received no support. Thirty-eight percent of students reported working outside school for an average of 15 hr per week. Participants were treated in accordance with Canadian Psychological Association/American Psychological Association ethical guidelines, as approved by the Research Ethics Board.

Design and procedure

This study used a two-wave prospective design. At Time 1, participants reported an academic goal and a physical activity goal and completed a measure of AP and CP regarding their physical activity goal. Six weeks later, at Time 2, participants completed measures of goal conflict, goal facilitation, and physical activity goal progress. All questionnaires were completed online via a secure website. Classroom participants received a small monetary compensation, and those recruited via the participant pool received two participation points.

Measures

Physical activity and academic goal (Time 1)

Participants were asked to report a specific, meaningful, and measurable goal for both their physical activity behavior and their academic behavior to pursue during the semester. Participants reported goals such as “I want to go jogging 3 times per week” (i.e., physical activity goal) and “To complete the practice problems for all my courses” (i.e., academic goal).

AP and CP for physical activity (Time 1)

AP was assessed using five items adapted from the measure of Rise, Thompson, and Verplanken (2003). Using a scale from 1 (not at all) to 7 (totally), participants indicated the extent to which they formed plans about when, where, and how to work toward their physical activity goal, and what they did in the service of their goal (“I made detailed plans about “what” to do to work on my physical activity behavior”). Prior work has reported high (>.90) internal consistency in the exercise domain (Brickell, Chatzisarantis, & Pretty, 2006; Rise et al., 2003) and support for factorial validity in a sample of university students (Brickell et al., 2006).

CP was assessed with a version of Sniehotta and colleagues’ (2005) scale adapted for physical activity goals. Participants used a scale from 1 (not at all) to 7 (totally) to rate the extent to which they formed plans to address potential obstacles, temptations, distractions, external events, and bad habits. Prior work has supported the factorial validity of the scale (Sniehotta et al., 2005).

Academic goal conflict and facilitation (Time 2)

Participants were asked to pair their academic goal with their physical activity goal to respond to an adapted version of the Inter-Goal Relations Questionnaire (IRQ; Riediger, 2001). Using a scale from 1 (not at all true or never/very rarely) to 5 (very true or very often), participants were asked to answer items with reference to their life over the last few weeks. Four items were used to measure academic goal conflict and goal facilitation. In addition to excellent internal consistency (α = .94), Riediger and Freund (2004) provided good evidence for the factorial, predictive, and discriminant validity of the IRQ.

Physical activity goal progress (Time 2)

Physical activity goal progress was assessed using five items rated on a scale from 1 (not at all) to 7 (totally). Referring to the last few weeks, participants indicated the extent to which they progressed on their goal, moved forward in the pursuit of their goal, came closer to reaching their goal, made progress toward the realization of their goal, and advanced toward their goal. This scale has demonstrated high internal consistency in prior studies (e.g., Carraro & Gaudreau, 2009).

Plan of analyses

Descriptive statistics and multiple imputations were carried out in SPSS 21. Assuming the data were missing at random, Little’s MCAR: χ²(5) = 1.662, p = .894, multiple imputation (m = 50 imputations) was used to handle the missing data (Enders, 2006; Graham, Olchowski, & Gilreath, 2007). Multiple imputation is preferable to listwise deletion because it minimizes the risks of generating biased parameter estimates while maximizing the statistical power (Schlomer, Bauman, & Card, 2010). It has been recommended that the number of imputations should at least correspond to the percentage of missing data (e.g., Bodner, 2008) because increasing the number of imputations is important to minimize biases in the standard error of parameter estimates and, hence, their p value. Although adding the number of imputations can be computationally demanding, our analyses were conducted with 50 imputed data sets.

Moderated hierarchical regression analyses were conducted in MPLUS (Version 7.01), and they used robust maximum likelihood estimation to adhere to best practice guidelines (Kline, 2011). The predictors and the moderators were centered. A two-step approach was used in which AP, CP, goal conflict, and goal facilitation were entered in the first step, and the AP × Goal conflict and CP × Goal conflict multiplicative terms were added in the second step. The significance of the block of predictors at Steps 1 and 2 (and the incremental change from Steps 1 to 2) was calculated using a Wald statistic (distributed like a chi-square with degrees of freedom corresponding to the number of predictors). Significant interactions were decomposed using simple slope analyses (Cohen, Cohen, West, & Aiken, 2003). Simple slopes at low and high levels of goal conflict were calculated at one standard deviation below and above the mean, respectively.

Results

The database of 318 participants was screened for outliers. Visual inspection of bivariate scatterplots for all combinations of variables revealed two potential outlying cases: one between AP and physical activity goal progress and one between CP and physical activity goal progress. Using the Mahalanobis distance critical value for two variables, χ²(2) = 13.816, p < .001, the case identified between AP and physical activity goal progress was confirmed as a significant outlier and therefore removed before running our main analyses (Meyers, Gamst, & Guarino, 2006). Descriptive statistics, reliability estimates, and correlations between the variables are reported in Table 1.

Table 1.

Study 1: Descriptive Statistics, Reliability Estimates, and Bivariate Correlations (N = 317).

	Range	M	SD	1.	2.	3.	4.	5.
1. Time 1 AP	1-7	3.71	1.69	.93
2. Time 1 CP	1-7	3.13	1.55	.77**	.95
3. Time 2 academic goal conflict	1-5	3.58	0.99	−.03	−.02	.93
4. Time 2 academic goal facilitation	1-5	2.60	0.91	.20**	.29**	−.29**	.91
5. Time 2 physical activity goal progress	1-7	4.40	2.09	.21**	.22**	−.51**	.42**	.98

Note. Cronbach’s alphas are presented on the diagonal. AP = action planning; CP = coping planning.

p < .01.

The results of the moderated hierarchical regression analyses showed significant main effects for goal conflict and goal facilitation, but not for AP and CP (see Table 2). The interaction AP × Goal conflict was significant, whereas the interaction CP × Goal conflict was marginally significant (see Table 2). The two interactions explained 2% of unique variance over and above the main effects. As illustrated in Figure 1 (see Panel A), simple slope analyses revealed that AP was significantly related to goal progress at low (B = .336, SE = 0.151, p < .05), but not at high levels of school-to-physical activity goal conflict (B = −.089, SE = 0.132, p = .50), thereby supporting Hypothesis 1. Conversely, simple slope analyses showed that CP was not significantly related to goal progress at low levels of conflict (B = −.114, SE = 0.163, p = .48), but showed a trend toward being positively related to goal progress at high levels of conflict (B = .25, SE = 0.152, p = .09). These results, shown in Figure 1 (see Panel B), partially supported Hypothesis 2.

