This is not a feature you can turn on today. But it is one of the more interesting examples of where smartwatch health tracking could go next, especially for people who deal with sudden fainting episodes and the injury risk that comes with them.

Why fainting prediction is different

Vasovagal syncope happens when heart rate and blood pressure drop suddenly, often after stress, pain, heat, standing or sitting for too long or another trigger. The episode itself usually passes quickly, but the fall can be the real problem.

That is why a few minutes of warning could be useful. It could give someone enough time to sit down, lie down or call for help before losing consciousness.

Samsung says up to 40 percent of people experience vasovagal syncope at some point in life. It also says around one third experience repeat episodes, which makes this less niche than it might sound at first glance.

What the study actually showed

The research team studied 132 patients with suspected vasovagal syncope during induced fainting tests. They used Galaxy Watch6 to collect biosignals through the optical PPG sensor, then analysed heart rate variability using an AI model.

The model predicted impending fainting episodes up to five minutes in advance. Samsung says the result came with 84.6 percent accuracy, 90 percent sensitivity and 64 percent specificity.

That mix is worth reading carefully. High sensitivity means the system caught most upcoming episodes. The lower specificity means false alerts remain an issue, which would need work before this could become a polished consumer warning system.

Why Galaxy Watch6 is the interesting part

The study used a commercial smartwatch rather than a lab-only device. That is the key point here, because it suggests common wrist hardware may already capture enough signal to support this kind of prediction with the right algorithm.

That does not mean every Galaxy Watch6 suddenly becomes a fainting detector. It means Samsung and its clinical partners have shown that the raw data from the watch can support the concept under controlled study conditions.

There is also a practical angle. Smartwatches already sit on the wrist all day, so a warning system like this would not need users to wear a specialist device if the feature ever reached real-world deployment.

Still a research result, not a finished feature

Samsung is framing the work as part of a shift from post-event care to preventive health. That is fair enough, but there is a long gap between a clinical study and a consumer feature that people can rely on.

The next steps would likely involve larger studies, real-world testing and regulatory questions. False alerts would also need careful handling, because a fainting warning that triggers too often could become something users ignore.

Still, this is a strong direction for smartwatch health tech. Many wearable features track what has already happened, while this points toward watches that warn before something happens.

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Selected users will be able to buy BodyScan 2 weeks ahead of the global release. They will also be asked to share feedback through surveys and may join 30 minute calls with Withings’ Product and Marketing teams.

The company says participants who complete at least 80 percent of the surveys will receive an exclusive reward. The program is Europe only for now, although Withings says BodyScan 2 will soon be available in the US as well. If you want in, you can apply via this link.

BodyScan 2 is moving closer to launch

The company says participants will receive final, production ready hardware, so this does not appear to be a prototype trial. The remaining work seems to be around firmware, feature activation and real world feedback before the wider rollout.

BodyScan 2 was announced earlier this year as Withings’ next flagship health scale. It is designed to go beyond weight and body composition, with a 90 second scan and a wider set of health metrics.

That includes cardiovascular and metabolic tracking, along with longer term trend features such as Health Trajectory. The device continues the idea of a smart scale as a home health station rather than just a weight tracker.

Early users may shape the final experience

The Insider Program should help Withings clean up the final experience before launch. That probably means checking how reliably the scans work, how clearly the app explains results and whether the firmware behaves properly in daily use.

That is important because BodyScan 2 is trying to do a lot. More metrics only help if users understand what changed and what it means over time. Withings will need the software layer to be as clear as the hardware pitch.

The 500 person limit also keeps the rollout controlled. Withings gets real feedback from engaged users, while buyers get the device before everyone else. It is a practical middle step before the global launch.

A wider release now looks close

Withings previously said BodyScan 2 would arrive in 2026, and this Insider Program suggests the launch window is now tightening. Europe gets the first early access route, while the US is expected to follow soon.

The product still needs to prove that users want this much health tracking from a scale. But the direction is clear. Withings is pushing BodyScan 2 as a more complete home health device, and this early access program looks like the final test before it lands properly.

