J Community Health DOI 10.1007/s10900-014-9828-8

ORIGINAL PAPER

Temporal Aspects of Psychosocial Predictors of Increased Fruit and Vegetable Intake in Adults with Severe Obesity: Mediation by Physical Activity James J. Annesi • Nicole Mareno

Ó Springer Science+Business Media New York 2014

Abstract Effective and reliable obesity treatments are lacking because of a poor understanding of the health behavior change process. Community-based organizations with the capacity to train existing staff members are particularly well-positioned to implement evidence-based treatment protocols to impact obesity-related behaviors such as unhealthy eating and lack of physical activity. The aim of this study was to assess temporal aspects of psychosocial predictors (self-regulation, mood, and self-efficacy) on increased fruit and vegetable intake in adults with severe obesity, while also accounting for mediation by physical activity volume. A 6-month, randomized field investigation was conducted. Severely obese adults volunteered for behavioral support of physical activity coupled with nutrition education (n = 73) or cognitivebehavioral methods for nutrition change (n = 71). Improvements in self-regulation, mood, self-efficacy, fruit and vegetable intake (FV), and physical activity (PA) were significant, with significantly greater self-regulation at month 6 for the cognitive-behavioral group. Increase in FV was predicted by changes in the above psychosocial variables over 6 months, with mood change over 3 months also a significant predictor. Change in PA mediated the above

J. J. Annesi Department of Health Promotion and Physical Education, Kennesaw State University, Kennesaw, GA, USA J. J. Annesi (&) Wellness Department, YMCA of Metropolitan Atlanta, 100 Edgewood Avenue NE, Suite 1100, Atlanta, GA 30303, USA e-mail: [email protected] N. Mareno Wellstar School of Nursing, Kennesaw State University, Kennesaw, GA, USA

relationships, with a reciprocal effect between changes in PA and FV. Findings have implications for the large-scale behavioral treatment of obesity. Keywords Weight loss  Obesity treatment  Behavioral  Psychological aspects  Physical activity

Introduction Obesity is a highly prevalent and important medical issue that affects more than one-third of U.S. adults [1]. Trogdon and colleagues estimate that obesity-attributable medical expenditures cost the U.S. economy $146.6 billion per year [2], making obesity a significant public health burden. The effect of obesity on morbidity and mortality is well-documented [3]. It is a contributing factor in the development of medical problems such as type II diabetes, cardiovascular diseases, and musculoskeletal diseases [4]. Obesity has also been associated with lower health-related quality-of-life in the dimensions of physical functioning, social functioning, and mental health [5]. Community-based organizations, especially multi-setting organizations such as the YMCA, could be influential in addressing obesity by assisting individuals with behavior-change methods [6]. Community-based organizations with the capacity to train existing staff members are particularly well-positioned to implement evidence-based treatment protocols to impact obesity-related behaviors such as unhealthy eating and lack of physical activity. Understanding temporal and contextual factors that influence an individual’s acquisition of self-management skills and confidence for engaging in the behavior-change process, as well as mitigating barriers, are important components of successful treatments. Presently, however,

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effective and reliable treatments are lacking because of a poor understanding of health behavior change in adults with obesity. In a comprehensive review of research on obesity treatments, Mann and colleagues [7] articulated the general failure of behavioral interventions, with weight loss being highly variable and nearly always regained in short order. They, however, suggested that physical activity and exercise may hold promise for weight loss in areas yet to be adequately explored, and thus suggested more detailed inquiry [7]. In fact, physical activity has been identified as the strongest correlate of maintained weight loss [8, 9]. Because obese, sedentary, and deconditioned individuals can complete only minimal volumes of physical activity [10], Baker and Brownell proposed that its benefits are from factors beyond its associated energy expenditure; possibly through the association of physical activity with improvements in psychological predictors of improved eating such as coping ability, self-esteem, and mood [11]. Recent research from Portugal [12, 13], Finland [14], and the U.S. [15, 16] directly investigated changes in various psychosocial factors associated with physical activity that might positively affect eating behaviors such as consumption of fats, and fruits and vegetables. The hopes were that findings would ultimately inform the architecture of behavioral treatments to increase their reliability and effectiveness. A recent review suggested that social cognitive theory and the theory of planned behavior produced the most promising psychosocial determinants of fruit and vegetable intake [17]. Borrowing from Bandura’s social cognitive and self-efficacy theories [18, 19] which posit that individuals set goals, acquire behavioral skills, enact new behaviors, and get reinforced by feelings of ability and positive psychological states, changes in usage of self-regulatory skills, mood, and self-efficacy were tested as predictors of controlled eating associated with newly initiated programs of physical activity [20]. It was thought that self-regulation methods such as cognitive restructuring, relapse prevention, and setting goals and attaining feedback on incremental goal progress would counter barriers to improved eating; mood improvement would energize behavioral effort and reduce emotional eating; and self-efficacy would enable behavioral changes through confidence and perceived ability, even in the presence of challenges such as strong social pressure and other prompts for inappropriate eating. A standardized protocol of behavioral support of physical activity was used in several trials to counter the expected rates of high attrition [21]. It was thought that associated volumes of physical activity might serve to mediate relationships between self-regulation, mood, and, self-efficacy, and improved eating [15]. Further research proposed that physical activity and improved eating, emanating from changes in their

