Effects of Intellectual Activities on Different Domains of Cognitive Function in Elderly People
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".