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This is the part most scales get wrong

Most smart scales still work the same way. You step on them barefoot, they send a signal through your lower body, and then software makes its best guess about the rest. It is useful, but only to a point.

BodyFit tries to do something more serious. The retractable handle lets the current travel through the full body, including the upper half, so the scale can track each limb and your trunk independently. That is a much better way to spot muscle imbalances, uneven fat loss or recovery issues after injury.

Withings says the system reaches 99% correlation with DEXA scans for fat mass results, which is about as strong a marketing claim as you can make in this category. It uses multifrequency BIS technology with eight electrodes and 13 frequencies up to 800 kHz, so this is clearly built to be more than a casual wellness gadget.

The scale also tracks things like visceral fat, basal metabolic rate, vascular age, pulse wave velocity and standing heart rate. The full scan takes about 20 seconds, so it is still quick enough for everyday use and not something that feels like a chore.

Where it sits next to Body Scan 2

This launch is more interesting because Withings has also been talking about Body Scan 2. This device picked up a CES 2026 Innovation Award and is positioned as the company’s cardiometabolic flagship.

That device goes much further than body composition. It is built around heart health, cardiovascular risk and long-term longevity tracking, with Withings describing it more like a checkup machine for your bathroom. It is meant to give users a broader picture of where their health is heading, not just whether their latest training block is working.

I found the same with the original Body Scan when I reviewed it. It felt less like a scale and more like a full health station. The retractable handle was a big part of that, helping it deliver much richer data than a standard smart scale.

BodyFit takes a similar but simpler route. It keeps the segmental body composition side of things, but leaves out the more medical-style focus and long-term cardiovascular checks.

US first, Europe next

Withings says the first limited batch will roll out to customers in the US first, with Europe following shortly after. The company specifically points to the rapid growth of GLP-1 use in the US as one reason for that priority.

That makes sense. As more people use GLP-1 medications for weight loss, there is more interest in tracking what is actually being lost. Weight alone does not tell you much if muscle mass is dropping too fast. A scale that can separate fat loss from muscle loss becomes a lot more useful in that situation.

That may end up being the strongest reason BodyFit exists at all. It is not trying to be the fanciest scale Withings has ever made. It is trying to be the one many people will want to buy.

Check it out on the company’s website.

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You know the feeling. You’ve woken up feeling strangely worse after what should have been a full night’s sleep. Well, something like this can help. The company is positioning it as the final piece of its “Sleep, by Design” platform. It sits alongside Sleep Assist for falling asleep and Deep Sleep Boost for supporting slow-wave sleep.

A smarter way to wake up

The idea here is simple. But the technology behind it is more interesting than the typical smartwatch-style smart alarm. Rather than estimating sleep stages from movement or heart rate, Muse is using EEG data directly from the headband.

That matters because the whole point of a smart alarm is timing. If you are being woken at the wrong point in your sleep cycle the grogginess can linger well into the morning. Muse says Smart Wakeup continuously monitors brain activity during your chosen wake window and starts a gradual audio alarm the moment it detects lighter sleep. If that moment does not arrive, it still wakes you at the latest set time.

That direct EEG approach is one of the things that stood out to me in my hands-on review of the Muse S Athena. The device’s brain visualisation tools and signal quality were already among its strongest points. So this feels like a natural extension of what the hardware is already good at.

The data behind it

Muse says the feature was developed using an internal study based on around 6,200 nights of sleep data. Some 1300 users in total were looked at, with each session paired to morning mood ratings.

The most interesting takeaway is that sleep duration still matters far more than anything else. In other words, this is not being presented as a fix for poor sleep habits. But for nights where users already got seven or more hours with good sleep efficiency, waking during lighter sleep was linked with noticeably better morning mood.

That feels believable. Anyone who tracks sleep regularly will know that seven hours can feel very different depending on when the alarm hits. Some mornings you wake naturally a few minutes before it goes off and feel clear-headed. Other mornings the same alarm time feels brutal.

