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Bidirectional associations of physical activity, sleep, and self-reported mental health in young adults participating in an online wellness intervention during the COVID-19 pandemic
PURPOSE: The purpose of this study was to examine the bidirectional associations of physical activity (PA), sleep, and mental health in young adults participating in an online wellness intervention from October 2021 to April 2022. METHODS: Participants were a sample of undergraduate students from on...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10264583/ https://www.ncbi.nlm.nih.gov/pubmed/37325310 http://dx.doi.org/10.3389/fpubh.2023.1168702 |
Sumario: | PURPOSE: The purpose of this study was to examine the bidirectional associations of physical activity (PA), sleep, and mental health in young adults participating in an online wellness intervention from October 2021 to April 2022. METHODS: Participants were a sample of undergraduate students from one US university (N = 89; 28.0% freshman; 73.0% female). The intervention was a 1-h health coaching session that was delivered either once or twice by peer health coaches on Zoom during COVID-19. The number of coaching sessions was determined by random allocation of participants to experimental groups. Lifestyle and mental health assessments were collected at two separate assessment timepoints after each session. PA was assessed using the International Physical Activity Questionnaire–Short Form. Weekday and weekend sleep were assessed by two one-item questionnaires and mental health was calculated from five items. Cross-lagged panel models (CLPMs) examined the crude bidirectional associations of PA, sleep, and mental health across four-time waves (i.e., T1 through T4). To control for individual unit effects and time-invariant covariates, linear dynamic panel-data estimation using maximum likelihood and structural equation modeling (ML-SEM) was also employed. RESULTS: ML-SEMs showed that mental health predicted future weekday sleep (β = 0.46, p < 0.001) and weekend sleep predicted future mental health (β = 0.11, p = 0.028). Although CLPMs showed significant associations between T2 PA and T3 mental health (β = 0.27, p = 0.002), no associations were observed when unit effects and time-invariant covariates were accounted for. CONCLUSION: Self-reported mental health was a positive predictor of weekday sleep and weekend sleep positively predicted mental health during the online wellness intervention. |
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