Perceived neighborhood safety and exercise behavior among community dwellers in Gauteng, South Africa
Notice bibliographique
Résumé
ABSTRACT: Maintaining a physically active life is an important determinant of overall health and psychosocial wellbeing among adults. Physical exercise behavior can be influenced by various social and environmental circumstances including neighborhood safety. Using data from South Africa Quality of life Survey 2015/16, this study aimed to assess the hypothesis that lack of perceived neighborhood safety (PNS) can reduce the likelihood of engaging in physical exercise (PE). The participants were 30,002 men and women aged 18 years and above. The association between self-reported PE behavior and neighborhood safety were assessed by multivariable regression method while adjusting for potentially confounding factors. Less than a quarter (23.41%) of the participants reported taking exercise on daily basis whereas 27.90% reported never taking any. Respectively 6.0% and 38.1% of the participants reported feeling very unsafe walking in the neighborhood during day and night. In regression analysis, both the pooled and stratified models indicated that lack of PNS was inversely associated with regular PE. Lack of PNS (bit unsafe) during day was associated with lower odds of PE both among men (OR = 0.776, P < .001) and women (OR = 0.874, P < .001). The negative association between lack of PNS and PE during day was significant among those living with disability (OR = 0.758, P < .001). Further analysis showed that the negative association between lack of PNS with regular PE during day was significant in Johannesburg (OR = 0.800, P < .001), Tshwane (OR = 0.735, P < .001) and Emfuleni (OR = 0.619, P < .001) only, while that during night was significant in Johannesburg (OR = 0.737, P < .001), Ekurhuleni (OR = 0.673, P < .001), Emfuleni (OR = 0.418, P < .001), Lesedi (OR = 0.385, P < .001), Mogale City (OR = 0.693, P < .001), and Randfontein (OR = 0.565, P < .001). Overall, the findings highlight a significantly inverse association between lack of PNS and PE behavior. In light of the current findings, it is recommended that PE promotion programs pay special attention on population living in the neighborhoods fraught with crime concerns.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 tête enseignante, 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 ».