Is exercise the key to a good night's sleep? Identifying the optimal dose of exercise for efficient sleep in older adults
Bibliographic record
Abstract
Sleep is a critical factor in the promotion of mental and physical health. Advancing age is associated with progressive decline in sleep efficiency. Critically, poor sleep efficiency is related to diminishing memory performance in healthy older adults and is a key symptom of cognitive impairment. With the rapidly growing aging population, there is an urgent need to identify evidence-based strategies to improve sleep efficiency in older adults. Physical exercise is a promising lifestyle factor to enhance sleep quality. However, the optimal dose of exercise for maximal sleep quality benefits remains unclear. We examined the dose-response relationship between exercise intensity and sleep quality in healthy older adults. Thirty-four participants were randomized into a high-intensity, moderate-intensity, or low-intensity exercise group. Each group received supervised training three times per week for 12 weeks. Subjective sleep quality was assessed at baseline and at study completion. Results demonstrate that moderate-intensity exercise improved sleep efficiency significantly more than high-intensity exercise (p < .05). Total sleep duration was not affected by the intervention. Overall, the results suggest that moderate intensity exercise may be the optimal dose for improving sleep quality in older adults. Ultimately, this research will help to inform exercise prescription guidelines for older adults to enhance sleep quality, cognition and physical health in advancing age.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".