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psychosocial predictors, could reinforce one another in a reciprocal manner [22]. Although treatments intended to leverage physical activity-induced psychosocial changes for their positive impact on eating are currently in development, several important research questions remain unanswered that might refine and extend their processes. For example, will self-regulation, mood, and self-efficacy be best-improved using a cognitive-behavioral nutrition program that emphasizes their development through methods extrapolated from social cognitive theory, or will a more typical nutrition education approach be associated with as much (or more) change? Some research suggests that exercise carries over to behavioral changes for eating improvements without further intervention [23], while other research indicates additional benefits through use of a cognitivebehavioral nutrition approach [12, 15]. Also, it is not known whether psychosocial improvements occurring over, for example, a year, 6 months, 3 months, from month 3 to month 6, at baseline, or at program end will best predict desired changes in eating behaviors. If self-regulatory changes occurring over the initial several months of treatment are most predictive of improved eating, then methods addressing development of those skills could be concentrated upon early in treatment. However, if selfregulation at treatment end is a superior predictor of nutrition change, attention given to the increase in those skills would be equally warranted throughout the duration of treatment. Baseline values of psychosocial measures may be indicative of behavioral traits [24], and might suggest that treatments be accordingly tailored. For example, low mood at baseline could indicate a need for an especially intense focus on self-regulation because of the possible depleting effects of anxiety and depression symptoms on the skills critical for dealing with inevitable barriers [11, 25]. Additionally, methods intended to regulate mood (e.g., deep breathing, cognitive restructuring) might become a priority to address emotional eating. Clearly, a more complete understanding of temporal factors related to psychosocial variables has the potential of enhancing treatments to maximize their effects. In order to address the above research questions, a 6-month, randomized field investigation of individuals with severe obesity was conducted. A validated protocol for behavioral support of physical activity [21] was paired with either educational or cognitive-behavioral nutrition methods. A naturalistic setting was chosen to facilitate rapid generalization of findings that could address an urgent medical problem for highly at-risk adults in a large scale manner. It was expected that the psychosocial variables addressed; self-regulation for eating, overall mood, and self-efficacy for controlled eating; would each demonstrate greater improvement, at all time frames assessed, in the

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cognitive-behavioral nutrition group. Although previous research supported an association of changes in self-regulation, mood, and self-efficacy with increased intake of fruits and vegetables over 6 months, it was left as research questions, without hypotheses, whether treatment-associated psychosocial changes from baseline to month 3, month 3 to month 6, or at baseline or month 6 would best predict eating changes. It was thought that change in physical activity volume would mediate all significant relationships between psychosocial changes and fruit and vegetable intake, and changes in eating and physical activity behaviors would have reciprocal, mutual reinforcing, relationships (because, as previously suggested [11], each was a part of a constellation of healthy behaviors).

Methods Participants Men and women from the southeast U.S. were recruited from newspaper advertisements. They were accepted into the study based on inclusion criteria of (1) age C21 years, (2) BMI C35 B55 kg/m2, (3) no regular exercise (selfreported average of \20 min/week) within the previous year, and (4) willingness to undertake a program of regular physical activity. Exclusion criteria were (1) planned or present pregnancy, (2) use of medications for weight loss or a psychological condition, and (3) current participation in a medically based or commercial weight-loss program. Additionally, a written statement of adequate health to participate was required from a medical professional. Appropriate approval was received from the Institutional Review Board of Kennesaw State University, and written informed consent was obtained from each participant. There was no significant difference in percentage of men (overall 22 %), age (overall Mean ± SD = 45.2 ± 9.2 years), BMI (40.7 ± 4.9 kg/m2), and racial/ethnic make-up (overall 52 % White, 46 % African American, and 2 % of other racial/ethnic groups) between those randomly assigned to groups of either nutrition education (n = 73) or cognitivebehavioral methods applied to nutritional change (n = 71). Participants were nearly all middle-class. Attrition from initial study acceptance to actual participation was minimal (6 %). It was due to reported illnesses, transportation problems, and an inability of study staff to make contact by phone or email, and did not differ by group. There was no cost or financial compensation for participation. Measures Self-regulation for controlled eating (SR) was measured using a scale adapted from a recently validated inventory

