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HRV baseline calculator: your 7-day average and range

Short answer

A single HRV reading is noisy, so research on athletes reads it as a 7-day rolling average of log-transformed values, plus how much it varies day to day. For a week of 62, 58, 65, 60, 55, 63, 48 ms, the average is 58 ms, the usual range 53–65 ms and the latest morning 18% below average. Compare each week with your own past weeks, not with other people.

Last reviewed 27 October 20264 published sourcesNot medical advice

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HRV baseline calculator

Enter 3 to 7 morning readings taken the same way each day, oldest first. Blank mornings are skipped.

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Oldest first. Use the same measure (rMSSD or SDNN) every day.

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Enter at least three mornings of HRV.

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How the baseline works

Take the natural log of each morning's reading, then their mean and standard deviation. The mean, converted back with the exponential, is your average; one standard deviation either side is your usual day-to-day range; and the standard deviation divided by the mean is the coefficient of variation (CV).

In an elite triathlete who became non-functionally overreached, the 7-day rolling average of Ln rMSSD fell towards the key race and its CV fell sharply, while both stayed stable in a teammate (Plews et al., 2012). Plews et al. later concluded that longitudinal monitoring is needed to learn each athlete's individual HRV pattern (Plews et al., 2013).

Buchheit advises interpreting a change against the measure's error and the smallest change that matters, and in the context of the training phase and load (Buchheit, 2014).

Four example weeks
Week (ms, oldest first)AverageUsual rangeCV of ln valuesLatest vs average
Steady week: 60, 62, 59, 61, 60, 63, 6161 ms60–62 ms0.5%+0%
Up-and-down week: 45, 70, 52, 68, 48, 72, 5558 ms48–70 ms4.7%−5%
Low latest morning: 62, 58, 65, 60, 55, 63, 4858 ms53–65 ms2.5%−18%
Higher-HRV person: 95, 102, 98, 90, 105, 99, 10198 ms94–104 ms1.1%+3%
Illustrative readings, worked out with the same maths as the calculator. Average = exp(mean ln). Range = exp(mean ± 1 SD).

Apple Watch and other devices

Apple Watch stores HRV as SDNN (Apple HealthKit: heartRateVariabilitySDNN), while most training studies use rMSSD. The maths here works for either, but never mix the two. See how your number compares with others your age in the HRV by age guide, and check your weekly training load with the training load calculator or the ACWR calculator.

Let your watch build the baseline

Reps reads the HRV your Apple Watch already records and turns it into readable trends you can compare with your recovery and training.

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Common questions

Why use a 7-day average instead of today's HRV?

Daily HRV swings a lot from one morning to the next. Plews et al. tracked elite triathletes with a 7-day rolling average of log-transformed rMSSD and showed its trend, not single days, marked the slide towards overreaching, and their 2013 review recommends appropriate averaging for day-to-day monitoring.

Why take the logarithm of HRV?

HRV values are skewed: big readings are more spread out than small ones. Working on the log scale makes a 10% change look the same whatever your baseline, which is why studies report Ln rMSSD. The calculator converts the average back to milliseconds for you.

Can I use Apple Watch HRV?

Yes, as long as you use the same measure every day. Apple Watch records SDNN, while most of this research used rMSSD, so compare your SDNN only with your own SDNN history.

Sources

Figures on this page were checked against these publications on 27 October 2026.

  1. Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. European Journal of Applied Physiology. 2012;112(11):3729–3741. doi.org/10.1007/s00421-012-2354-4
  2. Plews DJ, Laursen PB, Stanley J, Kilding AE, Buchheit M. Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring. Sports Medicine. 2013;43(9):773–781. doi.org/10.1007/s40279-013-0071-8
  3. Buchheit M. Monitoring training status with HR measures: do all roads lead to Rome? Frontiers in Physiology. 2014;5:73. doi.org/10.3389/fphys.2014.00073
  4. Apple. heartRateVariabilitySDNN. HealthKit developer documentation. developer.apple.com/documentation/healthkit/hkquantitytypeidentifier/heartratevariabilitysdnn

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