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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The key takeaway is that not all movement is equal, and more importantly, not all movement is interchangeable. Certain activities seem to offer stronger protective effects than others, and doing several of them regularly appears to compound those benefits.

A deeper dive into the numbers

This was not a short term experiment. Researchers used data from the Nurses’ Health Study and the Health Professionals Follow-Up Study, tracking self-reported physical activity from 1986 to 2018. They developed a “variety score” that counted how many different activity types a person engaged in regularly. People with the most variety had a 19 percent lower risk of all-cause mortality compared to those with the least.

Crucially, the associations held even after adjusting for total activity volume, smoking, diet, weight, and other lifestyle factors. That suggests it wasn’t just the more active people living longer. It was the ones who moved in different ways.

Walking, running and stair climbing top the list

Walking stood out with one of the strongest associations. Those in the highest walking category had a 17 percent lower mortality risk than those in the lowest. That tracks with what most fitness trackers encourage through daily step counts. But steps alone might miss the nuance.

Running also performed well, reducing mortality risk by about 13 percent. Stair climbing followed closely, at around 10 percent. These activities challenge the heart, lungs, and muscles in slightly different ways. Walking builds endurance and metabolic health. Running puts the cardiovascular system under more strain, while stairs engage both aerobic and muscular systems.

Essential reading: Top fitness trackers and health gadgets

Rowing and strength training were similarly linked with a 13 to 14 percent lower risk. These build muscle mass, coordination, and support healthy aging, especially as people lose muscle and bone density with age. Racquet sports also performed strongly. They add reactive movements, spatial awareness, and often a social element, which some studies have associated with mental health benefits.

Cycling and swimming showed weaker links

Interestingly, cycling and swimming were not associated with the same levels of mortality reduction. Swimming in particular showed no clear benefit in this analysis. That doesn’t necessarily mean those activities are ineffective, but the authors note that intensity, frequency, and difficulty measuring those exercises may have diluted the observed effects.

Could too much exercise be a problem?

The study wasn’t designed to examine excessive exercise, but researchers did include a sensitivity analysis that looked at the highest volumes. The findings suggest that beyond a certain point, adding more of one activity doesn’t necessarily provide extra protection and may even reduce the benefits.

For example, those who reported extremely high amounts of running didn’t show more protection than moderate runners. The same held for resistance training. There is a point where repetition, especially without variety, may actually create wear and tear, overtraining or limit overall recovery. Something to think about before that next marathon run!

This is especially relevant for people who chase daily rings or streaks on smartwatches. Hitting high weekly goals is not always better if it lacks variation. The study doesn’t suggest people should do less, but it does hint that constantly repeating the same workout might not be the optimal path.

What this means for wearables and app design

Most fitness trackers currently focus on volume: step counts, calorie burn, minutes of intensity. That’s understandable. It’s easy to measure and rewards consistency. But this study offers a different lens. A device that helps you vary your routine over time may be more beneficial for long term health.

It’s not hard to imagine smartwatches offering a weekly or monthly “movement diversity” score. By analysing GPS data, heart rate zones, rep counts, or workout tags, wearables could surface nudges like: “You’ve done a lot of walking this week, consider adding some resistance or sport-based movement.”

Apps like Garmin Connect and Apple Fitness already track activities by type, but they don’t always give weight to that variety. That could change. Even passive prompts to balance walking with strength or include stair climbing could support healthier habits.

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Let’s make this clear at the outset – the device is still firmly in prototype territory. There’s no release date or pricing and no firm plans to bring it to market. But what it does represent is a glimpse into the company’s broader ambitions for AI-driven nutrition tracking.

A closer look at the Vital prototype

The unit shown off in the video is roughly the size of an old flip phone, though quite a bit chunkier. It unfolds via a hinge to reveal a small embedded camera module, some LEDs and a live preview screen. There are tactile buttons on the base, a USB-C charging port and a clunky magnetic stand that props it up on a table. The outer casing feels more like a test shell than anything close to final hardware.

Check out the video below.

What’s surprising is that the interface appears to borrow heavily from Zepp Health’s existing watch OS. You get familiar menu options like Wi-Fi, Bluetooth, display, and sound. The main feature, however, is “Dining Mode.” Once tapped, the camera extends and activates, ready to scan your meal.

When placed next to a plate of food, the device records both a visual analysis and a time-lapse of the eating process. After finishing, the user can close the camera, triggering an upload to the Zepp Health app. Within moments, a breakdown appears showing estimated calories, macros and a short AI-generated coaching insight based on what was eaten, how fast it was consumed, ignoring what was left behind.

The software goes as far as to recommend meal adjustments for balance, taking into account things like protein deficiency or overreliance on processed carbs. In Chase the Summit’s test, it correctly identified everything on the plate. The cherry tomatoes, a avocado, orange and a handful of crackers. The calorie estimate landed surprisingly close to real-world expectations.

This is more than a nutrition gimmick

As I noted in my original article, the Amazfit Vital camera seems less about the hardware itself and more about developing a next-gen food tracking engine. Zepp Health already offers food logging inside the Zepp Health app, but this approach layers in automatic visual recognition, time-based insights and AI-generated feedback.

The prototype adds a clear behavioural component too. It doesn’t just measure what you ate, but how you ate it. That ties in with Zepp Health’s recent emphasis on health-span and readiness scoring in their Balance and Active watch lines. There’s a clear ambition here to extend biometric coaching into everyday habits without relying entirely on manual input.

