Smartwatch blood pressure readings are a mixed bag: under controlled conditions and shortly after calibration, some devices come close to clinical-grade accuracy, but the technology has consistent blind spots that make it unreliable for diagnosing or managing hypertension on its own. The core problem is that cuffless sensors estimate blood pressure indirectly, and those estimates tend to compress toward average values, missing the extremes that matter most in medicine. For someone with normal blood pressure curious about trends, the numbers can be informative. For someone whose doctor is adjusting medication based on readings, a standard cuff remains the tool to trust.
How Smartwatches Estimate Blood Pressure Without a Cuff
A traditional blood pressure cuff physically compresses an artery and listens for the return of blood flow. That mechanical process is what makes cuff readings reasonably accurate across a wide range of pressures. Smartwatches skip the cuff entirely. Most rely on photoplethysmography (PPG), which shines a light into the skin and measures how the reflected light changes with each pulse beat. The shape, timing, and amplitude of that pulse wave carry indirect clues about blood pressure. Some devices go further by combining PPG with an electrocardiogram sensor to calculate pulse arrival time, the delay between the heart’s electrical signal and the pulse reaching the wrist.1Biomedical Signal Processing and Control. A new approach for daily life Blood-Pressure estimation using smart watch The device then feeds these signals into an algorithm that converts them into a blood pressure estimate.
The critical thing to understand is that none of this directly measures the force of blood against artery walls. It measures something correlated with that force and translates it through a mathematical model. That model works best when conditions closely match the conditions under which it was trained. When they don’t, errors creep in.
What the Accuracy Studies Actually Show
Accuracy findings depend enormously on which device is tested and how the study is designed. One validation study of a smartwatch with a built-in inflatable cuff (a miniaturized version of the traditional approach) found that resting blood pressure differed from a standard device by less than 1 mmHg for systolic pressure and about 1.6 mmHg for diastolic pressure, with variability within the limits set by the international standard for blood pressure monitors.2PubMed Central. Validating the accuracy of a multifunctional smartwatch sphygmomanometer to monitor blood pressure That sounds impressive, and it is, but inflatable-cuff smartwatches are a niche product. Most consumer smartwatches use only optical sensors.
For optical-only devices, the picture is less rosy. A study specifically examining smartwatch-based blood pressure found that the watches showed proportional bias: they overestimated systolic blood pressure when it was below roughly 140 mmHg and underestimated it above that threshold. Diastolic readings were overestimated across the board, especially at lower values. Sensitivity for detecting hypertension was 83%, but specificity was only 41%, meaning the watch correctly flagged most people with high blood pressure but also flagged many people who didn’t have it.3Frontiers in Cardiovascular Medicine. Smartwatch-Based Blood Pressure Measurement Demonstrates Insufficient Accuracy In practical terms, that means if the watch says your blood pressure is elevated, there’s a decent chance it actually isn’t.
This compression effect, where low values get pushed up and high values get pulled down, is the single most important limitation to understand. It makes the watch appear accurate on average across a population, because the overestimates and underestimates roughly cancel out. But for any individual reading, especially at the clinical extremes where medical decisions happen, the error can be substantial.
The Calibration Problem
Most cuffless smartwatch blood pressure features require periodic calibration against a traditional cuff. You take a reading with a real cuff, enter that value into the watch, and the algorithm adjusts its model. Right after calibration, accuracy tends to be reasonable. The trouble is that the calibration drifts over time as your body changes, or simply because the algorithm’s assumptions stop matching your physiology.
A prospective study tracked this drift and found that when actual blood pressure deviated by 10 mmHg from where it was at the last calibration, the smartwatch’s systolic reading was off by about 3.4 mmHg on average, and the diastolic reading was off by about 5.1 mmHg.4PubMed Central. Long-term accuracy and stability of blood pressure measurements from a smartwatch: Prospective validation study That error grows the further your blood pressure moves from the calibration point. In other words, the watch is most accurate when your blood pressure is exactly where it was last time you calibrated, which is precisely the scenario where you least need the information.
