Agreement of Sleep Measures—A Comparison between a Sleep Diary and Three Consumer Wearable Devices

Klier, Kristina

doi:10.3390/s22166189

Cited by 10 publications

(6 citation statements)

References 52 publications

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“…Various devices have been developed for predicting OSA, including wearables and nearables. Wearables, such as smartwatches (eg, Apple Watch Series 8, Galaxy Watch5, Google Pixel Watch, Fitbit Sense 2) and ring-shaped devices (eg, Oura Ring 3), have gained popularity in the market . These devices typically use sensors like photoplethysmography and accelerometers or gyroscopes to track heart rate variability and movement.…”

Section: Discussionmentioning

confidence: 99%

“…Wearables, such as smartwatches (eg, Apple Watch Series 8, Galaxy Watch5, Google Pixel Watch, Fitbit Sense 2) and ring-shaped devices (eg, Oura Ring 3), have gained popularity in the market. [16][17][18][19][20] These devices typically use sensors like photoplethysmography and accelerometers or gyroscopes to track heart rate variability and movement. However, a limitation of wearables is their primary focus on measuring oxygen desaturation rather than accurately diagnosing OSA based on AHI.…”

Section: Roc Curve Cutoff 30mentioning

confidence: 99%

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

Han,

Kim,

Rhee

et al. 2024

JAMA Otolaryngol Head Neck Surg

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ImportanceConsumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important.ObjectiveTo validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home.Design, Setting, and ParticipantsThis diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. 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. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. The study was performed between February 2022 and February 2023.Main Outcomes and MeasuresSensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds.ResultsOf the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. 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. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 per hour were 92.2%, 90.0%, and 92.9%, respectively, with specificities of 84.0%, 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 was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30 per hour, respectively.Conclusions and RelevanceThis diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.

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Section: Discussionmentioning

confidence: 99%

Section: Roc Curve Cutoff 30mentioning

confidence: 99%

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

Han,

Kim,

Rhee

et al. 2024

JAMA Otolaryngol Head Neck Surg

View full text Add to dashboard Cite

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“…A sleep diary is considered the ‘gold standard' in terms of subjective sleep assessment 54 and in non-laboratory conditions. 54 , 55 Data collected included subjective sleep quality, sleep disturbances, caffeine and medication consumption, daily exercise and bedtime routine. Additional data collected included bedtime, wake-up time, number and duration of daytime naps and sleep disruptions.…”

Section: Methodsmentioning

confidence: 99%

A pilot randomised controlled trial exploring the feasibility and efficacy of a human-AI sleep coaching model for improving sleep among university students

Liu,

Ito,

Ngo

et al. 2024

DIGITAL HEALTH

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Objective Sleep quality is a crucial concern, particularly among youth. The integration of health coaching with question-answering (QA) systems presents the potential to foster behavioural changes and enhance health outcomes. This study proposes a novel human-AI sleep coaching model, combining health coaching by peers and a QA system, and assesses its feasibility and efficacy in improving university students’ sleep quality. Methods In a four-week unblinded pilot randomised controlled trial, 59 university students (mean age: 21.9; 64% males) were randomly assigned to the intervention (health coaching and QA system; n = 30) or the control conditions (QA system; n = 29). Outcomes included efficacy of the intervention on sleep quality (Pittsburgh Sleep Quality Index; PSQI), objective and self-reported sleep measures (obtained from Fitbit and sleep diaries) and feasibility of the study procedures and the intervention. Results Analysis revealed no significant differences in sleep quality (PSQI) between intervention and control groups (adjusted mean difference = −0.51, 95% CI: [−1.55–0.77], p = 0.40). The intervention group demonstrated significant improvements in Fitbit measures of total sleep time (adjusted mean difference = 32.5, 95% CI: [5.9–59.1], p = 0.02) and time in bed (adjusted mean difference = 32.3, 95% CI: [2.7–61.9], p = 0.03) compared to the control group, although other sleep measures were insignificant. Adherence was high, with the majority of the intervention group attending all health coaching sessions. Most participants completed baseline and post-intervention self-report measures, all diary entries, and consistently wore Fitbits during sleep. Conclusions The proposed model showed improvements in specific sleep measures for university students and the feasibility of the study procedures and intervention. Future research may extend the intervention period to see substantive sleep quality improvements.

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“…This study classified CSTs into 3 types: wearables, nearables, and airables. 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 [6,[12][13][14][15][16]. Nearable devices or nearables, placed near the body without direct contact, have radar or mattress pads to detect subtle movements during sleep [9,17].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study

Lee,

Cho,

Cha

et al. 2023

JMIR Mhealth Uhealth

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Background Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography. Objective This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment. Methods The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography. Results The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. 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. Each CST sleep recording covered an average of 353 hours. 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. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. 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. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. 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. Conclusions Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep.

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Agreement of Sleep Measures—A Comparison between a Sleep Diary and Three Consumer Wearable Devices

Cited by 10 publications

References 52 publications

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

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

A pilot randomised controlled trial exploring the feasibility and efficacy of a human-AI sleep coaching model for improving sleep among university students

Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study

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