Capturing the Interrelationship between Objectively Measured Physical Activity and Sedentary Behaviour in Children in the Context of Diverse Environmental Exposures
Bibliographic record
Abstract
Even though physical activity and sedentary behaviour are two distinct behaviours, their interdependent relationship needs to be studied in the same environment. This study examines the influence of urban design, neighbourhood built and social environment, and household and individual factors on the interdependent relationship between objectively measured physical activity and sedentary behaviour in children in the Canadian city of Saskatoon. Saskatoon's built environment was assessed by two validated observation tools. Neighbourhood socioeconomic variables were derived from 2006 Statistics Canada Census and 2010 G5 Census projections. A questionnaire was administered to 10-14 year old children to collect individual and household data, followed by accelerometry to collect physical activity and sedentary behaviour data. Multilevel logistic regression models were developed to understand the interrelationship between physical activity and sedentary behaviour in the context of diverse environmental exposures. A complex set of factors including denser built environment, positive peer relationships and consistent parental support influenced the interrelationship between physical activity and sedentary behaviour. In developing interventions to facilitate active living, it is not only imperative to delineate pathways through which diverse environmental exposures influence physical activity and sedentary behaviour, but also to account for the interrelationship between physical activity and sedentary behaviour.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".