Muse’s point is that the timing of your sleep stages shifts from night to night. Travel, stress, schedule changes and even minor routine differences can move REM and lighter sleep periods around, which means a fixed 7am alarm does not always hit the same sleep stage.

What comes next

The company is already talking about where this platform goes next in 2026, and this is where it gets especially interesting. Planned additions include waking once a recovery target has been met, a mode designed to wake users during REM for dream recall, and guided audio layered into the transition from sleep to wakefulness.

Some of that does stray into more speculative territory, but Smart Wakeup itself feels grounded and genuinely useful. Based on my experience with the Muse S Athena, this is exactly the kind of feature that makes sense for the platform. It builds on the headset’s clinical-style EEG strengths rather than trying to imitate what smartwatches are already doing.

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On paper, it sounds like another health dashboard. It is not really that. The more interesting part is that it tries to sit on top of everything you already use and make sense of it.

Less dashboards, more asking questions

Most health apps still expect you to dig through charts. You scroll, tap around, maybe spot a trend if you are paying attention.

This flips that around. You just ask. And presumably, its deeper that what you get on some AI chat features that you get in some fitness apps. Plus you can link up different sources.

For example, you can ask how your sleep has changed recently. Or whether your resting heart rate is trending up. Or how your activity lines up with recovery. The system pulls data from various places and gives you a straight answer.

That might sound simple, but it changes how you use this stuff. Instead of trying to interpret graphs yourself, you get something closer to an explanation.

There is also this idea of “health memory”. Basically, the system builds up a profile of you over time.

It keeps track of past data, previous questions and whatever you have uploaded. That context feeds into future answers, so things should get more relevant the longer you use it.

Pulling everything into one place

All of this only works if the data is actually there. So integrations are a big part of it.

You can connect Apple Health, Fitbit and Withings at launch. On top of that, you can bring in lab results, prescriptions and other records, or just upload files yourself.

There is some plumbing behind the scenes to make this work. Medical records come through a partner system, while fitness apps connect through APIs. You still have to approve everything manually, so it is not exactly plug and play. But once it is set up, the idea is that everything sits in one place.

Our takeaway

Strip away the features and this is really about control. Right now, your health data sits inside separate ecosystems. Each one gives you its own view, its own logic and its own limits. Perplexity is trying to sit above that and give you a single way to interact with everything.

That only works if the answers are actually better than what you get inside those apps. If it just repackages the same insights in a different format, there is not much value there. The whole pitch depends on whether it can connect things in a way the original platforms do not.

There is also a dependency problem. It does not own the data or the hardware. It relies entirely on other platforms continuing to provide access. That puts a ceiling on how far it can go, especially if those ecosystems decide to keep more of their insights locked in.

So the idea is clear. One interface, all your data. But will it tell you something new, or just save you a few taps? If it is just the second, it will not really be that useful. The real value is in connecting dots you would not spot yourself, and that is where things are heading. AI insights are the future.

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Huawei diabetes risk alerts

Huawei’s latest smartwatch update aims to offer early warning signs of diabetes risk, using optical heart rate sensors and a new app. Rather than attempting direct blood sugar measurements, it focuses on vascular and nerve signal patterns linked to long-term glucose problems.

The company is positioning the new feature in the wellness camp. Needless to say, this is not a diagnostic tool, nor is it being framed as a replacement for lab tests. Instead, the Diabetes Risk app is pitched as a prompt for further evaluation. If you land in the Medium or High category, Huawei recommends speaking with a doctor.

The core of the approach relies on PPG, the same tech used to track heart rate. Huawei’s system looks for long-term changes in those signals. Specifically, it’s tracking variations that could point to diabetes-related microvascular or neuropathic effects. These may develop silently over time and subtly affect how light is absorbed or reflected under the skin.

Huawei also leans on some scientific context here. At the Dubai launch, the company referenced studies suggesting shared genetic factors between resting heart rate and diabetes, along with vascular and nerve damage altering PPG waveforms. The idea is that these slow shifts in signal pattern, captured during daily wear, might be enough to flag emerging issues.

