0717 Peer-intervention Can Reduce Health Disparities In Patients With Obstructive Sleep Apnea
Bibliographic record
Abstract
Abstract Introduction In patients with obstructive sleep apnea (OSA), adherence to continuous positive airway pressure (CPAP) therapy is a major problem. Moreover, up to 20% of patients with suspected OSA who are referred to sleep study testing do not adhere to such diagnostic work-up. Although, peer-driven intervention with an interactive voice response system (PDI-IVR) can improve CPAP adherence, whether such an intervention can improve adherence to sleep study testing is unknown. Also, there remain health disparities with greater levels of CPAP nonadherence disproportionately affecting individuals of lower socioeconomic status. We aimed to determine whether PDI-IVR can improve adherence to sleep study testing and CPAP adherence in a lower income population. Methods We performed a prospective, randomized, parallel group, controlled trial wherein patients with suspected OSA were randomly assigned to receive PDI-IVR or provided with educational information regarding OSA and CPAP therapy (attention-control group) while both groups received usual care. The PDI-IVR interactions aimed at promoting adherence to sleep study testing and in patients diagnosed with OSA the peer-intervention was focused on improving CPAP adherence. In the PDI-IVR group, trained peers (peer-buddies) with OSA were paired with randomized patients over a 6-month period combined with an ability to meet in-person, email, text message, or phone an inter-disciplinary team of providers. Results In this pilot study, there were 63 patients (48.4 ± 12.5 years; 30 men) who were randomized to intervention (n=31) and attention-control (n=32) arms. There were 36 peer-buddies who mentored the patients in the intervention group. Intention to treat analysis revealed that failure to undergo sleep study testing was 15.6% of patients in the attention-control arm and 9.7% in the PDI-IVR arm (P=0.7). Per protocol analysis revealed that failure to undergo sleep study testing was 18.4% of patients in the attention-control arm and 4% in the PDI-IVR arm (P=0.13). At 6 months, CPAP adherence was greater in PDI-IVR arm (290 ± 45 min [SE]) than attention-control arm (181 ± 43 min; P=0.01). Conclusion In a lower income population, PDI-IVR improved CPAP adherence with a tendency for better adherence to sleep-study testing. Peer-intervention can reduce sleep health disparities. Support HL138377
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.014 | 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".