Hilo, Oura, and Cuffless Blood Pressure: Promise, Physiology, and Pitfalls

The PPG, Blood Pressure Paradox: Why Hilo and Oura May See the Pulse but Not the Pressure:

With the recent wave of wearable devices claiming some form of blood-pressure tracking, from Oura’s nighttime BP dipping to Whoop, Galaxy Watch, and now even the Pixel Watch 4/5, it seems like a good time for a reality check: what are these devices actually measuring, what do their BP-related numbers really mean, and how accurate are they?. To that end, first we will discuss the theoretical background of optical BP and then mention a more "dedicated" unit, the Hilo Core wristband vs the Oura ring, which I’ve used for the past few weeks.

What does a PPG blood-pressure device actually measure?

Photoplethysmography (PPG) has become one of the most widely used sensing technologies in consumer wearables. By lighting the skin with an LED and measuring changes in reflected or transmitted light, PPG detects the pulsatile changes in blood volume that accompany each heartbeat. This signal is well suited to estimating heart rate and, under favorable conditions, beat-to-beat timing for heart-rate variability. More recently, however, PPG has been extended to a much more ambitious target: blood pressure estimation without an inflatable cuff. This development has considerable appeal. Conventional ambulatory blood-pressure monitoring requires repeated cuff inflation, which can be uncomfortable, disruptive during sleep, and limited to intermittent measurements. I can attest to this personally. Several months ago I took part in a Pixel watch ambulatory BP study and wore a cuff for 24 hours. Every time it inflated, it woke me from sleep and I ended up just removing it before it drove me nuts. Therefore, a small wristband, watch, or ring capable of estimating blood pressure passively could potentially provide many measurements across days or weeks with little burden on the wearer. Devices such as Aktiia/Hilo and newer PPG-based features from Oura illustrate how rapidly this field is moving.

The important physiological distinction is that PPG does not measure pressure directly. A conventional cuff estimates arterial pressure by mechanically compressing an artery and observing the pressure at which arterial flow or oscillations change. A PPG sensor instead records an optical waveform generated by pulsatile changes in peripheral blood volume. Blood pressure influences that waveform, but it is only one of many factors that do so.

The shape of a PPG pulse contains substantial cardiovascular information. Features such as the systolic upstroke, systolic peak, dicrotic notch, reflected or diastolic wave, pulse width, and timing between waveform landmarks can change as arterial pressure changes. These relationships provide the physiological foundation for cuffless BP algorithms. Modern systems may combine many such waveform characteristics, along with heart rate and other sensor information, using statistical or machine-learning models to infer blood pressure or blood-pressure-related patterns.

The difficulty is that these waveform characteristics are not uniquely determined by blood pressure. Peripheral vascular tone, arterial stiffness, pulse-wave velocity, wave reflection, stroke volume, heart rate, skin temperature, local perfusion, body position, autonomic activity, and even the pressure of the sensor against the skin can modify the PPG waveform. Thus, a change in pulse shape does not necessarily mean that blood pressure changed by a corresponding amount.

Conceptually, the PPG signal might be represented as:

PPG waveform = a function of: (blood pressure, vascular tone, arterial stiffness, wave reflection, stroke volume, heart rate, temperature, perfusion, sensor conditions, …)

A cuffless BP algorithm attempts to solve the inverse problem:

blood pressure ≈ summated function⁻¹(PPG waveform)

That inverse relationship is considerably more difficult. If several physiological variables can produce similar changes in the optical pulse, there may be no unique blood-pressure value associated with a particular waveform. The problem becomes especially important when the physiological state changes substantially, for example during sleep, exercise, medication treatment, heat exposure, or sympathetic activation. This distinction helps explain an apparent paradox in the cuffless-BP literature. A PPG device can demonstrate superb agreement with a conventional cuff during controlled resting measurements yet perform much less well when asked to follow blood pressure changes over time. Calibration can place the estimated pressure close to the true value at baseline, but good baseline agreement does not necessarily demonstrate that the algorithm has the correct "dynamic response" or "gain" when blood pressure subsequently rises or falls.