A separate real-world study found that the average error after recalibration was about 6.8 mmHg for systolic blood pressure, and this error increased at higher systolic levels.5Hypertension Research. Feasibility and measurement stability of smartwatch-based cuffless blood pressure monitoring: A real-world prospective observational study Nobody has settled on how often recalibration should happen, which leaves users guessing. Samsung recommends every four weeks for its Galaxy Watch blood pressure feature, but the evidence suggests that may not be frequent enough for people whose blood pressure fluctuates significantly.
Why Accuracy Degrades Over Months
The drift problem isn’t just about software aging or battery wear. It reflects a fundamental biological challenge. The relationship between pulse wave timing and actual blood pressure changes over time because arteries themselves change. Smooth muscle in artery walls contracts and relaxes in response to hormones, stress, temperature, and long-term vascular aging. These changes alter how fast the pulse wave travels through your arteries, which throws off the algorithm’s assumptions about what a particular pulse shape means for blood pressure.
Research examining calibration models after one year found that the models relating pulse timing to blood pressure weren’t significantly better than a naive guess. The culprit was smooth muscle contraction, which modulates arterial stiffness independently of blood pressure.6PubMed Central. Assessment of Calibration Models for Cuff-Less Blood Pressure Measurement After One Year of Aging The wrist and finger arteries, where most smartwatches take readings, are particularly affected because they contain more smooth muscle than the larger central arteries. This is a physics problem as much as an engineering one, and it’s not easily solved by better software alone.
Does Skin Color or Body Size Affect Accuracy?
This is a legitimate concern, given that pulse oximetry, the related optical technology used to measure blood oxygen, has well-documented accuracy gaps in people with darker skin. For blood pressure specifically, at least one study directly tested PPG-based blood pressure measurement across skin tones using the Fitzpatrick scale and found no meaningful difference. The bias was less than 1 mmHg in both lighter (Fitzpatrick types 1 through 3) and darker (types 4 through 6) skin groups, and correlations with the reference device remained above 0.93 for all groups. Sex and body mass index also did not significantly affect accuracy in that study.7Frontiers in Physiology. Influence of Sex, BMI, and Skin Color on the Accuracy of Non-Invasive Cuffless Photoplethysmography-Based Blood Pressure Measurements
That said, this is one study with a specific device, and broader reviews have flagged that underrepresented populations, including older adults, people with darker skin, and those in low-resource settings, are underrepresented in the validation literature as a whole.8PubMed. Exploring the potential of five machine learning regression algorithms for noninvasive blood pressure estimation with photoplethysmography The finding that skin color doesn’t matter for one well-designed PPG device is encouraging, but it would be premature to assume that holds universally across all smartwatches on the market. If you have darker skin and are relying on a smartwatch for blood pressure trends, it’s worth verifying against a cuff periodically.
Getting the Most Accurate Readings
Even with the technology’s limitations, you can reduce measurement error with how you take the reading. The biggest modifiable factor is wrist position. Smartwatch blood pressure sensors are sensitive to the hydrostatic pressure created when your wrist is above or below your heart. Holding your wrist at heart level during measurement reduces this artifact significantly.9American Journal of Hypertension. Stress-Induced Blood Pressure Elevation Self-Measured by a Wearable Watch-Type Device Letting your arm dangle at your side during a reading can add several mmHg of error just from gravity acting on the blood column.
Other practical steps that reduce noise in the readings:
- Sit still: motion artifacts are the most common cause of failed or wildly inaccurate readings. Sit with your back supported, feet flat, and don’t talk during the reading.
- Tighten the band: a loose watch band reduces the quality of the optical signal. The sensor needs firm contact with the skin without cutting off circulation.
- Recalibrate regularly: if your watch allows it, recalibrate against a validated upper-arm cuff at least monthly, and more often if your blood pressure tends to swing widely.
- Take multiple readings: averaging two or three readings taken a minute apart gives a more stable estimate than any single snapshot.
