The effects of age and gender on heart rate variability as measured by spectral and time domain analysis of 24 h ECG recordings were evaluated in 101 healthy subjects, 49 men and 52 women (20-69 years of age). In the frequency domain, total power, very low-frequency power, low-frequency power and high-frequency power were negatively correlated to age (P < 0.001 for all variables). Total power decreased by 30% between 20-29 and 60-69 years of age. In the time domain, SDNN-index, the mean of the standard deviations of all normal R-R intervals for all 5 min segments of a 24 h ECG recording, was negatively correlated to age (P < 0.001). Total power, very low-frequency power, low-frequency power and the low-frequency/high-frequency ratio were lower in women (P < 0.05, P < 0.05, P < 0.01 and P < 0.01), although the absolute differences were much smaller than for age. There was a pronounced circadian variation; at night total power increased in all age groups (P < 0.01). The results show that age, and to a lesser degree gender, are important determinants of heart rate variability in healthy subjects. Heart rate variability is a valuable tool for risk stratification in cardiovascular disease, but the physiological effects of ageing, with diminishing heart rate variability in older age groups, must also be taken into account.

Forty‐eight hour Holter monitoring was undertaken of 16 male elite middle‐ and long‐distance runners, age 25±3 years, with peak oxygen uptake 4.83±0.43 1 O2/min or 73.0±3.9 ml O2/kg/min. The athletes had pronounced bradycardia during the night‐time, with heart rate calculated from four RR intervals <30 beats/min in five runners. Twelve of 16 runners had RR intervals >2 s. Of those, 10 runners had sinus pauses exceeding 2 s, the longest being 3.06 s. Three runners had AV block II, two with Mobitz type 1, and one with both Mobitz type 1 and 2. Autonomic function was estimated by time domain and power spectral analysis of heart rate variability. The runners were compared with a control group of 13 sedentary or moderately active subjects. The runners had a mean of 14 b.p.m. lower heart rate at night than the controls. The runners had higher heart rate variability in all spectral bands. In the time domain pNN50 and rMSSD, which are considered to reflect strongly vagal tone, were markedly higher in the runners than the controls. The findings suggest that an increased parasympathetic tone might at least partly explain the pronounced resting sinus bradycardias found in endurance‐trained runners.

BackgroundYoga exercises are known to decrease stress and restore autonomic balance. Yet knowledge about the physiological effects of inversion postures is limited. This study aimed to investigate the effects of inversion postures (head below the heart) on blood pressure (BP) and heart rate variability (HRV).MethodsTwelve healthy women and men took part in an 8-week yoga program (60 min once a week). BP was measured with an automatic Omron mx3 oscillometric monitoring device and HRV with a Holter 24-hour ECG at baseline and 8 weeks after the intervention.ResultsThere was no significant effect of inversion postures on BP. Nine out of 12 participants showed a significant increase in HRV (p < 0.05) at night (2 hours) on pNN50% (12.7 ± 12.5 to 18.2 ± 13.3). There were no significant changes in other HRV measures such as NN50, LF, HF, LF/HF ratio, LF normalized units (n.u.), HF n.u. and RMSSD.ConclusionEight weeks of hatha yoga improved HRV significantly which suggests an increased vagal tone and reduced sympathetic activity.

Decreased heart rate variability independently predicted poor prognosis after myocardial infarction. However, the cut-off points that should be used in clinical practice are still a matter for further investigation.

Analysis of heart rate variability (HRV) has been used in studies of autonomic function and risk assessment in different patient groups such as in patients with diabetes mellitus, after myocardial infarction (MI) and other cardiovascular disease. Ectopic beats can, however, interfere with HRV analysis and give erroneous results. We have therefore studied the impact of ectopic beats on HRV analysis and the ability of a filter algorithm to correct this. Power spectral analysis of synthetic data with an increasing proportion of ectopic beats and 24-h Holter recordings from 98 healthy subjects and 93 post MI patients was done with and without digital filtering and interpolation of errors in the data stream. The analysis of HRV was seriously hampered by less than 1% of ectopic beats. A filter algorithm based on detection and linear interpolation of ectopic beats and other noise in the data stream corrected effectively for this in the synthetic data employed. In the healthy subjects and the post MI patients, filtering markedly reduced the extra variability related to non-normal beats. The software could automatically analyse over one hundred 24-h files in one batch. HRV analysis should include filtering for ectopic beats even with a small number of such beats. It is possible to make a fast analysis automatically even in huge clinical series, which makes it possible to use the method both clinically and in epidemiological studies.