The Physical Activity Type Most Related to Cognitive Function and Quality of Life
Bibliographic record
Abstract
Background . Physical activity has been found to maintain and improve cognitive function and consequently improve health‐related quality of life (HRQoL). The relationships between different types of physical activities, cognitive function, and HRQoL have not been studied sufficiently and compared in different age and gender groups. This study is aimed at examining the relationship between different types of physical activity (high‐intensity, moderate‐intensity, and walking exercise), cognitive function, and HRQoL. In addition, this study is aimed at examining these relationships in different age and gender groups. Methods . This cross‐sectional study included 150 adults with a mean age of 50 ± 8.8 years. Participants completed the International Physical Activity Questionnaire (IPAQ) to assess the level of the physical activity types and the Short‐Form Health Survey (SF‐36) questionnaire to assess HRQoL. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA) screening instrument. Spearman correlation analysis was used to explore the relationships between the different variables of the study. Results . There were significant positive relationships between all types of physical activities, cognitive ability, and HRQoL. The relationships between moderate‐intensity physical activities and cognitive function ( r = 0.38) and HRQoL ( r = 0.33) were higher than the relationships with walking exercise and high‐intensity physical activity. The middle‐aged group had a significantly higher cognitive function compared to the senior adults ( p < 0.001), while there was no significant difference between the age groups in HRQoL ( p = 0.18). Conclusion . The cognitive function and HRQoL were more related to moderate‐intensity physical activities compared to walking exercise or high‐intensity physical activities. These relationships were more pronounced in the senior adult population compared to the middle‐aged group.
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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 | 0.005 |
| 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".