Table 2.

Study 1: Results of Moderated Regression Analyses on Physical Activity Goal Progress (N = 317).

	Wald	R ²	B	95% CI
Step 1	145.120** (df = 4)	.331**
AP			0.109 (0.111)	[−0.109, 0.327]
CP			0.094 (0.124)	[−0.149, 0.337]
Goal conflict			−0.871** (0.126)	[−1.118, −0.624]
Goal facilitation			0.578** (0.136)	[0.311, 0.845]
Step 2	178.294** (df = 6)	.346**
AP			0.123 (0.108)	[−0.089, 0.335]
CP			0.070 (0.120)	[−0.165, 0.305]
Goal conflict			−0.876** (0.121)	[−1.113, −0.639]
Goal facilitation			0.558** (0.135)	[0.293, 0.823]
AP × Conflict			−0.215* (0.093)	[−0.397, −0.033]
CP × Conflict			0.187 (0.102)^†	[−0.013, 0.387]

Note. Difference in Wald statistic from Steps 2 to 1 was significant, Δ Wald = 33.094, Δdf = 2, p < .01. All parameters are unstandardized. Standard errors are reported in parentheses. CI = confidence interval; AP = action planning; CP = coping planning.

†

p = .068. *p < .05. **p < .01.

Figure 1.

Study 1: Simple slopes for AP × Conflict and CP × Conflict interactions on physical activity goal progress.

Brief Discussion

Study 1 pointed to a pattern in which AP and CP produced divergent associations with physical activity goal progress in the presence of academic goal conflict. AP was positively related to physical activity goal progress, but only for individuals who experienced lower levels of conflict stemming from their academic goal pursuit. This finding is consistent with an emerging line of research examining the boundary conditions for AP (Dalton & Spiller, 2012). Conversely, a trend emerged in which CP was positively related to physical activity goal progress, but only for individuals with higher levels of academic goal conflict. Notably, as illustrated in Figure 1, students made greater progress toward their physical activity goals when academic goal conflict was lower.

Study 2

Study 2 was designed to examine the moderating role of goal conflict at the within-person level with data collected in a 1-week daily diary study. Daily measurement provides a number of distinct advantages, such as enhanced ecological validity, reduced memory recall biases, and greater external validity (Shiffman, Huffard, & Patty, 2001). This design enabled us to investigate the relation between planning and physical activity at the within-person level (Moskowitz, Russell, Sadikaj, & Sutton, 2009).

We added a measure of self-reported physical activity behavior to complement the goal progress measure of Study 1. The measure of goal progress is idiosyncratic because it reflects the amount of progress on each participant’s physical activity goal. Levels of physical activity and goal progress are empirically related but conceptually distinct (Dugas, Gaudreau, & Carraro, 2012). Ultimately, individuals may have attained their goal of performing physical activity for 30 min while still being less physically active than other people who had only partially attained their goal of performing physical activity for an hour. Therefore, daily goal progress and daily self-reported physical activity behavior were both measured in this study to offer a more comprehensive examination of the moderating role of goal conflict.

In line with results from previous studies looking at planning and physical activity at the within-person level (e.g., Scholz et al., 2008), we expected positive associations of daily AP and CP with daily goal progress and self-reported physical activity behavior. In other words, it was expected that individuals would be more physically active and make more progress on their goals on days during which their levels of AP and CP were higher than their own habitual level. Consistent with Study 1, our main hypothesis was that the relationship of daily AP with daily physical activity goal progress (Hypothesis 1a) and self-reported physical activity (Hypothesis 1b) would be weaker on days when students experienced higher academic goal conflict than their own average. Conversely, the within-person association of daily CP with daily physical activity goal progress (Hypothesis 2a) and daily self-reported physical activity (Hypothesis 2b) was expected to be stronger on days during which students experienced higher academic goal conflict than their own average.

Method

Participants

Participants were students recruited in classrooms and via a psychology participant pool at a large Canadian university. Of the recruited sample of 136 participants, 115 completed baseline measures. As in Study 1, only participants providing relevant goals were included in the study. Two independent coders assessed goal relevance with satisfactory agreement (kappa physical activity goals = .69; kappa academic goals = .80). Of the participants with complete baseline data and valid goals, 14 failed to complete any daily diaries. All remaining participants completed at least one valid daily diary. Attrition analyses revealed no significant differences on demographic variables such as gender, χ²(1) = .63, p = .43, or age, F(1, 99) = 20.01, p = .29. Participants completed an average of 3.9 (SD = 1.75) daily diaries. The percentage of daily diaries completed is as follows: one (12.4%), two (17.5%), three (8.2%), four (15.5%), five (23.7%), and six diaries (22.7%).

The final sample was comprised of 97 participants (68% female) ranging from 17 to 52 years of age (M = 20.45, SD = 4.61) mainly in their first year at the university (59%). Participants were ethnically diverse (71% Caucasian, 4% African Canadian, 15% Asian Canadian, and 10% “Other”). Ninety-six percent of students were enrolled in their studies full-time. Approximately 32% of students received full financial support from their parents during their studies, while 39% received partial support, and 29% received no such support. Forty-two percent of students reported working outside school for an average of 14 hr per week. Classroom participants received monetary compensation, and those recruited via participant pool received two participation points.

Design and procedure

The study followed a time-contingent daily diary design (Moskowitz et al., 2009), with measures taken on a fixed interval schedule (i.e., daily for 1 week). All questionnaires were completed online via a secure website, with the baseline questionnaire being completed on a Monday and six daily diaries being completed each evening the subsequent Tuesday to Sunday. The baseline questionnaire contained measures of demographics, physical activity and academic goals, and control variables. For the next 6 days, participants were asked to log into the secure website each evening, between 7:00 p.m. and 3:00 a.m. the following morning, to complete a series of daily measures. The website was inaccessible outside these specified hours, and responses were time-stamped. Participants were sent an email every day at 5:00 p.m. with a friendly reminder to complete the daily diary to improve compliance (Shiffman et al., 2001).

Measures

Physical activity and academic goal (baseline)

As in Study 1, participants were asked to report a specific, meaningful, and measurable physical activity goal and academic goal. All instructions were identical to Study 1, but participants were instructed to report a goal that could be pursued and assessed each day over the course of 1 week. Participants set goals such as “I will walk for 30 minutes each day,” and “I would like to review my schoolwork outside of class for at least one hour each evening.”

