ArticlePloS one2022
Clustering of longitudinal physical activity trajectories among young females with selection of associated factors.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- [Cluster predictors of trajectories of leisure-time physical activity intensity in men and women from ELSA-Brasil].Cadernos de saude publica · 2025Article
- Physical activity and psychopathology: are long-term developmental trajectories of physical activity in children and adolescents associated with trajectories of general mental health problems and of attention-deficit hyperactivity (ADHD) symptoms?European child & adolescent psychiatry · 2024Article
- Analytic Methods for Understanding the Temporal Patterning of Dietary and 24-H Movement Behaviors: A Scoping Review.Advances in nutrition (Bethesda, Md.) · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
We examined multi-level factors related to the longitudinal physical activity trajectories of adolescent girls to determine the important predictors for physical activity. The Trial of Activity in Adolescent Girls (TAAG) Maryland site recruited participants at age 14 (n = 566) and followed up with these girls at age 17 (n = 553) and age 23 (n = 442). Individual, social factors and perceived environmental factors were assessed by questionnaire; body mass index was measured at age 14 and age 17, and self-reported at age 23. Neighborhood factors were assessed by geographic information systems. The outcome, moderate-to-vigorous physical activity (MVPA) minutes in a day, was assessed from accelerometers. A mixture of linear mixed-effects models with double penalization on fixed effects and random effects was used to identify the intrinsic grouping of participants with similar physical activity trajectory patterns and the most relevant predictors within the groups simultaneously. Three clusters of participants were identified. Two hundred and forty participants were clustered as "maintainers" and had consistently low MVPA over time; 289 participants were clustered as "decreasers" who had decreasing MVPA over time; 39 participants were grouped as "increasers" and had increasing MVPA over time. Each of the three clusters has its own cluster-specific factors identified using the clustering method, indicating that each cluster has unique characteristics.
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