Investigating the Associations Between COVID-19, Long COVID, and Sleep Disturbances: Cross-Sectional Study
Bibliographic record
Abstract
Background: COVID-19 has not only resulted in acute health issues but also led to persistent symptoms known as long COVID, which have been linked to disruptions in sleep quality. Objective: This study aims to investigate the associations between COVID-19, long COVID, and sleep disturbances, focusing on demographic, socioeconomic, and psychological factors among a Chinese population. Methods: This cross-sectional study included 1062 participants from China. Demographic, socioeconomic, and clinical data were collected through web-based questionnaires. Participants were divided into 2 groups based on COVID-19 infection status: infected and noninfected. Within the infected group, participants were further categorized into those with long COVID and those without long COVID. Noninfected participants were included in the non-long COVID group for comparison. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while depression and anxiety were evaluated using the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder-7 (GAD-7) scales, respectively. Multivariable linear regression was conducted to examine the associations between COVID-19, long COVID, and sleep quality, adjusting for demographic and psychosocial factors. Results: COVID-19 infection was confirmed in 857 participants, with 273 of them developing long COVID. No significant sex disparities were observed in infection rates (P=.63). However, a marginal statistical difference was noted in the prevalence of long COVID among females (P=.051). Age was significantly associated with both infection rates (P<.001) and long COVID (P=.001). Participants aged 60-70 years were particularly vulnerable to both outcomes. Sleep latency was significantly longer in the infected group (mean 1.73, SD 0.83) compared to the uninfected group (mean 1.57, SD 0.78; P=.01), and PSQI scores were higher (mean 8.52, SD 4.10 vs. 7.76, SD 4.31; P=.02). Long COVID participants had significantly worse sleep outcomes across all metrics (P<.001), except for sleep medication use (P=.17). Conclusions: Our findings indicate that long COVID is strongly associated with significant sleep disturbances, while initial COVID-19 infection shows a more moderate association with sleep issues. Long COVID-related sleep disturbances were exacerbated by factors such as age, income, and chronic health conditions. The study highlights the need for targeted interventions that address the multifaceted impacts of long COVID on sleep, especially among vulnerable groups such as older adults and those with lower socioeconomic status. Future research should use longitudinal designs to better establish the temporal relationships and causal pathways between COVID-19 and sleep disturbances.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".