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Associations Among Lifestyle Behaviors, Academic Achievement, and Physical Diseases in Adolescents: A Cross-Lagged
Hui Xue1, Chunyan Luo2, Dongling Yang2
1School of Public Health, Southeast University, Nanjing 210009, China.
Abstract:
Objective: We aimed to examine the longitudinal associations between lifestyle behaviors, academic achievement, and physical diseases in adolescents. Study Design: Longitudinal cohort study. Methods: We recruited participants (n = 4330; mean age of 14.0 (SD = 1.51) years at the first time point and 16.0 (1.51) years at the second time point) from 16 districts in Shanghai, China, who completed a survey in 2021 (T1) and 2023 (T2). We employed a cross-lagged panel network model to explore the interconnected relationships among lifestyle behaviors, academic achievement, and physical condition (i.e., obesity, high blood pressure, high myopia, depressive symptoms). Results: Among the cross-lagged associations, the predictive effects of T1 obesity on T2 high blood pressure (OR = 2.39), T1 breakfast skipping on T2 TV screen time (OR = 1.49), (in cross-domain relationships) T1 symptoms of depression on T2 low fruit and vegetable consumption (OR = 2.43), T1 obesity on T2 TV screen time (OR = 1.53), and T1 computer time on T2 high BP (OR = 1.31) were particularly prominent. Nonetheless, the observed cross-lagged effect sizes were small. Based on the sum of expected influence on their connecting nodes, obesity, depressive symptoms, and breakfast skipping demonstrated their paramount roles in the network metrics. We found breakfast skipping showed the strongest bridging effect among all factors in association with coexisting conditions and academic performance in children. Conclusions: Our findings identified breakfast skipping as the pivotal bridge node with the highest centrality within the network of modifiable lifestyle factors. Although this does not imply direct causality, its prominent bridge effect highlights its essential role in maintaining network stability and mediating interactions across distinct variable clusters.
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