Optimizing Sleep in Older Adults: Where Does High-Intensity Interval Training Fit?
Bibliographic record
Abstract
The present community-based study evaluated the effect of three different exercise interventions on sleep quality. Older adults were enrolled in one of three exercise intervention groups: high-intensity interval training (HIIT; n=20), moderate-intensity continuous training (MICT; n=19) or stretching (STRETCH; n=22). Prior to and following the intervention, sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). The PSQI was used to classify participants as poor (global PSQI score 5) or good (global PSQI score >5) sleepers and the effect of the intervention was examined on poor sleepers only. 70% of our sample were classified as poor sleepers. Poor sleepers were significantly impaired across all PSQI components, except for the use of sleeping medication, such that neither group were heavily prescribed. Exercise improved sleep quality for poor sleepers, but the intensity mattered. Specifically, MICT and STRETCH improved sleep efficiency for poor sleepers, whereas HIIT did not (p < .05). The results suggest that both MICT and STRETCH may be more effective than HIIT for optimizing sleep in poor sleepers. These findings help to inform exercise guidelines for enhancing sleep in the aging population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".