Digital biometrics in predicting risk for obstructive sleep apnea and hypertension: A decentralized, prospective cohort study (Preprint)

Le résumé fourni par la source

BACKGROUND Sleep is an important component of human health and can be measured longitudinally using digital activity trackers. Further, decentralized digital research data collection has the potential to provide a real-world picture of sleep in large populations. We hypothesized that longitudinal sleep patterns from activity trackers could predict risk of obstructive sleep apnea (OSA) similar to the Berlin questionnaire and risk for hypertension based on self-report. OBJECTIVE To test the ability of biometric to predict risk for human disease. METHODS We recruited adults ≥18 years nationwide to join our sleep-focused smartphone-based study, called the Research Framework for Exploring Sleep Health (REFRESH). Participants were asked to fill out health-related surveys including the Berlin questionnaire, which also includes self-reported hypertension, and the Horne-Ostberg questionnaire for chronotype. Participants were asked to optionally link their own activity tracker to the application to collect longitudinal sleep data. RESULTS We analyzed sleep data from 391 participants, 67.9% of which were women, and 40.4% of whom demonstrated an evening chronotype. Collinearity testing showed that OSA risk and self-reported hypertension could be considered independently. Increased sleep variability predicted risk of both OSA and hypertension in this decentralized cohort, when using data from the Berlin questionnaire as the ground truth. CONCLUSIONS Decentralized sleep research could provide important information regarding risk for some health outcomes. Sleep variability is gaining increasing importance in the context of sleep health. CLINICALTRIAL NCT05197738

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.