Data-Driven Health Insights Using Wearable Technology

🧬 𝗬𝗼𝘂𝗿 𝗦𝗺𝗮𝗿𝘁𝘄𝗮𝘁𝗰𝗵 𝗠𝗶𝗴𝗵𝘁 𝗞𝗻𝗼𝘄 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗬𝗼𝘂 𝗧𝗵𝗶𝗻𝗸 What if your wearable could tell you not just how many steps you’ve taken, but how fast you’re aging? A fascinating new study in Nature Communications introduces 𝗣𝗽𝗴𝗔𝗴𝗲, a “wearable-based aging clock” that uses simple PPG (photoplethysmography) signals from consumer devices like smartwatches to estimate your 𝗯𝗶𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗮𝗴𝗲. Here’s why this is a game changer 👇 Researchers found that this digital aging clock can predict a person’s age with remarkable accuracy , within about 2–3 years on average. But the real breakthrough lies in the “𝗮𝗴𝗲 𝗴𝗮𝗽” , the difference between your predicted (biological) age and your actual chronological age. That gap turned out to be a powerful health indicator. People with an older PpgAge gap had higher risks of 𝗵𝗲𝗮𝗿𝘁 𝗱𝗶𝘀𝗲𝗮𝘀𝗲, 𝗱𝗶𝗮𝗯𝗲𝘁𝗲𝘀, 𝗵𝗲𝗮𝗿𝘁 𝗳𝗮𝗶𝗹𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗼𝘁𝗵𝗲𝗿 𝗺𝗲𝘁𝗮𝗯𝗼𝗹𝗶𝗰 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀. Even after accounting for traditional risk factors, the signal held up. It didn’t stop there , lifestyle factors also showed up clearly: 💨 Smokers, poor sleepers, and low-activity individuals tended to have a higher (older) age gap. 🏃♂️ Meanwhile, those who exercised regularly and slept better tended to appear biologically younger. Perhaps most impressively, the model was dynamic. It detected subtle physiological changes like during pregnancy or after cardiac events , suggesting real-time responsiveness to body changes. We’re still early in this space, and it’s not without limitations , self-reported data, specific populations, and no proven causality yet. But this work clearly shows how 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗯𝗶𝗼𝗺𝗮𝗿𝗸𝗲𝗿𝘀 from everyday wearables are becoming powerful tools in predictive health and longevity. The future of health isn’t just about diagnosis , it’s about 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀, 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 into how your body is truly aging. 🔗 Source: Nature Communications – “A wearable-based aging clock associates with disease and behavior” https://lnkd.in/eFW_739q #DigitalHealth #WearableTechnology #Longevity #Innovation #HealthTech #AIinHealthcare

Delighted to share our latest research introducing SensorFM for Wearable Health!! For this study, we pretrained a sensor foundation model on the world's largest wearable health dataset with over 1 trillion minutes of health sensor data from 5 million participants. We evaluated it on 35 downstream health predictions by combining de-identified data from multiple prospective IRB-approved observational studies involving cardiovascular health, metabolic risk, sleep disorders, mental health, lifestyle choices, and physiologically relevant demographics. Some key findings: 📈 Size Matters: Joint scaling of data volume and model capacity led to near-linear improvements in both generative pretraining and discriminative post-training. 🔄 Real-World Imputation: SensorFM learned to successfully impute missing or unobserved data so well that it outperformed the best-performing baselines by 75% on random imputation, 39% on temporal interpolation, 40% on temporal extrapolation, and 84% on sensor signal imputation! 🤖 Self-Evolving Algorithm Generation: We deployed a "classroom" of LLM agents to autonomously search the space of downstream predictive heads, resulting in broad performance improvements across health tasks. 🩺 Clinical Validation: Integrating these predictors into a Personal Health Agent yielded personalized health summaries that were judged by clinicians to be more relevant, contextually aware, and safe. Read the full preprint here: https://lnkd.in/g5zmfxDQ Kudos to all my wonderful collaborators across Google Health, Google Research and Google DeepMind!! #DigitalHealth #GoogleHealth #HealthAI

