Fruit and vegetable consumption has been associated with a decreased risk of several forms of cancer 12 and reduced risk of obesity 3. Many children 45 in the USA do not consume five servings of fruit and vegetables per day. Current behavioral models explain a relatively small percentage of the variance in children's fruit and vegetable consumption 6. Improved understanding of the factors that influence youth fruit and vegetable consumption and how they interact is needed to guide intervention design 7.

Psychological theories enhance our ability to understand behavior by identifying key mediators of the behavior of interest that can subsequently be manipulated in an intervention 8. Social cognitive theory 9 has been used extensively to design youth fruit and vegetable interventions 1011121314. A key feature of SCT is reciprocal determinism, the notion that patterns of behaviour are dynamic, constantly being affected by both characteristics and beliefs of the individual and the environment in which the behaviour is performed 915. Thus, while a number of studies have reported that youth fruit and vegetable consumption differ according to the age 16, gender 16, ethnicity 17 and socio-economic status 17 of the participants, a great deal of attention has also focused on psychosocial variables that could be manipulated in an intervention. Fruit and vegetable self efficacy 18 (perceived competence to select and eat fruit and vegetables) and preferences (the extent to which the child likes fruit and vegetables 71920) are two psychosocial variables consistent with SCT that have been shown to predict youth fruit and vegetable consumption.

Reciprocal determinism also specifies that environmental variables interact with participant psychosocial variables to influence behavior. Home availability of fruit and vegetables (whether fruit and vegetables are available to be eaten in the home) is a proximal environmental variable that has consistently predicted consumption 21. There is also evidence that broader environmental factors influence fruit and vegetable consumption. For example, among White and Black Americans fruit and vegetables consumption increased with access to supermarkets 22, while reduced access to grocery stores was associated with lower diet quality among pregnant women 23. The availability of fruit, fruit juice and vegetables at restaurants within the census tract in which Boy Scouts resided was also associated with fruit juice and vegetable consumption 24.

A limitation of existing research has been the consideration of just psychosocial or only environmental influences on fruit and vegetable consumption. Social cognitive theory suggests that the association between environmental variables and behavior could be either direct or indirect (e.g. a facilitating or buffering effect) 25. An indirect association would provide further support for SCT as it would suggest that the association is mediated by other variables such as psychosocial variables or home availability of fruits and vegetables, which is a more proximal environmental variable 26. These associations are shown in Figure 1. In this paper we test the hypothesis that the relationship between environmental variables and adolescent fruit and vegetables consumption is mediated by psychosocial variables or home availability. We assessed whether: 1) distance to the nearest/density of food outlets was associated with adolescent male fruit and vegetable consumption; 2) associations between fruit and vegetable consumption and environmental features change after accounting for psychosocial variables and home availability; and 3) psychosocial variables or home availability mediated the association between the fruit and vegetable consumption and distance to or density of food outlets.

Participants were 204 Boy Scouts (aged 10–14) recruited from 36 Boy Scout Troops within the greater Houston (TX) area with a mean of 5.8 participants per troop (range 2 – 12). Participants were a sub-sample from the baseline assessment of a physical activity intervention 27. Due to a shortage of accelerometers in the main intervention study, accelerometer data were only collected on a randomly selected sub-sample. The addresses of participants that met a minimum two-day accelerometer inclusion criteria were geo-coded and associations with environmental characteristics reported 2829. A series of tests indicated no differences in the BMI (t = .983, df = 449, p = 0.326), age (t = 1.33, df = 466, p = 0.894), fruit, juice or vegetable intake (t = .282, df = 448, p = 0.778) or parental education (χ² = 2.65, df = 3, p = 0.448) between these participants and the larger sample. The Baylor College of Medicine Institutional Review Board approved this study and written informed consent and assent were obtained for all participants.

Parental report provided participant ethnicity and place of residence. As socioeconomic status can vary within census tract and participants came from many census tracts within Houston we used the highest education obtained within the household as a family specific indicator of socioeconomic status. As over 70% of the participants were Euro-American, ethnicity was coded as Euro-American or Other. Stature was measured to the nearest 0.1 cm using a stadiometer (Shorr Height Measuring Board; Olney, MD) and weight was measured to the nearest 0.1 kg using a calibrated scale (Seca 770 Model Scale; Vogel and Halke, Hamburg). Body mass index (BMI = kg/m²) and BMI percentile were computed 30.

Participants were 12.8 years of age with a mean BMI percentile of 62.8 (Table 1). Over 70% of the participants had a parent who received a college education. The mean daily intake of FJV was 5.9 servings per day.

There was an average of 1.3 supermarkets, 7.3 small food stores, 8.9 fast food restaurants and 9 full service restaurants within 1 mile radius of participants' addresses (Table 2). The average distance to the nearest small food store was 778 meters while the average distance to the nearest fast food restaurant was 1051 meters.

