Categories
Academy > Biometrics 101 > whitepaper

Heart Rate Variability: how to measure it, why it’s relevant to many aspects of health

Reading time: 5 min

What is it?

Heart rate variability (HRV) is a measure of differences in the time intervals between heart beats. Heart rate by itself is the expression of how many contractions of the heart there are in a given unit of time; however, the rate itself is not constant. There is normal fluctuation of time between heartbeats, in a manner that speeds up and slows down heart rate. Therefore, HRV is a quantifiable measure that assesses these differences.

This variation in the time between heartbeats is thought to be a composite measure of parasympathetic and sympathetic neural inputs and hormonal inputs as regulated by the autonomic nervous system. Much is still unknown about the mechanism of action causing variability changes. However, many studies have shown correlations between HRV and diseased states, such as heart disease, Parkinson disease, and cardiovascular disease; emotional stress, such as depression; physical/mechanical stress, such as high-intensity or resistance training; sleep in the context of both acute stress and chronic stress; and meditation whether it’s “inward- attention” or Vipassana meditation. Therefore, HRV is becoming a more common non-invasive measure to examine the physiological state and responses.

How it’s measured

HRV can be measured by use of an electrocardiogram (ECG) or photoplethysmography (PPG). By referencing a common point in the ECG or PPG waveform, the time between each heart beat can be recorded in milliseconds (ms). Collecting each beat-to-beat interval in ms allows us to compute HRV, most commonly reported as rMSSD (root mean square of successive differences)

The rMSSD method of calculation takes each interval, squares the interval, takes the overall mean, and then the square root of that mean is taken. Biostrap computes the rMSSD using this method and remains the standard computational method for HRV. 

More complex measures of HRV, including frequency domain analysis can be performed to get further information out of heart rate patterns, which will be covered in another review. 

Correlation with health conditions

HRV is most notably correlated with stress conditions, such as anxiety disorders, depression, PTSD, and other psychological states, with lower HRV indicating higher-stressed states. The suggested mechanism is an increased sympathetic arousal, which affects HRV; HRV alone does not cause these states, but reflects and provides insight into the heightened stress on the physiological systems, which in turn have effects on other bodily systems, particularly the cardiovascular and endocrine systems. 

Because of the chronic effects of stress, as previously mentioned, HRV has been noted to be a predictor of all-cause mortality and correlated with obesity, cardiovascular disease, cancer, and neurodegenerative diseases, among other health conditions.

What is a “normal” range?

Heart rate variability has a large individual component and is often used to assess changes in health over time (see “Interpreting Trends” below).

Heart rate variability can fluctuate day-to-day based on exposure to stress, sleep quality, diet, and exercise. This leads to low repeatability, and therefore makes normative data difficult to collect.

In general, younger individuals, males, and more active individuals tend to have higher heart rate variability. However, the inter-subject variability tends to be too high to suggest proper normative ranges. This demonstrates a need to track HRV over time to understand the ‘profile’ of an individual.

When considering a normal range, there is not a normal scale of 0-100. HRV scale is 0-255. Many factors influence where your HRV sits on this scale, including; genetics, lifestyle, and age. Once you track HRV over a period of time you will have a baseline HRV. Once a baseline is established you will be able to see how day-to-day internal and external stressors influence your HRV, upward or downward.

Watching your HRV deviate positively or negatively from your baseline is the most important factor to observe. The actual HRV number matters less than how much it has varied from your “normal” baseline.

Interpreting trends

As previously mentioned, HRV is difficult to interpret and generally a nonspecific data point from a single spot check. However, since it is a dynamic measure that responds to various lifestyle factors, tracking HRV over time allows for non-invasive insight into changes in health status or efficacy of certain interventions.

In general, since higher HRV is preferable, a greater ability to manage stress results in an increased HRV. The results of the studies demonstrating the relationship between stress and HRV suggest that interventions aimed at reducing mental and physical stress could increase HRV and minimize day-to-day fluctuations. The increase in HRV itself will not reduce risk and improve health over the long term, but rather, it reflects positive changes in an individual’s physiology.

Biostrap

In a 2018 study, the Biostrap sensor as a wrist-worn device was shown to produce high-quality signals which are useful for the estimation of heart rate variability. 