Nocturnal blood-pressure dipping provides a particularly useful example. During sleep, blood pressure normally declines, but so do sympathetic activity, peripheral vascular resistance, heart rate, skin temperature, and other determinants of the peripheral pulse waveform. A PPG algorithm therefore must distinguish waveform changes caused specifically by falling arterial pressure from waveform changes caused by the broader transition from wakefulness to sleep.

This issue is not confined to devices that report systolic and diastolic pressure in mmHg. It also applies to algorithms that use PPG to classify people as normal dippers or non-dippers. Such systems may avoid the difficult task of reconstructing absolute blood pressure, but they still depend on a relationship between nocturnal pulse morphology and the underlying blood-pressure response.

The central question for PPG-based blood-pressure technology is therefore not whether the optical pulse contains information related to blood pressure, it clearly does. The more difficult question is whether an algorithm can reliably separate the effect of blood pressure from the many other physiological processes that alter the PPG waveform, particularly when the cardiovascular state changes over time.

Hardware/Software Considerations:

Sampling requirements differ substantially between ECG and PPG because the important information in the two signals is different. In an ECG, heart-rate variability depends primarily on accurately locating the R peak, a sharp and readily identifiable electrical fiducial point. At a sampling rate of 250 Hz, samples are separated by 4 ms; at 500 Hz by 2 ms; and at 1000 Hz by 1 ms. Studies using ECG signals down-sampled from 1000 Hz have generally found excellent agreement in HRV measures at 250–500 Hz, while lower sampling rates progressively increase timing quantization error. Interpolation around the R peak can improve timing considerably and permit useful HRV analysis even at lower sampling rates. 

PPG presents a different problem. Beat timing can be obtained from a single reproducible point on the pulse, such as the foot, maximum upslope, or systolic peak and reasonable pulse-rate variability can therefore be obtained at surprisingly low sampling rates. One study found that, with an interpolated upslope fiducial point, PPG could be reduced to 50 Hz without important changes in conventional variability indices. This does not, however, mean that 50-Hz PPG adequately reproduces pulse morphology.

For blood-pressure estimation, the algorithm may depend on the shape of the entire pulse: the systolic upstroke, systolic peak, dicrotic notch, reflected wave, and the timing and amplitude relationships among these features. At 50 Hz there is only one sample every 20 ms, and at 100 Hz one every 10 ms. Thus, a relatively small feature such as the dicrotic notch may be represented by only a few samples, and its apparent position can shift substantially depending on where it falls relative to the sampling grid. Interpolation can improve estimates of fiducial timing, but it cannot restore morphological information that was never acquired, particularly in the presence of noise or waveform distortion. Work specifically examining PPG fiducial localization confirms that sampling rate, filtering, interpolation, and signal quality all influence the localization of waveform landmarks used for BP estimation.

Consequently, 100 Hz is quite reasonable for gross PPG morphology and is the documented sampling rate of the validated Hilo system, but higher rates such as 200–250 Hz provide substantially denser representation of the pulse when precise timing of the dicrotic notch, reflected wave, or other morphological features is important. This is different from ECG R-wave detection, where a single sharp fiducial point can often be localized very accurately even from a moderately sampled signal. A high sampling rate therefore does not make PPG-derived blood pressure intrinsically accurate, but inadequate sampling can add yet another source of uncertainty to an already indirect physiological measurement. That said, Oura's historically reported 250-Hz nocturnal PPG could provide a prettier and more precisely sampled pulse waveform than Hilo’s 100-Hz signal, yet it still cannot solve the fundamental BP problem. Sampling improves measurement of the PPG waveform; it does not make PPG morphology uniquely determined by arterial pressure.