This is a passive feature. You wear the watch as usual, and over the course of three to 14 days, it collects enough data to assess your risk. The app then categorises results into Low, Medium or High, with only the latter two triggering a suggestion to seek medical input. The company makes clear it isn’t diagnosing anything.

The rollout starts with the Huawei GT 6 Pro. An over-the-air update activates the functionality. More models are expected to get support, though no full timeline has been confirmed yet. Users don’t need to trigger anything manually once the update is installed. Just wearing the device is enough for the system to start logging.

Professor Jiguang Wang, Director of the Shanghai Institute of Hypertension, joined Huawei on stage for the unveiling. He has worked with the brand before, including on the WATCH D series with its inflatable wrist-cuff for blood pressure readings. At the event, he spoke about the growing potential of PPG-based insights for population health, especially in early detection.

Garmin has something different in mind

It’s worth noting how this differs from what Garmin has proposed. Garmin’s recent patent goes after HbA1c estimation, a long-term blood glucose marker typically requiring a lab test. That system uses multi-wavelength light sensors and spectral analysis to derive estimated values over time. Huawei, by contrast, isn’t trying to quantify anything directly. It’s spotting trends that might correlate with risk, not estimating specific blood sugar markers.

For now, Huawei’s feature avoids regulatory complications by focusing on general wellness. It may also sidestep the accuracy and reliability hurdles that come with trying to deliver clinical-grade data. But as sensor quality improves and algorithms mature, the line between wellness and diagnostic tech is becoming harder to draw.

via Gulfbusiness.com

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Hypertension alerts were cleared by the FDA in 2025 as a tool to raise awareness of potential hypertension. It uses optical sensors to look for patterns in blood flow, then runs the data through an algorithm to decide whether to show an alert. There’s no cuff involved. The idea is to flag risk, not diagnose anything.

So how well does it work in the real world? That’s what researchers from the University of Utah and the University of Pennsylvania wanted to find out.

It misses a lot of people

Using publicly available accuracy data from Apple and national health survey data from the U.S., the researchers simulated how this alert system would perform at scale.

What they found was pretty straightforward. If someone actually has undiagnosed hypertension, there’s a good chance the Apple Watch won’t pick it up. In fact, about 59 percent of those people wouldn’t get an alert. On the other side, around 8 percent of users who don’t have hypertension would still get flagged anyway.

So the feature isn’t very accurate if you think of it like a medical test. That’s because it isn’t one. It’s not built to diagnose or replace a cuff. It’s built to nudge you into paying attention.

The researchers focused on how meaningful the alerts actually are, depending on who you are. And that’s where things get more interesting.

Age and risk level change what the alert means

For younger users under 30, the chance of having undiagnosed hypertension is low to begin with. So when the watch sends an alert, the chance you actually have high blood pressure jumps from 14 percent to 47 percent. That’s a decent signal, but still leaves a lot of uncertainty. If there’s no alert, your risk drops slightly to 10 percent.

Older adults, on the other hand, have a higher baseline risk. For people over 60, the probability of having hypertension without knowing it is already 45 percent. If they get an alert, that jumps to 81 percent. But even if they don’t get an alert, their risk is still sitting at 34 percent.

That’s the point the researchers keep coming back to. An alert might help confirm risk, but the lack of an alert is not all that reassuring for older or higher-risk users.

The analysis also broke things down by race and ethnicity.

For non-Hispanic Black adults, getting an alert bumps the chance of having hypertension from 36 percent to 75 percent. Without an alert, it drops to 26 percent. For Hispanic adults, the same alert moves the probability from 24 percent to 63 percent, and silence brings it down to 17 percent.

Should you trust the alert?

Not entirely. It can be a helpful early warning, especially for people who wouldn’t otherwise think to check their blood pressure. But it doesn’t offer the kind of accuracy you get from a cuff-based device.