Mayo Clinic has just published some fascinating work on epilepsy that, in my view, marks an important milestone for wearables in healthcare. Their team, led by biomedical engineer Benjamin H Brinkmann PhD, FACNS FAES , showed that an AI-enabled smartwatch can forecast epileptic seizures about 75% of the time with relatively few false alarms 🤯, using signals like heart rate, movement, skin temperature, and conductance. Over 15 months, a parallel implant behind the ear recorded more than 72,000 hours of brain activity and detected 754 seizures, almost twice as many as reported in patient diaries, underscoring how much clinical reality we miss without continuous monitoring. Why does this matter beyond epilepsy? Because it demonstrates that AI can extract clinically actionable insights from real-world physiological data. Long-term continuous monitoring fundamentally changes clinical understanding: it fills the gaps between visits, corrects recall bias in patient-reported data, and opens the door to earlier, more precise interventions. Even when devices are not strictly “medical grade,” their signal quality is now good enough to provide useful, decision-supporting information to the entire care triad: patient – payer – provider. This is exactly the direction I hope our healthcare ecosystems are heading: wearables and sensor-rich environments as complementary infrastructure, continuously feeding risk models, decision support tools, and personalized care pathways. Not replacing clinicians or traditional diagnostics, but augmenting them with a much richer, longitudinal picture of health. At Monterail HealthTech division, we see growing demand from clients who want to integrate wearable data into their platforms, build AI/ML pipelines on top of it, and surface insights directly into clinical workflows. The Mayo work is a strong validation that this is not just “wellness”, it’s the future fabric of healthcare. I’ve been a big supporter of wearables and their influence on human health for years. With results like these emerging from top clinical centers, it feels like we’re finally moving from promise to proof. And that shift will reshape how we design digital health products, how we measure outcomes, and ultimately how we think about staying healthy over the long term.

Can #cardiovascular patterns from #wearables reveal insights about #hormonal #health? 📄 A recent study in npj Digital Medicine led by Summer Jasinski, Emily Capodilupo, and the team at WHOOP takes an important step toward answering that question. Using data from over 45,000 menstrual cycles (from >11,000 women), they identified a consistent, physiological pattern: ↗️ Resting heart rate increases during the luteal phase ↘️ Heart rate variability* decreases before menstruation From this, they created a new "cardiovascular amplitude metric" — capturing how much these signals change across the menstrual cycle. 💡 The findings? -Most naturally cycling women show a clear, rhythmic pattern -It’s blunted in women using hormonal birth control, and in those with higher age or BMI This matters because it suggests we may be able to use wearable data — not just from WHOOP, but from wrist- and ring-based devices more broadly —as a non-invasive signal of hormonal health. That’s a meaningful step forward for #women’s #health #research. Right now, most tools for understanding the menstrual cycle rely on symptom tracking or hormone testing—methods that can be hard to access, expensive, or too reactive. But what if passive signals from wearables could help detect when something shifts—like if ovulation doesn’t occur that month, or if there’s a hormonal disruption worth paying attention to? Congratulations to the WHOOP team on this important contribution—and to all the researchers pushing this field forward. At GSD Health Research, we’re excited about the potential of digital biomarkers to complement traditional clinical tools and bring new precision to how we study and support menstrual health. #WomensHealthResearch #WearableTech #DigitalBiomarkers #MenstrualHealth #HormonalHealth #FemTech #ReproductiveHealth *Note: For those tracking HRV on wearables: the study used RMSSD, a common short-term HRV metric often available through devices like WHOOP, Oura, and others.