[26]. Consistent with suggestions from the inventory’s architects, items were adapted to reflect the content of the methods used within this research (e.g., ‘‘I say positive things to myself about eating well.’’). Responses to the 10 items ranged from 1 (never) to 5 (often). Internal consistency for the present version was a = 0.81, and test–retest reliability was 0.74 [27]. A high score indicated more use of self-regulation. Mood was measured by the Total Mood Disturbance scale (TMD), a 30-item measure of negative mood derived by aggregating responses from the Profile of Mood States Short Form subscales of tension, depression, fatigue, confusion, anger, and vigor [28]. Responses to feelings denoted by 1- to 3-word items (e.g., anxious, sad) ‘‘over the past week’’ ranged from 0 (not at all) to 4 (extremely). Internal consistency ranged from a = 0.84–0.95, and test–retest reliability results over 3 weeks averaged 0.69 [28]. A low score indicated less negative mood. Self-efficacy for controlled eating was measured by the Weight Efficacy Lifestyle Scale (WEL) [29]. It incorporates items from five factors (negative emotions, availability, physical discomfort, positive activities, and social pressure) (e.g., ‘‘I can resist eating even when others are pressuring me to eat’’) that are summed for a total score. Responses to the scale’s 20 items ranged from 0 (not confident) to 9 (very confident). Internal consistency ranged from a = 0.70–0.90 [29]. A high score indicated greater self-efficacy. Fruit and vegetable intake (FV) was measured by participants’ self-reporting of number of servings of fruits and vegetables consumed ‘‘in a typical day’’ over the past month. Items used for this were based on the U.S. Food Guide Pyramid’s descriptions and corresponding portion sizes. Responses for the 1 item for fruits and the 1 item for vegetables were summed for this research. Test–retest reliability over 2 weeks averaged 0.82, and concurrent validity was indicated through strong correlations of the present measure with lengthier food frequency questionnaires [30]. Research suggests that self-reported fruit and vegetable intake, alone, is a good predictor of quality of the overall diet [31, 32], and a uniquely strong predictor of both weight loss and weight-loss maintenance [33]. Additionally, research suggests that in the present context, single-item scales are appropriate [34]. Volume of physical activity (PA) was measured by the Godin-Shephard Leisure-Time Physical Activity Questionnaire [35]. It incorporated metabolic equivalents (METs) or the energy cost based on physical activity intensity where 1 MET approximates the use of 3.5 ml of O2/kg/min [36]. The Questionnaire required entry of number of weekly sessions of strenuous (approximately 9 METs; e.g., running), moderate (approximately 5 METs; e.g., fast walking), and light (approximately 3 METs; e.g.,

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easy walking) physical activity for ‘‘more than 15 min’’. Test–retest reliability over 2 weeks was 0.74 [37]. Construct validity was supported through strong correlations of Questionnaire scores with both accelerometer values and VO2 peak estimates [38, 39].

