Self-reported activities of daily living, health and quality of life among older adults in South Africa and Uganda: a cross sectional study
Notice bibliographique
Résumé
BACKGROUND: Difficulties in performing the activities of daily living (ADL) are common among middle-aged and older adults. Inability to perform the basic tasks as well as increased healthcare expenditure and dependence on care can have debilitating effects on health and quality of life. The objective of this study was to examine the relationship between self-reported difficulty in activities of daily living (ADL), health and quality of life among community-dwelling, older population in South Africa and Uganda. METHODS: We analyzed cross-sectional data on 1495 men and women from South Africa (n = 514) and Uganda (n = 981) which were extracted from the SAGE Well-Being of Older People Study (WOPS 2011-13). Outcome variables were self-reported health and quality of life (QoL). Difficulty in ADL was assessed by self-reported answers on 12 different questions covering various physical and cognitive aspects. The association between self-reported health and quality of life with ADL difficulties was calculated by using multivariable logistic regression models. RESULTS: Overall percentage of good health and good quality of life was 40.4% and 20%, respectively. The percentage of respondents who had 1-3, 3-6, > 6 ADL difficulties were 42.4%7, 30.97% and 14.85%, respectively. In South Africa, having > 6 ADL difficulties was associated with lower odds of good health among men [Odds ratio = 0.331, 95%CI = 0.245,0.448] and quality of life among men [Odds ratio = 0.609, 95%CI = 0.424,0.874] and women [Odds ratio = 0.129, 95%CI = 0.0697,0.240]. In Uganda, having > 6 ADL difficulties was associated lower odds of good health [Odds ratio = 0.364, 95%CI = 0.159,0.835] and quality of life [Odds ratio = 0.584, 95%CI = 0.357,0.954]. CONCLUSION: This study concludes that difficulty in ADL has a significant negative association with health and quality of life among community-dwelling older population (> 50 years) in South Africa and Uganda. The sex differences support previous findings on differential health outcomes among men and women, and underline the importance of designing sex-specific health intervention programs.
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 | 0,002 | 0,002 |
| 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 ».