AP and CP for physical activity (daily)

AP and CP were assessed in a manner identical to that of Study 1, except participants were asked to respond to items as they related to the present day.

Academic goal conflict and goal facilitation (daily)

The method of assessing academic goal conflict and facilitation was identical to that used in Study 1, but items were modified to reflect daily measurement.

Physical activity goal progress (daily)

The method was identical to Study 1, but items were modified to reflect daily measurement.

Self-reported physical activity behavior (daily)

Daily self-reported physical activity behavior was assessed using the Godin Leisure Time Exercise Questionnaire (Godin & Shephard, 1985), which distinguishes between three levels of exercise: strenuous (i.e., heart beats rapidly and quickly results in sweating), moderate (i.e., not exhausting and takes time before leading to sweating), and mild (i.e., does not necessarily lead to sweating and requires little effort). Each evening, participants were asked to indicate the number of times they engaged in each form of exercise for at least 15 min that day. A daily level of physical activity index was computed using the weighted sum of scores for each form of exercise (i.e., [9 × Strenuous] + [5 × Moderate] + [3 × Mild]).

Overview of analyses

The data generated by this daily diary study are hierarchically organized, with daily assessments (Level 1) nested within people (Level 2). Multilevel modeling (MLM) is considered the most appropriate statistical analysis for hierarchical data, as it allows for the simultaneous but independent investigation of variability at the within- and between-person levels (Raudenbush & Bryk, 2002). The HLM 6.04 software with full information maximum likelihood robust estimation was used to analyze the data. Before running our main analyses, the database was screened for outliers as per the method described in Study 1. No potential outliers were detected, and we proceeded to conduct the main analyses with the sample of 97 participants described in the “Method” section of this article. Descriptive statistics are presented in Table 3.

Table 3.

Study 2: Descriptive Statistics, Reliability Estimates, and Bivariate Correlations.

	Range	ICC	M	SD	1.	2.	3.	4.	5.	6.
1. Daily AP	1-7	.50	3.49	1.92	.91
2. Daily CP	1-7	.59	2.95	1.68	.75**	.93
3. Daily academic goal conflict	1-5	.48	2.38	1.42	−.15**	−.18**	.94
4. Daily academic goal facilitation	1-5	.55	1.74	0.99	.12*	.21**	.05	.88
5. Daily physical activity goal progress	1-7	.38	3.61	2.17	.65**	.56**	−.35**	.22**	.99
6. Daily self-reported physical activity	0-3,360	.45	412.29	428.90	.35**	.35**	−.31**	.15**	.49**	NA

Note. N = 378 daily reports from 97 participants. Cronbach’s alphas are presented on the diagonal. ICC = intra-class correlation; AP = action planning; CP = coping planning.

p < .05. **p < .01.

The first step in performing the analyses was to run the null/unconditional model (Model 1). The results of this model allowed for the calculation of the intra-class correlation (ICC), which identifies the proportion of total variance in the outcomes that is attributable to variation between individuals; the remainder of the total variance is therefore attributable to variability within individuals across days (Raudenbush & Bryk, 2002). A chi-square statistic is computed to determine whether the between-person variance significantly differs from zero.

The second step was to examine main effects by adding four Level 1 predictors (Model 2): daily AP, CP, goal conflict, and goal facilitation. All predictors were group mean centered, such that the intercept of each individual represented his or her average score on the dependent variables over the 6 days (either physical activity goal progress or self-reported physical activity behavior).

The third step was to create and incorporate two Level 1 interaction terms following the guidelines of Cohen et al. (2003): AP × Goal conflict and CP × Goal conflict. These product terms were not centered (Model 3). Significant interactions were probed with simple slope analyses estimating the effect of daily AP and daily CP at low (i.e., 1 SD below) and high (i.e., 1 SD above) daily goal conflict.² Pseudo R² values were computed for Models 2 and 3 to determine the percentage of within-person variance explained by the predictors.

Results

Daily physical activity goal progress

As shown in Table 4 (see Model 1), the within-person variance (σ²) was 2.88 and the between-person variance (τ₀₀) was 1.73. The ICC indicated that 38% of the variance in physical activity goal progress was attributable to between-person variability. This means that a greater proportion of variability in physical activity goal progress (62%) is attributable to daily variations. The addition of the Level 1 predictors showed that the within-person slopes for AP (B₁₀), CP (B₂₀), goal conflict (B₃₀), and goal facilitation (B₄₀) were all significantly different from zero (see Table 4, Model 2). These four predictors explained 57% of the variance in physical activity goal progress. As expected, both daily AP and daily CP were significantly and positively associated with physical activity goal progress. Next, the two interaction terms were added (see Table 4, Model 3), and both AP × Goal conflict (B₆₀) and the CP × Goal conflict (B₇₀) interactions have reached statistical significance, which explained 1% of additional and unique variance in daily physical activity goal progress.

Table 4.

Study 2: Results of Multilevel Model Predicting Daily Goal Progress.

Effects	Daily goal progress
Effects	Model 1	Model 2	Model 3
Fixed effect
B₀₀ = Intercept	3.544** (0.162)	3.544** (0.160)	3.558** (0.161)
B₁₀ = AP		0.574** (0.106)	0.547** (0.104) [0.343, 0.751]
B₂₀ = CP		0.284** (0.108)	0.300** (0.107) [0.090, 0.510]
B₃₀ = AC		−0.436** (0.074)	−0.443** (0.072) [−0.584, −0.302]
B₄₀ = AF		0.390** (0.124)	0.361** (0.130) [0.106, 0.616]
B₅₀ = AP × AC			−0.172* (0.080) [−0.329, −0.015]
B₆₀ = CP × AC			0.221** (0.073) [0.078, 0.364]
	Variance	Variance	Variance
Random effect
σ²	2.878	1.234	1.219
τ₀₀	1.728**	2.161**	2.148**
r1		0.256 p = .096	0.268 p = .085
r2		0.137 p = .051	0.162 p = .046
r3		0.041 p > .500	0.046 p > .500
r4		0.093 p = .051	0.122 p = .037
r5			Not estimated
r6			Not estimated

Note. Pseudo R² = .571. Pseudo R² = .013. r5 and r6 could not be simultaneously estimated. Models in which either r5 or r6 was estimated yielded nonsignificant variance component (ps > .35). The two random effects were not included in the final model. Standard errors are reported in parentheses. 95% CIs of Model 3 are reported in brackets. All parameters are unstandardized. AP = action planning; CP = coping planning; AC = academic conflict; AF = academic facilitation; CI = confidence interval.

p < .05. **p < .01.