Procedure Each participant was provided access to a YMCA center after receiving an orientation to study processes. Physical activity support was identical in both the nutrition education and cognitive-behavioral nutrition groups. It consisted of 6, 45-min meetings with a trained YMCA wellness specialist approximately monthly over 6 months using a previously validated protocol [21]. These one-on-one meetings included a physical activity plan based on each participant’s preference and tolerance. The current minimum U.S. government recommended volume of weekly physical activity (i.e., C150 min of moderate cardiovascular activity) [40] was described. Additionally, it was indicated that any volume of physical activity was likely to be beneficial for health—especially as he/she initiated the behavior. Much of the structured protocol was spent learning self-regulatory methods intended to promote adherence. For example, long-term physical activity goals were identified, documented, and broken down into process-oriented, short-term goals where incremental progress was graphically tracked for emphasis. Additional selfregulatory methods such as cognitive restructuring, stimulus control, self-reward, and relapse prevention were also taught and reinforced within the sessions. The nutrition component of the treatments differed by group. Each was led by a trained YMCA wellness specialist in 6, 60-min group sessions over 12 weeks. Sessions began approximately 6 weeks after initiation of the physical-activity support component. The protocol used in the nutrition education group provided information in the following areas related to healthy eating: (1) food purchasing, (2) recipes, (3) menu planning, (4) eating outside of the home, and (5) snacking [41]. The protocol used in the cognitive-behavioral nutrition group focused primarily on behavioral techniques consistent with social cognitive theory. Thus, the following were emphasized: (1) setting a daily caloric-consumption goal and tracking food and energy intake, (2) self-weighing at least once weekly, (3) restructuring unproductive self-statements, (4) relapse prevention training, and (5) managing cues to overeating. Wellness leaders administering the treatments were blind to the study’s purposes with approximately 10 % of treatment sessions checked for fidelity by study staff. Rarely occurring deviations in treatment protocols were quickly rectified in cooperation with YMCA supervisors.

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Surveys were administered privately at baseline, at the end of month 3, and at the end of month 6. Data Analyses An intention-to-treat approach was used where data from all individuals initiating participation in the study were retained. The expectation–maximization algorithm was used for imputation applied to the 14 % of overall missing cases (none missing at baseline, 14 % missing at month 3, and an additional 15 % missing at month 6) [42, 43]. Statistical significance was set at a = 0.05 (2-tailed), throughout, with the Bonferroni adjustment applied where appropriate. For the regression analyses used, detection of a small-moderate effect (f2 = 0.08) at the statistical power of 0.80 (a = 0.05), required 98 participants. Scores of all measures were approximately normally distributed (skew and kurtosis scores within ±2.0 standard errors). Mixed-model repeated measures ANOVAs (time 9 treatment type) simultaneously assessed whether changes in each variable were significant over 3 and 6 months, and whether those changes differed across the two types of nutrition treatments. Effects sizes were estimated through partial eta-squared (g2p) where 0.01, 0.06, and 0.14 denote small, moderate, and large effects, respectively. Follow-up dependent t tests assessed if within-group changes were significant. Group differences at baseline and month 6 were also assessed through the use of one-way ANOVAs. Data were then aggregated for regression analyses. Because the initial values of the psychosocial predictors of change in FV were associated with their change scores, they were controlled for in the analyses. Bivariate relationships of scores of SR, TMD, and WEL at baseline, month 6, change from baseline to month 3, and change from month 3 to month 6; with changes in FV over 6 months; were first calculated. Relationships of changes in the 3 psychosocial variables from baseline to month 3 with change in FV over the same time frame were also computed. After conversion to z scores, the relative strengths of the bivariate relationships of the psychosocial variables that significantly predicted increased FV were then contrasted with one another. Mediation (see Fig. 1) of those significant bivariate relationships by PA (over the same time frames as the predictors) was then calculated using a bias-corrected bootstrapping procedure incorporating 20,000 re-samples [44]. R2 was used to assess significance of the overall mediation models, and 95 % confidence intervals were used to assess significance of mediations. If the relationship of the predictor and outcome variable (path c) was no longer significant after entry of the mediator (path c0 ), complete mediation was considered to have occurred. To assess whether a reciprocal relationship between changes in FV and PA was present, a

J Community Health Mediator

Predictor

Pathc’ Effect of predictor on outcome, after controlling for mediator

Outcome

Pathc Total effect of predictor on the outcome

Fig. 1 Display of relationships within mediation analyses

complimentary mediation analysis was also calculated for each model where the mediator became the outcome variable and the outcome variable became the mediator [22]. A reciprocal effect was considered present if significant mediation occurred in both of the complimentary models [22].