Calibration: Why Good Agreement Does Not Necessarily Mean Good BP Tracking

Most PPG-based blood-pressure systems face an immediate problem: the optical waveform has no intrinsic scale in mmHg. A PPG sensor measures changes in reflected light associated with pulsatile blood volume, producing a waveform in arbitrary optical units. Some method is therefore needed to connect the optical signal to an actual arterial pressure. The simplest solution is calibration against a conventional cuff. This distinction is particularly clear in the published Hilo/Aktiia validation studies. The bracelet first analyzed the wrist PPG waveform to produce uncalibrated systolic and diastolic BP estimates. During initialization, the first paired cuff measurements were then used to calculate a subject-specific SBP offset and DBP offset, converting the optical estimates into mmHg (PMID: 33675592). Conceptually, this can be represented as:

Reported BP = PPG-derived BP estimate + individual calibration offset

Suppose the optical algorithm estimates an individual's resting systolic pressure as 112 arbitrary BP units, while the calibration cuff measures 124 mmHg. An offset of +12 mmHg can make the bracelet report:

112 + 12 = 124 mmHg

The device now agrees perfectly with the cuff at calibration. But that agreement tells us surprisingly little about what will happen when the person's BP changes. Imagine that several hours later the true systolic pressure falls by 20 mmHg:

Cuff: 124 → 104 mmHg

If the PPG morphology changes by an amount that the optical algorithm interprets as only a 6-mmHg fall, the device will calculate:

112 → 106 optical units

and, after applying the same +12-mmHg calibration:

124 → 118 mmHg

So…. The initial value is exactly correct, but the subsequent change is badly underestimated. The problem is not the calibration offset. The problem is that the dynamic sensitivity of the PPG model to changing BP is too small. An offset calibration primarily solves the problem: Where should the BP estimate sit on the mmHg scale. It does not necessarily solve the problem: How far should the BP estimate move when actual pressure changes?

If the correct gain is 1.0 but the PPG algorithm effectively behaves as though it were 0.3, a true 20-mmHg change may appear as only about a 6-mmHg change. No adjustment of the baseline can correct that. This is especially relevant because Aktiia's later 24-hour study describes these two functions explicitly as separate. The investigators state that initialization generates an offset, establishing the absolute reference around which subsequent measurements vary, whereas the device's ability to track BP trends is determined independently by the optical algorithm. Reinitialization changes the baseline but does not change the trend-tracking mechanism (PMID: 39927495).

Population accuracy is not the same as within-person tracking

This also illustrates an important limitation of conventional device-validation statistics. Suppose 100 people undergo seated testing. Each device is individually calibrated near the subject's resting BP. Across the population there may be people with pressures ranging from 100 to 180 mmHg. If calibration places each person's optical estimate near his or her cuff pressure, the device can show:

• ·       very small mean bias,
• good correlation across subjects,
• acceptable average error,
• an impressive Bland–Altman plot.

The original Aktiia validation study, for example, reported average differences of approximately 0.46 mmHg for SBP and 0.39 mmHg for DBP during standardized seated measurements after subject-specific calibration (PMID: 33675592). Those are encouraging results, but they primarily demonstrate that the system can provide reasonable calibrated absolute BP estimates under the tested conditions. They do not automatically demonstrate that, within a particular individual:

ΔBP from PPG ≈ ΔBP measured by the reference method.

This is a fundamentally different validation question. A device could therefore show excellent cross-sectional agreement because subjects with high BP generally receive high estimates and subjects with low BP receive low estimates, while still substantially underestimating the magnitude of BP changes occurring within each person.

Why estimating an individual slope is much harder

In principle, calibration could determine both the intercept and the slope of the true BP vs PPG waveform value. But this requires reference measurements obtained across a meaningful range of BP. If cuff calibration measurements are: 121, 123, 120, 124 mmHg they provide excellent information about the person's resting baseline but very little information about how the PPG should behave at: 100 or 150 mmHg.

To estimate an individual PPG-to-BP slope reliably, paired PPG and reference measurements would ideally span substantially different pressure states.  That might require controlled perturbations such as:

• ·       changes in posture,
• exercise or recovery,
• pharmacological BP changes,
• cold or heat exposure,
• sustained handgrip,
• lower-body pressure manipulation,
• or naturally occurring day-night changes.

But this introduces the next problem: these interventions alter vascular physiology as well as BP. The relationship between PPG morphology and pressure may itself change between conditions. Consequently, even a personalized linear slope may not be sufficient. The true relationship may be state dependent: BP = function of (PPG, vascular tone, temperature, posture, arterial stiffness, autonomic state, …) rather than a single fixed calibration equation.

