Identifying Risk Profiles for Sedentary Behavior in Youth Using Recursive Partitioning Based on Individual, Familial, and Neighborhood Environment Factors
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».