The Role of Physical Literacy in the Association Between Weather and Physical Activity: A Longitudinal Multilevel Analysis With 951 Children
Bibliographic record
Abstract
BACKGROUND: Numerous studies showed an effect of weather on physical activity (PA) levels in children. However, no study has yet examined the relevance of personal factors in this relationship. Therefore, this study analyzes (1) whether there are systematic interindividual differences in the extent to which weather affects the PA behavior and (2) whether physical literacy (PL) moderates the weather-PA association in children. METHODS: A total of 951 children in 12 Danish schools (age 9.76 [1.59] y; 54.3% girls) completed objective PA assessments via accelerometry (moderate to vigorous PA, light PA, and sedentary behavior). Local weather data (precipitation, wind speed, temperature, and sunshine duration) were provided by the Danish Meteorological Institute. Participants' PL was measured employing the Danish version of the Canadian Assessment of Physical Literacy-2. The 4116 accelerometer days underwent longitudinal multilevel analyses while considering their nesting into pupils and school classes (n = 51). RESULTS: Fluctuations in all PA indicators were significantly explained by variations in weather conditions, especially precipitation (P ≤ .035). Significant interindividual differences were found for 9 of 12 analytical dimensions, suggesting that weather changes influence PA behavior differently across individuals (especially moderate to vigorous PA, χ2[4] ≥ 11.5, P ≤ .021). However, PL moderated the relationship between weather and PA in only 2 of the 48 analytical constellations. CONCLUSIONS: Despite the varying impact of weather on PA across individuals, the present study favors a main effect model in which weather and PL exert independent effects on children's PA. The insufficient support for PL as a moderating factor calls for future studies to test alternative mechanisms in the weather-PA association.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".