Changes in Positive Airway Pressure Use in Adults with Sleep-Related Breathing Disorder during the COVID-19 Pandemic: A Cross-Sectional National Community-Based Survey
Bibliographic record
Abstract
Abstract Purpose: To better understand: i) a positive airway pressure (PAP) therapy use during the pandemic, ii) how PAP use may relate to sleep, health, and COVID-19-related outcomes, and iii) factors associated with PAP use during the pandemic. Methods: This study is based on data collected between Apr 2020 and Jan 2021 as part of the online cross-sectional national community-based survey. The included participants were located in North America, 18 years and older, with self-reported sleep-related breathing disorder (SBD) and usage of a PAP device in the last month before and during the COVID-19 pandemic (“in the past 7 days”). Results: Of all respondents, 7.2% (41/570) stopped using PAP during the pandemic. There were no significant differences between individuals who continued and stopped using PAP in the time elapsed since the pandemic declaration, age, sex, education level, occupational status, family income, or the proportions of individuals endorsing symptoms that could be related to COVID-19. Compared to individuals who continued using PAP, those who stopped had significantly shorter sleep time, lower sleep efficiency, and poorer sleep quality. Higher stress levels and living with someone who experienced symptoms that could be attributable to COVID-19 were independently associated with stopping using PAP.Conclusions: In this survey study, we found that most individuals with SBD continued PAP therapy during the pandemic. However, even 7% of participants who stopped using PAP cannot be ignored. Identifying individuals at risk of discontinuing PAP treatment would help design targeted interventions for patients and health professionals to improve PAP use.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".