A total of 75 individuals were recruited from Seoul National University Bundang Hospital (SNUBH) and Clionic Lifecare Clinic (CLC). The demographic information of the study participants is presented in Table 1. By performing the most extensive simultaneous comparison of widely used CSTs and conducting a multicenter study with diverse demographic groups, this study offers comprehensive insights into the performance and applicability of these CSTs for sleep monitoring. Subgroup analysis was also performed to assess the impact of demographic factors on performance, including sex assigned at birth, apnea-hypopnea index (AHI), and BMI.

By simultaneously assessing multiple CSTs, we aimed to minimize bias and evaluate their performance across various metrics. To address these limitations, we conducted a multicenter study comparing widely used or newly released CSTs with in-lab polysomnography in a hospital setting. However, this previous study showed limitations of recruitment from a single institution and exclusion of widely used CSTs available commercially. Chinoy et al compared the performance of 7 CSTs with polysomnography (Fatigue Science Readiband, Fitbit Alta HR, Garmin Fenix 5S, Garmin Vivosmart 3, EarlySense Live, ResMed S+, and SleepScore Max). Some studies compared CSTs and alternative tools available for sleep analysis, such as electroencephalography headbands or subjective sleep diaries (without employing the gold standard polysomnography), which failed to validate the consistency between CSTs and polysomnography. Given the surge of diverse CSTs, it is necessary to conduct comprehensive and objective evaluations of the performance of these CSTs available in the market. This classification is based on the measurement methods and biological signals used in each category. Airable devices or airables use mobile phones to analyze sleep via built-in microphones or environmental sensors.

Nearable devices or nearables, placed near the body without direct contact, have radar or mattress pads to detect subtle movements during sleep. Wearable devices or wearables, such as smartwatches and ring-shaped devices, are generally worn by users to track sleep using sensors like photoplethysmography sensors and accelerometers. This study classified CSTs into 3 types: wearables, nearables, and airables. CSTs are widely used by not only individuals interested in wellness who wish to understand and proactively manage their own sleep, but also those who want to self-check and screen for sleep disorders. As a result, CSTs have emerged as accessible solutions for home sleep monitoring. These companies have made significant contributions to enhancing the performance of CSTs by integrating deep learning algorithms and biosignal sensing technologies. Recently, many big tech companies, including Apple, Samsung, and Google, as well as health care startups like Withings and Oura, have released their own CSTs. While the laboratory monitoring of sleep using the traditional sleep analysis tool polysomnography has several limitations associated with the need for cumbersome sensors, CSTs facilitate individual monitoring of sleep at home using minimal equipment. With the growing recognition of the importance of sleep for overall health, there has been a significant rise in public interest in monitoring sleep patterns using consumer sleep trackers (CSTs). Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. There was a distinct trend in sleep measure estimation according to the type of device. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. Each CST sleep recording covered an average of 353 hours. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings.

The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea.

Awake rem core deep apple watch