Regression models predicting fruit and juice consumption are shown in Table 3. In the first step, none of the personal characteristics were significant predictors of fruit and juice intake and the model accounted for a low proportion of the variance (less than 6%). In step two, distance to the nearest small food store was positively associated with fruit and juice consumption (Beta = 0.001 (95% CI = 0.00, 0.00), z = 3.07, p = 0.002) while distance to the nearest fast food restaurant was negatively associated (Beta = -0.000, (95% CI = -0.001, -0.000), z = -2.76 p = 0.006). The variance accounted for increased to 11% overall. In step three both fruit and juice home availability (Beta = 0.269 (95% CI = 0.18, 0.35), z = 6.37, p < 0.001) and preferences (Beta = 0.061 (95% CI = 0.02, 0.10), z = 2.80 p = 0.005) were associated with fruit and juice consumption. The addition of the psychosocial variables substantially increased the variance accounted for with the overall R² increasing to 25%. The mediation tests performed indicated that distance to the nearest small food store was associated with fruit and juice preferences (z = 2.17, p = 0.030), but not home availability. Distance to the nearest fast food restaurant was not associated with either fruit and juice preferences or home availability (p > 0.05). In step five, distance to the nearest small food store, distance to the nearest fast food restaurant, home availability and preferences were all (p < 0.05) associated with fruit and juice consumption.

Comparison of steps two and five indicates that the association between distance to the nearest small food store and fruit and juice consumption was attenuated (z reduced from 3.07 to 2.63) after preferences were added to the model suggesting the fruit and juice preferences functioned as a mediator. A Sobel test indicated that fruit and juice preferences partially mediated (34%) the association between fruit and juice intake and distance to the nearest small food store, but the mediation effect was not statistically significant (z = 1.805, p = 0.071).

Regression models predicting the consumption of low fat vegetables are shown in Table 4. None of the personal characteristics were significant predictors in step 1. In step 2, distance to the nearest small food store was associated with low fat vegetables consumption (Beta = 0.001, (95% CI = 0.00, 0.001), z = 2.74, p = 0.006). In step three, preferences (Beta = 0.067 (95% CI = 0.02, 0.09), z = 3.04, p = 0.002) and home availability (Beta = 0.182 (95% CI = 0.10, 0.26), p < 0.001) were positively associated with consumption. The addition of these psychosocial variables to the demographics also substantially increased the overall variance accounted by the model from less than 4% to greater than 21%. In step four, distance to the nearest small food store was associated with low fat vegetables preferences (z = 2.32, p = 0.020), but not home availability (z = 0.47, p = 0.638). In step five low fat vegetables preferences (Beta = 0.050 (95% CI = 0.1, 0.09), Z = 2.70 p = 0.007) and availability (= 0.177 (95% CI = 0.09, 0.25), p < 0.001) predicted consumption while distance to the nearest small food store approached significance (Beta = 0.003 (95% CI = -0.00, 0.00), z = 1.87, p = 0.060).

Reduction in the strength of association between distance to the nearest small food store and low fat vegetables consumption before and after the addition of low fat vegetables preferences (z reduced from 2.74 to 1.87) suggested a mediation effect. This hypothesis was supported by the Sobel test which indicated that low fat vegetable preferences partially mediated (26%) the relationship between distance to the nearest small food store and low fat vegetables intake (z = 2.13, p = 0.032).

Regression models predicting the consumption of high fat vegetables are shown in Table 5. None of the personal characteristics were significant in step one. In step two, distance to the nearest small food store was positively associated with high fat vegetable consumption (Beta = 0.003 (95% CI = 0.00, 0.00), z = 3.41, p < 0.001) while distance to the nearest fast food restaurant was negatively associated (Beta = -0.001, (95% CI = -0.00, -0.00), z = -2.94 p = 0.003). The model accounted for 8% of the variance within troops and 10% of the overall variance. In step three, home availability (Beta = 0.165 (95% CI = 0.07, 0.26), z = 3.60, p < 0.001) and preferences (Std. Beta = 0.174 (95% CI = 0.07, 0.28), z = 3.22 p = 0.001) were associated with consumption. In step four neither distance to the nearest small food store nor distance to the nearest fast food restaurant was associated with preferences or availability indicating no mediation effects. In step five, distance to the nearest small food store (Beta = 0.003 (95% CI = 0.00, 0.00), z = 3.69, p < 0.001), home availability (Beta = 0.169 (95% CI = 0.08, 0.26), z = 3.79, p < 0.001) and preferences (Beta = 0.174 (95% CI = 0.07, 0.27), z = 3.31, p = 0.001) were associated with consumption while distance to the nearest fast food restaurant was negatively associated (Beta = -0.001 (95% CI = -0.00, -0.00), z = -3.21, p = 0.001). The step five model accounted for 22.5% of the overall variance.

