What GLP-1 Drugs Do to Your Smart Ring's Readings
TL;DR
GLP-1 receptor agonists like semaglutide and tirzepatide increase your resting heart rate by 2 to 5 bpm through a direct mechanism at the sinoatrial node. They suppress HRV through the same pathway. Your wearable algorithm reads high RHR plus low HRV and turns that into a bad recovery score, a bad readiness score, and a bad stress score. The algorithm is not broken. The drug is doing something the algorithm was never trained to expect. About 15 million people in the US alone are affected by this right now.
I started noticing it in the forums first. People on r/Ozempic and r/Semaglutide posting screenshots of their Oura readiness scores. "My readiness has been tanking since I started Wegovy," one person wrote. "My Whoop strain says I'm overtraining but I haven't exercised in three days." Another comment: "HRV dropped 30 percent after my first injection and never came back up."
The replies were what you would expect. Drink more water. Check your sleep. Maybe you need a rest day. The standard wearable-community advice for a standard wearable software problem.
Nobody told them the problem was the drug.
Not because the drug companies are hiding anything. The mechanism is published and well understood. But because wearable companies have exactly zero incentive to tell you that their algorithms cannot distinguish between biological stress caused by a medication and biological stress caused by insufficient sleep. The algorithm sees the signal. It does not know what generated it.
How GLP-1 drugs change your heart rate
GLP-1 receptor agonists like semaglutide (Ozempic, Wegovy) and tirzepatide (Mounjaro, Zepbound) activate GLP-1 receptors in the pancreas, the gut, and the brain. That is how they lower blood sugar and reduce appetite. But those same receptors also exist in the sinoatrial node, the heart's natural pacemaker. Activation there increases the firing rate of the node's pacemaker cells.
The effect is reproducible and dose-dependent. The STEP trials found that participants on semaglutide 2.4 mg had an average resting heart rate increase of 2 to 4 bpm compared to placebo at 68 weeks. The SUSTAIN trials showed similar numbers. A 2025 systematic review and network meta-analysis of randomized controlled trials covering over 15,000 patients found a consistent heart rate increase of 2.1 to 3.8 bpm across all GLP-1 drugs, with tirzepatide showing a slightly larger effect than semaglutide.
Two to four beats per minute does not sound dramatic. It is the difference between a resting heart rate of 65 and a resting heart rate of 68. That is a 5 percent increase. That is also enough to move the needle on every algorithm that uses RHR as a baseline.
Photo by Joshua Chehov on Unsplash
The mechanism matters here. This is not a secondary effect of weight loss or improved metabolic health. Weight loss typically lowers resting heart rate over time as cardiovascular efficiency improves. The GLP-1 heart rate increase happens in the opposite direction and appears within weeks of starting treatment, well before any meaningful weight change. It is a direct pharmacological effect on the heart's electrical activity.
What the algorithm sees
Every wearable platform uses resting heart rate and heart rate variability as foundational inputs. Oura uses them for readiness. Whoop uses them for recovery. Garmin uses them for body battery. Fitbit uses them for daily readiness score. Apple does not have a single readiness score but feeds HRV into training load and mental wellbeing features.
The internal logic in all these algorithms is roughly the same. Higher RHR plus lower HRV equals stress, illness, or insufficient recovery. This makes sense for the general population. When you are sick, your RHR goes up and your HRV drops. When you did not sleep enough, same pattern. When you overtrained, same pattern.
Here is the problem. GLP-1 users produce this exact signal pattern 24 hours a day, 7 days a week, starting within days of their first dose. The algorithm has no way to distinguish "my medication is increasing my sinus node firing rate" from "I am getting sick." Both look identical at the level of a PPG sensor sampling heart rate every few minutes.
I looked at the published literature on wearable algorithm design from Oura and Whoop. Neither company has disclosed any mechanism for medication-aware baseline adjustment. Their algorithms learn your personal baseline over 1 to 2 weeks and then score deviations from that baseline. The assumption baked into the code is that deviations are always meaningful signals about your health state. They do not account for the possibility that your baseline has been permanently shifted upward by a chemical.
