Exploring the minds of rural seniors: A journey into cognitive health in aging communities
Notice bibliographique
Résumé
ABSTRACT Background: Cognitive function in older adults is a crucial aspect of overall health and well-being, particularly as the global population continues to age. Rural areas often face unique challenges that can impact cognitive health, including limited access to health-care services, lower educational opportunities, and lifestyle factors that may differ significantly from urban counterparts. By identifying the predictors of cognitive function and understanding the geographical disparities, the study seeks to inform targeted public health strategies and interventions to support cognitive health in rural populations. Aim: This study aims to investigate the level of cognitive function among older adults in four different rural areas, examining how demographic, socioeconomic, and lifestyle factors contribute to cognitive health. Methodology: A cross-sectional study was conducted with 800 participants (200 from each rural area). Cognitive function was assessed using the mini–mental state examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Demographic, socioeconomic, and lifestyle variables were recorded. Correlation analyses, analysis of variance, analysis of covariance, and multivariable regression analyses were performed to identify significant relationships and differences. Results: The study’s participants had a mean age of 72.4 years, with females comprising 55% of the sample. A quarter of participants reported education beyond primary school, and 42.5% had low socioeconomic status. Smoking was reported by 28.75% of participants, while 46.25% engaged in regular physical activity. Significant differences were observed in MMSE and MoCA scores between rural areas ( P < 0.001), with rural area D scoring the highest (MMSE: 27.5, MoCA: 24.2) and rural area C scoring the lowest (MMSE: 24.1, MoCA: 20.7). Positive correlations were found between cognitive scores and education level (MMSE: r = 0.35, MoCA: r = 0.40) and physical activity (MMSE: r = 0.21, MoCA: r = 0.22), while negative correlations were observed with age (MMSE: r = −0.15, MoCA: r = −0.12), smoking status (MMSE: r = −0.28, MoCA: r = −0.27), and alcohol use (MMSE: r = −0.25, MoCA: r = −0.23). ANOVA indicated significant differences in MMSE scores between areas ( F [3, 796] =12.34, P < 0.001). ANCOVA, adjusting for confounders, confirmed these differences (F[3, 792] =10.47, P < 0.001). Post hoc Tukey tests revealed that rural area D had significantly higher MMSE scores than Areas B and C ( P < 0.01), and Area A had higher scores than Area C ( P < 0.05). Significant factors associated with MMSE scores included age (β = −0.12, P = 0.01), education level (β =0.35, P < 0.001), physical activity (β =0.21, P < 0.05), smoking status (β = −0.28, P < 0.01), and alcohol use (β = −0.25, P < 0.01). Conclusion: Cognitive function among older adults varies significantly across different rural areas. Higher education levels and regular physical activity are associated with better cognitive performance, while older age, smoking, and alcohol use are negatively associated. These findings underscore the importance of targeted interventions to improve cognitive health in rural aging populations.
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 | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».