Sleep quality during the COVID-19 pandemic and High frequency- heart rate variability as a moderator: A longitudinal study
Bibliographic record
Abstract
A growing body of literature describes the effect of the confinement during the COVID-19 pandemic on sleep quality and duration. Sleep quality seems to be worsening during the confinement for vulnerable individuals, but seems to be improving for others with less rigid school and work schedules. High frequency heart rate variability (HF-HRV) has been conceptualized as a biomarker of vulnerability to stress-related sleep disturbances. The goal of this study was to investigate the effect the confinement on sleep quality, sleep efficiency, and sleep duration and to investigate HF-HRV as an individual difference in sleep reactivity to confinement. One hundred and fifty participants (Mage = 50.62, SD = 6.0) completed the Pittsburg Sleep Quality Index (PSQI) at the beginning of the confinement, about a month later, and at the end of confinement period of the first wave of the COVID-19 pandemic in Montreal, Canada. HF-HRV was collected few years prior to the onset of the pandemic. Results from hierarchical linear models demonstrated a curvilinear effect for sleep duration and sleep quality with poorer sleep quality and less sleep duration a month into confinement compared to the beginning of the confinement, followed by improved sleep quality and greater sleep duration during the deconfinement period; whereas sleep efficiency linearly decreased over time. Financial stress predicted between-person differences in PSQI scores at the beginning of the confinement, but HF-HRV did not predict within-person changes in PSQI scores. These results highlight the importance of providing sleep interventions to individuals and families affected by the pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".