Understanding parental support for children's 24‐hour movement behaviors based on an adapted HAPA framework: A three‐wave prospective study
Bibliographic record
Abstract
Parental supportive behavior (PSB) plays a pivotal role in shaping children's 24-hour movement behaviors (24-HMB), including light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), sedentary screen time (SST), and sleep. However, the psychosocial determinants and the changing process of PSB remain understudied. Using a three-wave prospective design over four months, this study examined the psychosocial mechanisms of PSB towards children's 24-HMB based on an adapted health action process approach (HAPA) framework among 812 parents (36.61 ± 3.80 years; 68.7% female). The adapted HAPA model demonstrated acceptable fit indices (CFI = .952-.980, TLI = .946-.967), incorporating the original HAPA model along with past behavior and affective attitude. The model explained 31.6%-54.8% of the variance in PSB across the four outcomes (LPA, MVPA, SST, and sleep). Motivational self-efficacy and outcome expectancy consistently predicted intentions, while intentions and action control emerged as stable predictors of PSB across all four outcomes. The prediction of planning, and volitional self-efficacy on PSB varied by movement behaviors. Both past behavior and positive affective attitude were directly associated with PSB, while their inclusion attenuated most pathways in the original HAPA model. Further, intention and action control served as prominent mediators between past behavior, affective attitude, and PSB across all outcomes. Future research could leverage the adapted HAPA framework to guide the development of parent-based interventions aimed at improving children's 24-HMB.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".