Sleep Quality Assessment Tools

A new study showed that consumer sleep trackers ŌURA Ring, Fitbit (now part of Google), and Apple Watch achieved >90% sleep-wake accuracy and 70-78% accuracy when determining sleep stages compared to polysomnography With the growing popularity of wearable #sleep tracking devices, millions of consumers now rely on these technologies to monitor and improve their sleep health. Given this widespread adoption, it's crucial to evaluate the accuracy of these devices against gold standard measurements. A recent study published in Sensors MDPI addressed this need by comparing three popular consumer sleep tracking devices - the Oura Ring Gen3, Fitbit Sense 2, and Apple Watch Series 8 - to polysomnography (PSG), the benchmark for sleep assessment. Conducted on 35 healthy adults aged 20-50 years, this research provides valuable insights into how well these wearable devices measure various aspects of sleep in a controlled setting. Key findings include: 1. All three devices demonstrated high sensitivity (≥90%) in detecting sleep versus wake states, surpassing many older research-grade actigraphy devices. The Oura Ring showed substantial agreement with PSG in determining specific sleep stages (Kappa > 0.61), while the Fitbit and Apple Watch demonstrated moderate agreement (Kappa < 0.61). 2. For nightly summary estimates, the Oura Ring was not significantly different from PSG in 7 out of 8 measures, only overestimating sleep latency by 5 minutes. 3. The Fitbit significantly overestimated light sleep by 18 minutes and underestimated deep sleep by 15 minutes compared to PSG. 4. The Apple Watch underestimated wake time by 7 minutes, deep sleep by 43 minutes, and wake after sleep onset by 10 minutes, while overestimating light sleep by 45 minutes. 5. A limitation is that only a single night of data was collected, and the devices were only compared to PSG during scheduled sleep episodes in healthy participants rather than across a 24 h interval, which is the way most wearables are used. The study highlights that while these consumer devices perform well in distinguishing between sleep and wake states, their accuracy in measuring specific sleep stages varies. The Oura Ring demonstrated the most consistent performance across different sleep parameters, although all devices had limitations in accurately measuring deep and REM sleep. This research provides valuable information for consumers and healthcare professionals considering the use of wearable sleep tracking devices. However, it's important to note that the study was conducted on healthy adults in a controlled setting, and further research is needed to evaluate device performance in populations with sleep disorders or in more naturalistic environments. P.S. Congrats to the sleep team at Brigham and Women's Hospital and Harvard Medical School for doing the study! Study: https://lnkd.in/dZThiegT #sleepmedicine #sleephealth #neuroscience #medtech #healthtech #science #research #education

Published today in the Proceedings of the National Academy of Sciences PNAS, our paper on data analytics approaches for monitoring sleep patterns using a soft, wireless electronic device designed with a high-bandwidth accelerometer and configured to gently mount on the suprasternal notch – an information-rich anatomical location for recording diverse mechano-acoustic activities, from subtle vibrations of the skin to bulk movements of the body. Digital filtering of the resulting data yields a broad range of characteristic features associated with heart rate, respiratory rate, respiratory sounds, body orientation and many others. This paper focuses on advanced machine learning algorithms that operate not only on these features but also on the raw data and an associated collection derived quantities. Training relies on recordings from human subjects in a sleep laboratory, where clinical-grade polysomnography systems and scoring by professional sleep clinicians set the ground truth. The resulting technology – soft, skin-interfaced sensors and machine learning algorithms – determine sleep patterns with fidelity that lies beyond that of traditional wrist or finger-mounted wearables. One interesting and intuitive finding - especially for anyone who has had children – is that respiratory sounds, rates, durations, depths and their temporal variations are powerful indicators of sleep onset and quality, yet not typically captured directly with home sleep monitors. Prof. Yayun Du (former postdoc, now on the faculty at Vanderbilt University), Jianyu Gu (former MS student, now a PhD student at Dartmouth College with former postdoc Prof. Wei Ouyang) and Shiyuan Duan (former MS student, now a PhD student at the University of Illinois Urbana-Champaign with former postdoc Prof. Cunjiang Yu) and Jacob Trueb (software engineer and data scientist at our Querrey Simpson Institute for Bioelectronics) contributed equally to this project. Deeply grateful to them for their excellent work, and to our main clinical collaborator on this project – Dr. Charles Davies, head of Sleep Medicine at Carle Hospital. We also thank senior colleagues Prof. Yonggang Huang (Northwestern University) and Dr. Andrew N. Carr (Procter & Gamble) for their important contributions. On-going work involves the use of this system to quantify sleep in pediatric patients, including those with Down syndrome, in collaborations with clinicians and sleep medicine experts at Ann & Robert H. Lurie Children's Hospital of Chicago – Dr. Debra Weese-Mayer and Dr. Ilya Khaytin.  Looking forward to publishing the results of these studies in the near future! https://lnkd.in/gnPk-K7h

The Athlete Sleep Screening Questionnaire: a new tool for assessing and managing sleep in elite athletes Link: https://lnkd.in/ef47Ekmg A comparison of existing sleep-screening tools with determination of clinical need from a sleep specialist showed low consistency, indicating that current sleep-screening tools are unsuitable for assessing athlete sleep. A new 15-item tool was developed (ASSQ) by selecting items from existing tools that more closely associated with the sleep specialist's reviews. Based on test-retest percentage agreement and the κ-statistic, we found good internal consistency and reliability of the ASSQ. To date, 349 athletes have been screened, and 46 (13.2%) identified as requiring follow-up consultation with a sleep specialist. Results from the follow-up consultations demonstrated that those athletes identified by the ASSQ as abnormal sleepers have required intervention. The research developed a new athlete-specific sleep-screening questionnaire. Our findings suggest that existing sleep-screening tools are unsuitable for assessing sleep in elite athletes. The ASSQ appears to be more accurate in assessing athlete sleep (based on comparison with expert clinical assessment). The ASSQ can be deployed online and provides clinical cut-off scores associated with specific clinical interventions to guide management of athletes’ sleep disturbance. The next phase of the research is to conduct a series of studies comparing results from the ASSQ to blinded clinical reviews and to data from objective sleep monitoring to further establish the validity of the ASSQ as a reliable sleep screening tool for elite athletes. #sleep #sleephealth #athlete #sleep2024 #research #sleepapnea #cpap #osa #health #healthcare #performance #hme #wellness