Are there sex differences in HRV? Why age, metric and measurement method change the answer

First: a sex difference is a group average

In most studies, a sex difference means that the average in a female group differed from the average in a male group. It does not mean that the two distributions were separate or that HRV can identify the sex of each person. Values in women and men of the same age overlap widely even when an average difference is statistically detectable.

Many older datasets record sex only as a female/male binary. That is the variable discussed here. HRV does not reveal gender identity, menstrual phase, pregnancy, menopause or hormone treatment.

HRV is not a single measurement

SDNN describes the overall spread of normal beat-to-beat intervals across a recording. RMSSD describes changes between adjacent intervals and, in short resting recordings, is relatively closely associated with cardiac vagal modulation. Apple HealthKit stores HRV as SDNN.

Frequency-domain measures include LF, HF and LF/HF. LF has several physiological contributors, so LF/HF cannot be read directly as sympathetic activity divided by parasympathetic activity. LF_norm and HF_norm are also constrained to a fixed sum and therefore are not independent pieces of evidence.

• SDNN: overall variation in beat intervals during the recording
• RMSSD: short-term changes between adjacent beat intervals
• HF: a high-frequency component strongly affected by breathing
• LF/HF: a context-dependent ratio, not a simple autonomic balance meter

What do large studies show?

A 2016 meta-analysis combined 172 studies with 63,612 healthy participants. On average, the female group had a shorter mean RR interval — and therefore a higher heart rate — as well as lower SDNN, total power and LF, while the relative share of HF was higher and LF/HF was lower. The standardized mean differences, reported as Hedges' g, were −0.44 for mean RR, −0.24 for SDNN, −0.38 for normalized LF, +0.38 for normalized HF and −0.39 for LF/HF. RMSSD and pNN50 did not show a consistent significant difference across the fixed- and random-effects models.

A Lifelines study of 149,205 ten-second ECGs, however, reported consistently higher RMSSD in women. A wrist-PPG study of about 8.2 million Fitbit users found sex differences in SDRR and LF, but no clear sex difference in RMSSD or HF.

What Feelmo did in the four-dataset re-analysis

The main analysis used 1,073 people from Autonomic Aging, 40 from Fantasia, 42 from the PhysioNet/Computing in Cardiology Challenge 2018 dataset and 18 from the MIT-BIH Normal Sinus Rhythm Database: 1,173 people across four datasets. A separate sensitivity analysis added 47 people from the MIT-BIH Arrhythmia Database. The total of 1,220 includes those arrhythmia records.

Within each dataset, multiple segments from the same person were averaged first so that each person contributed one set of features. We calculated Hedges' g as the female-group mean minus the male-group mean and then pooled the four dataset estimates with a DerSimonian–Laird random-effects model. This was not a causal analysis with consistent adjustment for age, breathing, BMI, medication or other possible explanations.

• Main analysis: four datasets and 1,173 people
• Sensitivity analysis: 47 arrhythmia records added, for 1,220 people in total
• Effect size: female group minus male group, reported as Hedges' g
• Pooling method: DerSimonian–Laird random-effects model

Group-average differences in the exploratory re-analysis

The pooled point estimates across the four datasets were −0.80 for LF_norm, +0.80 for HF_norm, −0.63 for LF/HF and −0.72 for DFA α1. By contrast, RMSSD was −0.09 and pNN50 was −0.16, and both 95% confidence intervals crossed zero. This analysis therefore cannot establish a consistent direction of group difference for RMSSD or pNN50.

The directions of LF_norm, HF_norm and LF/HF were consistent with the earlier large meta-analysis. The larger effect-size estimates do not show that the "true" difference is larger. The populations, recording conditions and calculations differed, so comparing the analyses as simple multiples has no clear physiological meaning.

• LF_norm: g = −0.80, 95% confidence interval −0.93 to −0.68
• HF_norm: g = +0.80, 95% confidence interval +0.68 to +0.93
• LF/HF: g = −0.63, 95% confidence interval −0.75 to −0.50
• DFA α1: g = −0.72, 95% confidence interval −0.84 to −0.60
• RMSSD: g = −0.09, 95% confidence interval −0.46 to +0.28
• pNN50: g = −0.16, 95% confidence interval −0.71 to +0.38

Why LF/HF cannot be called "autonomic balance"

At rest, HF is relatively closely related to cardiac vagal modulation, but it is also strongly affected by breathing rate and depth. LF has several contributors, including sympathetic and parasympathetic influences and the baroreflex. LF/HF therefore cannot be read as "sympathetic divided by parasympathetic" or as a direct measure of overall autonomic balance.

Under common definitions, LF_norm and HF_norm are calculated from the same LF and HF values and are constrained so that one falls when the other rises. LF/HF is another ratio made from those same two components. They cannot be counted as three independent replications. DFA α1 describes short-term fractal correlation in beat intervals; it is not autonomic balance itself.

Why I²=0% does not mean complete agreement

For LF_norm, HF_norm, LF/HF and DFA α1, the point estimate of I² — a statistic describing inconsistency between datasets — was 0%. This means that the model estimated no additional between-dataset variance in this analysis. It does not prove that every dataset had an identical effect or that the same difference exists in every population.

The main analysis had only four datasets, and three contained 42 people or fewer. With so few datasets and small samples, the ability to detect heterogeneity is limited and both I² and between-dataset variance are uncertain. I² was 51% for RMSSD and 75% for pNN50, showing substantial differences between datasets for the time-domain measures.

An age pattern does not prove menopause is the cause

A study of five-minute ECGs in 1,906 healthy people reported associations of age and sex with several HRV measures. Sex-group differences were no longer observed for most indices at ages 55–64 and for all indices except fractal dimension (FD) at ages 65–74. It was cross-sectional: it did not follow the same people over time and did not explain the pattern solely with hormone levels or menopausal stage.

Age groups can also differ in medication, illness, body composition, physical activity, breathing and measurement time. HRV alone cannot work backwards from a change after age 50 and identify menopause as the cause.

How should you use an Apple Watch value?

Apple Watch records HRV in HealthKit as SDNN. Research RMSSD, five-minute resting ECG, 24-hour ECG and another manufacturer's wrist-PPG value are not interchangeable simply because each is called HRV.

In daily use, compare measurements from the same device under a similar time, posture and sleep context, and follow your own trend over several weeks. Do not use HRV alone to diagnose health, fatigue, stress or menopause. If you have symptoms, seek medical advice based on the symptoms rather than the number.

Scientific limitations

• Most studies discussed are observational and cannot prove that sex caused an HRV difference
• A female/male binary in source data is not a measure of gender identity or hormonal state
• The four datasets were not selected through a systematic review; they were available public data used for an exploratory analysis
• Autonomic Aging contributed about 92% of the main-analysis participants and therefore strongly influences the pooled result
• Resting, sleeping and long-duration recordings were mixed, without consistent adjustment for age, breathing, BMI or medication
• CinC 2018 contains people assessed for sleep-disorder diagnosis, so the four datasets cannot all be described as healthy cohorts
• DerSimonian–Laird and I² estimates from only four datasets carry substantial uncertainty
• ECG and wrist PPG, and recordings lasting 10 seconds, five minutes or 24 hours, are not directly comparable
• A population-average difference does not measure accuracy for classifying an individual's sex, health or autonomic state
• Feelmo's re-analysis is unreviewed and has not yet received independent replication or external validation