2014 Global Summit on the Physical Activity of Children: Late-Breaking Abstracts
Bibliographic record
Abstract
Objective: We tested the association between neighbourhood typology based on features of the built environment and accelerometermeasured sedentary time in youth aged 8-10 years.Methods: Baseline data from the QUALITY cohort, an ongoing study of Quebec youth aged 8-10 years with at least one obese parent were used (n=512).Built environment features were obtained through systematic observations of residential neighbourhoods and from a geographic information system.Cluster analysis was used to identify distinct neighbourhood types.Physical activity and sedentary behaviour were assessed by accelerometer (Actigraph activity monitor).Participants with ≥ 10 hours of valid wear time on ≥ 4 days were retained.Wear time was standardized to 600 minutes/ day.Children were categorized as sedentary if they accumulated at least 300 minutes per day of less than 100 counts/minute.Associations between neighbourhood type and being sedentary were examined in multivariate logistic regression models controlling for sex, age, and parental education; we further examined the influence of weight status and of engaging in 60 minutes of MVPA daily.Results: Neighbourhood type was associated with being sedentary among heavy children only.Heavy children living in neighbourhoods with few parks, low land use mix, heavy traffic, and few traffic calming devices were most likely to be sedentary (OR: 3.1, 95%CI: 1.1-9.2).Conclusion The health risks associated with excessive sedentary time appear to be independent of physical activity behaviour.Excessive sedentary time may be one pathway through which neighbourhoods lead to obesity, possibly because they provide few appealing outdoor alternatives.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".