Prevalent, incident, and persistent insomnia in a population-based cohort tested before (2018) and during the first-wave of COVID-19 pandemic (2020)
Bibliographic record
Abstract
STUDY OBJECTIVES: High rates of sleep and mental health problems have been reported during the COVID-19 pandemic, but most of the evidence is retrospective without pre-pandemic data. This study documented rates of prevalent, incident, and persistent insomnia and psychological symptoms during the COVID-19 pandemic (2020) compared to pre-pandemic data (2018). METHODS: Data were derived from a longitudinal, population-based study of insomnia in Canada. When the first lockdown started in the province of Quebec, a subsample of participants who had completed the latest 2018 follow-up were surveyed (April to May 2020) about their sleep, insomnia, and psychological symptoms since the beginning of the pandemic. Prevalence, incidence, and persistence rates of insomnia, and severity of stress, anxiety, and depressive symptoms were estimated, as well as their associations with confinement, loneliness, social support, use of electronics, and other lifestyle changes occurring during the pandemic. A sleep/health survey and validated questionnaires of insomnia, sleep quality, stress, fatigue, anxiety, and depression were administered at both assessments. RESULTS: The sample consisted of 594 adults (mean age: 48.3 ± 13.1 years; 64.0% women). Prevalence of insomnia increased from 25.4% to 32.2% (symptoms) and from 16.8% to 19% (syndrome) from 2018 to 2020, for an overall 26.7% increase in insomnia rates. Of those classified as good sleepers in 2018 (n = 343), 32.6% (n = 112) had developed new insomnia during the COVID-19 pandemic. Among participants who had insomnia in 2018, the persistence rate was 76.5% 2 years later. There was a significant worsening of sleep quality, fatigue, anxiety, and depression (all ps < .005) during the COVID-19 pandemic compared to 2018. Significant associations were found between sleep and psychological symptoms and with living alone and being in confinement, lower social support, increased time using electronic devices, reduced physical exercise, and higher financial stress. CONCLUSIONS: The COVID-19 pandemic is associated with significant increases in insomnia and psychological symptoms compared to the pre-pandemic period. Large scale public sleep and mental health intervention programs should be prioritized during and after a pandemic such as the COVID-19.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".