0160 Sleep quality affects the heart rate signal when facing the different stress sources
Bibliographic record
Abstract
Abstract Introduction Stressful conditions can impact our judgement and increase the risk of adverse events such as workplace accidents, falls, and automobile collisions, among others. Sleep quality has also been shown to influence stress responses and heart rate variability (HRV) has proved to be a useful indicator of stress in a variety of scenarios. Therefore, wearable devices such as heart rate monitors may be able to reduce adverse events by measuring stress responses. Among subjects reporting different sleep quality, we measured heart rate variability (HRV) before and after different stressors to understand whether these stressful events could be detected by a wearable device. Methods Twenty-four subjects (male = 10; female = 14) with no known health conditions participated in this study. Subjects were divided into good sleep quality (N = 12) and bad sleep quality (N = 12) groups based on their Pittsburgh Sleep Quality Index (PSQI) score. Each subject engaged in a physical stress and mental stress on separate days and HRV was assessed before and after each stress intervention. T-tests were used to assess the change in HRV from pre- to post stress condition for each group. Results In the good sleep quality group we found several statistically significant differences across several HRV frequency-domains before and after physical stress (Low frequency: p = 0.0292; High frequency: p = 0.0287; Low frequency/ High frequency ratio: p = 0.0245) and mental stress (Low frequency: p = 0.0394; High frequency: p = 0.0387; Low frequency/ High frequency ratio: p = 0.0373). Conclusion Our findings indicate that sleep quality may influence the HRV response to different stressors. This suggests that HRV measured via wearable device may be used to provide warning under stressful conditions that could help prevent accidents and other adverse events. Support (if any)
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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 | 0.000 | 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) | 0.005 | 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".