This concern is supported by the independent evaluation of Aktiia by Tan and colleagues, in which antihypertensive treatment produced an approximately 19.7/11.5 mmHg reduction by cuff-based home BP monitoring, while Aktiia detected only about 1.0/0.8 mmHg of change (PMID: 37016925). Although this medication-change subgroup was very small and requires replication, the result is important because it illustrates precisely the distinction between obtaining a reasonable calibrated BP value and accurately tracking a substantial within-person BP change.

The same issue appears in nocturnal measurements. In a later 24-hour comparison, conventional ambulatory monitoring showed a substantially larger nighttime systolic decline than Aktiia, despite relatively close agreement in daytime mean pressure (PMID: 39927495). An independent ambulatory comparison similarly found marked underestimation of normal nocturnal BP dipping (PMID: 37016925). These findings raise the possibility that the optical system can track the *direction* of some BP changes while compressing their magnitude.

Thus, the crucial question for a calibrated PPG blood-pressure system is not simply: “Does its BP value agree with the cuff after calibration?” It is: does the optical signal correctly track both the direction and the magnitude of subsequent changes in blood pressure? The two are not equivalent—and distinguishing them is essential when interpreting validation studies of cuffless BP technology.

Oura Nighttime Blood Pressure: A Different Approach, but the Same PPG Limitation

Oura has taken a somewhat different approach to PPG-based blood-pressure assessment than Hilo/Aktiia. Rather than attempting to provide systolic and diastolic pressure in mmHg, Oura's Nighttime BP feature analyzes nocturnal PPG patterns and estimates the degree to which blood pressure appears to fall during sleep. The distinction is significant. Oura is not claiming that the ring directly measured, for example: Daytime SBP = 126 mmHg → nighttime SBP = 108 mmHg. Instead, it classifies the nighttime pattern into categories such as: 

• ·       typical dipping: 10–20%
• reduced dipping: <10%
• pronounced dipping: >20%
• rising: no fall or an increase overnight.

The displayed pattern is based on approximately 30 nights of data, rather than a single night's recording. Oura explicitly states that nighttime BP is a wellness feature, does not display BP in mmHg, and should not replace conventional blood-pressure monitoring. This represents a more modest target than reconstruction of absolute BP. However, it does not eliminate the fundamental physiological problem inherent in PPG-derived blood pressure: Oura is still inferring BP from a waveform that is not determined solely by BP!

As noted, the optical pulse recorded at the finger is affected by blood pressure, but also by many other variables (vascular tone, arterial stiffness, wave reflection, stroke volume, heart rate, temperature, peripheral perfusion, autonomic activity, sensor conditions, …) All of these factors can change during the transition from wakefulness to sleep. Therefore, if Oura observes a nocturnal change in pulse-wave morphology, that change cannot automatically be attributed to a corresponding change in arterial pressure. This is the same fundamental inverse problem encountered by other PPG-based BP systems. The American Heart Association's recent scientific statement on cuffless BP measurement emphasizes that PPG-derived pressure estimates depend on physiological relationships that may be altered by changes in vascular properties and measurement conditions and concludes that currently available cuffless devices have not yet been adequately validated across all typical use conditions (Cohen et al., 2026; PMID: 41376592).

What Oura's validation actually shows

 Oura reports that its science team compared Oura Ring 4 PPG data with 48-hour ambulatory BP monitoring in 134 participants. For discrimination between dippers and non-dippers, Oura reports:

• ·         Sensitivity: 84%
•  Specificity: 69%
•  Area under the ROC curve: 0.87

These results are encouraging for a noninvasive screening signal. However, as of August 2026, this 134-participant Oura validation has not been identified as a PubMed-indexed peer-reviewed publication, and therefore no PMID is currently available. The performance figures are reported by Oura. The results also need to be interpreted according to what was actually tested. An AUC of 0.87 means that the PPG-derived features contain substantial information related to ABPM-defined dipping status. It does not mean that Oura measured nighttime blood pressure with 87% accuracy. Likewise, the reported 69% specificity means that, at the chosen classification threshold, approximately 31% of ABPM-defined non-dippers would not be correctly identified as non-dippers. That is meaningful uncertainty if the feature is interpreted at the level of an individual. More importantly, important methodological details remain unavailable or insufficiently described publicly, including:

