Examining sleep patterns and cardiovascular disease risk profiles in community-dwelling middle age and older females
Bibliographic record
Abstract
Poor sleep patterning is associated with increased risk for future adverse cardiovascular events. An exaggerated blood pressure response (EBPR) to a moderate bout of physical activity may better and earlier detect autonomic dysfunction and CVD risk compared with resting blood pressure (BP). To date, determining if poor sleep patterning is associated with an EBPR to 3-minutes of moderate physical activity has not yet been assessed. The purpose of my thesis is to determine if differing sleep patterns are associated with an EBPR to 3-minutes of moderate physical activity in 206 women aged 55 years or older. Objective sleep data was collected through accelerometry to assess both sleep duration (total sleep time; TST; sleep durationTST) and sleep quality (sleep efficiency; SE; Sleep QualitySE). Self-reported sleep quality data was also collected using the Pittsburgh Sleep Quality Index (PSQI; sleep qualityPSQI). The BP response was collected after 3-minutes of moderate physical activity performed on a treadmill and categorized into a typical BP response or an absolute or relative EBPR (EBPRAbsolute; EBPRrelative). Differing sleep patterning prevalence was classified as: 1) 40.8% of the cohort had short sleep durationTST, 57.3% had normal sleep durationTST and 1.9% had long sleep durationTST; 2) 4.9% had poor sleep qualitySE, and 95.1% had adequate sleep qualitySE; and, 3) 57.3% had poor sleep qualityPSQI and 42.7 had adequate sleep qualityPSQI. No significant associations were determined between the objectively measured sleep durationTST or sleep qualitySE and EBPR categories. A significant association between sleep qualityPSQI and an EBPRrelative was detected, where participants with better sleep qualityPSQI score had a 63% reduction of having an EBPRrelative. The findings of no association between sleep durationTST or sleep qualitySE variables may be due to my study’s limitation of a small sample size and the large amount of variance. These associations should be re-assessed while using a larger sample size. If this relationship is confirmed in the future, health care professionals could implement sleep interventions to reduce CVD risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".