Heart Rate Variability (HRV)

Heart Rate Variability (HRV) can be used to measure stress, either to evaluate recovery status or exercise intensity. As a guide to whether you are ready to train, resting HRV is a weak signal on its own: it needs to be read as a trend, and it can rise rather than fall as fatigue builds during heavy training. See Training Readiness for how HRV fits with other ways of judging readiness.

What is HRV?

Heart Rate Variability (HRV) is a measure of the irregularity of the Heart Rate. The time between heartbeats varies slightly, even when the average Heart Rate is steady. For example, a Heart Rate of 60 BPM is an average of one beat per second. However, the actual time between heartbeats could vary so that some beats occur after 0.8 seconds, and some after 1.2 seconds. In the context of HRV, this irregularity is a good thing. Lower HRV often indicates stress, illness or fatigue, but HRV can also rise with fatigue during heavy training (see HRV during heavy training).

HRV to Measure Recovery Status

• HRV can be measured during exercise or at rest, usually overnight or first thing in the morning.
• There are various ways of analyzing HRV that provide different values, and these methods have different benefits (see HRV Metrics).
• Single readings are noisy. The day-to-day typical error of resting lnRMSSD is about 12%, against a smallest worthwhile change of about 3%. A 7-day rolling average compared with your own normal range is far more useful than last night's value, and at least three valid measurements a week are needed to match a full weekly average.
• Illness tends to lower HRV and raise resting heart rate. In data from 28,175 users of an HRV app, illness lowered HRV by about 10% and raised resting heart rate by about 6%; the data came from HRV4Training, whose developer is the first author.
• Generally, HRV is greatest at rest and the variability declines as the heart rate rises. Therefore, looking at HRV to Heart Rate ratios is important rather than looking at raw HRV values.
• HRV is linked to aerobic fitness, with the fittest individuals having the greatest variability, and this can be used to predict V̇O₂max .
• Lower HRV is associated with greater risk of death after heart attacks.
• Some Running Watches can record or display HRV, and some have software to use HRV to predict recovery or V̇O₂max.

HRV during heavy training

The direction of change in HRV during heavy training is not consistent, which is the biggest practical trap in using it:

• In 21 trained male triathletes, weekly-averaged HRV rose in those who became functionally overreached, and isolated, once-weekly readings may not detect this change. A meta-analysis found that weekly-averaged HRV rose in functionally overreached athletes while single readings did not. This rise is sometimes described as parasympathetic hyperactivity or parasympathetic fatigue, but the mechanism is not settled.
• In an elite triathlete who became non-functionally overreached, the 7-day rolling HRV and its day-to-day variation both declined in the weeks before a failed race.
• In very fit athletes, HRV can fall while resting heart rate also falls, a pattern called "saturation".
• In French national-team Nordic skiers followed for four years, fatigue came with higher heart rate, lower HRV and much more scattered HRV.
• A review of markers of functional overreaching concluded that HRV did not reliably reflect it.

In practice, an HRV trend that is well above your normal range during a heavy block, together with high perceived effort or heavy legs, is a possible sign of fatigue rather than good recovery. See Good heart rate and HRV numbers can mean fatigue.

Does HRV-guided training work?

Several trials have adjusted training using HRV, typically doing hard sessions only when HRV is within a personal normal range. Meta-analyses suggest at most a small, uncertain benefit over a sensible fixed plan:

Meta-analysis	Performance effect versus a fixed plan	Other findings
Düking et al. 2021	Trivial and not significant (g = 0.08)	Submaximal physiology improved (g = 0.30); fewer non-responders; HRV groups did fewer hard sessions
Manresa-Rocamora et al. 2021	Small and not significant (0.20; 95% confidence interval −0.09 to 0.48)	Superior only by a small margin
Granero-Gallegos et al. 2020	Small positive effect on V̇O2max (0.40)	Effect varied with training level and sex

The consistent secondary finding, fewer non-responders while doing less high-intensity work, is plausibly the real benefit: HRV mostly stops people doing hard sessions on bad days.

HRV and Overtraining Syndrome

Overtraining Syndrome is a serious long term problem for athletes. The science around HRV and Overtraining Syndrome is tricky to interpret for several reasons:

• Many of the studies evaluate the change in HRV with increasing training load (overload). This overload is quite different from Overtraining Syndrome and the results do not necessarily transfer. No prospective study has followed athletes from health into Overtraining Syndrome.
• Differing HRV metrics (see below) are used in different studies, making comparison difficult.
• The HRV is often measured while resting but awake, and HRV can be sensitive to changes in mood or stress which are more variable while conscious.
• Relatively short time periods are used, and Overtraining Syndrome typically requires a longer study period.

HRV Metrics

There are a number of mathematical approaches to evaluating HRV. Most of these metrics do not adjust for heart rate, so HRV appears disproportionately higher at lower heart rates, confounding analysis. These include:

• rMSSD. This is the square root of the mean of the squared differences between successive R–R intervals. Using rMSSD typically has less measurement error and is less influenced by breathing rate than other metrics. It is also used as the basis of the next two metrics.
• Ln rMSSD. This is the natural logarithm rMSSD, and this produces a smaller number which tends to be in the range 3.0-8.0.
• Ln rMSSD to R-R Interval Ratio. Using the ratio of Ln rMSSD to the heart rate (interval between beats or R-R Interval) adjusts for changes in Resting Heart Rate (RHR). An athlete could have a reduced HRV purely due to a slightly elevated RHR.
• SDNN. The standard deviation of R-R intervals. The problem with SDNN is that if the heart rate is changing, (going up or down steadily), then the SDNN will be inappropriately high.
• High Frequency Power (HF). Spectral analysis can provide the power in the high frequencies, typically 0.15 to 0.4 Hz (high frequency here is relative.)
• Low Frequency Power (LF). Like HF but for the low frequencies, typically 0.04 to 0.15 Hz,
• Normalized LF power (LFn). This is LF/(LF+HF).
• pNN50. The percentage of R-R intervals that differ by more than 50ms. I find this is far too sensitive to heart rate to be of much use.

Recording HRV

Most current sport watches, rings and HRV apps measure overnight or morning HRV automatically, usually as rMSSD, and many feed it into a "readiness" score. They differ in whether they use a rolling average or last night alone, and in how they treat HRV that is above your normal range; see Wearable HRV methods by brand. Accuracy also varies: over 536 nights in 13 adults, the Oura ring matched an electrocardiogram reference best for nocturnal HRV, WHOOP moderately, and the Garmin Fenix 6 and Polar Grit X Pro poorly. Apps such as HRV4Training take a morning reading using a phone camera or a Bluetooth heart rate sensor.

To record beat-to-beat (R–R) data during workouts for your own analysis, some older Garmin watches need HRV logging to be enabled by copying the enable_hrv_settings_file.fit file to the "GARMIN\NEWFILES" folder on the watch (on Windows this may require you to show hidden folders); disable_hrv_settings_file.fit turns it off again. The Garmin Fenix 5X has a menu option instead. Software such as RUNALYZE can then display the data.

HRV from the Fenix 5X in RUNALYZE

See also

• Training Readiness
• Resting Heart Rate
• Orthostatic Heart Rate Test
• Overtraining Syndrome