Sleep Tracking Technologies

Performance of seven consumer sleep-tracking devices compared with polysomnography Link: https://lnkd.in/enH5U5Gs Study Objectives Consumer sleep-tracking devices are widely used and becoming more technologically advanced, creating strong interest from researchers and clinicians for their possible use as alternatives to standard actigraphy. We, therefore, tested the performance of many of the latest consumer sleep-tracking devices, alongside actigraphy, versus the gold-standard sleep assessment technique, polysomnography (PSG). Methods In total, 34 healthy young adults (22 women; 28.1 ± 3.9 years, mean ± SD) were tested on three consecutive nights (including a disrupted sleep condition) in a sleep laboratory with PSG, along with actigraphy (Philips Respironics Actiwatch 2) and a subset of consumer sleep-tracking devices. Altogether, four wearable (Fatigue Science Readiband, Fitbit Alta HR, Garmin Fenix 5S, Garmin Vivosmart 3) and three nonwearable (EarlySense Live, ResMed S+, SleepScore Max) devices were tested. Sleep/wake summary and epoch-by-epoch agreement measures were compared with PSG. Results Most devices (Fatigue Science Readiband, Fitbit Alta HR, EarlySense Live, ResMed S+, SleepScore Max) performed as well as or better than actigraphy on sleep/wake performance measures, while the Garmin devices performed worse. Overall, epoch-by-epoch sensitivity was high (all ≥0.93), specificity was low-to-medium (0.18–0.54), sleep stage comparisons were mixed, and devices tended to perform worse on nights with poorer/disrupted sleep. Conclusions Consumer sleep-tracking devices exhibited high performance in detecting sleep, and most performed equivalent to (or better than) actigraphy in detecting wake. Device sleep stage assessments were inconsistent. Findings indicate that many newer sleep-tracking devices demonstrate promising performance for tracking sleep and wake. Devices should be tested in different populations and settings to further examine their wider validity and utility. #sleep #sleephealth #sleepapnea #sleeptrends #sleep2025 #health #healthcare #sleepapnea #osa #cpap

The biggest raise in sleep tech history. Eight Sleep just closed a $100M round. The company is calling its next phase an AI Sleep Agent, a personalized layer between your body and your environment that learns from your nightly data and adapts in real time. But the bigger story? They’re going after FDA clearance. That’s not just sleep optimization anymore, it’s a jump into regulated health tech. And it’s exactly the kind of wellness → healthcare convergence I’ve been talking about. Eight Sleep is just the latest, and one of the clearest, examples of this shift. For them, it unlocks clinical claims, physician adoption, and potentially employer or insurer channels. Why this matters... → 𝗙𝗿𝗼𝗺 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝘁𝗼 𝗶𝗻𝘁𝗲𝗿𝘃𝗲𝗻𝘁𝗶𝗼𝗻 Most sleep tools just report, but Eight Sleep is moving toward active adjustments and health monitoring. They say their system can already track cardiovascular and respiratory patterns with strong validation results. If proven, sleep becomes a measurable lever for metabolism, recovery, and cognition. → 𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝘁𝗼 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹 FDA approval would legitimize Eight Sleep with doctors and hospitals while creating new reimbursement pathways. Wellness brands rarely cross this line, if they succeed, it sets a template for other recovery tools. → 𝗙𝘂𝗹𝗹-𝘀𝘁𝗮𝗰𝗸 𝘀𝗹𝗲𝗲𝗽 𝗵𝗲𝗮𝗹𝘁𝗵 This isn’t just a mattress cover. It’s hardware + sensors, AI + software, and now a regulated pathway. That end-to-end stack could define how sleep is treated at home, in clinics, and in performance settings. → 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗲𝘅𝗽𝗮𝗻𝗱𝘀 Plans include retail stores, international expansion (with China in focus), and new price tiers. If they pair clinical credibility with broader access, sleep tech could move from premium gadget to mainstream health utility. What to watch -> regulatory path (which clearances they pursue), clinical outcomes (not just dashboards), pricing and access, and how they handle sensitive biometric data. TLDR: If Eight Sleep can turn sleep into a regulated, personalized intervention, it upgrades the bed from comfort product to health platform, with ripple effects across recovery, cardiometabolic care, and longevity. ♻️ Repost this to share with anyone tracking recovery and longevity. Follow me at Delphine Le Grand for more.

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

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