Urban Quality and Biochemical, Hematological, and Nutritional Markers in Older Adults: Cross-Sectional Geospatial Study
Notice bibliographique
Résumé
Background: The urban environment is an important determinant of frailty, primarily through factors such as infrastructure that supports physical activity, availability of social and medical support, and access to nutritious food. Given the increasing aging population, understanding the link between urban quality, frailty, and metabolic health is crucial for effective public health and urban planning interventions. Objective: This study aims to quantify the impact of distinct urban domains (built-environment characteristics, accessibility to essential services, availability of green and recreational spaces, and neighborhood socioeconomic context) on frailty status, nutritional profile, and hematological or biochemical biomarkers in community-dwelling older adults by integrating geospatial analysis. Methods: A cohort of 251 older adults (aged older than 65 years) was studied. Frailty was assessed using the Frailty Trait Scale 5, and nutritional status was determined using the Controlling Nutritional Status score. Hematological and biochemical parameters were evaluated in a subset of 70 participants by MINDRAY automatic equipment. A spatial analysis of frailty was conducted by incorporating Geographic Information System layers that mapped the distribution of urban facilities, including fruit and vegetable shops, senior centers, pharmacies, emergency health centers, parks and squares, community centers, and exercise facilities. Statistical analyses included t tests, Mann-Whitney U test, ANOVA, and correlation analyses. Results: The prevalence of frailty was 17.5%. Frail individuals exhibited significantly higher BMI (mean 31.5, SD 4.4 vs mean 28.5, SD 4.5 kg/m²; P=.0001). When comparing the upper (Q4) and lower (Q1) quartiles of urban quality, Q4 participants had higher Frailty Trait Scale 5 scores (mean 15.2, SD 7.4 vs mean 11.8, SD 6.4; P=.0334) and lower handgrip strength (mean 19.1, SD 4.4 vs mean 22.8, SD 7.3 kg; P=.006). Frail individuals resided significantly closer to emergency health centers (P=.0010), family health centers (P=.0412), and exercise facilities (P=.0322). In addition, bilirubin (Spearman ρ=0.33; P=.0049), serum iron (Spearman ρ=0.27; P=.0272), transferrin saturation (Spearman ρ=0.24; P=.0386), red blood cell count (Spearman ρ=0.26; P=.0303), and red blood cell distribution width (Spearman ρ=0.23; P=.0462) were positively correlated with urban quality. Frail participants also had higher Controlling Nutritional Status scores (P=.0323), which were positively correlated with urban quality (Spearman ρ=0.25; P=.0359). Conclusions: Urban quality was significantly associated with hematological parameters, nutritional status, and frailty. Frail individuals in areas with better urban quality exhibit lower handgrip strength, higher frailty scores, and greater proximity to emergency rooms, community health centers, and exercise facilities. This spatial distribution may reflect higher accessibility to health care and recreational resources among frail participants. Urban planning and public health strategies should focus on creating age-friendly environments to prevent frailty and improve health outcomes.
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 | 0,000 | 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 | 0,000 |
| 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) | 0,000 | 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 ».