Specific insomnia symptoms and self-efficacy explain CPAP compliance in a sample of OSAS patients
Bibliographic record
Abstract
This study explores the association between specific insomnia symptoms (sleep onset, sleep maintenance and early morning awakenings symptoms) and self-efficacy (perceived self-confidence in the ability to use CPAP) with CPAP compliance in French patients with obstructive sleep apnea syndrome (OSAS). We performed a retrospective, cross-sectional analysis of CPAP compliance in a cohort of 404 patients diagnosed with OSAS. Patients completed mailed questionnaires on sleepiness (ESS), insomnia (ISI) and self-efficacy in sleep apnea (SEMSA). Linear regression modeling analyses were performed to explore the impact of measured variables on the number of hours of CPAP use. Of the initial pool of 404 patients, 288 returned the questionnaires (71% response rate). Their mean age was 63.16±12.73 yrs, 31% were females, mean BMI was 30.39±6.31 kg/m2, mean daily CPAP use was 6.19±2.03 h, mean number of years of use was 6.58±6.03 yrs, and mean initial AHI before CPAP use was 34.61±20.71 /h. Age (p<0.01), BMI (p<0.01), sleep onset insomnia symptoms (p<0.01), sleep maintenance insomnia symptoms (p<0.01) and self-efficacy (p<0.01) were significantly associated with mean daily CPAP use. We found that specific insomnia symptoms and self-efficacy were associated with CPAP compliance. Our findings underline the need to demonstrate that interventions that reduce insomnia symptoms and improve self-efficacy will increase CPAP compliance.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".