In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea: A Validation Study with Level 2 Home Polysomnography

Nov 16, 2023
JAMA otolaryngology 2023



Consumer-level sleep analysis technologies have the potential to revolutionize the screening of obstructive sleep apnea (OSA). However, assessment of OSA prediction model based on in-home recording data usually performed concurrently with level 1 in-lab polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important.


To validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home.


This study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Breathing sounds were recorded during sleep using two smartphones, one with an iOS and the other with an Android operating system, simultaneously with home PSG in participants’ own home environment.


The study was conducted at the tertiary center.


The study included individuals aged 19 years and older, who slept alone, and had either been diagnosed with OSA or had no previous diagnosis.

Main Outcomes and Measures: Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds.


Total 101 participants were included during the study duration of February 2022 to February 2023. Participants were composed of 50 males and 51 females, and mean age of the participants was 48.3 (14.9) years. For the iOS smartphone, the sensitivity values at AHI levels of 5, 15, and 30 were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 were 92.2%, 90%, and 92.9%, respectively, with specificities of 84%, 94.4%, and 94.3%, respectively. The accuracy for the iOS smartphone was 88.6%, 93.3%, and 94.3%, respectively, and for the Android smartphone, it was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30, respectively.

Conclusions and Relevance:

Our study demonstrates the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.


Seung Cheol Han MD*
Daewoo Kim, MS*
Chae-Seo Rhee MD PhD
Sung-Woo Cho MD
Vu Linh Le, MS
Eun Sung Cho, MS
Hyunggug Kim, MS
In-Young Yoon, MD PhD
Hyeryung Jang, PhD
Joonki Hong PhD


* Seung Cheol Han and Daewoo Kim equally contributed to the work

** Corresponding author