![]() For the iOS smartphone, the sensitivity values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Results Of the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. ![]() Main Outcomes and Measures Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds. The study was performed between February 2022 and February 2023. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. Breathing sounds were recorded during sleep using 2 smartphones, one with an iOS operating system and the other with an Android operating system, simultaneously with home PSG in participants’ own home environment. Objective To validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home.Äesign, Setting, and Participants This diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important. However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Importance Consumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA).
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