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The demographic parameters within the Step one accounted for a mathematically extreme ratio of the difference, Roentgen dos=?0

017, F(4,732) = 3.22, p < 0.05. In particular, age and BMI were significant predictors with ? = ?0.78 (p < 0.05) and ? = 0.082 (p < 0.05) coefficient, respectively. In Step 2, the addition of the main effects of the restrained eating, academic stress, and emotional eating (positive and negative) contributed significantly to the explained variance (?R 2= 0.028, F(8,728) = 4.30, p < 0.001). Age, BMI, positive emotional eating, and restrained eating were significant predictors in this step. Finally, in Step 3, the stepwise method identified a significant interaction between academic stress and nationality (? = ?0.162, p < 0.001), indicating that the nationality significantly moderated the impact of academic stress on junk food consumption. According to the R 2 adjusted the best model was the third (R 2= 0.062, F(nine,727) = 5.31, p < 0.001). About the main effects at this step, BMI (? = 0.093, p < 0.05), positive and negative emotional eating (? = 0.130, ? = 0.124, p < 0.01), and academic stress (? = 0.127, p < 0.01) predicted positively the ;= ?0.095, p < 0.05), nationality (? = ?0.102, p < 0.05) and restrained eating (? = ?0.106, p < 0.01) negatively predicted it. These results (showed in Table 3 ) were consistent with Hypothesis 1. Contrary to what we expected, junk food consumption was higher in non-restrained eaters. Regarding nationality, results showed that, on average, Italian students ate more junk food than French ones in the presence of academic stress. To interpret the moderation effect of the nationality on junk food consumption in relation to academic stress, simple slope analysis (Dawson, 2014) was conducted. As it can be seen in Figure 1 , academic stress significantly predicted junk food consumption for both Italian and French students, but differently: when academic stress increased, junk food intake increased in Italian students (? = 0.12, p < 0.01) but ;?0.21, p < 0.05). This result confirmed Hypothesis 5.

Sweet restaurants

The Step 1 model explained a statistically significant proportion of variance (R 2= 0.060, F(4,732) = , p < 0.001) but only the variable nationality (? = 0.24, p < 0.001) was a significant predictor of sweet food consumption among the demographic variables ( Table 4 ). The addition of the main effects in Step 2 caused a significant increase of the explained variance (?R 2= 0.025, F(8,728) = 8.42, p < 0.001): sex, nationality, negative emotional eating, and academic stress had a significant effect on sweet food intake. Finally, in Step 3 the following two significant interactions were identified by the procedure: academic stress ? nationality (? = ?0.98; p < 0.05) and academic stress ? negative emotional eating (? = 0.078; p < 0.05). According to the R 2 adjusted the latter model was the best one (R 2= 0.098, F(ten,726) = 7.92, p < 0.001). The main effects of negative emotional eating (? = 0.14, p < 0.01) and academic stress (? = 0.14, p < 0.01) predicted positively sweet food consumption, whereas BMI (? = ?0.08; p < 0.05) predicted it negatively. Moreover, being French was associated with higher sweet food intake (? = 0.20; p < 0.001). Simple slope analysis tests showed that academic stress significantly predicted sweet food consumption only for the Italian students (? = 0.14; p < 0.05) and for moderate (? = 0.14; p < 0.05) and high levels (+1SD; ? = 0.20; p < 0.01) of negative emotional eating. These latter results (depicted in Figures 2 and ? and3) 3 ) were consistent with Hypotheses 4 and 5 about the moderating effect of nationality and BMI on the relationship between academic stress and sweet food consumption.

Snacking

For snacking, only the nationality, among the demographic variables included in Step 1, affected snack consumption ( Table 5 ). This model accounted for a statistically significant proportion of the variance, R 2= 0.061, F(cuatro,731) = 11.9, p < 0.001. In Step 2, the entrance of the main effects contributed significantly to the variance explained (?R 2= 0.040, F(8,727) = , p < 0.001): nationality, negative emotional eating, and restrained eating were significant predictors. About interaction terms, the stepwise methods in Step 3 identified the following as significant: academic stress ? nationality, academic stress ? BMI, academic stress ? restrained eating. According to the R 2 adjusted, the latter model was the best one (R 2= 0.124, F(11,724) = 9.28, p < 0.001). In Step 3, the main effects of negative emotional eating and academic stress predicted snacking positively (? = 0.22, ? = 0.18, respectively). Moreover, results showed that snack consumption was higher in non-restrained eaters (? = ?0.08; p < 0.05), men (? = ?0.07; p < 0.05) and Italian students (? = ?0.25; p < 0.001). Simple slope analyses about moderation effects showed that academic stress predicted snacking only for the Italian students (? = 0.05, p < 0.001), non-restrained eaters (? = 0.05, p < 0.001) and for moderate (? = 0.05, p < 0.001) and high (+1SD; ? = 0.07; p < 0.001) levels of BMI. These latter results are shown in Figures 4 to ? to6. 6 . It is worth noting that, for snacking, almost all the Hypotheses were confirmed.