Social Jetlag Changes During the COVID-19 Pandemic as a Predictor of Insomnia – A Multi-National Survey Study
Bibliographic record
Abstract
PURPOSE: Lifestyle and work habits have been drastically altered by restrictions due to the COVID-19 pandemic. Whether the associated changes in sleep timing modulate the risk of suffering from symptoms of insomnia, the most prevalent sleep disorder, is however incompletely understood. Here, we evaluate the association between the early pandemic-associated change in 1) the magnitude of social jetlag (SJL) - ie, the difference between sleep timing on working vs free days - and 2) symptoms of insomnia. PATIENTS AND METHODS: A total of 14,968 anonymous participants (mean age: 40 years; 64% females) responded to a standardized internet-based survey distributed across 14 countries. Using logistic multivariate regression, we examined the association between the degree of social jetlag and symptoms of insomnia, controlling for important confounders like social restriction extension, country specific COVID-19 severity and psychological distress, for example. RESULTS: In response to the pandemic, participants reported later sleep timing, especially during workdays. Most participants (46%) exhibited a reduction in their SJL, whereas 20% increased it; and 34% reported no change in SJL. Notably, we found that both increased and decreased SJL, as a result of the COVID-19 pandemic, were associated with later sleep midpoint (indicating a later chronotype) as well as more recurrent and moderate-to-severe symptoms of insomnia (about 23-54% higher odds ratio than subjects with unchanged SJL). Primarily those with reduced SJL shifted their bedtimes to a later timepoint, compared with those without changes in SJL. CONCLUSION: Our findings offer important insights into how self-reported changes to the stability of sleep/wake timing, as reflected by changes in SJL, can be a critical marker of the risk of experiencing insomnia-related symptoms - even when individuals manage to reduce their social jetlag. These findings emphasize the clinical importance of analyzing sleep-wake regularity.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".