Health Monitoring Wearables

Our steps, sleep, heart rate, stress - we are self tracking everything! "Oura Paranoia" is the new anxiety which is emerging with this data driven wellness A recent New York Times article explored this growing phenomenon where health tracking leads not to peace of mind, but to worry, overthinking, and constant self-monitoring. You get a wearable. It feels smart. Empowering, even. But before you know it, you’re checking your vitals constantly. Real stories from the article: • One person bought a blood pressure cuff just to double-check her ring • Another couldn’t sleep — not from restlessness, but because her device said she wasn’t sleeping “efficiently” • One said, “I just felt like I couldn’t do anything right to make the ring happy” A therapist told her patient to stop wearing the device. A doctor told another: “You’re healthy. Consider ditching the ring.” The article summed it up perfectly: “Collecting data promises a happier, healthier life. But what if it’s also heightening our stress? Is there a metric for that?” Sometimes, one might be experiencing paranoia and not even know it — because it shows up not as panic, but as fixation. Not as illness, but as the pursuit of “optimization.” This isn’t an attack on health tech. Let’s be clear: ✔️ Wearables have helped detect real health issues for some ❌ But constantly chasing scores can disconnect us from our own body’s wisdom If you wake up feeling fine, but a number tells you otherwise… If you feel rested, but your “sleep score” says poor… If you’re checking stats more than signals from your own body… Then maybe the tech is doing the opposite of what it promised. As one professor in the piece asked her students: “But what did your body say?” That might be the most important question of all. Not to throw out the tech, but to reclaim balance. To remember: your body is not a dashboard. It’s not a data stream. It’s a sensing, adaptive, living system that often knows exactly what it needs — if we pause long enough to listen. The best health decisions might come not from graphs or scores, but from noticing: Am I tired? Am I anxious? Am I okay? Let’s not confuse being connected to a device with being connected to ourselves. Has tracking your data helped you or made it harder to trust your instincts? Would love to hear your reflections. #MentalHealth #DigitalWellbeing #QuantifiedSelf #OuraParanoia #SelfAwareness #HealthTech #WellnessCulture #NYT Image - NYT

I tracked sleep for 30 days. Almost tanked my sleep in the process. There’s a term for it now. Orthosomnia: the obsession with perfect sleep scores that ironically ruins your sleep. Three brutal truths: 1. Data ≠ Biology. Trackers get time asleep mostly right. But REM, deep sleep, latency? They’re guessing. Yet we chase those numbers as if they were gospel. 2. Stress transfers. I found myself lying awake, anxious because my tracker said I’d slept badly. Self-fulfilling insomnia. 3. We’re human, not robots. Normal sleep fluctuates. 3–6 nightly wake-ups? Normal. But one “poor” score and your brain hits panic mode. So I ran the experiment in reverse. Ditched the Oura. Went pen-and-paper. Logged one thing: how I felt at 7 a.m. Result? Better sleep. Less rumination. And a painful realization: Sleep isn’t a performance metric. It’s biology. The relentless pursuit of 8 hours, 25% deep, no wake-ups? It’s a fantasy. Precision kills. It introduces anxiety where calm is needed. Track if it helps. But if your sleep stack is stressing you out? The most powerful optimization might be letting go. #Recover #UpwardARC

Struggling with productivity at work due to tiredness? Use your best sleep tracker and consider these metrics. And no, not from your Oura, Apple, or Whoop. Use how you feel and what you’ve done: 1. Sleep duration: How many hours are you actually sleeping? 2. Sleep consistency: Are you going to bed and waking up at the same time each day? 3. Morning restfulness: How refreshed do you feel upon waking? 4. Sustained energy: How do you feel two hours after waking up? 5. Stress levels: How stressed are you throughout the day, especially before bed? 6. Wind-down period: Did you have a calming routine before bed to help you relax? 7. Meal timing: How long before bed was your last big meal? 8. Caffeine dependence: How quickly do you reach for your first cup of coffee? For clients with sleep issues and fatigue, I recommend: Simple tracking ↓ Make precise changes ↓ Assess effectiveness ↓ Optimise what works ↓ Significant improvements However, chronic sleep issues may need further investigation with your doctor. Do what you can and focus on what you can control. How do you track sleep?

