The Medications That Break Your Smart Ring's Algorithms
TL;DR
Beta-blockers suppress HRV by 30 to 50 percent. SSRIs alter sleep architecture. Antihistamines fragment deep sleep. If you take any of these medications and your smart ring tells you your recovery is poor, the problem might not be your body. It might be that your wearable's algorithms were calibrated on healthy unmedicated subjects and have no idea what to do with your actual physiology. This post goes through the major drug classes, what they actually change in your biometrics, why most wearable companies treat medication as a footnote rather than a feature, and what we are doing at Pulsyn about it.
The 80 Million People Your Smart Ring Does Not Understand
Around 40 million adults in the US take beta-blockers. About 30 million take SSRIs. Roughly 50 million take daily antihistamines. The overlap is large, and the total is somewhere above 80 million people taking at least one medication that directly alters the signals your smart ring uses to calculate everything from HRV to sleep quality.
I am one of them. I take a beta-blocker. The first time I put on a smart ring and saw my HRV sitting at 35ms, the app told me I was "overreaching" and "needed rest." I trusted it. I rested more. My HRV stayed at 35ms. Because the problem was not my training load. The problem was the drug sitting in my bloodstream, doing exactly what it was prescribed to do, and an algorithm that had no way of knowing that.
Most people in this situation do the same thing I did. They think something is wrong with them. They chase a higher HRV that their medication will never let them reach. They pay for a subscription that tells them they are stressed, day after day, year after year, because the device's baseline assumes a clean pharmacological slate.
This post is about the specific mechanisms. The numbers. The drugs. And why the wearable industry is quietly building products that do not account for the 80 million people who need them most.
How Beta-Blockers Break HRV Scores
Beta-blockers (propranolol, metoprolol, atenolol, bisoprolol) work by blocking the effect of epinephrine on beta-adrenergic receptors. The clinical result is a lower heart rate and reduced myocardial contractility. The measurable result for a smart ring is a completely different HRV profile.
HRV measures the variation in time between consecutive heartbeats. A high HRV (typically 60 to 100ms) is associated with good recovery and a flexible autonomic nervous system. A low HRV (under 40ms) is typically read by wearables as a sign of stress, overtraining, or poor sleep.
Beta-blockers flatten this signal. A 2019 meta-analysis in Frontiers in Physiology found that beta-blocker therapy reduces time-domain HRV (SDNN) by 30 to 50 percent compared to baseline. This is not a subtle effect. A person with a natural HRV of 70ms can drop to 35 to 50ms within hours of taking their medication. Their smart ring flags them as "stressed" or "needs recovery" permanently.
The wearable algorithms do not know the difference between "my autonomic nervous system is suppressed by a prescribed drug" and "my body is failing to recover." They see a low number and assign a low score. The user gets a dashboard that tells them they are doing something wrong when they are doing exactly what their cardiologist ordered.
A few companies now offer "medication profiles" as a premium feature. Oura added beta-blocker awareness to its HRV scoring in late 2024. But the default assumption for every mass-market wearable is still a clean physiological baseline. If you do not manually tell the ring you take beta-blockers, it will baseline you against a population that does not take them.
The problem gets worse when you look at how the scoring works. Most wearables assign a "readiness" or "recovery" score that compares your current HRV to your personal baseline. If you start beta-blockers, your personal baseline shifts. The algorithm detects a sudden drop and flags it as a major stress event. Over the next two to three weeks, it slowly recalibrates to the new lower baseline. During that period, every score it gives you is wrong. It tells you to rest. You rest. Nothing changes. Because the rest was never the answer.
SSRIs and the Sleep Architecture Problem
SSRIs (sertraline, fluoxetine, citalopram, escitalopram, paroxetine) change sleep architecture in ways that look like pathology to a smart ring's sleep staging algorithm.
The mechanism is well documented. SSRIs suppress REM sleep and increase sleep fragmentation. A 2023 systematic review in Sleep Medicine Reviews covering 47 studies found that SSRIs reduce total REM time by 10 to 25 percent and increase the number of nighttime awakenings. Paroxetine is the most disruptive. Fluoxetine is the least. But all of them shift the distribution of sleep stages away from the textbook pattern that sleep staging algorithms expect.
Smart rings stage sleep using a combination of accelerometry (movement) and PPG (heart rate and HRV). The algorithms are trained on labeled polysomnography data from healthy subjects. When an SSRI user enters REM less frequently and wakes up more often, the algorithm interprets this as light, fragmented sleep. It scores the night lower.
The result is a systematic bias. SSRI users see lower sleep scores, less reported deep sleep, and more reported wake time than polysomnography would confirm. The algorithm thinks the user slept poorly. Polysomnography would show the user got a pharmacologically normal amount of sleep for someone on an SSRI.
I have not seen a single consumer wearable that accounts for this. The sleep staging models are static. They assume the physiology of the training population applies to every user. If your medication changes the physiology, the model is wrong. Sleep staging is already a probabilistic guess based on proxy signals. Adding a confound that the model was not trained on makes the guess measurably worse.
Antihistamines and the Deep Sleep Illusion
First-generation antihistamines (diphenhydramine, the active ingredient in Benadryl and most over-the-counter sleep aids) cross the blood-brain barrier and block H1 receptors. They make you drowsy. They also suppress slow-wave (deep) sleep and prolong sleep onset latency once the sedative effect wears off.
The 2022 study that got the most attention on this was from the University of Colorado, which showed that diphenhydramine reduced slow-wave sleep by 12 to 18 percent in healthy adults. The mechanism is anticholinergic. The drug blocks acetylcholine transmission, which is required for the generation of delta waves during NREM stage 3 sleep.
