How to Improve HRV

If you wear an Oura Ring, a WHOOP strap, or a smartwatch, you have probably watched your heart rate variability (HRV) score rise and fall and wondered what to do about it. A low number after a hard week can feel like a verdict. It is not. It is a reading, and like any reading it is most useful when you know what it measures, how to interpret it, and which levers actually move it.

This article covers all three, based on peer-reviewed research. It ends with the one approach that trains the underlying reflex directly rather than waiting for the number to drift: HRV biofeedback, which we describe in detail on our HRV biofeedback program page.

What Your HRV Score Is Measuring

Your heart does not beat like a metronome. The interval between beats speeds up slightly when you breathe in and slows when you breathe out, and it shifts constantly in response to effort, temperature, and stress. HRV is the size of that beat-to-beat variation. Most wearables report a measure called RMSSD, which mainly reflects activity of the vagus nerve, the body's built-in brake on heart rate. A higher value, relative to your own baseline, generally means that brake is engaged and responsive. A lower value often means your system is working harder: fighting off a late night, a drink, a hard session, or a stressful stretch.

Your Wearable Is a Good Gauge, Read Correctly

Consumer devices have improved a great deal, and the research supports using them, with some caveats.

In a study comparing the Oura Ring with a simultaneous electrocardiogram (ECG) overnight, Liang and colleagues (2024, Sensors) found strong agreement for both heart rate and HRV. Accuracy improved when poor-quality data were filtered out and when values were averaged over at least 30 minutes or the whole night. Discrepancies were larger in older participants. The practical lesson: trust the nightly average, not a single spot reading.

A meta-analysis by Xu and colleagues (2026, Sensors) pooled studies comparing optical (PPG) sensors, the kind in rings and watches, against ECG. Under resting, controlled conditions the error for RMSSD was small. The authors cautioned that this does not automatically extend to exercise or everyday movement and that the two signals should not be treated as interchangeable. Optical HRV is a close approximation, not a clinical ECG.

Miller and colleagues (2022, Sensors) tested six popular wearables, including Oura and WHOOP, against laboratory sleep recordings. All were valid for sleep timing and duration, while sleep-stage estimates still have room to improve. So your total sleep time is a solid number; your exact minutes of deep sleep are an estimate.

Read It Like a Clinician

Two habits make the score far more useful.

Compare yourself only to yourself. HRV varies widely between people for reasons that have nothing to do with fitness or resilience. In a study of 260 healthy people aged 10 to 99, Umetani and colleagues (1998, Journal of the American College of Cardiology) found that HRV falls steadily with age: by the sixth decade of life, RMSSD had dropped to roughly half of its level in people in their twenties. Under age 30, women tended to have lower values than men, a gap that disappeared by 50. A review of normal values across more than 21,000 healthy adults by Nunan and colleagues (2010, Pacing and Clinical Electrophysiology) likewise found large differences between individuals and between measurement methods. A colleague's score of 90 says nothing about your score of 40.

Watch the trend, not the day. A single low night is noise until it repeats. Look at your 7-day average against your own longer baseline, and look for patterns: which kinds of days come before your lowest and highest readings.

The Everyday Levers

Several lifestyle factors reliably show up in HRV. Each is something you can test on yourself.

Alcohol

Alcohol is the most consistent short-term lever in the data. In a real-world study of 4,098 Finnish employees, Pietilä and colleagues (2018, JMIR Mental Health) compared each person's nights with and without alcohol. Drinking reduced parasympathetic (vagal) activity during the first hours of sleep in a dose-dependent way, and even low intake had a measurable effect. Being younger or fitter did not protect against it. If your score dips after an evening out, this is likely why.

Sleep

Sleep loss pushes HRV down. A meta-analysis of 11 randomized trials by Zhang and colleagues (2025, Frontiers in Neurology) found that sleep deprivation significantly lowered RMSSD, the measure most wearables report. Other indices were less consistent: changes in SDNN and high-frequency power were not significant. Consistent sleep timing and enough of it is the foundation. Our sleep and recovery program addresses this directly.

Stress

Stress tends to show up as reduced vagal activity. A review and meta-analysis of 37 studies by Kim and colleagues (2018) found that stress most often lowered high-frequency HRV, the component tied to the vagus nerve, and linked these changes to brain regions that appraise threat. A run of low readings during a demanding stretch at work is your physiology reporting the load, not a sign that something is wrong with you.

Training Load

Regular aerobic exercise raises HRV over time. A meta-analysis by Sandercock and colleagues (2005, Medicine and Science in Sports and Exercise) found that exercise training increased high-frequency HRV with a moderate effect, with smaller responses in older adults. In the short term, though, a hard session lowers HRV the next morning, which is why many athletes use the score to adjust their plan. A meta-analysis by Düking and colleagues (2021, Journal of Science and Medicine in Sport) found that HRV-guided endurance training produced modest gains in submaximal fitness compared with fixed programs, with small, non-significant effects on performance and fewer non-responders. Use the number to decide when to push and when to recover. We work with athletes on exactly this.

Training the Reflex Itself: HRV Biofeedback

The levers above remove things that suppress HRV. HRV biofeedback works differently: it trains the reflex that produces it.

In HRV biofeedback, a sensor displays your heart rhythm in real time while you learn to breathe at your resonance frequency, an individual rate, typically around six breaths per minute, at which the swings in heart rate become as large as they get. Slow breathing raises vagal HRV while you are doing it; a meta-analysis by Laborde and colleagues (2022, Neuroscience and Biobehavioral Reviews) confirmed this acute effect across many studies. The aim of training is to make that response stronger and easier to call on.