During the CES briefing, Zepp Health’s reps hinted that this may not be a standalone device forever. One idea floated was embedding similar camera tech into smart glasses, which would remove the awkwardness of table-mounted hardware. Something like that would make more sense, as it would not disrupt mealtime routines.

The road ahead is unclear, but the intent is not

This kind of product is unlikely to hit the market in its current form. The hardware is too niche for daily use. But the software behind it? That feels like the real play.

Zepp Health seems to be testing the waters for a more ambient, feedback-driven approach to nutrition tracking. It’s not hard to imagine future Amazfit devices incorporating some version of this feature – perhaps through the camera on your phone or smart glasses.

For now, the Vital camera remains a quirky, unfinished glimpse at what’s possible. But it also suggests that food tracking, long stuck in barcode-scanning limbo, might be on the verge of something a bit smarter.

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Both use three pods that are connected with straps. Both rely on red and infrared light for deep tissue treatment and focus on pain relief and recovery. The wavelengths are fairly similar. But their design choices, power delivery and day to day usability set them apart in some important ways.

You can check out the devices on Prongo’s website (use code INY15 at checkout for 15% off). and the Kineon website. Let’s get into it.

Form factor and flexibility

At a glance the two systems look similar. Each uses three modular pods and a strap system. The similarity ends once you start using them. FluxGo allows the three modules to work together or be split up so you can treat different areas at the same time. That gives it a lot of flexibility if you want to target more than one joint or muscle group in a single session. The strap mechanism, although not perfect, is also easier to understand.

MOVE Plus Pro takes a more fixed approach. Its three modules are designed to sit in a specific configuration. This suits knee focused use very well, but it limits how freely you can spread the modules around the body. The design choice makes setup more predictable, but also less adaptable.

FluxGo’s modules measure 72 x 48 x 29 millimetres, with a total system weight of 970 grams including the case. MOVE Plus Pro modules are smaller at 59 x 37 x 37 millimetres, and the total weight comes in at 790 grams.

But hidden in these specs is an important difference which because evident during real-world use. The three FluxGo models have a much larger surface area than Move Plus. Which is actually our preference. Perhaps not as easy to use to treat very small or hard-to-reach areas, but for the majority of problems people experience – the larger modules work better.

Wavelengths and output levels

Both devices rely on red and infrared light, but the specifics differ. FluxGo uses 650nm red LEDs and 850nm infrared lasers, delivering between 50 and 100mW per square centimetre with a total output of 540mW per module. MOVE Plus Pro uses 660nm red LEDs and 808nm infrared lasers, with irradiance between 5 and 25mW per square centimetre and 210mW per module.

This difference in output shapes how sessions feel. FluxGo delivers a up to 6x stronger dose per session, which pairs with its shorter overall battery life. MOVE Plus Pro spreads its energy over longer runtimes, which aligns with its longer per module battery endurance.

Light sources and optics

FluxGo packs more emitters into each pod. Every module includes 18 LEDs and 12 VCSEL laser diodes, combined with a focusing lens for LEDs and a polarising filter for lasers. This setup aims to concentrate light where it is needed.

MOVE Plus Pro uses 8 LEDs and 10 VCSEL laser diodes per module, split across two emission areas. The design spreads light more evenly across the treated area, rather than concentrating it as tightly. Both approaches support deep tissue penetration, but they reflect different philosophies in how light should be delivered.

Treatment modes and session control

FluxGo gives you three treatment modes called Mild, Mid and Strong. These map to session lengths of 8, 15 and 20 minutes. You choose the intensity and the device handles the rest. This makes it easy to adjust based on how you feel that day.

MOVE Plus Pro sticks to preset session times of 5, 10 or 15 minutes. The simplicity helps if you want a quick routine, but it offers less flexibility. You either run another session or stop early, there is no middle ground.

FluxGo uses two buttons per pod and includes a small display on each module. This gives direct feedback and control without guesswork. MOVE Plus Pro relies on a single button per pod, also paired with a display. It feels simpler, but both solutions work well.

Battery life and charging

Battery strategy is another big difference. FluxGo offers up to 75 minutes of runtime per module and relies on a rechargeable storage case to top everything up. The case becomes part of the system and keeps charging simple.

MOVE Plus Pro delivers up to four hours of battery life per module, which is significantly longer. It also comes with a rechargeable storage case, but each module lasts much longer between charges. The tradeoff shows up in output levels.

Bottom line

Both Prungo FluxGo and Kineon MOVE Plus Pro do what they set out to do. They deliver focused red and infrared light therapy in a modular wearable format, backed by real science and noticeable effects over time. But the way they go about it is slightly different.

FluxGo hits harder per session, offers more flexibility in how you arrange the modules, and comes in at a much lower price. It’s less refined in some areas but makes up for it with simplicity and strong output. Plus you get a larger surface area of each pod which works better for the majority of use cases.

MOVE Plus Pro leans into comfort and longer battery life. It feels more polished and self-contained, especially for hard to reach knee-specific routines. But that refinement comes at a cost. You’re paying for the streamlined setup and extra runtime.

So it depends what you want. For use, the power, versatility and better value make FluxGo our favourite. If you prefer a more elegant design with fewer charging stops and a slightly smaller footprint, MOVE Plus Pro might suit you better.

Finally, don’t forget to check out our detailed hands-on reviews of the Prungo FluxGo and the Kineon MOVE Plus Pro. The first can be purchased on Prongo’s website (use code INY15 at checkout for 15% off), the second on the Kineon website.

Tech specs comparison

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The post Prungo FluxGo vs Kineon MOVE Plus Pro: What really separates them appeared first on Gadgets & Wearables.