7 wearable and sensor innovations pushing health beyond “wellness” tracking this month: 🔘 Sibel Health is developing an AI-enabled wearable that tracks scratching behaviour in people with atopic dermatitis, turning something usually seen as a subjective symptom into a measurable clinical signal that could also support drug development. 🔘 CranioSense is working on a non-invasive approach to measuring intracranial pressure, which today often requires invasive procedures, and if validated could make brain pressure monitoring safer and more continuous in routine clinical care. 🔘 University of Technology Sydney researchers are developing AI-powered sweat sensors that can decode body chemistry in real time, tracking hormones, medication levels and potential early warning signs of disease, potentially offering a non-invasive alternative to some forms of blood testing 🔘 ŌURA rings are being used within Medicare Advantage Plans, with around one-third of eligible members opting in and sharing biometric data, which is already leading to improvements in sleep and light activity and is paving the way for deeper clinical use cases such as hypertension monitoring 🔘 Samsung Electronics is preparing to launch an AI Brain Health tool that uses data from smartphones and wearables, including speech, movement and sleep behaviour, to help detect early signs of dementia while aiming to keep the experience privacy-aware and clinically relevant 🔘 Researchers at the University of Arizona have created a wearable mesh sleeve that monitors gait and subtle movement patterns to identify early signs of frailty in older adults, with the goal of shifting care from reacting after a fall to proactively supporting prevention through continuous remote monitoring 🔘 And China is testing “smart urinals” that analyse urine in real time for markers like glucose and protein, which opens up interesting conversations about passive health screening, consent, and how health data might be gathered in everyday environments. 💬We are steadily moving from episodic health snapshots to passive, continuous and contextual signals across movement, sleep, behaviour and even body chemistry. The technology is getting closer. Now the real work is around validation, governance, reimbursement and making sure the data actually makes a difference in peoples lives 👇 Links to articles in comments #DigitalHealth #Wearables #AI

A breakthrough in wearable health technology is emerging from Singapore, where researchers have developed a flexible skin patch capable of detecting up to 12 different diseases using sweat alone. The patch continuously analyzes biomarkers such as glucose levels, proteins, inflammation indicators, and stress hormones, all without needles or blood samples. Using ultra-thin microfluidic channels and embedded biosensors, the device captures tiny amounts of sweat and processes data in real time. The system wirelessly transmits results to a smartphone, allowing users to monitor their health as they go about daily life. Powered by body heat, the patch operates without batteries or charging, functioning like a compact medical lab worn on the skin. This innovation could transform healthcare by enabling early detection and predictive monitoring, catching potential health issues long before symptoms appear. For chronic conditions like diabetes, it offers continuous glucose tracking without finger-prick tests. Researchers also suggest that some cancer-related biomarkers may be detectable in sweat months before traditional imaging methods identify tumors. With projected production costs under $20 per patch and a lifespan of up to three months, clinical trials are expected to begin in 2026. If successful, routine health monitoring may soon become effortless, affordable, and non-invasive. #MedicalInnovation #WearableTech #HealthcareTechnology #Biotechnology #drkevinramdhun

NEW: ŌURA just put a doctor inside its ring app. Not a chatbot. A licensed physician, reachable in minutes. Here is why this matters more than another wearable update 👇 Oura has partnered with Counsel Health to build care directly into the Oura App. You describe a symptom. A headache. Nausea. A PCOS flare. A medical AI reads it first, alongside your history and your ring data. If you need more, it passes you to a licensed physician in the same chat. Within minutes. No clinic booking. No video call. No switching apps. 𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐬𝐢𝐠𝐧𝐚𝐥 𝐈 𝐬𝐞𝐞 For a decade, wearables had one job. Show you data. Steps. Sleep. Heart rate. A readiness score. Interesting to look at. Rarely acted on. This is not the first time the signal, the interpretation, the clinician and the prescription all sit in one consumer app. WHOOP is also taking a similar approach. The loop finally closes. Passive data turns into a decision. That changes what a ring is. It stops being a tracker. It becomes a front door to care. 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥𝐥𝐲 The examples they give are telling. A drop in overnight oxygen saturation helping separate a simple viral cough from an asthma flare. Ring trends hinting at an infection before you feel unwell. This is context a doctor almost never has at first contact. We usually meet you at the moment of crisis, with no baseline. Continuous data flips that. We start from your normal, and watch for the drift. 𝐖𝐡𝐞𝐫𝐞 𝐈 𝐭𝐡𝐢𝐧𝐤 𝐭𝐡𝐢𝐬 𝐠𝐨𝐞𝐬 I wrote a while back that wearables, AI and health records were converging into actionable intelligence, not just prettier dashboards. This is the next rung. From insight to action. But I would hold the excitement against three real problems. → Continuity. A run of one-off chats is not a relationship that knows you. Primary care is more than transactions. → Signal quality. A consumer oxygen reading is not a clinical-grade test. If that data starts driving prescriptions, the bar for validation has to rise sharply. → The worried well - yep this one is not new. Always-on monitoring can catch disease early. It can also manufacture anxiety and over-treatment in healthy people. The direction is clear though. The entry point to healthcare is quietly moving from the GP's front desk onto your finger. The companies that own the device may end up owning the first clinical conversation. The question is, who's building towards that next? Follow me, Dr. Youssef Aboufandi, MD, for what is next in healthcare AI.

