Differences in Cognitive Health and Brain Activity According to Mild Cognitive Impairment and Physical Activity Levels in Older Women
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to investigate differences in cognitive well-being and brain activity between older women with and without mild cognitive impairment (MCI) across varying levels of physical activity. METHOD: A total of 126 female participants aged over 65 years were recruited and categorized into MCI and non-MCI groups. Cognitive health was evaluated using the Korean versions of the Mini-Mental State Examination (MMSE-K) and the Montreal Cognitive Assessment (MoCA-K), while physical activity levels were quantified with the Physical Activity Scale for the Elderly (PASE-K). Brain activity was assessed through electroencephalography (EEG). Data were analyzed using two-way ANOVA. RESULTS: Results indicated that non-MCI participants consistently demonstrated higher cognitive scores across all physical activity levels. Moreover, individuals with higher physical activity exhibited greater theta wave activity compared with those in the MCI group. Significant group differences were also observed in concentration and stress levels. CONCLUSION: These findings suggest that higher physical activity levels are associated with better cognitive performance and brain activity in older women. Promoting physical activity may therefore contribute to strategies aimed at supporting healthy cognitive aging, although longitudinal research is required to establish causal relationships.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".