Features of the Neighbourhood Built Environment and Their Ability to Predict Fitness in Youth: A Random Forest Approach
Notice bibliographique
Résumé
Background: Cardiorespiratory Fitness (CRF) is an important marker of health among youth, strongly linked to improved cardiometabolic outcomes and reduced risk of early-onset cardiovascular disease. Factors at the individual, behavioural, and environmental levels are all implicated in shaping CRF. Notably, favourable Neighbourhood Built Environment (NBE) features are generally recognized as supportive of physical activity and active living; however, the nature of specific features, and the magnitude of their potential contribution to CRF in youth, require further investigation.Objectives: The primary objective of this thesis was to estimate the extent to which NBE features predict CRF in youth. A supplementary analysis examined gender-specific differences in the associations between NBE features and predicted CRF using stratified analyses.Methods: This study used data from the Quebec Adipose and Lifestyle InvesTigation in Youth (QUALITY) cohort. The QUALITY cohort comprised 630 families, including a child aged 8–10 years at baseline, and both biological parents, with at least one parent living with obesity. Data included those collecting during the initial (baseline) visit, and analyses were restricted to participants in the Greater Montreal Area (n = 504). CRF was assessed using peak oxygen volume consumption (VO2peak) during a cycling test on an electromagnetic bike, adjusted for Fat-Free Mass (FFM) as measured by DXA, and expressed as VO2peak/FFM (mL·min⁻¹·kg⁻¹ FFM). Moderate to vigorous physical activity was measured using a uniaxial accelerometer worn for 7 days, with valid wear time defined as a minimum of 10 hours per day on at least 3 weekdays and 1 weekend day. Salient NBE features were captured through a Geographic Information System (GIS) and on-site audits. Random forest models were applied to examine the predictive capability of NBE features in relation to VO2peak/FFM as a proxy for CRF. Bivariate associations between6predictors and predicted CRF were visualized using scatter plots and violin plots, for the full dataset, and stratified by gender.Results: Among 504 children (mean age 9.6 ± 0.9 years; 54% boys) boys exhibited higher mean VO2peak/FFM (71.2 min⁻¹·kg FFM⁻¹, SD = 13.8) compared to girls (63.4 min⁻¹·kg FFM⁻¹, SD = 11.4). The random forest model explained 33% of the variance in CRF, with BMI z-score, sex, and moderate to vigorous physical activity emerging as the top three predictors, followed by several NBE features, including the density of streets with normal traffic, number of intersections, vegetation index (NDVI), land use mix, building density, and signs of social disorder. Sex-specific analyses point to potential gendered-patterns: a modest positive association was observed between NDVI and predicted CRF, while nonlinear trends between number of intersections, land use mix, and predicted CRF among boys; in contrast, associations were largely negligible among girls.Conclusion: This study identified several NBE features with a moderate to weak contribution in predicting CRF, in addition to established sociodemographic factors and levels of physical activity. While the contributions of NBE features were relatively modest, they suggest potential for targets for neighbourhood transformations that may foster healthier lifestyles and improve CRF in youth populations. These findings could be useful to inform urban planning and public health research and policies, and should be extended to address the specific needs of diverse populations more broadly
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 | 0,021 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,005 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».