The conversation around wearables is evolving fast. For years, the debate was whether consumer sleep trackers were accurate enough. Today, the bigger question is whether they can help more people recognize a problem and get to the right care. This is critical, because sleep disorders are still underdiagnosed and often overlooked in everyday clinical conversations. In primary care, the average physician may spend just 8 to 10 minutes a year with a patient. That’s not much time to surface a sleep issue that may have been building for months or years. That is why I find the Resmed and ŌURA collaboration so compelling. It is designed to connect overnight breathing disturbance data with education and pathways to clinical evaluation and care. I mention in the article, “We’re at an inflection point in how people engage with their sleep health, driven by rising awareness and more accessible technology. By partnering with Oura, we are turning insight into action by guiding people across their sleep health journey and making it easier for them to seek clinical evaluation and care if they have concerns about their sleep.” That shift is important because the data can start a better conversation. Resmed research has shown why longitudinal sleep information matters: An analysis of data from more than 312,000 adults using CPAP therapy helped establish global benchmarks for sleep duration, efficiency, and physiology. A second analysis of 117,000 adults with OSA found that higher sleep efficiency was linked to longer sleep duration, greater physical activity, and a lower resting heart rate. Ricky Bloomfield, CMO at Oura put it well too: “We know from many anecdotal reports…that our members really appreciate the insights that they get from the sleep metrics within Oura. When people see that they are not sleeping well and have a large number of breathing disturbances, that has motivated them to go seek out clinical care with their doctor.” That is the real opportunity here. Not replacing clinical care. Not turning wearables into diagnostics. But using consumer technology to help more people move from awareness to action, and from action to better sleep health conversations. Are wearables becoming the bridge between noticing a problem and doing something about it? #SleepHealth #GlobalSleepPriority #HumanHealth #HealthTech #SleepDiagnostics #WearableTech Read the Sleep Review article here: https://lnkd.in/eqGYW49c

GenAI can generate personalized health insights from raw wearable data. This moves beyond just data collection towards true data interpretation for patients. Merrill MA, Paruchuri A, Rezaei N et al. published this work in 2026. They developed the Personal Health Insights Agent (PHIA) to address the numerical reasoning challenges common in standard large language models. PHIA employs a multi-step reasoning process, integrating both code generation and information retrieval to analyze behavioral health data. The research team built two comprehensive benchmark datasets, containing over 4000 health insights questions, to rigorously test PHIA. A human expert evaluation, involving 650 hours, compared its performance against a strong code generation baseline. PHIA demonstrated superior performance across the board. For objective, numerical questions, it achieved 84% accuracy. When assessing open-ended questions, PHIA received 83% favorable ratings and was twice as likely to get the highest quality rating compared to the baseline model. ✅ I've seen patients struggle to make sense of their own health data — this could be transformative. The abstract emphasizes PHIA's potential to empower individuals and enable personalized, data-driven wellness. This was an abstract-only publication, so the real-world scalability in diverse populations and implementation specifics still need full exploration. This work is essential reading for anyone exploring how to make individual health data truly actionable. Publication date: January 2026 Publication source: Nature Communications Read more here: https://lnkd.in/egxuSK4e Subscribe free to AI Rounds for a weekly AI × healthcare digest: https://www.airounds.net/