Waking up at 5 AM to train sleep deprived? You might be killing your gains🙈 Most people think sacrificing sleep for the gym is what separates the committed from the lazy. It feels like discipline. Like dedication. Like the price you have to pay. But science says otherwise. 📉 A study from the Journal of the American Medical Association (JAMA) found that sleep-deprived individuals built 60% less muscle than those who got enough rest—even with the same training and nutrition plan. Because here’s the truth: ❌ Muscle isn’t built in the gym. It’s built while you sleep. ❌ Lack of sleep spikes cortisol. More stress = more muscle breakdown = more fat storage. ❌ Poor recovery = weaker workouts. Meaning you’re working harder but getting less out of it. So if you think grinding on low sleep is giving you an edge… think again. The real high-performance move is prioritising sleep like you prioritise training. How many hours sleep do you average? A = less than 5 hrs B = 5-6 hrs C = 7-8 hrs D = More than 8 hrs

The Sleep-Heart Connection: Can Technology Help Detect the Link Between Sleep Apnea and Atrial Fibrillation? Did you know that new wearable technologies like the Apple Watch and other smart devices are making it easier to detect potential heart and sleep disorders? ✅ The Sleep Apnea-AFib Link Obstructive Sleep Apnea (OSA) causes repeated oxygen drops during sleep, putting strain on the heart and increasing the risk of atrial fibrillation (AFib). This irregular heart rhythm can lead to stroke, blood clots, and heart failure if untreated. ✅ How Technology Can Help Devices like the Apple Watch and other wearables now have heart rate and rhythm tracking features that can detect irregular patterns, such as AFib episodes. Some technologies can even monitor oxygen levels and sleep patterns, offering clues about underlying sleep apnea. ✅ Why It Matters Many people don’t realize they have sleep apnea or AFib until complications arise. Early detection through wearable tech gives patients and clinicians the power to intervene sooner—whether it’s scheduling a sleep study, starting CPAP therapy, or managing AFib through medications or procedures like ablation. Wearable technology is bridging gaps between sleep and cardiovascular health. As AI-powered devices become more accurate, they can provide real-time insights, enabling early diagnosis and better management of these interconnected conditions. Do you use wearable technology to track your sleep or heart health? #HeartHealth #SleepApnea #AtrialFibrillation #WearableTech #AppleWatch #PreventiveCare #AIInHealthcare #SleepMedicine #HealthAwareness #StrokePrevention #CPAP #DigitalHealth #HeartMonitoring #AFibAwareness #TechForGood #CardiovascularCare

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

From $5B to $11B in one year. ŌURA is not slowing down. In health & wellness, Oura has been a breakout success, but the truth is, it’s now much bigger than “wellness.” In venture/investor circles, the question is always, how big is the market? More often than not, investors anchor on the market as it looks today, not how it could evolve. If you’d asked a decade ago whether millions of people would be wearing rings to track sleep, HRV, and recovery, most would have said the market was too small to matter. Fast forward to 2025, and Oura alone has shipped 5.5M devices. So looking ahead, what does this mean for the space? → 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 Oura essentially created the smart ring market, and is still the one to beat. A mix of hardware sales and subscription revenue has given it both scale and staying power. With $1B+ in annual revenue already, it’s not just a "gadget" anymore, it’s a category leader. → 𝗧𝗵𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗿𝗮𝗰𝗲 Giants like Apple and Samsung Electronics are both entering the ring market so the question is, can Oura defend its head start against platforms with muchhhh deeper pockets and existing ecosystems? Their bet is that focus & first-mover advantage will matter more than just size. → 𝗧𝗼𝘄𝗮𝗿𝗱 𝗮 𝗵𝗲𝗮𝗹𝘁𝗵 𝗢𝗦 Everyone is talking about who will own the "personal OS". With Dexcom integration and biomarker tracking, Oura is starting to position itself less and less as a wearable and more as a daily health OS. The ring becomes a gateway to continuous monitoring of sleep, recovery, metabolism, etc. That’s what investors are paying for, not just jewelry with sensors, but infrastructure for the future of health data. 𝗪𝗵𝗮𝘁 𝗜'𝗹𝗹 𝗯𝗲 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 --> – Competition from tech giants, like does Apple make this a feature, or a standalone category? – How far Oura pushes into regulated health vs. staying consumer-first (merge of healthcare & wellness one of the biggest trends I'm seeing) – Whether subscriptions continue to drive sticky revenue – If the $11B valuation can be justified by growth beyond earlyish adopters And if you’re building in the space, this is a reminder that entire categories in health are being created right now, this is the time to innovate. ♻️ Repost this to share with anyone tracking wearables and longevity. Follow me at Delphine Le Grand for more.