And that’s where this feature could backfire a bit. If someone sees the alert and follows up, that’s good. But if someone assumes they’re fine just because the watch is quiet, they could miss a diagnosis that needs attention. That’s a real concern for older users and people in higher-risk categories.

Current guidelines still recommend cuff-based screening every few years for adults under 40, and yearly after that. Apple’s alerts aren’t changing that advice.

Source: Jama Network

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What makes Smartbuds different

Most sleep wearables just sit back and watch. Smartbuds actually do something. They’ve got six dry-contact EEG sensors that track your brainwaves in real time, then play gentle sounds to help keep you in deep sleep. It’s all done with a feedback loop that kicks in when your brain starts drifting out of that restorative zone.

Inside, you’ve got two high-res EEG channels sampling at 1000 Hz. That’s the kind of quality you’d normally see in lab gear. The sensors are placed around the ear canal and outer ear, using a special Tecticoat layer to keep readings stable without any messy gels.

Instead of guessing sleep stages based on movement or heart rate, Smartbuds go straight to the source. You get brainwave data, not ballpark estimates.

Comfort and audio matter too

The earbuds weigh around 5 grams each and come with three sizes of tips and wings. Conductive silicone ensures both comfort and signal fidelity. Battery life hits nine hours on a single charge, and the case holds four extra charges via USB-C.

You can stream your own music, podcasts or use NextSense’s own soundscapes. Audio quality uses 6mm drivers with a standard 20Hz to 20kHz frequency response, and users in the beta rated them highly. The company says – among those with prior earbud experience, 69 percent said the sound quality was better than what they were used to.

They’re not just for sleep either. Since the EEG sensors can pick up alertness and neural patterns during the day, there’s future potential for cognitive tracking during waking hours. The tech is already being explored with academic partners.

Backed by research and tested in real-world use

NextSense ran a controlled test over 106 nights and found Smartbuds increased slow-wave activity. Around half the participants said their sleep was better or much better, and the improvements extended into morning recovery scores.

The company says it has gathered over 1,000 nights of in-ear EEG data so far, making this one of the largest consumer-facing datasets of its kind. Clinical collaborations are ongoing, with published results expected later in the year.

Jonathan Berent, founder and CEO, says the whole idea stemmed from a moment of frustration with heart health tech. After getting an atrial fibrillation alert from a smartwatch, he realised there was still no simple way to get meaningful brain data at home. That gap led to his decision to leave Alphabet’s X division and create NextSense.

Pricing, app support and availability

Smartbuds cost $249 during the early launch window, with full retail set at $399. They come with three months of the Fit Kit subscription, which sends fresh ear tips and wings each month to preserve EEG signal quality. After that, it renews at $14.99 per month but can be paused or cancelled at any time.

The earbuds currently work only with iPhones running iOS 17 or later. There’s no mention yet of Android support. Syncing is event-driven and continuous, using Bluetooth 5.3 with LE Audio and standard streaming protocols.

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Deep Sleep Boost works with your brain

This isn’t about calming bedtime stories or generic white noise. Deep Sleep Boost detects slow-wave sleep with EEG in real time and delivers pink-noise pulses that are phase-timed to the user’s actual brain oscillations. These pulses are designed to gently reinforce slow-wave activity, the part of sleep most tightly linked to physical recovery, memory consolidation and long-term brain health.

In my review of the Athena headband I found it feels familiar to earlier Muse models but packs noticeably more detail under the hood, with upgraded EEG sensors and the addition of fNIRS for tracking both brain activity and how hard your brain is working during sessions. The sleep tracking was accurate and detailed, with clear visualisations of brainwaves and solid overall performance night after night.

What’s new here is not just sound during sleep, but smart, adaptive stimulation based on the exact timing of brainwaves. That level of precision is what Muse believes sets it apart from traditional sound-based sleep tools.

The science behind it isn’t new, but now it’s usable

Closed-loop audio stimulation has been around in research for years. Trials have shown that when delivered correctly, these cues can boost slow-wave activity by as much as 50 percent and improve memory retention overnight by around 20 percent. One Alzheimer’s study saw a 60 percent increase in time spent in deep sleep using similar stimulation protocols.