Results There was no significant group difference on any of the three psychosocial measures at baseline (p values [0.15). For WEL, mixed-model repeated measure ANOVAs indicated a significant increase from baseline to month 6 (F1, 142 = 215.39, p \ 0.001, g2p = 0.603), but not from month 3 to month 6. Post hoc testing indicated that within-group improvements from baseline to month 3 and from baseline to month 6 were each significant (Table 1). For TMD, there was a significant decrease from baseline to month 6 (F1, 142 = 126.49, p \ 0.001, g2p = 0.471), but not from month 3 to month 6. Post hoc testing indicated that within-group improvements from baseline to month 3 and from baseline to month 6 were each significant (Table 1). For SE, there was a significant increase from baseline to month 6 (F1, 142 = 144.59, p \ 0.001, g2p = 0.505), and from month 3 to month 6 (F1, 142 = 10.79, p = 0.001, g2p = 0.071). Post hoc testing indicated that within-group improvements from baseline to month 3, from month 3 to month 6, and baseline-month 6 were each significant (Table 1). For each of the psychosocial measures, the time 9 treatment type interaction was not significant. At month 6, the behavioral treatment group had a significantly higher score on SR than the education group (F1, 142 = 8.71, p = 0.004, g2p = 0.058) (Table 1). There were no significant group differences in TMD or WEL at month 6. There were significant relationships of change in SR from baseline to month 6 (b = 0.20, p = 0.015) and SR at month 6 (b = 0.17, p = 0.045), with change in FV. There were significant relationships of changes in TMD from baseline to month 3 (b = -0.19, p = 0.027), TMD from

baseline to month 6 (b = -0.22, p = 0.007), and TMD at month 6 (b = -0.21, p = 0.010), with change in FV. There was a significant inverse relationship between WEL at baseline, and FV change (b = -0.24, p = 0.003), and a significant positive relationship between change in WEL from baseline to month 6, and FV change (b = 0.25, p = 0.003). No significant difference in the strengths the above bivariate relationships for any of the 3 psychosocial measures was found (all p values [0.70). Over 6 months, change in PA significantly mediated the relationship of change in SR with FV change (complete mediation); and FV change significantly mediated the relationship of SR change with change in PA. Thus, within those complementary equations, a reciprocal effect between changes in PA and FV was identified (see Table 2, upper section). Over the initial 3 months, change in PA significantly mediated the relationship of change in TMD with FV change (complete mediation); and FV change significantly mediated the relationship of TMD change with change in PA. Thus, within those complementary equations, a reciprocal effect between PA and FV changes was found (see Table 2, middle section). Over 6 months, change in PA significantly mediated the relationship of change in WEL with FV change (complete mediation); and FV change significantly mediated the relationship of WEL change with change in PA. Thus, those complementary equations also indicated a reciprocal effect between changes in PA and FV. PA change did not significantly mediate the relationship of WEL at baseline with change in FV (see Table 2, lower section). No other significant mediations or reciprocal effects were found (Table 2).

Discussion This multi-tiered investigation of psychosocial predictors of increased fruit and vegetable consumption in severely obese adults supported several previous findings [12–17], and yielded new insights into temporal effects that could have implications for improving behavioral weight-management treatments. The study included analyses of the mediation of increased physical activity on associations between psychosocial predictors and eating change, and how changes in fruit and vegetable intake and physical activity might serve to reinforce one another. Findings extended possible explanations of psychosocial predictors of improved eating, and the role of physical activity in (possibly indirectly) facilitating nutrition changes leading to weight management. Although the substantial improvements over 3 and 6 months in self-regulation for controlled eating, mood, and self-efficacy for controlled eating did not significantly differ between groups incorporating cognitive-behavioral

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J Community Health Table 1 Changes in study measures over 3 and 6 months (Mean ± SD) Month 1

