Tell me where you live, and I will predict your exercise levels: How self‐regulatory action control, objective and perceived physical environment jointly explain physical activity time
Bibliographic record
Abstract
OBJECTIVES: This study investigated how self-regulatory action control indicators (self-regulatory effort, awareness of standards, self-monitoring) and perceived physical environment (perceived physical environment at home, in the neighbourhood, and availability of health promotion programs) are connected to explain moderate-to-vigorous physical activity (MVPA) over time. Furthermore, we examined whether these associations were moderated by an 'objective' physical environmental indicator, comparing small towns and rural areas with fewer PA facilities with a large city with more physical activity (PA) facilities. METHODS AND DESIGN: The study applied a prospective design, with participants (N = 593) providing data twice, spanning 8 months between the measurements. MVPA time was assessed using ActiGraph GT3X-BT accelerometers. Two-group mediation models were tested with path analyses. RESULTS: The associations representing mediating effects, encompassing perceived home environment → awareness of standards → MVPA were significant and positive in the city (with more PA facilities), but no mediation effects were found for data collected in towns/rural areas (with fewer PA facilities). High perceived availability of health promotion programmes was directly related to lower MVPA, but only in towns/rural areas (with fewer PA facilities). CONCLUSIONS: The findings suggest distinct patterns of associations in the larger city, compared to smaller towns/rural areas. Different perceived environmental characteristics and different self-regulatory action control facets may directly and indirectly predict MVPA of citizens living in these two types of locations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".