These steps don’t fix the fundamental accuracy ceiling, but they remove the avoidable sources of error that make readings even less reliable.
Tracking Stress and Emotional Blood Pressure Spikes
One area where wrist-worn monitors show genuine clinical promise is capturing blood pressure responses to stress throughout the day. A study comparing a wearable watch-type device to a conventional ambulatory monitor found that both devices detected the blood pressure rise associated with negative emotions like anxiety and tension. Systolic blood pressure during stressed moments averaged about 141 mmHg on the wearable, compared with 140 mmHg on the conventional monitor, a statistically indistinguishable difference. Even after adjusting for age, sex, body position, physical activity, and time of day, the blood pressure bump during negative emotions was about 5 to 7 mmHg on both devices.9American Journal of Hypertension. Stress-Induced Blood Pressure Elevation Self-Measured by a Wearable Watch-Type Device
This matters because stress-related blood pressure spikes, sometimes called masked hypertension when they occur outside the doctor’s office, are invisible to traditional clinic measurements. A device that captures these spikes in real time could help identify people whose blood pressure looks fine at checkups but climbs dangerously during their workday. The watch may not nail the absolute number, but it can reliably detect that a spike is happening, which is the clinically useful signal.
Nighttime Monitoring and Dipping Patterns
Blood pressure normally drops during sleep, a pattern called dipping. People whose blood pressure doesn’t dip at night, called non-dippers, face a higher risk of heart disease and stroke. Identifying non-dippers currently requires wearing an inflatable cuff that automatically inflates throughout the night, which disrupts sleep and limits how often the test gets done.
A method-comparison study of 67 subjects found that a cuff-less optical sensor worn on the wrist or upper arm tracked 24-hour averages within about 2 mmHg of a conventional ambulatory monitor, and the agreement on whether someone was a dipper or non-dipper was 98.5% for both systolic and diastolic blood pressure.10Scientific Reports. Method-comparison study between a watch-like sensor and a cuff-based device for 24-h ambulatory blood pressure monitoring A separate study using a different smartwatch achieved about 83% accuracy in classifying dipping patterns based on 24-hour PPG monitoring.11Circulation Research. Abstract Wed168: Smartwatch-based 24-hour photoplethysmography monitoring to distinguish between dipping and non-dipping blood pressure patterns
This is arguably the most exciting near-term application of smartwatch blood pressure technology. Even if the absolute blood pressure number from a given reading isn’t perfect, the pattern across a full day and night, captured without the misery of an inflating cuff, gives doctors information that simply wasn’t available before at scale. For large-scale screening of dipping patterns, the technology is surprisingly close to useful.
Machine Learning Is Pushing the Boundaries
Much of the improvement in cuffless blood pressure accuracy is coming from better algorithms rather than better sensors. Researchers are extracting dozens of features from the PPG waveform, things like the shape of the rising edge, the position of the dicrotic notch, and the ratio between different peaks, and feeding them into machine learning models that learn the complex, nonlinear relationship between these features and actual blood pressure.
Recent work using Gaussian process regression with carefully selected PPG features has achieved root mean square errors of about 6.7 mmHg for systolic and 3.6 mmHg for diastolic blood pressure.12PubMed Central. Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques Other groups using ensemble tree-based algorithms have pushed systolic errors down to about 5.2 mmHg.8PubMed. Exploring the potential of five machine learning regression algorithms for noninvasive blood pressure estimation with photoplethysmography For context, the international standard for blood pressure monitors allows a mean error of up to 5 mmHg with a standard deviation of up to 8 mmHg, so these algorithms are approaching but haven’t consistently cleared the bar in independent validation on diverse populations.
A key research direction involves reducing the number of features the model needs. Smaller, more interpretable models are faster to run on watch hardware and less prone to overfitting, where the model memorizes the training data rather than learning the actual relationship.13PubMed Central. Exploring supervised machine learning models to estimate blood pressure using non-fiducial features of the photoplethysmogram (PPG) and its derivatives The trajectory is encouraging, but it’s worth noting that most of these results come from controlled laboratory datasets. Real-world performance, with motion, sweat, varying skin temperatures, and inconsistent band tightness, tends to be worse.