Muse has essentially translated this into a real-world feature for nightly use. According to the company, their AI models were trained on large-scale EEG datasets and refined using years of longitudinal brain data from Muse users. The goal was to bring clinical insight into a consumer setting, without the clunky lab gear.

The system is also modular. Muse users can combine Deep Sleep Boost with other features like Sleep Assist, which plays EEG-guided sounds to help users fall asleep faster, or pair it with Sleep Sounds like “Ocean Stillness.” Everything runs inside the same mobile app, with an option to preview results the next day.

It’s designed to help with depth, not just duration

Many people sleep long enough but still wake up groggy. Muse is aiming at the quality side of the equation. Deep sleep tends to decline with age, stress, or irregular routines, and boosting it without drugs or wearables that just guess at your sleep stage is a tough problem. That’s where Muse’s EEG tracking and adaptive timing come in.

The feature only kicks in once you’ve actually reached deep sleep. A visual in the app shows a confirmation: “Will be active when you reach deep sleep.” It also tracks how many times it triggered during the night and displays that next to your sleep staging and slow-wave intensity score the following morning.

The system supports customization too. Protocol 7 is the current preset, but the app offers advanced settings for intensity and timing. Muse recommends using wired headphones for the most accurate audio timing, though the experience still works wirelessly.

Included for free with Athena

Deep Sleep Boost is already live and free for anyone using a Muse S Athena, whether you’re on Android or iPhone. It’s part of Muse’s bigger Sleep by Design toolkit, which also includes features like the upcoming Smart Alarm and the Enso AI coach. The idea is to go beyond just tracking sleep and actually help improve it using real brain data.

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Real time insights may replace one-off visits

The UK’s NHS is working on a new approach that would allow patients to receive personalised cancer risk updates in real time using wearable devices. The idea is to move beyond the traditional model of diagnosing illness during occasional GP visits or screenings, and instead offer ongoing feedback based on data collected by gadgets like smartwatches.

This is part of a wider digital health strategy aimed at earlier detection and prevention. The technology wouldn’t diagnose cancer directly. But it could support continuous risk modelling based on lifestyle, physiological data and other metrics gathered over time. Patients could be notified if their risk profile begins to shift, triggering follow-up with clinicians before symptoms emerge.

A ten-year timeline for nationwide rollout

The ambition is big, but the timeline is long. The reports suggest that everyone in the UK should be able to access this within the next decade. It’s not a short-term change. Rather, it’s a shift that builds slowly alongside improvements in health data infrastructure and public adoption of wearables.

Essential reading: Top fitness trackers and health gadgets

No single brand of smartwatch or health tracker is named. And the types of signals being considered haven’t been publicly disclosed. But you can imagine your typical Apple and Garmin watches being used for this sort of thing. The underlying idea aligns with the broader NHS move toward integrated health apps, at-home sensors, and machine learning models that crunch data in the background to highlight patterns that clinicians might otherwise miss.

What wearables might be tracking

Typical smartwatch data includes heart rate, heart rate variability, sleep, respiration, skin temperature and activity levels. Some studies suggest that subtle shifts in these metrics can indicate physiological stress or inflammation, both of which could be relevant to disease risk models. Over time, the combination of data and context could help flag unusual trends.

To get there, however, wearable devices would need to be validated for medical use, and the software powering these risk models would need to meet rigorous clinical standards. That adds complexity, but it’s already starting to happen in other areas of health. Similar models have been proposed for cardiovascular disease, diabetes and mental health. Cancer is more complex, but the long-term goal is the same – early, data-driven intervention.

This approach reflects a growing belief in health systems that data collected daily may offer better insight into long-term health than snapshots taken during brief doctor visits. That shift has been gathering steam for years, and this new NHS plan seems to lock it in as part of national strategy.

How quickly it becomes reality will depend on partnerships between government, device makers and regulators. But the vision is clear.

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