Objectively Measured Environmental Correlates of Toddlers’ Physical Activity and Sedentary Behavior
Bibliographic record
Abstract
PURPOSE: Examine objectively measured environmental correlates of physical activity and sedentary behavior in toddlers (12-35 mo). METHODS: Participants were recruited at immunization appointments in Edmonton, Canada. Physical activity and sedentary time were objectively measured via accelerometers (n = 149). The parents reported screen time and demographic characteristics via a questionnaire (n = 249). Postal codes were used to link neighborhood data via geographic information systems. Neighborhood data included 4 environmental domains: functional (ie, walkability), safety (ie, crime), esthetic (ie, tree density), and destination (ie, cul-de-sac density, wooded area percentage, green space percentage, recreation density, park density). Weather data (temperature and precipitation) were obtained via historical weather records. Multilevel multiple linear regression models were used to account for clustering of participants within neighborhoods and adjustment of demographic variables. RESULTS: Each additional 10°C of mean temperature was significantly associated with 5.74 (95% confidence interval, 0.96-10.50) minutes per day of higher light-intensity physical activity, though the effect size was small (f2 = 0.08). No other significant associations were observed. CONCLUSIONS: The lack of significant findings for neighborhood environment factors suggests proximal factors (eg, features of the home environment) may be more important in predicting toddlers' physical activity and sedentary behavior. More indoor physical activity opportunities may be needed on colder days for toddlers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".