Better Subjective Sleep Quality Partly Explains the Association Between Self-Reported Physical Activity and Better Cognitive Function
Bibliographic record
Abstract
Background: Physical activity has been associated with better cognitive function and better sleep quality. Yet, whether the beneficial effect of physical activity on cognitive function can be explained by an indirect pathway involving better sleep quality is unclear. Objective: To investigate whether sleep quality mediates the association between physical activity and cognitive function in adults 50 years of age or older. Methods: 86,541 community-dwelling European adults were included in the study. Physical activity and sleep quality were self-reported. Indicators of cognitive function (immediate recall, delayed recall, verbal fluency) were assessed using objective tests. All measures were collected six times between 2004 and 2017. The mediation was tested using multilevel mediation analyses. Results: Results showed that self-reported physical activity was associated with better self-reported sleep quality, which was associated with better performance in all three indicators of cognitive function, demonstrating an indirect effect of physical activity on cognitive function through sleep quality. The mediating effect of sleep quality accounted for 0.41%, 1.46%, and 8.88% of the total association of physical activity with verbal fluency, immediate recall, and delayed recall, respectively. Conclusion: These findings suggest that self-reported sleep quality partly mediates the association between self-reported physical activity and cognitive function. These results need to be confirmed by device-based data of physical activity and sleep quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".