Peer-Driven Intervention for Care Coordination and Adherence Promotion for Obstructive Sleep Apnea: A Randomized, Parallel-Group Clinical Trial
Bibliographic record
Abstract
Abstract Rationale Obstructive sleep apnea (OSA) is a common condition that is usually treated by continuous positive airway pressure (CPAP) therapy, but poor adherence is common and is associated with worse patient outcomes and experiences. Patient satisfaction is increasingly adopted as a quality indicator by healthcare systems. Objectives We tested the hypothesis that peer-driven intervention effected through an interactive voice response (IVR) system leads to better patient satisfaction (primary outcome), care coordination, and CPAP adherence than active control. Methods We performed a 6-month randomized, parallel-group, controlled trial with CPAP-naive patients recruited from four centers and CPAP-adherent patients who were trained to be mentors delivering support through an IVR system. Measurements and Main Results In 263 patients, intention-to-treat analysis of global satisfaction for sleep-specific services was better in the intervention group (4.57 ± 0.71 Likert scale score; mean ± SD) than in the active-control group (4.10 ± 1.13; P < 0.001). CPAP adherence was greater in the intervention group (4.5 ± 0.2 h/night; 62.0% ± 3.0% of nights >4 h use) versus the active-control group (3.7 ± 0.2 h/night; 51.4% ± 3.0% of nights >4 h use; P = 0.014 and P = 0.023). When compared with the active-control group, the Patient Assessment of Chronic Illness Care rating was moderately increased by an adjusted difference of 0.33 ± 0.12 (P = 0.009), Consumer Assessment of Healthcare Provider and Systems rating was not different (adjusted difference, 0.46 ± 0.26; P = 0.076), and Client Perception of Coordination Questionnaire was mildly better in the intervention group (adjusted difference, 0.15 ± 0.07; P = 0.035). Conclusions Patient satisfaction with care delivery, CPAP adherence, and care coordination were improved by peer-driven intervention through an IVR system. New payor policies compensating peer support may enable implementation of this approach. Clinical trial registered with www.clinicaltrials.gov (NCT02056002).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".