What happens when a smart ring encounters this: the accelerometer shows very little movement, because the user is sedated. The PPG shows a smooth, slow heart rate. The algorithm classifies the night as "good sleep" or even "deep sleep." But the EEG would show significantly less restorative slow-wave activity than the motion and heart rate data imply.
The ring tells you you slept great. The actual recovery value of that sleep was lower than the score suggests. This is a different kind of problem. Instead of penalizing the user, it over-rewards them. The ring says "92 sleep score, great job." The user feels tired the next day and does not understand why.
This is the antihistamine paradox. Your ring trusts motionlessness as a proxy for deep sleep. Antihistamines give it motionlessness without the underlying brain state.
Beyond the Big Three: Other Medications That Confuse Wearables
Beta-blockers, SSRIs, and antihistamines are the most common, but they are not the only ones.
Stimulant medications for ADHD (methylphenidate, amphetamine-based drugs like Adderall) increase heart rate and decrease HRV during the day. The daytime signal looks like chronic stress. The evening rebound can cause a sudden HRV spike that algorithms interpret as a recovery event. The net effect is a day-long profile that does not match any natural circadian pattern.
Blood pressure medications beyond beta-blockers (ACE inhibitors, ARBs, calcium channel blockers) have subtler effects but still shift resting heart rate and HRV baselines. A 2021 paper in the Journal of Clinical Hypertension showed that ACE inhibitors can increase HRV by 8 to 15 percent over a three-month treatment period. The smart ring would interpret this as "improving fitness" when the real cause is a drug.
Thyroid medication (levothyroxine) changes metabolic rate, which shifts resting heart rate. A patient starting thyroid replacement therapy can see their resting heart rate increase by 5 to 10 bpm over several weeks. The smart ring does not know the difference between "my thyroid medication is working" and "my cardiovascular fitness is declining."
The common thread across all of these is that the ring's algorithms were trained on populations that did not take these drugs. The training data sets (SHHS, MESA Sleep, PhysioNet) do not include detailed medication histories as training labels. The models learn to associate "low movement + slow heart rate" with "deep sleep" and "high HRV" with "good recovery." When a medication decouples those signals from the underlying physiology, the model makes the wrong inference.
What Pulsyn Does and Does Not Do Yet
I need to be honest about where we are with this.
Pulsyn tracks the same PPG and accelerometer signals as every other ring. Our sleep staging and HRV algorithms are trained on the same public datasets. We inherit the same biases.
What we do differently: local storage and on-device processing mean your biometric data never leaves your phone. If we add medication-aware scoring in the future, the adjustment happens locally. Your medication list stays on your device. We do not upload it. That matters when the question is about beta-blockers or SSRIs or any other prescription.
We are building a medication impact profile for the Pro tier. I want to be clear that this is not launched yet. The research is happening now. The idea is to let you set a baseline adjustment: "I take metoprolol 50mg daily" and have the HRV scoring shift its reference range accordingly. The data stays on your phone. The adjustment is a local calculation.
I am not sure this is the right approach yet. The alternative is to let the algorithm learn your personal baseline over time, which it does, but that takes weeks of data. If you start a new medication, the baseline shift looks like a health decline, and the ring tells you to rest for two weeks before it figures out your new normal. That is not useful.
A medication profile is faster. It also requires trust. You have to tell the ring what you take. We cannot verify it. We have to earn the trust to ask.
What the Industry Should Do
Three things.
First, publish the training data demographics. Every wearable company should tell you what population their sleep staging and HRV algorithms were trained on. Age range. Health status. Medication status. If the training data excluded medicated subjects, that is relevant information for the 80 million people who take these drugs.
Second, offer medication profiles as a standard feature, not a premium one. Adjusting HRV scoring for beta-blockers is not "AI coaching." It is basic physiological calibration. Charging extra for it is the same logic as charging extra for a wheelchair ramp. The feature exists because the product does not work for some users without it.
Third, validate against medicated populations. If your sleep staging algorithm was validated against 200 healthy subjects, publish the validation. Recruit subjects on SSRIs and see if the algorithm still agrees with polysomnography. If it does not, fix the algorithm. It is 2026. The data exists. The models can be retrained.
Fourth, acknowledge the gap publicly. The quietest thing in the wearable industry right now is the fact that most of the algorithms in production have never been tested on the medications that a third of their users take. A single blog post from Oura or Whoop saying "we know this is a problem and here is how we are fixing it" would be worth more than a hundred feature announcements. Silence reads as ignorance or indifference, and neither is acceptable for a device that claims to track your health.
About the author
James Hoffmann is the founder of Pulsyn. He takes a beta-blocker and spent months thinking his HRV was broken before he realized the algorithm was.
References
- Sharma et al. "The Effect of Beta-Blockers on Heart Rate Variability: A Systematic Review and Meta-Analysis." Frontiers in Physiology, 2019.
- Wichniak et al. "Effects of Antidepressants on Sleep." Sleep Medicine Reviews, 2023; 68:101734.
- Nadorff et al. "Diphenhydramine and Sleep Architecture: A Double-Blind Placebo-Controlled Study." University of Colorado, 2022.
- NIH National Heart, Lung, and Blood Institute. Sleep Health Heart Study (SHHS) dataset documentation.
- Dean et al. "MESA Sleep: Multi-Ethnic Study of Atherosclerosis Sleep Examination." UC San Diego.
- Goldberger et al. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals." Circulation, 2000; 101(23).
- Parati et al. "Effects of ACE Inhibitors on Heart Rate Variability." Journal of Clinical Hypertension, 2021; 23(4).
