Familial Environment and Overweight/Obese Adolescents’ Physical Activity
Bibliographic record
Abstract
(1) Background: Family environments can impact obesity risk among adolescents. Little is known about the mechanisms by which parents can influence obesity-related adolescent health behaviours and specifically how parenting practices (e.g., rules or routines) and/or their own health behaviours relate to their adolescent's behaviours. The primary aim of the study explored, in a sample of overweight/obese adolescents, how parenting practices and/or parental modeling of physical activity (PA) behaviours relate to adolescents' PA while examining the moderating role of parenting styles and family functioning. (2) Methods: A total of 172 parent-adolescent dyads completed surveys about their PA and wore an accelerometer for eight days to objectively measure PA. Parents completed questionnaires about their family functioning, parenting practices, and styles (authoritative and permissive). Path analysis was used for the analyses. (3) Results: More healthful PA parenting practices and parental modeling of PA were both associated with higher levels of adolescents' self-reported moderate-vigorous physical activity (MVPA). For accelerometer PA, more healthful PA parenting practices were associated with adolescents' increased MVPA when parents used a more permissive parenting style. (4) Conclusions: This study suggests that parenting practices and parental modeling play a role in adolescent's PA. The family's emotional/relational context also warrants consideration since parenting style moderated these effects. This study emphasizes the importance of incorporating parenting styles into current familial interventions to improve their efficacy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".