Does COVID-19 Infection Continue to Affect Self-Reported and Objective Sleep? A Longitudinal Study of Good Sleepers
Bibliographic record
Abstract
Jun Wu,1– 3 Baixin Chen,1– 3 Qingsong Qin,4 Yanyuan Dai,1– 3 Le Chen,1– 3 Dandan Zheng,1– 3 Jiansheng Zhang,1– 3 Yun Li1– 3 1Department of Sleep Medicine, Mental Health Center of Shantou University, Shantou, Guangdong, People’s Republic of China; 2Sleep Medicine Center, Shantou University Medical College, Shantou, Guangdong, People’s Republic of China; 3Shantou University Medical College—Faculty of Medicine of University of Manitoba Joint Laboratory of Biological Psychiatry, Shantou, Guangdong, People’s Republic of China; 4Laboratory of Human Virology and Oncology, Shantou University Medical College, Shantou, People’s Republic of ChinaCorrespondence: Yun Li, Department of Sleep Medicine,Mental Health Center of Shantou University, North Taishan Road, Wanji District, Shantou, Guangdong, 515041, People’s Republic of China, Email s_liyun@stu.edu.cnBackground: Whether COVID-19 infection continues to affect both self-reported and objective sleep is not clear. This longitudinal study aims to investigate the impact of COVID-19 infection on self-reported and objective sleep of good sleepers.Methods: Fifteen good sleepers, with prior COVID infection, completed self-reported and objective sleep assessments at 3 time points: pre-COVID-19 infection, short-term post-COVID-19 infection for 1 month and long-term post-COVID-19 infection for 6 months. Self-reported sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). Self-reported sleep onset latency (s-SOL) and sleep efficiency (s-SE) were extracted from the PSQI. Objective sleep was assessed by overnight polysomnography. Nighttime electroencephalogram (EEG) relative power at central EEG derivations during sleep was calculated.Results: Total scores of the PSQI (P=0.003), s-SOL (P=0.017) and s-SE (P=0.040) changed across the 3 time points. Specifically, total PSQI scores (P=0.002) and s-SOL (P=0.011) increased, while s-SE decreased (P=0.019) from the pre-COVID-19 infected period to the short-term post-COVID-19 infected period. However, there were no significant differences regarding PSQI scores, s-SOL or s-SE between short-term and long-term post-COVID-19 infected periods, or between pre-COVID-19 and long-term post-COVID-19 infected periods (all P> 0.999). The changes in objective sleep were not significant across different periods except shorter o-SOL at the long-term post-COVID-19 infected period compared to the pre-COVID-19 (P=0.028) and short-term post-COVID-19 infected periods (P=0.010). Similarly, the changes in EEG relative power were not significant across different periods except the relative alpha EEG power during REM sleep (P=0.007).Conclusion: COVID-19 infection has temporary adverse effect on self-reported sleep but no effect on objective sleep of good sleepers.Keywords: COVID-19 infection, self-reported sleep, objective sleep
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".