Within-Person Associations Between Physical and Social Contexts With Movement Behavior Compositions in Adolescents: An Ecological Momentary Assessment Study Using a Compositional Data Analysis Approach
Bibliographic record
Abstract
BACKGROUND: External contexts, including the social and physical contexts, are independent predictors of momentary physical activity and sedentary behaviors. However, no studies to date have examined how external contexts are related to overall momentary movement behavior compositions using compositional data analysis. Therefore, this study aimed to determine differences in momentary movement behavior compositions between different social and physical contexts in adolescents. METHODS: Overall, 119 adolescents (mean age 14.7 y, SD = 1.44) provided details about their momentary physical and social contexts over 4 days using ecological momentary assessment. Sedentary behaviors, light-intensity physical activity, and moderate to vigorous physical activity were assessed using ActiGraph GT3X+ accelerometers. Compositional multivariate multilevel models were estimated to determine if movement behavior compositions differed between contexts. RESULTS: Participants engaged in significantly less sedentary behaviors when outdoors compared with indoors and replaced it with moderate to vigorous physical activity. Participants also engaged in significantly less sedentary behaviors when with friends or friends and family and replaced it with light-intensity physical activity. CONCLUSION: These results highlight the potential of targeting external contexts to increase physical activity and to reduce sedentary behavior in adolescents' daily lives. These factors could be targeted in mobile health and just-in-time adaptive interventions to improve young people's movement behavior compositions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".