Simple slope analyses revealed that daily AP was positively associated with daily goal progress (B₁₀ = 0.72, SE = 0.13, p < .01) on days during which individuals experienced lower goal conflict from their academic goal. The relation between daily AP and daily physical activity goal progress was significant, but substantially weaker (B₁₀ = 0.48, SE = 0.14, p < .01) on days during which individuals experienced higher goal conflict, which provides support for Hypothesis 1a (see Figure 2, Panel A).

Figure 2.

Study 2: Simple slopes for AP × Conflict and CP × Conflict interactions on daily physical activity goal progress and daily self-reported physical activity behavior.

Daily CP was not significantly associated with daily goal progress on days during which individuals experienced lower goal conflict (B₁₀ = 0.12, SE = 0.14, p > .05). However, the relation between daily CP and daily progress was positive and significant on days during which individuals experienced higher goal conflict (B₁₀ = 0.45, SE = 0.14, p < .01), which supports Hypothesis 2a (see Figure 2, Panel B).

Daily self-reported physical activity behavior

As shown in Table 5 (see Model 1), the within-person variance (σ²) was 96,864 and the between-person variance (τ₀₀) was 80,134. The ICC indicated that 45% of the variance in self-reported physical activity behavior was attributable to between-person variability; 55% of the variance is therefore attributable to daily variations. The addition of the Level 1 predictors showed that the within-person slopes for AP (B₁₀), goal conflict (B₃₀), and goal facilitation (B₄₀) were all significantly different from zero (see Table 5 Model 2). These four predictors explained 52% of variance in self-reported physical activity behavior. As expected, both daily AP and daily CP were positively and significantly associated with daily self-reported physical activity behavior. Finally, only the AP × Goal conflict (B₆₀) reached statistical significance (see Table 5, Model 3), thus supporting Hypothesis 1b but not 2b.

Table 5.

Study 2: Results of Multilevel Model Predicting Daily Self-Reported Physical Activity.

Effects	Daily self-reported physical activity
Effects	Model 1	Model 2	Model 3
Fixed effect
B₀₀ = Intercept	397.748** (33.184)	401.761** (34.233)	399.859** (34.052)
B₁₀ = AP		52.432** (17.683)	50.660** (17.313) [16.7, 84.6]
B₂₀ = CP		18.624 (19.237)	23.454 (19.593) [−14.8, 61.7]
B₃₀ = AC		−78.901** (18.800)	−79.830** (18.338) [−115.8, −43.9]
B₄₀ = AF		57.650 (37.120)	57.978 (37.501) [−15.5, 131.5]
B₅₀ = AP × AC			−33.128** (11.084) [−54.9, −11.4]
B₆₀ = CP × AC			11.384 (12.150) [−12.4, 35.2]
	Variance	Variance	Variance
Random effect
σ²	96,864	46,600	47,616
τ₀₀	80,134**	101,443**	100,176**
r1		2,662, p = .090	2,337, p = .092
r2		2,204, p = .023	3,253, p = .024
r3		15,353, p = .090	14,250, p = .111
r4		41,154**	39,164**
r5			Not estimated
r6			Not estimated

Note. Pseudo R² = .519. Pseudo R² cannot be estimated. r5 and r6 could not be simultaneously estimated. Models in which either r5 or r6 was estimated yielded nonsignificant variance component (ps > .35). The two random effects were not included in the final model. Standard errors are reported in parentheses. The 95% CIs of Model 3 are reported in brackets. All parameters are unstandardized. AP = action planning; CP = coping planning; AC = academic conflict; AF = academic facilitation; CI = confidence interval.

p < .05. **p < .01.

Simple slope analyses for the significant AP × Goal conflict interaction showed that daily AP was positively associated with daily self-reported physical activity behavior on days during which individuals experienced lower goal conflict (B₁₀ = 60.78, SE = 21.19, p < .01). The relation between daily AP and daily self-reported physical activity behavior was not significant on days during which individuals experienced higher goal conflict (B₁₀ = 20.86, SE = 22.94, p > .10; see Figure 2, Panel C).

Brief Discussion

The results of this daily diary study offered additional support for the moderating role of goal conflict in the respective relation of AP and CP with physical activity outcomes. Consistent with the results of Study 1, our results lent credence for the differential role of AP and CP in predicting physical activity outcomes when students are experiencing goal conflict. On the one hand, the positive relation between daily AP and daily physical activity outcomes was stronger on days during which the students experienced lower levels of goal conflict. On the other hand, the positive relation between daily CP and daily physical activity goal progress was stronger on days during which the students experienced higher levels of goal conflict. Overall, these findings supported three of the four hypotheses—this time, in a study looking at the within-person association of AP and CP with two conceptually different—albeit empirically related—indicators of physical activity.

Discussion

This research contributes to an emerging line of study that examines the pursuit of more than one goal at a time to more closely capture how people strive toward valued aims in their everyday lives. Two studies, using different methodologies and outcome measures, provided convergent support for the overarching thesis of the present research, which is that the relation between planning and physical activity is moderated by the level of goal conflict students experience from the academic domain.

Review and Integration of the Main Findings

The results of both studies dovetail nicely to reveal two key findings. First, academic goal conflict moderated the influence of planning on physical activity outcomes. This result is in line with prior research showing the deleterious influence of goal conflict at both the between- (e.g., Bailis, Thacher, Aird, & Lipschitz, 2011; Gebhardt & Maes, 1998) and within-person levels (O’Connor et al., 2008; Payne et al., 2010). Second, the more striking finding to arise was that AP and CP play differential roles in predicting physical activity outcomes when students are experiencing goal conflict. AP related to better physical activity outcomes at lower levels of academic goal conflict, whereas CP related to better physical activity outcomes at higher levels of academic goal conflict, suggesting these two planning strategies play a different, yet complementary role in goal pursuit. Although AP and CP are highly interrelated (and both positively relate to physical activity), their differential function becomes apparent when examining their impact across varying levels of goal conflict.

