0518 Patterns Of Long-term CPAP Adherence: A 3-year Follow-up From The Tele-OSA Study
Bibliographic record
Abstract
The Tele-OSA study demonstrated that auto-telemonitoring (TM) with messaging (U-Sleep; ResMed Corp) significantly improves CPAP adherence at 3 months and 1 year. This 3-year follow-up evaluates long-term adherence patterns and whether TM continues to impact adherence. The Tele-OSA study was a 4-arm randomized controlled trial (details available at Hwang et al. Am J Respir Crit Care Med. 2018 Jan 1;197(1):117-126.) which demonstrated that TM improved CPAP adherence while tele-education did not. This study is a post-hoc analysis of the following cohorts that were longitudinally followed over 3 years: a) TM (messaging continued); b) TM (messaging discontinued at 3 months); c) no TM. 556 patients were followed for 3 years. 356 (64%), 294 (53%), and 273 (49%) had evidence of any degree of usage at the 1-year, 2-year, and 3-year mark. 204 (37%), 211 (38%), and 177 (32%) used CPAP “≥70% of days with ≥4 hours”, also at those three time-points. “TM messaging continued” (n=73) had the highest usage ("any usage" was 75% at 1 year, 60% at 2 years, 55% at 3 years) compared to "TM discontinued" (n=190) (60% at 1 year, 49% at 2 years, 46% at 3 years) and compared to "no TM" (64% at 1 year, 53% at 2 years, 50% at 3 years), although differences were not statistically significant at 3 years. Moderate/severe OSA (n=384 patients) had better usage vs. mild OSA (n=172). Proportion of patients with "any usage" was 70% vs. 54% at 1 year; 57% vs 43% at 2 years; 54% vs 38% at 3 years. Those with “≥70% of days with ≥4 hours" was 41% vs 29% at 1 year; 41% vs 31% at 2 years; 35% vs 25% at 3 years -- p=0.02 at 3 years. Over 3 years after initiation of CPAP, a significant proportion of OSA patients discontinue CPAP. However, moderate/severe OSA have better adherence while overall usage stabilizes between 1 and 3 years. The Tele-OSA study was funded by the American Sleep Medicine Foundation and ResMed Corp.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".