Month 3

Change from baseline to month 3

Month 6

Change from month 3 to month 6

Change from baseline to month 6

Self-regulation for eating (SR) Nutrition education group

21.12 ± 5.98

28.86 ± 5.53

7.74 ± 5.50 

28.35 ± 5.91

-0.52 ± 4.33

7.22 ± 6.55 

Cognitivebehavioral group

22.46 ± 5.47

30.54 ± 5.47

8.07 ± 7.05 

31.01 ± 4.89

0.48 ± 3.61

8.55 ± 6.34 

Overall sample

21.78 ± 5.76

29.69 ± 5.55

7.90 ± 6.29 

29.66 ± 5.57

-0.03 ± 4.01

7.88 ± 6.46 

Total mood disturbance (TMD) Nutrition education group

17.51 ± 12.16

4.04 ± 11.54

-13.47 ± 14.10 

3.37 ± 11.34

-0.67 ± 6.95

-14.14 ± 14.92 

Cognitivebehavioral group

18.38 ± 13.26

2.35 ± 13.24

-16.03 ± 15.11 

2.86 ± 14.73

0.51 ± 9.28

-15.52 ± 16.70 

Overall sample

17.94 ± 12.68

3.21 ± 12.39

-14.73 ± 14.61 

3.12 ± 13.07

-0.09 ± 8.17

-14.82 ± 15.78 

Self-efficacy for eating (WEL) Nutrition education group

101.79 ± 36.54

126.01 ± 27.85

24.22 ± 29.63 

131.52 ± 25.89

5.51 ± 19.51*

29.73 ± 32.02 

Cognitivebehavioral group

102.49 ± 30.87

133.61 ± 26.78

31.11 ± 31.04 

137.37 ± 25.95

3.77 ± 13.77*

34.87 ± 32.44 

Overall sample

102.14 ± 33.75

129.76 ± 27.50

27.62 ± 30.42 

134.40 ± 26.00

4.65 ± 16.89**

32.26 ± 32.22 

5.48 ± 2.02

1.03 ± 1.99 

5.57 ± 1.93

0.09 ± 1.21

1.13 ± 1.84 

36.08 ± 15.75

28.41 ± 15.58 

34.23 ± 15.87

-1.85 ± 13.59

26.56 ± 15.78 

Fruit and vegetable intake (FV) Overall sample

4.44 ± 1.89

Physical activity (PA) Overall sample

7.67 ± 6.32

Nutrition education group n = 73. Cognitive-behavioral group n = 71. Overall sample N = 144 * p \ 0.05, ** p \ 0.01,

 

p \ 0.001

exercise support paired with either nutrition education or a behavioral nutrition approach, the amount of self-regulation utilized at treatment end was, as expected, greater in the behavioral nutrition condition (where there was more time focused in that area). For both groups, self-efficacy for controlled eating continued to significantly improve from month 3 to month 6, where changes in self-regulation and mood beyond month 3 were negligible. For mood, this temporal effect of fairly rapid improvement followed by stabilization of the improved psychological profile is consistent with previous research on individuals initiating moderate physical activity [45]. The finding that changes in self-regulation occurred early may indicate either an initial motivational effect linked to initiating an obesity treatment, or a true ceiling effect. Until this is better understood, treatments might emphasize learning resilient self-regulatory skills in the initial months, and seek consistent improvement in self-efficacy throughout by, for example, emphasizing short-term nutritional goals that are distinct

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and measurable (e.g., increasing fruit and vegetable consumption by 1 serving per day every month) that can demonstrate continually increasing competence. In aggregated data analyses, weekly physical activity estimates approached the standard recommended volume for health by month 3 of treatment, and remained there through month 6. Fruit and vegetable intake increased by slightly more than 1 serving per day by month 3, and remained at that level (above five servings per day) through treatment end. Consistent with previous research [25], improvements in self-regulation, mood, and self-efficacy over 6 months were significantly associated with increased fruit and vegetable intake. Self-regulation and mood scores at month 6 were also significantly related to fruit and vegetable intake. Additionally, improvement in mood over the initial 3 months was associated with improved eating. The strength of each of these significant relationships was similar. This further reinforced the validity of focusing upon the factors of self-regulation, mood, and self-efficacy

J Community Health Table 2 Mediation analyses of psychosocial predictors and change in fruit and vegetable intake Predictor

