The Impact of a Telemedicine Monitoring System on Positive Airway Pressure Adherence in Patients with Obstructive Sleep Apnea: A Randomized Controlled Trial
Bibliographic record
Abstract
STUDY OBJECTIVES: First-line therapy for patients with moderate to severe obstructive sleep apnea (OSA) is positive airway pressure (PAP). Although PAP is a highly efficacious treatment, adherence to PAP is still a substantial clinical problem. The objective of this study was to determine whether PAP adherence can be improved with a telemedicine monitoring system. DESIGN: A nonblinded, single-center, randomized controlled trial that compared standard PAP treatment versus PAP treatment and a telemedicine monitoring system SETTING: University sleep disorders program in British Columbia, Canada PATIENTS: Adult patients (≥ 19 yr of age) with moderate to severe OSA (apnea hypopnea index (AHI) ≥ 15 events/hr determined by polysomnography) prescribed PAP INTERVENTIONS: Patients were randomized to either standard care with an autotitrating PAP machine or an autotitrating PAP machine that transmitted physiologic information (i.e., adherence, air leak, residual AHI) daily to a website that could be reviewed. If problems were identified from information from the website, the patient was contacted by telephone as necessary. MEASUREMENTS: PAP adherence after 3 mo, subjective sleep quality, and side effects RESULTS: Seventy-five patients were enrolled; 39 were randomized to telemedicine and 36 to standard care. The mean age ± standard deviation (SD) was 53.5 ± 11.2 yr, mean AHI was 41.6 ± 22.1 events/hr, and 80% of patients were male. After 3 mo, mean PAP adherence was significantly greater in the telemedicine arm (191 min per day) versus the standard arm (105 min per day; mean difference = 87 min, 95% confidence interval (CI): 25-148 min, P = 0.006, unpaired t test). On days when PAP was used, mean adherence was 321 min in the telemedicine arm and 207 min in the standard arm (difference = 113 min, 95% CI: 62-164 min, P < 0.0001). Significant independent predictors of adherence included age, baseline Epworth Sleepiness Scale score, and use of telemedicine. On average, an additional 67 min of technician time was spent on patients in the telemedicine arm compared with the standard arm (P = 0.0001). CONCLUSIONS: PAP adherence can be improved with the use of a web-based telemedicine system at the initiation of treatment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".