Effect of Sleep Quality on Hemodynamic Response to Exercise and Heart Rate Recovery in Apparently Healthy Individuals
Bibliographic record
Abstract
PURPOSE: Poor sleep quality has an unfavorable impact on autonomic nervous system activity, especially that of the cardiovascular (CV) system. The heart rate (HR) and blood pressure (BP) at rest and during exercise, along with the heart rate recovery (HRR), were examined in poor sleepers and compared with individuals with good sleep quality. METHODS: A total of 113 healthy individuals were enrolled to the study. All participants performed treadmill stress testing. Sleep quality of participants was assessed by using the Pittsburgh Sleep Quality Index (PSQI) questionnaire: 48 subjects were categorized as ‘poor sleepers’ (PSQI score > 6 points), and the rest were grouped as ‘good sleepers’. RESULTS: The poor sleepers showed higher resting HR (p <0.001), higher diastolic BP (p=0.006), similar systolic BP (p=0.095), more frequent hypertensive response to exercise (p=0.046) and less HR increase with exercise (chronotropic incompetence) (p=0.002) compared with individuals who reported good sleep quality. In addition, the poor sleepers demonstrated reduced heart rate recovery at the 1st and 3rd minute of recovery (p=0.005 and 0.037, respectively) compared with good sleepers. Multivariate logistic regression analysis revealed that only resting diastolic BP was the independent predictor of HRE. The PSQI score was positively correlated with resting HR; while it was negatively correlated with HR response to exercise, HRR1 and HRR index-1. CONCLUSION: This cross-sectional study emphasizes the effect of poor sleep quality on unfavorable cardiovascular outcome indicators of the treadmill stress test.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".