Identifying Risk Profiles for Sedentary Behavior in Youth Using Recursive Partitioning Based on Individual, Familial, and Neighborhood Environment Factors
Bibliographic record
Abstract
Background: Being sedentary is an established risk factor for obesity and related cardiometabolic complications, independently of physical activity (PA) levels. We used recursive partitioning analysis (RPA) to identify unique combinations of individual, familial, and neighborhood factors that increased the likelihood of being very sedentary. Methods: Baseline data were collected in 2005-2008 for 512 Quebec youth (aged 8-10 years) with a history of parental obesity (QUALITY study). Sedentary behavior and PA were assessed by accelerometry (Actigraph). Children with ≥10h of valid wear time on at least 4 days were retained for analysis. Children were categorized as being very sedentary if they accumulated at least 300 minutes/day of ≤100 counts/min on average. Fifteen variables were submitted to the recursive partitioning process in order to identify sub-groups by likelihood of being very sedentary. MLR was used to estimate likelihood of being very sedentary across subgroups, controlling for the child’s age, sex, household income, and PA level. Indicator variables were used, retaining the lowest risk group as the reference. Results: Data were complete for 445/512 participants. Six variables were retained to construct the classification tree. A total of 7 subgroups were identified, with proportions very sedentary equal to 5%, 22%, 31%, 26%, 33%, 40%, and 77%, respectively. The 7 risk subgroups, in order of increasing likelihood of being very sedentary, comprised children who: (1) met MVPA guidelines (engaged in at least 60 min/day of MVPA), (2) did not meet MVPA guidelines, but resided in lower poverty areas, (3) did not meet MVPA guidelines, resided in higher poverty areas, but with a higher park area ratio; (4) did not meet MVPA guidelines, resided in higher poverty areas, had a lower park area ratio, but did not have an obese father; (5) did not meet MVPA guidelines, resided in higher poverty areas, had a lower park area ratio, had an obese father, but lived in more urban areas; (6) same as subgroup (5) but living in less urban areas and not exceeding 2 hours/day of screentime on weekends; and finally (7) same as subgroup (6) but exceeding 2 hours per day of screentime on weekends. In multivariable logistic regressions, compared to subgroup 1, groups 2 to 7 were significantly more likely to be very sedentary, after controlling for age, sex and household income. However, after further controlling for minutes of MVPA, only children in group 7 remained significantly more likely to be categorized as very sedentary (OR: 7.3, 95% CI: 1.3-45.1). Conclusion: The relationship between physical activity and sedentary behaviour is complex. Specific combinations of factors appear particularly conducive to engaging in excessive sedentary behaviour, with weekend screen time possibly being the most salient individual contributor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".