Long-term consequences of COVID-19 on sleep, mental health, fatigue, and cognition: a preliminary study
Bibliographic record
Abstract
INTRODUCTION: Post-COVID-19 Syndrome (PCS) is defined as symptoms persisting beyond 12 weeks from the onset of symptoms. Notably, COVID-19 has been associated with long-term effects on the brain and mental health. This cross-sectional study aims to investigate depression, fatigue, sleep quality, and cognitive dysfunction, particularly working memory, in individuals with PCS compared to a healthy control group. MATERIAL AND METHODS: Between April and December 2021, 45 COVID-19 individuals and 60 healthy individuals met the eligibility criteria. Demographic information and the Montreal Cognitive Assessment were collected. Two visual working memory tasks, Delayed Match-to-Sample (DMS) and n-back, were performed, along with self-report questionnaires: Beck Depression Inventory, Modified Fatigue Impact Scale, and Pittsburgh Sleep Quality Index. RESULTS: A total of 105 participants were enrolled. Findings reveal that the PCS group exhibited notably higher levels of cognitive impairment (13.3% vs. 1.6%, p = 0.04), depression (53.9% vs. 25.9%, p = 0.03), and sleep disturbances (53.9% vs. 18.6%, p = 0.01) compared to the healthy control group. Sleep latency and sleep duration were particularly affected. No significant differences in working memory function were observed between the two groups (p = 0.90 for DMS and p = 0.98 for n-back). CONCLUSION: The study highlights the higher prevalence of sleep disturbance, depression, and cognitive impairment in the PCS phase, with inflammation likely playing a significant role. Moreover, the study suggests that untreated depression and sleep disturbances may pose long-term risks for dementia. Understanding the underlying mechanisms is crucial for developing effective interventions and support for individuals recovering from the infection. Prospective longitudinal studies with larger and more diverse samples are warranted to confirm and expand upon these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".