On the one hand, it was found that the positive relation between AP and physical activity outcomes was stronger when students experienced lower levels of academic goal conflict over the course of the semester (Study 1) or on a given day (Study 2). This finding, at first glance, appears to contradict some evidence suggesting that AP is more effective for difficult rather than easy goals (Brandstätter et al., 2001; Study 1). However, goal conflict and goal difficulty should not be considered synonymous. Although difficult goals are less likely to be fully attained, they nonetheless yield more behavioral output (Locke & Latham, 2013). The rather positive effect of goal difficulty is in sharp opposition with the negative effect of goal conflict generally observed in the literature and in our two studies. On both theoretical and empirical grounds, we did not expect that goal difficulty and goal conflict would display a similar moderating effect in the relation between planning and goal progress. From a practical perspective, interventions fostering AP have traditionally been targeted at helping individuals articulate when, where, and how to pursue their physical activity goals without considering other important life strivings. Our findings could explain why such AP interventions sometimes fail to report strong effect sizes (e.g., Carraro & Gaudreau, 2013) insofar as people might be struggling to avoid conflicts stemming from other goals. A second generation of AP interventions could be devised to help minimize the risk of goals conflicting with one another.

On the other hand, it was found that the positive relation between CP and physical activity outcomes was generally stronger when students experienced higher levels of academic goal conflict over the course of the semester (Study 1), or a given day of the week (Study 2). Interventions including CP have typically involved proactively identifying potential obstacles and problems that could eventually interfere with goal striving (Sniehotta et al., 2005). Our findings could also explain why CP interventions sometimes fail to report strong effect sizes, because CP seems largely inconsequential when individuals are not experiencing goal conflict. As shown in our studies, however, the importance of CP becomes more obvious when individuals start experiencing some conflict in the pursuit of their physical activity goals. These findings offer emerging empirical support for the notion that CP should be systematically included in planning interventions insofar as it can prevent goal striving from getting derailed “when the going gets tough.”

Differences in findings across the two studies are worth noting. In particular, the CP × Conflict interaction was only marginally significant in Study 1, and it was nonsignificant for the physical activity behavior outcome in Study 2. One reason might simply be related to the inherently different meanings associated with prospective and concurrent within-day effects that we, respectively, estimated in Studies 1 and 2. Such considerations strengthen the argument for research to be performed at the within-person level to amass a larger body of evidence to be considered alongside traditional between-person research. Another reason could be that the CP × Conflict interaction is more likely to predict idiosyncratic outcomes, such as goal progress, compared with an outcome tapping the absolute amount of physical activity behavior—thus strengthening the arguments to consider both goal progress and behavior.

From a practical standpoint, interventionists might seek to optimize AP and CP by encouraging people to devise plans for getting started and coping with obstacles that specifically consider how concurrent goals might interfere with focal goal pursuit. Notwithstanding, students still made greater overall physical activity gains when goal conflict was lower. This suggests that planning may not be the most appropriate “first line” strategy against goal conflict. An interesting line of future research would be to examine whether it may be more fruitful to intervene at the goal setting stage, by devising goals at the front end that take into account potential ways of minimizing conflict and even increasing facilitation.

Limitations and Additional Future Directions

Study limitations should be considered when examining our results and may serve as a springboard for future research. For one, we used a dual goal model, which is one way of assessing interrelationships among goals. One limitation of this approach is that the two goals assessed were only a small subset of a broader goal structure. Other approaches have been used to study multiple goal pursuit, such as personal projects analysis (e.g., Presseau et al., 2013) and striving instrumentality matrix (e.g., Segerstrom & Nes, 2006), which both involve more exhaustive goal matrices. Alternatively, industrial organizational researchers have also focused on two life domains, such as work and family (Wiese & Salmela-Aro, 2008), but they included multiple goals within each domain and examined the degree of conflict and facilitation between each of them. It would be interesting to examine whether the results obtained across our two studies can be replicated using these alternative methods.

Another limitation concerns our use of university student samples, which limits the generalizability of our findings. From an applied perspective, it is fruitful to study this demographic group given the downward trajectory of physical activity behavior beginning in university (Stone et al., 1998). However, it would be important to study other populations (e.g., employees and new parents) and combinations of goals (e.g., work/family/friendship/romantic relationships)—particularly those involving combinations of goals relating to primary social roles and leisure endeavors. Often, people strive to lead a more “balanced life” that includes both types of pursuits, but they live in environments (e.g., office, home) that favor their social role–related goals by priming reminders and cues that increase their salience within the goal system and interfere with alternative goals (e.g., Bargh, Gollwitzer, Lee-Chai, Barndollar, & Trotschel, 2001). In addition to examining different goal domains, it would be important to examine the extent to which our results generalize to goals requiring recurrent action (e.g., regular physical activity, tooth brushing, diabetes management) versus one-time action (e.g., attending a doctor’s appointment, donating blood), as this is a potential boundary condition of our reported effects. Similarly, Presseau, Sniehotta, Francis, and Campbell (2009) have proposed that goal conflict and facilitation can also occur along a temporal dimension. For example, some forms of conflict are more transient or “one off” (e.g., surprise visit from relative), whereas other forms of conflict are more enduring (e.g., ongoing learning). The implication is that management of the different forms of conflict likely requires separate strategies. Whereas transient conflict is amenable to deferral strategies (Presseau et al., 2009), enduring conflict is—by its very nature—recurring, and so, continual deferral is not effective. It is perhaps in such cases that planning strategies are most likely to be helpful—a question that is ripe for empirical investigation.

In addition to contextual factors, it would be fruitful to examine motivational variables that could influence the observed relationships. For example, a recent study (Vogt, De Houwer, & Crombez, 2011) demonstrated that the motivational strength of a goal directly affects attention allocation in a multiple goal context. More specifically, participants were found to orient toward goals with higher value (Experiment 1) and expectancy of success (Experiment 2) when faced with competing goals. Relatedly, theories such as the Self-Concordance Model (Sheldon & Elliot, 1998) suggest that the type of motivation underlying goals may also matter. Goals can be considered “autonomous” if they are pursued for reasons related to personal interests and values, whereas they can be considered “controlled” if they are pursued for reasons relating to external or internal pressure. The model posits that goals pursued out of controlled motivation are likely to generate a sense of conflict within the self, whereas autonomous goals are likely to promote the effective usage of effortful volitional strategies (Gaudreau, Carraro, & Miranda, 2012; Koestner, Otis, Powers, Pelletier, & Gagnon, 2008). Hence, it may be that autonomous goal motivation buffers the deleterious impact of goal conflict on AP, another interesting avenue for future research. Also, a recent systematic review (Adriaanse, Vinkers, De Ridder, Hox, & De Wit, 2011) has shown that AP interventions for approach-oriented goals result in larger effects on behavior than planning interventions for avoidance-oriented goals, and may thus be important to consider in future replications of this research.