DSRB-M6

Mediator

DPAB-M6

Outcome

DFVB-M6

Path a coef

Path b coef

Path c coef

Path c0 coef

Indirect effect

Model R2

SE

SE

SE

SE

SE

p

0.75 0.24

DSRB-M6

DFVB-M6

DPAB-M6

SRM6

PAM6

DFVB-M6

SRM6

DFVB-M6

PAM6

DPAB-M3

DFVB-M3

0.06 0.03

0.01

0.015

0.69 0.01

0.045

1.17 0.70

-0.44

0.03

0.11 \0.001

0.01 1.87

DTMDB-M3

DFVB-M3

DPAB-M3

-0.07

DTMDB-M6

DPAB-M6

DFVB-M6

-0.42 0.10 \0.001

0.01

DTMDB-M6

DFVB-M6

DPAB-M6

-0.03

1.13

TMDM6

PAM6

DFVB-M6

-0.42

0.01

0.10 \0.001

0.01 0.86

0.01

0.01

0.016

0.61

0.055

0.68

0.24

0.096

0.03

0.096

0.61 0.23 0.01

0.003

0.098

0.045

0.03

0.010

0.55 0.24

0.211

0.016

0.11 \0.001

0.11 -0.03

0.01

0.006

0.01

0.10 \0.001

0.10 -0.03

0.010

0.01

0.01

0.022

0.07 0.05

\0.001 0.040 \0.001 0.046

DFVB-M6

0.17 0.003

0.01 1.42

0.051

0.01 0.17

0.003

0.01 0.14

0.016

DPAB-M6

0.05 0.02 0.01

0.003

0.72

0.051

0.05

0.003

0.06

0.013

DWELB-M6 WELB

DFVB-M6 DPAB-M6

DFVB-M6

0.01 0.04

WELB

DFVB-M6

DPAB-M6

0.03 0.871

-0.01 0.01

0.01 0.71

0.10 \0.001

0.10 0.02

-0.01 0.010

1.87 0.005

-0.39

0.02

0.01 0.01

0.010

0.04

0.01

0.001

0.04

0.12 -0.030, -0.004

0.01 0.03 0.01

-0.162, -0.011 \0.001 0.09 -0.019, 0.001 -0.119, 0.004

\0.001 0.06

-0.016, 0.003

0.017 0.13

-0.102, 0.011

\0.001

0.002 0.027

0.000,0.008

\0.001 0.12

0.019

0.000, 0.073

0.001

-0.002, 0.003

0.001

0.03

0.15

0.001

0.12

-0.02 0.436

0.010 0.17

0.0002 0.003

0.001 0.19

0.003

0.03 0.871

-0.010, 0.213

-0.03

-0.01 0.005

0.034 0.06 0.009

-0.01

DPAB-M6

0.211

-0.002, 0.031

-0.04

DWELB-M6

0.68

0.04

0.001 0.05

-0.01

-0.03

0.02

0.000, 0.027

-0.06

PAM6

0.010

0.01

0.011 0.12

-0.01 0.185

DFVB-M6

0.01

0.10

0.106

TMDM6

-0.42

0.09 0.000, 0.038

0.01

-0.38

-0.03 0.01

0.009

-0.38

-0.03

0.01 0.10

-0.02 0.016

-0.42 0.098

0.25

95 % CI

0.01 0.056

0.05

-0.44 0.003

0.03 0.65

-0.03 0.003

p

0.06 0.015

0.06

0.02

0.006

0.03 0.75

0.02 0.010

p

0.07 0.055

1.34

0.61 0.23

p

0.02 0.003

0.07 0.03

DTMDB-M3

p

0.01

0.08 -0.057, -0.005

0.008

Coef coefficient, 95 % CI 95 % confidence interval, D change in score over designated time, SR self-regulation for eating, TMD total mood disturbance, WEL self-efficacy for controlled eating, B baseline, M6 month 6. Path a = predictor ? mediator, Path b = mediator ? outcome, Path c = predictor ? outcome, Path c0 = predictor ? outcome, while controlling for the mediator

within behavioral nutrition interventions. Somewhat unexpectedly, self-efficacy at baseline was inversely related to change in fruit and vegetable intake. This finding does, however, support previous research suggesting deleterious effects of ‘‘overconfidence’’ (i.e., inflated selfefficacy) on health behavior changes [46]. Thus, when selfefficacy is very high at treatment start, participants should be specifically monitored to ensure their receptivity to selfregulatory skills needed to manage unexpected barriers to adherence over time (because they may not acknowledge such a need). Changes in physical activity volume served to mediate the relationships of improvements in self-regulation (baseline to month 6), mood (baseline to month 3), and self-efficacy (baseline to month 6), with increased fruit and vegetable consumption over the same time frames. In the

case of self-regulation and mood, their relationships with changes in eating were no longer significant after entry of physical activity change as the mediator. This finding points to the absolute need for the early inclusion of physical activity in obesity treatments due to its effect on the association of better eating with its behavioral predictors. Recent research suggests that the role physical activity plays in psychosocial changes that may affect better eating may actually be more important than its (often minimal) energy expenditures [20]. This novel premise is supported within this study by the mutually reinforcing relationships between the moderate increases physical activity and eating improvements that were identified through reciprocal effects analyses. If the aforementioned premise is ultimately confirmed through further research, then dosages of physical activity for inducing weight loss could become far