• ·         the exact PPG features entering the dipping model,
•  the sampling rate used specifically for the Nighttime BP algorithm,
•  signal filtering and pulse-rejection procedures,
•  the proportion of nighttime data rejected for poor signal quality,
• Whether sleep stages enter the model,
•  the continuous relationship between estimated and ABPM-measured percentage dipping,
•  Bland–Altman limits for percentage dipping,
•  and performance in clinically important subgroups.

Until those data are published, the reported sensitivity, specificity, and AUC are best regarded as promising preliminary validation, rather than definitive evidence of quantitative BP-dipping accuracy.

Is the finger a better site for BP:

There are reasons why Oura could potentially perform better than wrist PPG. The finger generally provides a stronger peripheral pulse signal, and Oura has demonstrated very good performance for nocturnal heart rate and interbeat-interval measurement. But obtaining a high-quality waveform and interpreting its physiological meaning are two different problems. Higher sampling frequency, lower motion artifact and a clearer dicrotic notch can provide: a more accurate measurement of the PPG waveform without necessarily providing a more accurate measurement of blood pressure.

The Aktiia studies are particularly relevant because they demonstrate experimentally that PPG morphology can contain enough information to produce plausible BP estimates while still substantially underrepresenting nocturnal BP changes. That physiological limitation does not disappear when the PPG sensor is moved from the wrist to the finger.

Thirty night averaging: potentially an advantage, but also a different measurement

Oura's decision to average nighttime patterns over approximately 30 nights is potentially valuable. Dipping status itself is not perfectly reproducible from night to night. Burgos-Alonso and colleagues examined 225 high-cardiovascular-risk patients who underwent four 24-hour ABPM recordings over five months. Only about two-thirds of participants retained their initial systolic dipper/non-dipper classification across subsequent recordings, leading the authors to characterize individual dipping reproducibility as modest (Burgos-Alonso et al., 2021; PMID: 33591600). A second large study by McGowan and colleagues examined 512 untreated patients with two ABPM studies approximately 29 months apart. Binary dipper/non-dipper classification remained unchanged in 76% of participants, but agreement was relatively weak. In contrast, nocturnal dipping expressed as a continuous percentage was considerably more reproducible, supporting the concept that dipping may be better regarded as a continuous physiological variable rather than a rigid categorical state (McGowan et al., 2009; PMID: 19641455). Repeated passive PPG measurements, therefore, offer a genuine potential advantage: they can characterize the person's typical nocturnal physiology over many nights without repeatedly inflating a cuff and disturbing sleep. However, this also raises an important validation issue.

Oura's reported reference study used 48-hour ABPM, whereas the consumer feature characterizes patterns over approximately 30 nights. Those are not necessarily the same phenotype. A 30-night PPG average could conceivably be a more stable measure of nocturnal vascular physiology than a one- or two-night ABPM classification—but demonstrating that would require prospective outcome studies, not simply agreement with a short ABPM recording.

Oura's approach is conceptually more conservative than attempting to reconstruct systolic and diastolic BP in mmHg. A PPG-based classifier may indeed identify useful nocturnal cardiovascular patterns even when PPG cannot accurately reproduce absolute blood pressure. But the underlying physiological limitation remains: The nocturnal PPG waveform is affected by blood pressure, but it is also strongly affected by the same autonomic and vascular changes that accompany sleep. Oura may therefore be measuring a valuable surrogate of nocturnal BP physiology, rather than nocturnal BP itself. That distinction should remain central when interpreting the new Nighttime BP feature.

Some screen shots from my Hilo Core and Oura 4 ring from the same night:

Oura 4 

compared to the Hilo:

Both show some nocturnal dips, but as noted above, YMMV. I do like the form factor of the Hilo and hope to see improvements in calibration over time.