From a measurement perspective, one limitation concerns our use of self-report measures of physical activity, which are often critiqued for over-estimating absolute physical activity values due to social desirability and retrospective recall bias (Sallis & Saelens, 2000). Notably, a recent systematic review (Prince et al., 2008) showed no clear overall trends regarding the degree of correspondence between self-report and direct measures of physical activity, but did highlight the strengths and weaknesses of each approach, and recommended selecting appropriate measures on the basis of study objectives. Given that our primary aim centered on the relationships between predictor and physical activity outcome variables, and less on absolute physical activity levels, the use of well-validated self-report physical activity measures was deemed appropriate. Nevertheless, it would be valuable for future work to include objective measures of goal conflict and/or physical activity to safeguard against potential threats of shared method variance. Also, measures of social desirability could be included in studies with subjective outcomes. Finally, measurement issues such as the potential dilution effect of multiple plans (Verhoeven, Adriaanse, De Ridder, de Vet, & Fennis, 2013), plan format (Chapman, Armitage, & Norman, 2009), and degree of rehearsal of the plans (Knauper et al., 2011) would be potential characteristics to consider to clarify the conditions under which goal conflict moderates the effects of AP and CP (please see Bélanger-Gravel et al., 2013; Carraro & Gaudreau, 2013; Hagger & Luszczynska, 2014, for further discussion).

Another measurement-related consideration concerns the fact that goal conflict and goal progress were measured at the same time point in both studies. This could mean that participants were subject to “self-protective effects” or a sort of attribution bias in which they may have been inclined to point to conflict as an explanation for their lack of progress. Future studies could consider measuring these constructs at separate time points. Alternatively, a cross-lagged design with all variables measured at each time point would enable a stringent examination of whether goal conflict influences progress, progress influences goal conflict, or both. We also acknowledge that some people may have reported personal goals according to a priori estimates of anticipated levels of conflict/facilitation based on past goal striving experiences. Future work could replicate the studies herein while controlling for such variables as level of intention or priority when considering idiosyncratic goals. Alternatively, it would be interesting to replicate the study with experimenter-set goals. From a design standpoint, one limitation concerns our use of correlational designs in both Studies 1 and 2, which do not speak to potential causal pathways. Future work could use an experimental design by randomizing participants to higher or lower conflict groups, and testing for a potential interaction with AP and CP.

Conclusion

The two studies reported herein provided convergent support for the moderating role of academic goal conflict in the relation between AP and CP with physical activity at both the between- and within-person levels of analysis. The key finding to emerge was that AP and CP play differential roles in predicting physical activity outcomes, with AP showing greater associations with physical activity at lower levels of academic goal conflict and CP showing greater associations with physical activity at higher levels. Although the reported results require further replication and elaboration, they extend our current understanding of self-regulation in dual goal pursuit. Future research is encouraged that continues to chart the conditions under which AP and CP are, respectively, more or less helpful to maximize desired outcomes in the real world.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Sport Canada Sport Participation Research Initiative from the Social Sciences and Humanities Research Council of Canada (#862-2009-0007). This research was supported by a Joseph-Armand Bombardier Canada Graduate Scholarship from the Social Sciences and Humanities Research Council (SSHRC) awarded to the first author, and by a Sport Canada Sport Participation Research Initiative from SSHRC (#862-2009-0007) awarded to the second author. We would like to thank our colleagues at the Laboratory for research on Achievement, Motivation, and the Regulation of Action (LAMRA) for their help in editing an earlier draft of this manuscript. We would also like to thank Andrew Lumb and Eve-Marie Blouin for double-coding the goals.

Supplemental Material

The online supplemental material is available at .

Notes

References

Adriaanse

M. A.

Vinkers

C. D.

De Ridder

D. T. D.

Hox

J. J.

De Wit

J. B.

(2011). Do implementation intentions help to eat a healthy diet? A systematic review and meta-analysis of the empirical evidence. Appetite, 56, 183-193. https://dx-doi-org.web.bisu.edu.cn/10.1016/j.appet.2010.10.012

Austin

J. T.

Vancouver

J. B.

(1996). Goal constructs in psychology: Structure, process, and content. Psychological Bulletin, 120, 338-375.

Bailis

D. S.

Thacher

T. M.

Aird

N. C.

Lipschitz

L. J.

(2011). Affective and behavioral traces of goal conflict with physical activity. Basic and Applied Social Psychology, 33, 128-144. doi:10.1080/01973533.2011.568836

Bargh

J. A.

Gollwitzer

P. M.

Lee-Chai

Barndollar

Trotschel

(2001). The automated will: Nonconscious activation and pursuit of behavioral goals. Journal of Personality and Social Psychology, 81, 1014-1027. https://dx-doi-org.web.bisu.edu.cn/10.1037/0022-3514.81.6.1014

Bélanger-Gravel

Godin

Amireault

(2013). A meta-analytic review of the effect of implementation intentions on physical activity. Health Psychology Review, 7, 23-54. doi:10.1080/17437199.2011.560095

Bodner

T. E.

(2008). What improves with increased missing data imputations? Structural Equation Modeling: A Multidisciplinary Journal, 15, 651-675. doi:10.1080/10705510802339072

Brandstätter

Lengfelder

Gollwitzer

P. M.

(2001). Implementation intentions and efficient action initiation. Journal of Personality and Social Psychology, 81, 946-960. https://dx-doi-org.web.bisu.edu.cn/10.1037//0022-3514.81.5.946

Brickell

Chatzisarantis

Pretty

G. M.

(2006). Using past behaviour and spontaneous implementation intentions to enhance the utility of the theory of planned behaviour in predicting exercise. British Journal of Health Psychology, 11, 249-262. doi:10.1348/135910705X52471

Carraro

Gaudreau

(2009, March). Validation of a new measure of goal progress. Paper presented at the Société Québécoise pour la Recherche en Psychologie Ottawa, ON, Canada.

10.

Carraro

Gaudreau

(2013). Spontaneous and experimentally induced action planning and coping planning for physical activity: A meta-analysis. Psychology of Sport and Exercise, 14, 228-248. doi:10.1016/j.psychsport.2012.10.004

11.