Beyond the Wrist
The wrist isn’t necessarily the best place to measure blood pressure. Finger-based and ring-form sensors are emerging as alternatives. A bioimpedance ring device demonstrated systolic errors of about 0.1 ± 5.3 mmHg and diastolic errors of about 0.1 ± 3.9 mmHg across more than 2,000 readings covering a wide blood pressure range from 89 to 213 mmHg systolic.14Scientific Reports. Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device The ring form factor offers tighter skin contact and less motion artifact than a wrist-worn device, and bioimpedance measures the electrical properties of tissue rather than relying solely on light, which could sidestep some of the skin-color concerns associated with optical sensors.
Upper-arm optical sensors are another approach. As noted in the dipping study, placing the sensor on the upper arm rather than the wrist puts it closer to the brachial artery, which is where traditional cuffs take their readings. This sacrifices the convenience of a watch form factor but gains a more reliable signal. The trend in the field is toward multi-site sensing, where a wrist sensor, a ring, and perhaps a chest patch share data to improve the overall estimate. None of these are mainstream consumer products yet, but they represent the likely next generation of cuffless monitoring.
When Smartwatch Blood Pressure Readings Are Misleading
Certain groups and conditions make smartwatch blood pressure readings less trustworthy than usual. If you have atrial fibrillation or another irregular heart rhythm, the timing-based algorithms that underpin cuffless blood pressure measurement lose one of their key inputs. An irregular pulse makes pulse arrival time unpredictable, and the PPG waveform shape changes beat to beat in ways that confuse the algorithm.
People with very high blood pressure are particularly poorly served by the current technology because of the proportional bias described earlier. When systolic blood pressure is above 140, the watch tends to report it as lower than it actually is.3Frontiers in Cardiovascular Medicine. Smartwatch-Based Blood Pressure Measurement Demonstrates Insufficient Accuracy This is exactly the population that most needs accurate monitoring. Similarly, the calibration drift problem means that people whose blood pressure changes a lot from day to day, such as those with poorly controlled hypertension or kidney disease, will see larger errors as their pressure moves away from the calibration point.4PubMed Central. Long-term accuracy and stability of blood pressure measurements from a smartwatch: Prospective validation study
Cold fingers, vasoconstriction from caffeine or nicotine, and peripheral artery disease all reduce the quality of the PPG signal by narrowing or stiffening the small arteries where the watch takes its reading. If you notice that your watch frequently fails to produce a reading or gives implausible numbers, it’s likely struggling with signal quality rather than encountering genuinely wild blood pressure swings.
Regulatory Status and What “Cleared” Actually Means
Samsung’s Galaxy Watch blood pressure feature has received regulatory clearance in South Korea and some other markets. The FDA has not cleared any smartwatch for blood pressure measurement in the United States as of mid-2025, though several companies have applied. Regulatory clearance in any country does not mean the device replaces a clinical blood pressure monitor. It typically means the device has met a minimum standard for accuracy in a controlled validation study, which as we’ve seen can look very different from real-world performance.
The international validation standard allows a mean error of up to 5 mmHg with a standard deviation up to 8 mmHg. A device can meet this standard on aggregate while still being dangerously inaccurate at the clinical extremes. Passing validation means the average error across a study population was acceptable, not that any individual reading will be within 5 mmHg. This distinction matters: a device could reliably read 120/80 when your actual pressure is 125/82, pass validation, and still read 145 when your actual pressure is 165, potentially masking a hypertensive crisis.
If you’re using a smartwatch for blood pressure and your readings consistently seem normal but you have risk factors for hypertension, like a family history, obesity, or high sodium intake, get checked with a proper cuff. The watch’s tendency to compress readings toward the middle means it’s more likely to give false reassurance than false alarm at the high end.