Impact of <scp>COVID</scp>‐19 pandemic on sleep parameters and characteristics in individuals living with overweight and obesity
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) has been very challenging for those living with overweight and obesity. The magnitude of this impact on sleep requires further attention to optimise patient care and outcomes. This study assessed the impact of the COVID-19 lockdown on sleep duration and quality as well as identify predictors of poor sleep quality in individuals with reported diagnoses of obstructive sleep apnoea and those without sleep apnoea. An online survey (June-October 2020) was conducted with two samples; one representative of Canadians living with overweight and obesity (n = 1089) and a second of individuals recruited through obesity clinical services or patient organisations (n = 980). While overall sleep duration did not decline much, there were identifiable groups with reduced or increased sleep. Those with changed sleep habits, especially reduced sleep, had much poorer sleep quality, were younger, gained more weight and were more likely to be female. Poor sleep quality was associated with medical, social and eating concerns as well as mood disturbance. Those with sleep apnoea had poorer quality sleep although this was offset to some degree by use of CPAP. Sleep quality and quantity has been significantly impacted during the early part of the COVID-19 pandemic in those living with overweight and obesity. Predictors of poor sleep and the impact of sleep apnoea with and without CPAP therapy on sleep parameters has been evaluated. Identifying those at increased risk of sleep alterations and its impact requires further clinical consideration.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".