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Tracking and changes in the clustering of physical activity, sedentary behavior, diet, and sleep across childhood and adolescence: A systematic review
Author(s) -
Blyth Finn,
Haycraft Emma,
PeralSuarez Africa,
Pearson Natalie
Publication year - 2025
Publication title -
obesity reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.845
H-Index - 162
eISSN - 1467-789X
pISSN - 1467-7881
DOI - 10.1111/obr.13909
Subject(s) - psychological intervention , sedentary behavior , cluster (spacecraft) , physical activity , childhood obesity , obesity , scopus , tracking (education) , medicine , early childhood , clinical psychology , psychology , gerontology , developmental psychology , medline , psychiatry , overweight , physical therapy , pedagogy , computer science , political science , law , programming language
Summary Introduction Clusters of health behaviors (e.g. physical activity/sedentary behavior/diet/sleep) can exert synergistic influences on health outcomes, such as obesity. Understanding how clusters of health behaviors change throughout childhood and adolescence is essential for developing interventions aimed at uncoupling unhealthy behaviors. This review synthesizes prospective studies examining changes in clusters of physical activity, sedentary behavior, diet, and sleep through childhood and adolescence. Methods Electronic searches (PubMed, Embase, Web of Science, Scopus) identified prospective studies, published in English up to/including January 2024, of children/adolescents (0‐19 years) which used data‐driven methods to identify clusters of 2/more behaviors (physical activity, sedentary behaviors, diet, sleep) at multiple timepoints. A narrative synthesis was conducted due to methodological heterogeneity. Results Eighteen studies reporting data from 26,772 individual participants were included. Eleven studies determined clusters at each timepoint (i.e. identified clusters at T1 and T2, respectively), while seven determined clusters longitudinally using behavioral data across multiple timepoints. Among studies that identified clusters at each timepoint, participants commonly transitioned to similarly characterized clusters between timepoints. Where cluster tracking was examined, 64% of clusters had stable transition probabilities of 60‐100%. The most prevalent longitudinal cluster trajectories were characterized by co‐occurring healthy behaviors which remained stable. Remaining within unhealthy clusters at multiple timepoints was associated with higher markers of adiposity. Conclusion ‘Healthy’ and ‘unhealthy’ clusters remained highly stable over time, suggesting behavioral patterns developed early can become entrenched and resistant to change. Interventions focused on instilling healthy behaviors early are required to provide a strong foundation for behavioral stability throughout life.