Does it raise your resting number? Sometimes. In a five-week randomized trial, Jung and colleagues (2025, Applied Psychophysiology and Biofeedback) compared slow breathing designed to increase heart rate oscillations with a control condition designed to reduce them. In the training group, larger increases in resting HRV went along with larger decreases in negative emotion, and the change in HRV statistically explained part of the emotional improvement.

The evidence is not uniform, and it is worth knowing that. In a sham-controlled trial of 53 healthy older adults, Allen and colleagues (2026, Applied Psychophysiology and Biofeedback) found no difference between real and sham biofeedback in resting HRV after five weeks. The real training did raise low-frequency HRV, the rhythm that slow breathing amplifies, during recovery from a laboratory stressor. Both groups also improved their sleep and several HRV measures, which shows how much of the benefit can come from simply practicing slow, regular breathing. A fair reading: biofeedback reliably trains how well you recover from stress, while a higher resting score may take longer or depend on the person.

That distinction matters for anyone chasing a number. Your morning reading is one snapshot. The capacity to bring your heart rate down in the minutes after a tense meeting or a missed shot is the skill that changes how you perform.

A Two-Week Self-Experiment

You can learn a lot about your own HRV in two weeks.

1. Set a baseline. Wear your device every night for a week without changing anything. Note your 7-day average.
2. Remove one suppressor at a time. Try a week of alcohol-free nights, or a fixed bedtime and wake time, and compare your average with the baseline.
3. Practice slow breathing daily. Spend 10 minutes a day breathing slowly and evenly, around six breaths per minute, with a slightly longer exhale. This is not a substitute for a resonance frequency assessment, but it is a useful start.
4. Log context. Note hard training days, late meetings, travel, and illness. Patterns emerge quickly.
5. Read the trend, not the day. Judge changes by the weekly average against your own baseline.

A note of caution: HRV is a training signal, not a diagnosis. A sudden, sustained drop alongside symptoms such as unusual fatigue, chest discomfort, or illness is a reason to see your physician.

Working With Dr. Lagos

Dr. Leah Lagos has 18 years of clinical experience with HRV biofeedback. Her program begins with an individual resonance frequency assessment and builds structured training around your schedule and goals, with your wearable as the gauge that tracks progress. She works with executives managing sustained pressure, athletes preparing for competition, and performers, in New York City and remotely. To learn more or request a consultation, visit the HRV biofeedback program page.

References

Allen WD, Talbert LD, Wheeler A, Hartwell B, Jensen T, Young E, Baldwin T, Olpin K, Baldwin SA, Steffen PR, Larson MJ. (2026). A randomized sham-controlled study of heart rate variability biofeedback in an older adult sample. Applied Psychophysiology and Biofeedback. Online ahead of print. PMID 42565919.

Düking P, Zinner C, Trabelsi K, Reed JL, Holmberg HC, Kunz P, Sperlich B. (2021). Monitoring and adapting endurance training on the basis of heart rate variability monitored by wearable technologies: a systematic review with meta-analysis. Journal of Science and Medicine in Sport, 24(11), 1180-1192. PMID 34489178.

Jung H, Yoo HJ, Choi P, Nashiro K, Min J, Cho C, Thayer JF, Lehrer P, Mather M. (2025). Changes in negative emotions across five weeks of HRV biofeedback intervention were mediated by changes in resting heart rate variability. Applied Psychophysiology and Biofeedback, 50(1), 25-48. PMID 39516353.

Kim HG, Cheon EJ, Bai DS, Lee YH, Koo BH. (2018). Stress and heart rate variability: a meta-analysis and review of the literature. Psychiatry Investigation, 15(3), 235-245. PMID 29486547.

Laborde S, Allen MS, Borges U, Dosseville F, Hosang TJ, Iskra M, Mosley E, Salvotti C, Spolverato L, Zammit N, Javelle F. (2022). Effects of voluntary slow breathing on heart rate and heart rate variability: a systematic review and a meta-analysis. Neuroscience and Biobehavioral Reviews, 138, 104711. PMID 35623448.

Liang T, Yilmaz G, Soon CS. (2024). Deriving accurate nocturnal heart rate, rMSSD and frequency HRV from the Oura Ring. Sensors, 24(23), 7475. PMID 39686012.

Miller DJ, Sargent C, Roach GD. (2022). A validation of six wearable devices for estimating sleep, heart rate and heart rate variability in healthy adults. Sensors, 22(16), 6317. PMID 36016077.

Nunan D, Sandercock GR, Brodie DA. (2010). A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. Pacing and Clinical Electrophysiology, 33(11), 1407-1417. PMID 20663071.

Pietilä J, Helander E, Korhonen I, Myllymäki T, Kujala UM, Lindholm H. (2018). Acute effect of alcohol intake on cardiovascular autonomic regulation during the first hours of sleep in a large real-world sample of Finnish employees: observational study. JMIR Mental Health, 5(1), e23. PMID 29549064.

Sandercock GR, Bromley PD, Brodie DA. (2005). Effects of exercise on heart rate variability: inferences from meta-analysis. Medicine and Science in Sports and Exercise, 37(3), 433-439. PMID 15741842.

Umetani K, Singer DH, McCraty R, Atkinson M. (1998). Twenty-four hour time domain heart rate variability and heart rate: relations to age and gender over nine decades. Journal of the American College of Cardiology, 31(3), 593-601. PMID 9502641.

Xu S, Liu H, Liu Z, Su P, Gu Z. (2026). Accuracy of photoplethysmography-derived pulse rate variability compared with electrocardiography-derived heart rate variability: a systematic review and meta-analysis. Sensors, 26(16), 5192. PMID 42655500.