Self‐reported sleep quality and exercise in polycystic ovary syndrome: A secondary analysis of a pilot randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To examine the proportion of participants with poor sleep quality, evaluate the associations between sleep quality and anthropometric and cardiometabolic health markers, and evaluate the effect of high intensity interval training (HIIT) and continuous aerobic exercise training (CAET) on sleep quality in polycystic ovary syndrome (PCOS). DESIGN: Secondary analysis of a pilot randomized controlled trial. PATIENTS: Women with PCOS aged 18-40 years. MEASUREMENTS: The Pittsburgh Sleep Quality Index (PSQI) was measured at baseline and following a 6-month exercise intervention. A PSQI score >5 indicates poor sleep. Linear regression was used to evaluate the associations between PSQI score and anthropometric and cardiometabolic health markers, and the effect of exercise training on these associations. RESULTS: Thirty-four participants completed the PSQI at baseline, and 29 postintervention: no-exercise control (n = 9), HIIT (n = 12) and CAET (n = 8). At baseline, 79% had poor sleep quality. Baseline PSQI score was positively correlated with body mass index, waist circumference, body weight, haemoglobin A1c and insulin resistance. Mean PSQI score changes were -0.4 (SD 1.1), -0.7 (SD 0.6) and -0.5 (SD 0.9) for control, HIIT and CAET, respectively. For HIIT participants, change in PSQI score was associated with changes in body weight (B = .27, 95% CI 0.10-0.45) and waist circumference (B = .09, 95% CI 0.02-0.17). CONCLUSION: Most participants had poor sleep quality which was associated with poorer anthropometric and cardiometabolic health markers. There were no statistically significant changes in PSQI score with exercise training. With HIIT training, decreases in the sleep efficiency score were associated with reductions in body weight and waist circumference. Further studies are needed to determine the effect of exercise training on 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".