Effects of Intellectual Activities on Different Domains of Cognitive Function in Elderly People
Notice bibliographique
Résumé
Background Intellectual activities such as reading and playing puzzle games can slow the decline of cognitive function in the elderly, but the effects of specific types of such activities on cognitive function and cognitive domains need to be further studied. Objective To explore the influence of common types of intellectual activities on cognitive function and cognitive domains of the elderly in the community. Methods From May to August 2022, stratified convenience sampling was used to select elderly people from four communities in Nanjing, Changzhou, Nantong and Xuzhou of Jiangsu Province. A face-to-face survey was conducted with a general information questionnaire and the Montreal Cognitive Assessment (MoCA) Beijing edition to collect data regarding sociodemographics, frequency and types of intellectual activities, and cognitive function. Stepwise multiple regression analysis was used to explore the relationship between intellectual activities and different cognitive domains. Results In total, 782 cases attended the survey, and 758 of them (96.93%) who completed it were included for analysis, including123 from Nanjing, 197 from Changzhou, 240 from Nantong, and 198 from Xuzhou. The intellectual activities done by these older people include learning new knowledge (n=170), playing chess and cards (n=228), reading (n=228), singing (n=59), playing puzzle games (n=57), helping grand children with their homework (n=42), painting (n=16), playing a musical instrument (n=47), and practicing calligraphy (n=30). Stepwise multiple linear regression analysis showed that learning new knowledge, reading, helping grand children with their homework, playing puzzle games and playing musical instruments were associated with cognitive function (P<0.05). Learning new knowledge (B=0.250), reading (B=0.590), playing puzzle games (B=0.585), helping grand children with their homework (B=0.711), and playing musical instruments (B=0.643) were the influencing factors of Visuospatial/Executive (P<0.05). Learning new knowledge (B=0.219) was an influencing factor of Abstraction and Delayed recall/Memory (B=0.727) (P<0.05). Reading was a factor affecting Naming (B=0.095), Attention (B=0.207), Language (B=0.290), Abstraction (B=0.241), and Delayed recall/Memory (B=0.377) (P<0.05). Playing puzzle games (B=0.290) and playing musical instruments (B=0.278) were the influencing factors of Language (P<0.05). Among various types of activities, reading was included in a total of seven regression equations, with a standardized regression coefficient of 0.225 for its impact on the total score of MoCA, which was higher than that of the other types. Conclusion Intellectual activities such as reading, learning new knowledge, playing puzzle games, helping grand children with their homework and playing a musical instrument can maintain or improve the cognitive function of the elderly in the community. The effects of different types of intellectual activities on cognitive function are domain-specific, which has a positive significance for the prevention and intervention of cognitive function decline of the elderly.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 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 ».