References

  1. Mccraty R, Shaffer F. Heart Rate Variability: New Perspectives on Physiological Mechanisms, Assessment of Self-regulatory Capacity, and Health Risk. Global Advances in Health and Medicine. 2015;4(1):46–61. doi:10.7453/gahmj.2014.073
  2. Silva LEV, Silva CAA, Salgado HC, Fazan R. The role of sympathetic and vagal cardiac control on complexity of heart rate dynamics. American Journal of Physiology-Heart and Circulatory Physiology. 2016;312(3):H469–H477. doi:10.1152/ajpheart.00507.2016
  3. Dobrek Ł, Skowron B, Baranowska A, Malska-Woźniak A, Ciesielczyk K, Thor PJ. Spectral heart rate variability and selected biochemical markers for autonomic activity in rats under pentobarbital anesthesia. Polish Annals of Medicine. 2017;24(2):180–187. doi:10.1016/j.poamed.2017.01.001
  4. Huikuri HV, Mäkikallio TH. Heart rate variability in ischemic heart disease. Autonomic Neuroscience. 2001;90(1):95–101. (Neural Regulation of Cardiovascular Function Explored in the Frequency Domain). doi:10.1016/S1566-0702(01)00273-9
  5. Alonso A, Huang X, Mosley TH, Heiss G, Chen H. Heart rate variability and the risk of Parkinson disease: The Atherosclerosis Risk in Communities study. Annals of Neurology. 2015;77(5):877–883. doi:https://doi.org/10.1002/ana.24393
  6. Thayer JF, Yamamoto SS, Brosschot JF. The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. International Journal of Cardiology. 2010;141(2):122–131. doi:10.1016/j.ijcard.2009.09.543
  7. McCraty R, Atkinson M, Tiller WA, Rein G, Watkins AD. The effects of emotions on short-term power spectrum analysis of heart rate variability. The American Journal of Cardiology. 1995;76(14):1089–1093. doi:10.1016/S0002-9149(99)80309-9
  8. CARNEY RM, FREEDLAND KE. Depression and heart rate variability in patients with coronary heart disease. Cleveland Clinic journal of medicine. 2009;76(Suppl 2):S13–S17. doi:10.3949/ccjm.76.s2.03
  9. Sarmiento S, García-Manso JM, Martín-González JM, Vaamonde D, Calderón J, Da Silva-Grigoletto ME. Heart rate variability during high-intensity exercise. Journal of Systems Science and Complexity. 2013;26(1):104–116. doi:10.1007/s11424-013-2287-y
  10. Kingsley JD, Figueroa A. Acute and training effects of resistance exercise on heart rate variability. Clinical Physiology and Functional Imaging. 2016;36(3):179–187. doi:https://doi.org/10.1111/cpf.12223
  11. Hall M, Vasko R, Buysse D, Ombao H, Chen Q, Cashmere JD, Kupfer D, Thayer JF. Acute Stress Affects Heart Rate Variability During Sleep. Psychosomatic Medicine. 2004;66(1):56–62. doi:10.1097/01.PSY.0000106884.58744.09
  12. da Estrela C, McGrath J, Booij L, Gouin J-P. Heart Rate Variability, Sleep Quality, and Depression in the Context of Chronic Stress. Annals of Behavioral Medicine. 2021;55(2):155–164. doi:10.1093/abm/kaaa039
  13. Busek P, Vanková J, Opavsky J, Salinger J, Nevsimalova S. Spectral analysis of heart rate variability in sleep. Physiological research / Academia Scientiarum Bohemoslovaca. 2005;54:369–76.
  14. Krygier JR, Heathers JAJ, Shahrestani S, Abbott M, Gross JJ, Kemp AH. Mindfulness meditation, well-being, and heart rate variability: A preliminary investigation into the impact of intensive Vipassana meditation. International Journal of Psychophysiology. 2013;89(3):305–313. (Psychophysiology in Australasia – ASP conference – November 28-30 2012). doi:10.1016/j.ijpsycho.2013.06.017
  15. Wu S-D, Lo P-C. Inward-attention meditation increases parasympathetic activity: a study based on heart rate variability. Biomedical Research. 2008;29(5):245–250. doi:10.2220/biomedres.29.245
  16. Jarchi D, Salvi D, Velardo C, Mahdi A, Tarassenko L, Clifton DA. Estimation of HRV and SpO2 from wrist-worn commercial sensors for clinical settings. In: 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). 2018. p. 144–147. doi:10.1109/BSN.2018.8329679
  17. Shaffer F, Ginsberg JP. An Overview of Heart Rate Variability Metrics and Norms. Frontiers in Public Health. 2017 [accessed 2021 Apr 14];5. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624990/. doi:10.3389/fpubh.2017.00258
  18. Chalmers JA, Quintana DS, Abbott MJ-A, Kemp AH. Anxiety Disorders are Associated with Reduced Heart Rate Variability: A Meta-Analysis. Frontiers in Psychiatry. 2014 [accessed 2021 Apr 21];5. https://www.frontiersin.org/articles/10.3389/fpsyt.2014.00080/full. doi:10.3389/fpsyt.2014.00080
  19. Hauschildt M, Peters MJV, Moritz S, Jelinek L. Heart rate variability in response to affective scenes in posttraumatic stress disorder. Biological Psychology. 2011;88(2):215–222. doi:10.1016/j.biopsycho.2011.08.004
  20. Cohen H, Kotler M, Matar MA, Kaplan Z, Miodownik H, Cassuto Y. Power spectral analysis of heart rate variability in posttraumatic stress disorder patients. Biological Psychiatry. 1997;41(5):627–629. doi:10.1016/S0006-3223(96)00525-2
  21. Tsuji H, Venditti F J, Manders E S, Evans J C, Larson M G, Feldman C L, Levy D. Reduced heart rate variability and mortality risk in an elderly cohort. The Framingham Heart Study. Circulation. 1994;90(2):878–883. doi:10.1161/01.CIR.90.2.878
  22. Karason K, Mølgaard H, Wikstrand J, Sjöström L. Heart rate variability in obesity and the effect of weight loss. The American Journal of Cardiology. 1999;83(8):1242–1247. doi:10.1016/S0002-9149(99)00066-1
  23. Stein PK, Reddy A. Non-Linear Heart Rate Variability and Risk Stratification in Cardiovascular Disease. Indian Pacing and Electrophysiology Journal. 2005;5(3):210–220.
  24. Sandercock G. Normative values, reliability and sample size estimates in heart rate variability. Clinical Science. 2007;113(3):129–130. doi:10.1042/CS20070137

 

Did we miss anything?

If you have any questions, suggestions or topic requests, please reach out.

Skip to content