 

Conclusion:

• PPG-based blood-pressure technology is attractive because it promises something conventional cuff measurement cannot easily provide: frequent, unobtrusive cardiovascular monitoring across daily life and sleep. The optical pulse clearly contains information related to arterial pressure, and increasingly sophisticated algorithms can extract timing, amplitude, and morphological features that correlate with BP and vascular state. 
• The central limitation, however, is physiological rather than merely technical. A PPG sensor does not measure arterial pressure directly. It measures changes in peripheral blood volume, and the resulting waveform reflects the combined influence of blood pressure, vascular tone, arterial stiffness, pulse-wave reflection, stroke volume, heart rate, temperature, perfusion, autonomic activity, body position, and sensor conditions. Blood pressure is therefore one contributor to the waveform, not its sole determinant. That distinction has practical consequences. 
• A device can agree closely with a cuff after calibration without necessarily tracking subsequent BP changes accurately. Calibration may establish the correct baseline or offset while leaving errors in the dynamic gain of the PPG-to-BP relationship. The Aktiia/Hilo literature illustrates this particularly well. Controlled seated measurements can show minimal average bias, while independent and ambulatory studies have demonstrated substantial attenuation of nocturnal BP dipping and, in limited data, large medication-induced changes in BP (PMID: 33675592; PMID: 37016925; PMID: 39927495). This should change how cuffless BP validation is interpreted. Low mean bias around the calibration condition is important, but it does not answer the more physiologically relevant question: Does the device correctly reproduce the magnitude of within-person BP changes across different physiological states? For many applications, including nocturnal dipping, medication response, BP variability, exercise recovery, and stress responses—that may be the more important test.
• Nighttime monitoring is especially challenging because sleep alters many of the same physiological variables that determine PPG morphology. Sympathetic activity, vascular resistance, arterial wave reflection, skin temperature, peripheral perfusion, heart rate, and stroke volume all change during sleep. A nocturnal change in the optical pulse therefore cannot automatically be interpreted as a proportional change in arterial pressure. This limitation also applies to devices such as Oura that take a more conservative approach and infer BP-dipping patterns rather than reporting absolute SBP and DBP. Classification is an easier problem than reconstruction of pressure in mmHg, and a PPG-derived dipping phenotype may ultimately prove useful. But successful classification does not establish that the ring directly measured the magnitude of the BP decline. The algorithm may instead be detecting autonomic and vascular changes that are associated with normal dipping.
• Signal quality and sampling rate matter, but they do not solve the underlying physiology. A higher sampling rate can improve identification of the systolic peak, dicrotic notch, reflected wave, and other pulse landmarks. Better sensor placement can reduce noise and motion artifact. More sophisticated machine learning can identify complex patterns that simple regression cannot. Yet none of these improvements make PPG morphology uniquely determined by blood pressure. Likewise, aggressive signal-quality filtering can create another potential source of bias. Wearables commonly reject periods containing motion, poor perfusion, atypical pulse morphology, or inadequate sensor contact. This improves technical signal quality but may also preferentially exclude unusual physiological states. A device may therefore provide very clean estimates during selected low-motion periods while incompletely representing the full variability of blood pressure across the day or night.
• For future validation studies, the most informative analysis may therefore be less about absolute agreement after calibration and more about tracking change. Studies should deliberately expose participants to a broad range of pressures and physiological states. None of this means that PPG-based BP technology lacks value. On the contrary, PPG may ultimately prove extremely useful for long-term cardiovascular phenotyping, detecting trends, identifying individuals who warrant formal BP evaluation, and recognizing physiological patterns that conventional intermittent measurements miss. A multi-night optical phenotype may even prove more reproducible or prognostically informative than a single night of cuff-based monitoring.
• “Can PPG predict blood pressure?” It clearly can to some degree. The more important question is: “Can a particular PPG algorithm distinguish a true change in arterial pressure from the many other physiological changes that alter the peripheral pulse waveform—and can it do so accurately enough for the intended use?” That is the standard by which PPG-based blood-pressure technology should ultimately be judged.