The biggest fitness mistake your making isn’t in the gym. It’s in your bedroom. After helping 300+ men transform their bodies, I can tell you with 100% certainty: The quality of your fat loss is determined by the quality of your sleep. Here’s why (and how you can fix it — even if you’re a busy professional): 1/ Your metabolism is crippled by poor sleep Most guys obsess over their workout routine while ignoring the metabolic disaster happening each night. Just ONE night of poor sleep: - Decreases testosterone by up to 15% - Spikes ghrelin (your hunger hormone) - Crashes leptin (your fullness hormone) - Increases cortisol (stress hormone that triggers fat storage) The math is simple: Sleep less, store more fat. 2/ Recovery is impossible Your muscles don't grow during workouts. They grow during sleep. Without 7+ hours of quality sleep: - Growth hormone secretion plummets - Protein synthesis decreases - Inflammation stays elevated - Recovery time doubles or triples The result? You're breaking down tissue in the gym but never fully rebuilding it. Guys wonder why they're not seeing gains despite consistent workouts. Look at your sleep first. 3/ Say goodbye to your willpower Ever notice how your nutrition falls apart when you're tired? That's not a coincidence. Poor sleep literally makes your brain crave sugar, salt, and fat as emergency energy sources. You don't lack discipline—your brain is in survival mode. This is why most diets fail on weekdays when executives are running on 5 hours of sleep. 4/ The protocol Here’s what I tell my high-performing clients who need to prioritize sleep on a busy schedule: - Same bedtime every night - 7-9 hours of sleep (non-negotiable) - 30-60 minute wind-down before bed - Set phone to airplane mode during this time - No screens 60 mins before sleep - Wear blue light blockers 3 hours before bed Optimize it: - Mouth tape (nose breathing = deeper sleep) - Breathe right strip (opens nasal pathway) - Magnesium Glycinate + L-Theanine - Eye mask or black-out curtains - White noise (if that’s your thing) Extra, I know. But it’s worth it. I've helped 300+ busy executives lose 20-50 pounds of stubborn fat while improving energy, focus, and drive. The ones who take sleep seriously see results 2-3X faster. I'm looking for a few more men who want to lose 15-25lbs in the next 90 days. - No restrictive diets - Without endless cardio - Only 3 hours/week required DM me "FIT" and let's chat (Results Guaranteed). It’s not too late to get lean before summer. Cheers, Jimmy

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

Sometimes, it’s better not to look at your wearable sleep data. I had an overnight flight earlier this week. I got a few hours of sleep, but when I woke up, I was reminded it's sometimes better not to look at your sleep data. Our perception of sleep can profoundly impact performance, particularly if we have not slept well. For example, in one study, researchers recruited participants & assigned them to one of two groups (PMID: 29989248) Both groups received wearables, which they believed provided feedback about the previous night’s sleep. However, the feedback was false. Group 1 received positive false feedback indicating sleep quality was “91.4%: Very Good’. Group 2 received negative false feedback indicating sleep quality was “61.4%: Very Poor”. The feedback was not connected with their actual sleep - only their perception changed based on the sleep data. Participants in the negative group exhibited impaired cognitive performance. Participants in the positive‐feedback group were in a better mood, more alert and less sleepiness & fatigue. Perception can be reality. ❓Have you noticed how your wearable data influences how you feel? #health #wellbeing #performance ------ I'm James, a speaker & scientist who equips knowledge workers with science-based tools to improve their wellbeing & performance. Like this post? Want to see more? 🔔 Ring the bell on my profile 🔝 Connect with me 📰 💥 Subscribe to my newsletter (link in profile)