Distance to the nearest small food store was a positive predictor of fruit and juice, low fat vegetable and high fat vegetable consumption, but proximity to large food stores was not associated with any of the dietary variables. Theoretically, shopping behavior will be conditional on local supply and the catchments of local grocery stores. If this were true eating behavior would be a function of distance to a large grocery store as people would need to drive further to get to where they do their regular large shop. We did not find this association in our data, but we did show that fruit and vegetable consumption was inversely associated with access to small stores. Since small stores usually provide a limited supply of fruit and vegetable 46, reduced proximity to these stores may limit consumption of higher calorie foods which negatively impacts fruit, juice and vegetable consumption. Moreover, among adolescents, who do not drive, it may be that access to small stores is more important than access to the larger grocery stores that require car access.

Twenty six percent of the association between distance to the nearest small food store and low fat vegetable consumption was mediated by preferences. Although the data are cross-sectional, and therefore the ability to detect the true nature of associations is not possible, the results indicate that participants who lived further away from small grocery stores had increased preferences for fruit and vegetables. Thus, adolescents who have less access to the smaller food stores, which traditionally carry a wider variety of processed foods and less fresh fruit and vegetables are perhaps more likely to consume fruit and vegetables [at home or in other locations] and develop preferences for them. Moreover, this group of adolescents are perhaps less likely to visit small stores and buy processed foods. Alternatively, it may be the case that families that like fruit and vegetables elect to live in neighborhoods that are further away from small food stores.

Distance to the nearest fast food outlet was negatively associated with both fruit and juice intake and high fat vegetable consumption. As proximity to a fast food restaurant increased so did consumption of fruit and juice and high fat vegetables. This seems logical because high fat vegetables such as French Fries are sold at these restaurants. Thus, perhaps adolescents who live close to a fast-food restaurant are more likely to consume the high fat vegetables provided at these stores, a simple facilitating effect. The association with fruit and juice is more difficult to tease out, but it may be that this association is a function of the fruit and juice that these stores sell, or children mistakenly reporting fruit pies or fruit flavored beverages as fruit consumed.

Although distance to small food stores and fast food restaurants was associated with consumption, no other proximity indicator was significant, nor were any density measures. This finding is in contrast to the research which has shown that access to supermarkets is associated with adult fruit and vegetables consumption 222347484950. The cause of this disparity is not clear. However, no previous research has focused specifically on US "Sun-Belt" cities or male adolescents. Thus, while the results of this study appear at odds with earlier research this study provides key information about a previously understudied group.

There was a small (~4%) increase in the variance accounted for by the addition of environmental variables. Approximately 30% of the total variance in fruit and vegetable consumption was explained by personal characteristics, psychosocial and environmental variables. This is comparable to previous studies 515253 that have not included environmental variables. Home availability was the strongest predictor of fruit and juice and low fat vegetable consumption, but it did not function as a mediator of distance to the nearest grocery stores, suggesting that strategies that focus directly on increasing home fruit, juice and vegetable availability will increase intake 21. The greater influence of home availability suggests that increasing home availability may be an effective intervention approach for this group.

For fruit and juice as well as low fat vegetables, the amount of variance accounted for by the models was much greater between troops than within troops when psychosocial variables were included in the models. For example, in Table 3 the between troop variance was 13% when just personal characteristics and environmental variables were in the model but this increased to 54% when personal and psychosocial variables were in the model. This suggests that participant self-efficacy, preferences and home availability account for more of the variability between troops whereas participants in the same troop are exposed to similar influences and also impact the self-efficacy and preferences of each other. This finding therefore supports our decision to control for clustering effects and implies that understanding why psychosocial variables differ between clusters could be important for developing new, more effective intervention approaches.

The mean daily fruit and vegetable consumption of this sample was 5.9 servings per day which is comparable to the 5.2 servings per day that were previously reported among Houston Boy Scouts using the same instrument 54. The 5.9 servings per day found in this study is greater than the 4.3 servings per day that were obtained using the BRFSS fruit and vegetable intake questionnaire among adolescent males in Minnesota 5. As the physical activity and BMI profiles of this group are comparable to other studies of similar aged US adolescents 27 the sample does not appear to be an unusually "healthy group". Since food frequency questionnaires that assess intake of individual fruits and vegetables have been shown to yield higher estimates of fruit and vegetable intake 55 the relatively high levels reported in this study could be a by-product of the assessment method.

Among adolescent males, residing further away from a small food store and close to a fast food restaurant was associated with increased fruit, juice and vegetable consumption. The association between distance to a small food store and low fat vegetable consumption was mediated by low fat vegetable preferences.

The author(s) declare that they have no competing interests.

This study was conceived by RJ and TB. RJ performed the analysis with all authors providing input on data interpretation. RJ wrote the first draft of the paper. All authors provided critical input and revisions to the paper. All authors approved the submission of this paper.