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Updated: Jul 11, 2025

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography.

Seung Cheol Han¹, Daewoo Kim², Chae-Seo Rhee^1,3

¹Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea.

JAMA Otolaryngology-- Head & Neck Surgery

|November 16, 2023

Summary

This summary is machine-generated.

Consumer smartphones can effectively screen for obstructive sleep apnea (OSA) using breathing sounds. This study validates smartphone-based OSA prediction against home polysomnography, showing promising accuracy for early detection.

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Area of Science:

Sleep Medicine
Medical Technology
Diagnostic Tools

Background:

Consumer sleep analysis technologies offer potential for obstructive sleep apnea (OSA) screening.
Current OSA prediction models often rely on in-laboratory polysomnography (PSG).
Validating smartphone-based OSA prediction using at-home, level 2 PSG is crucial.

Purpose of the Study:

To validate the performance of a prediction model for OSA using smartphone-recorded breathing sounds.
To assess the model's accuracy in conjunction with level 2 home polysomnography (PSG).

Main Methods:

Prospective diagnostic study involving 101 participants aged 19+.
Unattended level 2 home PSG was conducted simultaneously with breathing sound recordings from iOS and Android smartphones.
Participants slept alone and had either a prior OSA diagnosis or no previous diagnosis.

Main Results:

Both iOS and Android smartphones demonstrated high sensitivity and specificity in predicting OSA across different apnea-hypopnea index (AHI) levels (5, 15, 30 per hour).
Accuracy for the iOS smartphone ranged from 88.6% to 94.3%, and for the Android smartphone from 88.1% to 94.1%.
High performance metrics were observed for predicting moderate to severe OSA.

Conclusions:

Smartphone-based breathing sound analysis is a feasible method for predicting OSA with reasonable accuracy.
This approach holds potential for revolutionizing OSA screening in a home environment.
Further validation could lead to widespread adoption of consumer-level sleep analysis for OSA detection.