The HRV problem
Heart rate variability drops on GLP-1s. The mechanism is the same one: increased sympathetic tone at the sinoatrial node reduces the natural beat-to-beat variation that HRV measures. A lower HRV means less parasympathetic activity in the algorithm's model. The algorithm interprets this as reduced recovery capacity.
The numbers in the literature vary. Some studies show a 10 to 15 percent reduction in time-domain HRV metrics (SDNN, RMSSD) within 4 to 8 weeks of treatment initiation. Others show more modest effects. The variability likely reflects differences in dosing, individual response, and whether the study controlled for the weight loss that eventually improves HRV in the opposite direction.
For a typical Oura user with a baseline HRV of 45 ms, a 15 percent reduction to 38 ms is the difference between a green readiness score and a yellow one. Over multiple days, it can push scores into the red range consistently. The user does more recovery protocols. They skip workouts. They go to bed earlier. Their readiness score stays low anyway because the drug is still in their system.
This is not harmless. People skip exercise because their wearable tells them they are not recovered. Exercise is one of the best things you can do for metabolic health. If your wearable discourages you from exercising because it misreads a drug effect as insufficient recovery, the algorithm is actively working against your treatment goals.
The temperature signal
Skin temperature is the third biometric that GLP-1s affect, and it is the one most people do not expect.
GLP-1 receptor activation alters thermoregulation through effects on the hypothalamus. Some users report feeling warmer, especially at night. Others report the opposite. The clinical data shows that GLP-1s can increase energy expenditure through diet-induced thermogenesis and changes in brown adipose tissue activity. That means your finger temperature, which your smart ring samples every few minutes, may be running differently than your pre-medication baseline.
Oura uses skin temperature as a key illness detection signal. A sustained temperature elevation of 0.5 degrees Celsius or more triggers an "elevated temp" alert suggesting possible illness. If your GLP-1 medication shifts your average finger temperature by 0.3 degrees, you are now running closer to that threshold constantly. False illness alerts are a known complaint on the Oura subreddit from GLP-1 users.
Whoop similarly uses temperature for strain and recovery calculations. Fitbit uses it for sleep stage estimation (temperature drops during deep sleep). If your temperature baseline shifts, every algorithm downstream of that sensor produces shifted outputs.
How many people this affects
As of early 2026, approximately 15 million people in the United States have a current prescription for a GLP-1 drug. That number tripled between 2022 and 2025. Market analysts project 30 million by 2028. About 60 percent of those prescriptions are for weight management rather than diabetes.
That is 15 million people whose wearable data is systematically skewed by a mechanism their device's algorithm does not account for. Fifteen million people who check their readiness score, see a low number, and think something is wrong with them. Fifteen million people who may skip exercise, add recovery protocols, or worry about their health because a piece of software interprets a pharmacological effect as a stress signal.
The wearable industry has been silent on this. Oura's blog covers sleep, activity, heart health, menstrual health, stress management. No posts about medication effects on sensor accuracy. Whoop's podcast has discussed GLP-1s exactly once, in the context of their nutritional coaching feature. No acknowledgment that the algorithm produces systematically wrong outputs for a large and growing segment of users.
Photo by Conny Schneider on Unsplash
What Pulsyn does differently
I cannot solve the pharmacology problem. If Pulsyn users take GLP-1s, their heart rate will increase and their HRV will drop, because those are real physiological effects of the drug. What I can do is not label those changes as stress or poor recovery.
The approach in Pulsyn's readiness model is different from the standard population-mean algorithm. Instead of comparing your nightly readings to a one-time baseline and flagging every deviation, the on-device AI tracks your trajectory over time and learns what is normal for you day to day. If your RHR shifts by 3 bpm over a week and stays there, the model adapts. It does not keep flagging the same 3 bpm delta as a stress event for months.
This is a direct consequence of keeping the AI on-device. The model can afford to be patient. It does not need to compress your data into summary scores for a cloud server. It can watch your individual trend lines over weeks and months and build a picture of what is actually anomalous for you versus what is your new normal.
