Sleep quality in individuals with post-COVID-19 condition: Relation with emotional, cognitive and functional variables
Bibliographic record
Abstract
The study aimed to assess sleep quality in PCC patients and its predictors by analysing its relationship with emotional, cognitive and functional variables, as well as possible differences based on COVID-19 severity. We included 368 individuals with PCC and 123 healthy controls (HCs) from the NAUTILUS Project (NCT05307549 and NCT05307575). We assessed sleep quality (Pittsburgh Sleep Quality Index, PSQI), anxiety (Generalized Anxiety Disorder, GAD-7), depression (Patient Health Questionnaire, PHQ-9), global cognition (Montreal Cognitive Assessment, MoCA), everyday memory failures (Memory Failures of Everyday Questionnaire, MFE-30), fatigue (Chadler Fatigue Questionnaire, CFQ), quality of life (European Quality of Life-5 Dimensions, EQ-5D), and physical activity levels (International Physical Activity Questionnaire, IPAQ). 203 were nonhospitalized, 83 were hospitalized and 82 were admitted to the intensive care unit (ICU). We found statistically significant differences in the PSQI total score between the PCC and HC groups (p < 0.0001), but there were no differences among the PCC groups. In the multiple linear regressions, the PHQ-9 score was a predictor of poor sleep quality for mild PCC patients (p = 0.003); GAD-7 (p = 0.032) and EQ-5D (p = 0.011) scores were predictors of poor sleep quality in the hospitalized PCC group; and GAD-7 (p = 0.045) and IPAQ (p = 0.005) scores were predictors of poor sleep quality in the group of ICU-PCC. These results indicate that worse sleep quality is related to higher levels of depression and anxiety, worse quality of life and less physical activity. Therapeutic strategies should focus on these factors to have a positive impact on the quality of 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".