Chapman

Armitage

C. J.

Norman

(2009). Comparing implementation intention interventions in relation to young adults’ intake of fruits and vegetables. Psychology & Health, 24, 317-332. doi:10.1080/08870440701864538

12.

Cohen

West

S. G.

Aiken

(2003). Applied multiple regression/correlation analysis for the behavioral sciences. London, England: Lawrence Erlbaum.

13.

Conroy

D. E.

Elavsky

Doerksen

S. E.

Maher

J. P.

(2013). A daily process analysis of intentions and physical activity in college students. Journal of Sport & Exercise Psychology, 35, 493-502. https://dx-doi-org.web.bisu.edu.cn/10.1016/j.psychsport.2014.04.008

14.

Conroy

D. E.

Elavsky

Hyde

A. L.

Doerksen

S. E.

(2011). The dynamic nature of physical activity intentions: A within-person perspective on intention-behavior coupling. Journal of Sport & Exercise Psychology, 33, 807-827.

15.

Dalton

A. N.

Spiller

S. A.

(2012). Too much of a good thing: The benefits of implementation intentions depend on the number of goals. Journal of Consumer Research, 39, 600-614. https://dx-doi-org.web.bisu.edu.cn/10.1086/664500

16.

Dugas

Gaudreau

Carraro

(2012). Implementation planning and progress on physical activity goals: The mediating role of life-management strategies. Research Quarterly for Exercise and Sport, 83, 77-89. doi:10.5641/027013612X13330442605331

17.

Emmons

R. A.

King

L. A.

(1988). Conflict among personal strivings: Immediate and long-term implications for psychological and physical well-being. Journal of Personality and Social Psychology, 54, 1040-1048.

18.

Enders

C. K.

(2006). Analyzing structural equation models with missing data. In Hancock

G. R.

Mueller

R. O.

(Eds.), Structural Equation Modeling: A second course (pp. 313-342). Greenwich, CT: Information Age Publishing.

19.

Gaudreau

Carraro

Miranda

(2012). From goal motivation to goal progress: The mediating role of coping in the Self-Concordance Model. Anxiety, Stress, & Coping, 25, 507-528. doi:10.1080/10615806.2011.628015

20.

Gebhardt

(2007). The role of facilitation and goal conflict in motivation. In Brown

(Ed.), Psychology of motivation (pp. 129-140). New York, NY: Nova Science.

21.

Gebhardt

Maes

(1998). Competing personal goals and exercise behavior. Perceptual & Motor Skill, 86, 755-759.

22.

Gebhardt

Maes

(2001). Integrating social-psychological frameworks for health behaviour research. American Journal of Health Behavior, 25, 528-536. https://dx-doi-org.web.bisu.edu.cn/10.5993/ajhb.25.6.2

23.

Godin

Shephard

R. J.

(1985). A simple method to assess exercise behaviour in the community. Canadian Journal of Applied Sport Sciences, 10, 141-146.

24.

Gollwitzer

P. M.

Schaal

(1998). Metacognition in action: The importance of implementation intentions. Personality and Social Psychology Review, 2, 124-136.

25.

Graham

Olchowski

Gilreath

(2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8, 206-213. https://dx-doi-org.web.bisu.edu.cn/10.1007/s11121-007-0070-9

26.

Gyurcsik

N. C.

Bray

S. R.

Brittain

D. R.

(2004). Coping with barriers to vigorous physical activity during transition to university. Family & Community Health, 27, 130-142. https://dx-doi-org.web.bisu.edu.cn/10.1097/00003727-200404000-00006

27.

Hagger

M. S.

Luszczynska

(2014). Implementation intention and action planning interventions in health contexts: State of the research and proposals for the way forward. Applied Psychology: Health and Well-Being, 6, 1-47. doi:10.1111/aphw.12017

28.

Hekler

Buman

Ahn

Dunton

Atienza

King

(2012). Are daily fluctuations in perceived environment associated with walking? Psychology & Health, 27, 1009-1020. doi:10.1080/08870446.2011.64.5213

29.

Kline

(2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.

30.

Knauper

McCollam

Rosen-Brown

Lacaille

Kelso

Roseman

(2011). Fruitful plans: Adding targeted mental imagery to implementation intentions increases fruit consumption. Psychology & Health, 26, 601-617. https://dx-doi-org.web.bisu.edu.cn/10.1080/08870441003703218

31.

Koestner

Otis

Powers

Pelletier

Gagnon

(2008). Autonomous motivation, controlled motivation, and goal progress. Journal of Personality, 76, 1-42. doi:10.1111/j.1467-6494.2008.00519.x

32.

Kruglanski

A. W.

Kopetz

Bélanger

Chun

W. Y.

Orehek

Fishbach

(2012). Features of multifinality. Personality and Social Psychology Review, 20, 1-18. doi:10.1177/1088868312453087

33.

Kruglanski

A. W.

Shah

J. Y.

Fishbach

Friedman

R. S.

Chun

W. Y.

Sleeth-Keppler

(2002). A theory of goal systems. In Zanna

M. P.

(Ed.), Advances in experimental social psychology (Vol. 34, pp. 331-378). San Diego, CA: Academic Press.

34.

K.-K.

Chan

D. K.

(2008). Goal conflict and the moderating effects of intention stability in intention-behavior relations: Physical activity among Hong Kong Chinese. Journal of Sport & Exercise Psychology, 30, 39-55. https://dx-doi-org.web.bisu.edu.cn/10.1016/j.midw.2013.07.015

35.

Locke

E. A.

Latham

G. P.

(2013). New developments in goal setting and task performance. New York, NY: Routledge.

36.

Locke

E. A.

Smith

Erez

Chah

D.-O.

Schaffer

(1994). The effects of intra-individual goal conflict on performance. Journal of Management, 20, 67-91.

37.

Meyers

L. S.

Gamst

Guarino

A. J.

(2006). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: SAGE.

38.

Moskowitz

Russell

Sadikaj

Sutton

(2009). Measuring people intensively. Canadian Psychology, 50, 131-140. doi:10.1037/a0016625

39.

Nezlek

J. B.

(2001). Multilevel random coefficient analyses of event- and interval-contingent data in social and personality psychology research. Personality and Social Psychology Bulletin, 27, 771-785. https://dx-doi-org.web.bisu.edu.cn/10.1177/0146167201277001

40.

Nezlek

J. B.

(2012). Multilevel modeling analyses of diary-style data. In Mehl

M. R.

Conner

T. S.

(Eds.), Handbook of research methods for studying daily life (pp. 357-383). New York, NY: Guilford Press.