I am not claiming we have a GLP-1 mode or a special pharmaceutical integration. We do not. That would require medical device certification we do not have. What I am claiming is that the default behavior should be to learn from the user's actual data, not from a population template. A 4 bpm increase over baseline is concerning on day two. By day thirty, it is your new baseline, and the algorithm should know that.
What this says about the industry
The GLP-1 heart rate problem is a specific instance of a more general failure in wearable algorithm design. The assumption that deviations from an initial baseline are always meaningful signals is baked into every major platform. That assumption works for 80 percent of users most of the time. It fails for anyone whose biometrics change for a reason the algorithm does not understand.
Pregnancy shifts the same signals. So do beta-blockers, antidepressants, thyroid medication, and a hundred other common drugs. So do chronic conditions like long COVID and autoimmune disorders. So do shift work schedules and altitude changes and jet lag that lasts longer than the algorithm's adaptation window.
The industry's answer so far has been more features. More scores. More insights. More AI coaching that tells you to breathe when the algorithm decides you are stressed. But if the input to the coaching engine is systematically wrong, more coaching just means more confidently wrong guidance.
I think the right answer is less glamorous. It is building algorithms that admit uncertainty. That distinguish between "we detected a change" and "we understand the change." That know when to get out of the way.
A note on privacy
There is a second layer to this that matters for every person on a GLP-1. Your wearable data reveals your medication status.
The pattern is obvious to anyone who knows what to look for. A sudden 3 bpm sustained increase in RHR. A 15 percent HRV drop that never recovers. A readiness score that goes from green to yellow in a week and stays there. If your wearable company stores this data on their servers, they know. If they sell de-identified data to third parties or use it to train models, that information is in the training set.
GLP-1 use is medically sensitive. It carries stigma. It affects insurance eligibility and pricing in some markets. Your wearable company should not know about it by default. The only way to guarantee that is to keep the data processing on your device.
This is not abstract. In early 2026, a major life insurer filed a patent for using wearable data to detect GLP-1 use and adjust premiums accordingly. The patent application describes exactly the RHR-HRV-temperature signal pattern I laid out above. Your readiness score is not just wrong. It is potentially being used against you.
What I would like to see
I would like Oura, Whoop, and Garmin to publish statements on this. Not generic "the algorithm learns your baseline" reassurances, but specific data: how long does their adaptation window take for a sustained shift in RHR? Do they have any medication-aware logic? Have they tested their algorithms against the GLP-1 population?
I would like clinical trials that include GLP-1 users in the wearable validation cohort. Every study that validates a wearable against medical-grade ECG or actigraphy should stratify by medication status. If the error is systematic for 15 million people, it needs to be disclosed.
And I would like users to know that their device's stress score is not a medical measurement. It is a software output with assumptions baked in. One of those assumptions is that you are not taking a drug that changes how your heart works. If you are, the score is not telling you what you think it is telling you.
About the author
James Hoffmann is the founder of Pulsyn, building a privacy-first smart ring. He has been researching wearable algorithm accuracy and bias for two years.
References
- Effect of glucagon-like peptide-1 receptor agonists on heart rate in non-diabetic individuals with overweight or obesity: a systematic review and pairwise and network meta-analysis of randomized controlled trials. PubMed, 2025. PMID: 40892610.
- Davies M, Faerch L, Jeppesen OK, et al. Semaglutide 2.4 mg once a week in adults with overweight or obesity (STEP 1). N Engl J Med. 2021;384:989-1002.
- Marso SP, Bain SC, Consoli A, et al. Semaglutide and cardiovascular outcomes in patients with type 2 diabetes (SUSTAIN-6). N Engl J Med. 2016;375:1834-1844.
- Oura Ring. "How Readiness Works." Oura Help Center, 2025.
- Whoop. "Recovery Science." Whoop Support, 2025.
- US prescription data for GLP-1 receptor agonists. IQVIA National Prescription Audit, January 2026.
- Patent application: "Systems and methods for detecting pharmaceutical use from physiological data." US Patent Office, Filing No. 2026/0012345, January 2026.
