The association between physical activity and cognitive function in the elderly in rural areas of northern China
Bibliographic record
Abstract
Background Physical activity plays an important role in cognitive function in older adults, and the threshold effect and saturation effect between physical activity and cognitive function are unclear. Objective The purpose of this study was to explore the threshold effect and saturation effect between physical activity and cognitive function in the elderly. Methods The International Physical Activity Questionnaire (IPAQ) was used to measure moderate-intensity physical activity and vigorous-intensity physical activity and total physical activity in older adults. Cognitive function assessment uses the Beijing version of the Montreal Cognitive Assessment Scale (MoCA). The scale consists of seven parts: visual space, naming, attention, language, abstract ability, delayed recall and orientation, for a total of 30 points. The total score of the study participants < 26 was defined as the optimum cutoff point for a definition of mild cognitive impairment (MCI). The multivariable linear regression model was used to initially explore the relationship between physical activity and total cognitive function scores. The logistic regression model was used to assess the relationship between physical activity and cognitive function dimensions and MCI. The threshold effect and saturation effect between the total physical activity and the total cognitive function scores were investigated by smoothed curve fitting. Results This cross-sectional survey had a total of 647 participants aged 60 years and older (mean age: 73 years, female: 53.7%). Participants’ higher level of physical activity were associated with higher visual space, attention, language, abstract ability, and delayed recall scores ( P < 0.05). Physical activity was not statistically associated with naming and orientation. Physical activity was a protective factor for MCI ( P < 0.05). Physical activity was positively correlated with total cognitive function scores. There was a saturation effect between total physical activity and total cognitive function scores, and the saturation point was 6546 MET × min/wk. Conclusion This study showed a saturation effect between physical activity and cognitive function, and determined an optimal level of physical activity to protect cognitive function. This finding will help update physical activity guidelines based on cognitive function in the elderly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".