41.

O’Connor

Jones

Conner

McMillan

Ferguson

(2008). Effects of daily hassles and eating style on eating behavior. Health Psychology, 27, S20-S31. doi:10.1037/0278-6133.27.1.S20

42.

Parks-Stamm

E. J.

Gollwitzer

P. M.

Oettingen

(2007). Action control by implementation intentions: Effective cue detection and efficient response initiation. Social Cognition, 25, 248-266. https://dx-doi-org.web.bisu.edu.cn/10.1521/soco.2007.25.2.248

43.

Payne

Jones

Harris

(2010). A daily diary investigation of the effects of work stress on exercise intention implementation: Can planning overcome the disruptive effects of work? Psychology & Health, 25, 111-129. doi:10.1080/08870440903337622

44.

Presseau

Sniehotta

F. F.

Francis

Campbell

(2009). Multiple goals and time constraints: Perceived impact on physicians’ performance of evidence-based behaviours. Implementation Science, 4, Article 77. doi:10.1186/1748-5908-4-77

45.

Presseau

Sniehotta

F. F.

Francis

J. J.

Gebhardt

W. A.

(2010). With a little help from my goals: Integrating intergoal facilitation with the theory of planned behaviour to predict physical activity. British Journal of Health Psychology, 15, 905-919. https://dx-doi-org.web.bisu.edu.cn/10.1348/135910710x494105

46.

Presseau

Tait

R. I.

Johnston

D. W.

Francis

J. J.

Sniehotta

F. F.

(2013). Goal conflict and goal facilitation as predictors of daily accelerometer-assessed physical activity. Health Psychology, 32, 1179-1187. doi:10.1037/a0029430

47.

Prince

Adamo

Hamel

Hardt

Connor Gorber

Tremblay

(2008). A comparison of direct versus self-report measures for assessing physical activity in adults: A systematic review. International Journal of Behavioral Nutrition and Physical Activity, 5, Article 56. doi:10.1186/1479-5868-5-56

48.

Raudenbush

S. W.

Bryk

(2002). Hierarchical linear models: Applications and data analysis methods (2 ed.). Thousand Oaks, CA: SAGE.

49.

Riediger

(2001). On the dynamic relations among multiple goals: Intergoal conflict and intergoal facilitation in younger and older adulthood. Doctoral dissertation, Freien Universität Berlin, Germany.

50.

Riediger

Freund

A. M.

(2004). Interference and facilitation among personal goals: Differential associations with subjective well-being and goal pursuit. Personality and Social Psychology Bulletin, 30, 1511-1523. doi:10.1177/0146167204271184

51.

Rise

Thompson

Verplanken

(2003). Measuring implementation intentions in the context of the theory of planned behavior. Scandinavian Journal of Psychology, 44, 87-95. doi:10.1111/1467-9450.00325

52.

Sallis

Saelens

(2000). Assessment of physical activity by self-report: Status, limitations, and future directions. Research Quarterly for Exercise and Sport, 71, 1-14. https://dx-doi-org.web.bisu.edu.cn/10.1080/02701367.2000.11082780

53.

Schlomer

Bauman

Card

(2010). Best practices for missing data management in counseling psychology. Journal of Counseling Psychology, 57, S1-S10. doi:10.1037/a0018082

54.

Scholz

Keller

Perren

(2009). Predicting behavioral intentions and physical exercise: A test of the health action process approach at the intrapersonal level. Health Psychology, 28, 702-708. doi:10.1037/a0016088

55.

Scholz

Nagy

Schüz

Ziegelmann

J. P.

(2008). The role of motivational and volitional factors for self-regulated running training: Associations on the between- and within-person level. British Journal of Social Psychology, 47, 421-439. doi:10.1348/014466607X266606

56.

Segerstrom

S. C.

Nes

L. S.

(2006). When goals conflict but people prosper: The case of dispositional optimism. Journal of Research in Personality, 40, 675-693. doi:10.1016/j.jrp.2005.08.001

57.

Shah

J. Y.

Friedman

Kruglanski

A. W.

(2002). Forgetting all else: On the antecedents and consequences of goal shielding. Journal of Personality and Social Psychology, 83, 1261-1280. https://dx-doi-org.web.bisu.edu.cn/10.1037//0022-3514.83.6.1261

58.

Sheldon

K. M.

Elliot

A. J.

(1998). Not all personal goals are personal: Comparing autonomous and controlled reasons for goals as predictors of effort and attainment. Personality and Social Psychology Bulletin, 24, 546-557.

59.

Shiffman

Huffard

Patty

(2001). Subject experience diaries in clinical research: Part I: The patient experience movement. Applied Clinical Trials, 10, 46-56.

60.

Sniehotta

F. F.

Schwarzer

Scholz

Schüz

(2005). Action planning and coping planning for long-term lifestyle change: Theory and assessment. European Journal of Social Psychology, 35, 565-576. doi:10.1002/ejsp.258

61.

Steptoe

Wardle

Pollard

T. M.

Canaan

Davies

(1996). Stress, social support, and health-related behaviour: A study of smoking, alcohol consumption, and physical exercise. Journal of Psychosomatic Research, 41, 171-180.

62.

Stone

McKenzie

Welk

Booth

(1998). Effects of physical activity interventions in youth: Review and synthesis. American Journal of Preventive Medicine, 15, 298-315.

63.

Vancouver

J. B.

Weinhardt

J. M.

Schmidt

A. M.

(2010). A formal, computational theory of multiple-goal pursuit: Integrating goal-choice and goal-striving processes. Journal of Applied Psychology, 95, 985-1008. doi:10.1037/a0020628

64.

Verhoeven

Adriaanse

M. A.

De Ridder

D. T.

de Vet

Fennis

(2013). Less is more: The effect of multiple implementation intentions targeting unhealthy snacking habits. European Journal of Social Psychology, 43, 344-354. doi:10.1002/ejsp.1963

65.

Vogt

De Houwer

Crombez

(2011). Multiple goal management starts with attention: Goal prioritizing affects the allocation of spatial attention to goal-relevant events. Experimental Psychology, 58, 55-61. doi:10.1027/1864-9335/a000042

66.

Wiese

B. S.

Salmela-Aro

(2008). Goal conflict and facilitation as predictors of work-family satisfaction and engagement. Journal of Vocational Behavior, 73, 490-497. doi:10.1016/j.jvb.2008.09.007

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