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less intense than current recommendations, where a maximization of energy expenditures is typically sought (often to the point of considerable discomfort, resulting in drop out). Because less than 5 % of adults (of all weights) in the U.S. and Canada complete even the minimum recommended volume of weekly physical activity [47–49], more realistic and manageable amounts are likely to be far more attainable and maintainable for individuals with obesity. Future research should extend these findings to determine where, temporally, physical activity initiation should be positioned within the overall obesity treatment in relation to diet change. For example, it has been suggested that physical activity change might best be initiated several weeks to months prior to changes in eating so that carryover effects of key psychosocial factors (e.g., self-regulation) are best utilized [20]. This substantially differs from typical practices where physical activity is positioned as an optional adjunct to severe energy restriction. There is sometimes even a concern that initiating physical activity along with energy restriction might deplete limited selfmanagement resources [50]. However, based on the present findings, improved timing and emphases on moderate physical activity for targeting specific psychosocial changes over the course of treatment could help to maximize nutrition changes, weight loss, and behavioral predictors of maintained weight loss (e.g., improved ability to deal with barriers to controlled eating after a plateau in lost weight, feelings of confidence to persevere, positive mood states that may have a protective effect over emotional overeating). Even with manageable physical activity prescriptions, evidence-based exercise adherence support will be essential because drop-out rates of newly initiated physical activity range from 50 to 65 % [21]. Because of the key role maintained physical activity has in the proposed treatment paradigm, dropout would present a strong possibility for early derailment of the desired behavioral changes. Although several possibilities for treatment improvements were found, replication across subsamples (e.g., different degrees of overweight/obesity; diagnosis of diabetes) is required to assess generalizability of findings. When temporal effects of psychosocial predictors of eating and effects of physical activity are transposed into revised treatments, they will then require considerable testing and decomposition to assess causal patterns. However, given persistent failures in the behavioral treatment of obesity, recent authorities’ suggestions point to the strong practical value in the use of field designs, as here, that facilitate evidence-based improvements in treatment components while continuing to scrutinize theory-based predictors and patterns of change [51–53]. Thus, variables from additional health behavior-change models will also require field testing for their relative efficacy in advancing both theory

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and treatment. This research also indirectly addressed a call to evaluate whether physical activity is such a robust predictor of long-term success with weight loss because of its associated effects on psychosocial factors (rather than through energy expenditures directly) [7]. Beyond those already stated, limitations of this research include the use of change scores that inflated the measurement error of scales because of their multiple administration times [54]. However, accounting for the dynamic process of changes in psychosocial predictors of eating was a key purpose and strength of this investigation. Although adequate validation data were presented, assessment of physical activity and nutrition behaviors may benefit from the use of more sensitive instrumentation in the future such as accelerometers and detailed food logging or use of comprehensive food frequency questionnaires. Although internal validity could be compromised in field investigations such as this (e.g., through expectation and social support effects), the design advantages include the ability to address theory and extend a poorly developed area of research while simultaneously translating findings into treatment development. Findings from this study are useful for moving science forward in an under-attended to area. The results have several important implications for theory testing and evidence-based treatment. First, theory-based behavioral change predictors can be further explored for refinement of a weight management protocol that is applicable within community-based settings. Second, weight loss treatment protocols can be revised to include, temporally, where physical activity initiation and emphases on key psychosocial predictors could be positioned to best effect improved eating. Testing of the revised treatment protocols among diverse community subsamples would then be necessary. For example, inclusion of individuals with multiple co-morbidities (e.g., metabolic syndrome) could help to identify additional factors that have an impact on the health behavior change process. Sufficient testing is also necessary to facilitate dissemination and use in multisite community settings in a standardized manner. Ultimately, however, community organizations will be wellpositioned to deliver, and to assess the outcome of, evidence-based weight management treatments that are efficient, palatable, and reliable. In conclusion, obesity is a significant public health issue in the U.S. Numerous health risks associated with obesity have been demonstrated [3–5]. Difficulty in achieving weight loss, and failure to sustain weight loss with behavioral interventions, is widespread [7]. In this study, temporal aspects of psychosocial predictors were examined in relation to increased fruit and vegetable intake among obese adults. Increased fruit and vegetable intake was predicted by self-regulation skills for controlled eating,

J Community Health

mood, and self-efficacy for controlled eating. The importance of incorporating moderate physical activity early in the weight-loss process was demonstrated, along with its mutually reinforcing effect on improved eating. The results have promising implications for community-based administration of validated weight-loss treatments that are efficient, palatable, and reliable and greatly impact the high prevalence of obesity within society.

15.

16.

17. Acknowledgments We acknowledge Ms. Kristin McEwen for her help in facilitating treatment administration within the YMCA.

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