Nightmares in People with COVID-19: Did Coronavirus Infect Our Dreams?
Bibliographic record
Abstract
Introduction: A growing number of studies have demonstrated that the coronavirus disease-19 (COVID-19) pandemic has severely affected sleep and dream activity in healthy people. To date, no investigation has examined dream activity specifically in COVID-19 patients.\nMethods: As part of the International COVID-19 Sleep Study (ICOSS), we compared 544 COVID-19 participants with 544 matched-controls. A within-subjects comparison between pre-pandemic and pandemic periods computed separately for controls and COVID-19 participants were performed on dream recall and nightmare frequency (DRF; NF). Also, non-parametric comparisons between controls and COVID-19 participants were carried out. Further, we compared psychological measures between the groups collected during pandemic. Ordinal logistic regression to detect the best predictors of NF was performed.\nResults: We found that people reported greater dream activity during the pandemic. Comparisons between controls and COVID-19 participants revealed a) no difference between groups concerning DRF in the pre-pandemic period and during the pandemic; b) no difference between groups concerning nightmare frequency in the pre-pandemic period; and c) COVID-19 participants reported significantly higher NF than controls during pandemic (p = 0.003). Additionally, we showed that a) anxiety, depression, post-traumatic stress-disorder (PTSD) symptom scores were higher in COVID-19 participants than controls; and b) quality of life and health as well as wellbeing (WHO-5) scores were significantly higher in controls than COVID-19 participants. Finally, ordinal logistic regression indicates that DRF (p < 0.001), PTSD (p < 0.001), anxiety (p = 0.018), insomnia (p = 0.039), COVID-19 severity (p = 0.014), sleep duration (p = 0.003) and age (p = 0.001) predicted NF.\nDiscussion: Our work shows strong associations between increased nightmares in those reporting having had COVID-19. This suggests that the more that people were affected by COVID-19, the greater the impact upon dream activity and quality of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 teacher head, 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".