The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index.
Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study
Open accessAbstract
Background Heart rate variability, or the variation in the time interval between consecutive heart beats, is a non-invasive dynamic metric of the autonomic nervous system and an independent risk factor for cardiovascular death. Consumer wrist-worn tracking devices using photoplethysmography, such as Fitbit, now provide the unique potential of continuously measuring surrogates of sympathetic and parasympathetic nervous system activity through the analysis of interbeat intervals. We aimed to leverage wrist-worn trackers to derive and describe diverse measures of cardiac autonomic function among Fitbit device users. Methods In this cross-sectional study, we collected interbeat interval data that are sent to a central database from Fitbit devices during a randomly selected 24 h period. Age, sex, body-mass index, and steps per day in the 90 days preceding the measurement were extracted. Interbeat interval data were cleaned and heart rate variability features were computed. We analysed heart rate variability metrics across the time (measured via the root mean square of successive RR interval differences [RMSSD] and SD of the RR interval [SDRR]), frequency (measured by high-frequency and low-frequency power), and graphical (measured by Poincare plots) domains. We considered 5 min windows for the time and frequency domain metrics and 60 min measurements for graphical domain metrics. Data from participants were analysed to establish the correlation between heart rate variability metrics and age, sex, time of day, and physical activity. We also determined benchmarks for heart rate variability (HRV) metrics among the users. Findings We included data from 8 203 261 Fitbit users, collected on Sept 1, 2018. HRV metrics decrease with age, and parasympathetic function declines faster than sympathetic function. We observe a strong diurnal variation in the heart rate variability. SDRR, low-frequency power, and Poincare S 2 show a significant variation with sex, whereas such a difference is not seen with RMSSD, high-frequency power, and Poincare S 1 . For males, when measured from 0600 h to 0700 h, the mean low-frequency power decreased by a factor of 66·5% and high-frequency power decreased by a factor of 82·0% from the age of 20 years to 60 years. For females, the equivalent factors were 69·3% and 80·9%, respectively. Comparing low-frequency power between males and females at the ages of 40–41 years, measured from 0600 h to 0700 h, we found excess power in males, with a Cohen's d effect size of 0·33. For high-frequency power, the equivalent effect size was −0·04. Increased daily physical activity, across age and sex, was highly correlated with improvement in diverse measures of heart rate variability in a dose-dependent manner. We provide benchmark tables for RMSSD, SDRR, high and low frequency powers, and Poincare S 1 and S 2 , separately for different ages and sex and computed at two times of the day. Interpretation Diverse metrics of cardiac autonomic health can be derived from wrist-worn trackers. Empirical distributions of heart rate variability can potentially be used as a framework for individual-level interpretation. Increased physical activity might yield improvement in heart rate variability and requires prospective trials for confirmation. Funding Fitbit.
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“For example, a study on over 8 million fitbit users (Natarajan et al., 2020) reported that young people who took 12,000–13,000 steps a day displayed 21.2% (males) and 22.9% (females) greater high frequency HRV than those who took 5,000–6,000 steps.”
From The Complex Construct of Wellbeing and the Role of Vagal Function (2022) · cites this paper as Natarajan et al., 2020
In the citing paper
“The largest study we identified was conducted in 2019 in North America and included over 8 million participants (75.71%) [153]; the second largest was a European study comprising 742,000 participants (6.85%) [162].”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [153]
In the citing paper
“Some large-scale population-based studies (11/179, 6.1%) relied on secondary data collected with mobile apps [87] or web-based wearable platforms [153] or provided through a wearable company [189].”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [153]
In the citing paper
“One study in North America, conducted in 2019 with over 8 million participants [153], dominated the image of the distribution of participants.”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [153]
Citations
Cites
Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use
Electrophysiology, T. F. O. T. E. S. O. C. T. N. A. (1996) · Circulation · doi:10.1161/01.cir.93.5.1043 · Cited by 17,159
Heart rate variability: Standards of measurement, physiological interpretation, and clinical use
Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., & Schwartz, P. J. (1996) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a014868 · Cited by 15,289
An overview of heart rate variability metrics and norms
Shaffer, F., & Ginsberg, J. P. (2017) · Frontiers in Public Health · doi:10.3389/fpubh.2017.00258 · Cited by 7,185
Photoplethysmography and its application in clinical physiological measurement
Allen, J. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/3/r01 · Cited by 3,967
On the Analysis of Fingertip Photoplethysmogram Signals
Elgendi, M. (2012) · Current Cardiology Reviews · doi:10.2174/157340312801215782 · Cited by 1,206
Umetani, K., Singer, D. H., McCraty, R., & Atkinson, M. (1998) · Journal of the American College of Cardiology · doi:10.1016/s0735-1097(97)00554-8 · Cited by 1,145
Reduced heart rate variability and mortality risk in an elderly cohort. The Framingham Heart Study
Tsuji, H., Venditti, F. J., Manders, E. S., Evans, J. C., Larson, M. G., Feldman, C. L., & Levy, D. (1994) · Circulation · doi:10.1161/01.cir.90.2.878 · Cited by 1,114
Dekker, J. M., Crow, R. S., Folsom, A. R., Hannan, P. J., Liao, D., Swenne, C. A., & Schouten, E. G. (2000) · Circulation · doi:10.1161/01.cir.102.11.1239 · Cited by 876
NUNAN, D., SANDERCOCK, G. R. H., & BRODIE, D. A. (2010) · Pacing and Clinical Electrophysiology · doi:10.1111/j.1540-8159.2010.02841.x · Cited by 874
Sassi, R., Cerutti, S., Lombardi, F., Malik, M., Huikuri, H. V., Peng, C.-K., Schmidt, G., Yamamoto, Y., Reviewers:, D., Gorenek, B., et al. (2015) · Europace · doi:10.1093/europace/euv015 · Cited by 727
La Rovere, M. T., Pinna, G. D., Hohnloser, S. H., Marcus, F. I., Mortara, A., Nohara, R., Bigger, J. T., Camm, A. J., & Schwartz, P. J. (2001) · Circulation · doi:10.1161/01.cir.103.16.2072 · Cited by 697
Short-Term Heart Rate Variability—Influence of Gender and Age in Healthy Subjects
Voss, A., Schroeder, R., Heitmann, A., Peters, A., & Perz, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0118308 · Cited by 516
Hillebrand, S., Gast, K. B., de Mutsert, R., Swenne, C. A., Jukema, J. W., Middeldorp, S., Rosendaal, F. R., & Dekkers, O. M. (2013) · EP Europace · doi:10.1093/europace/eus341 · Cited by 515
Rennie, K. L. (2003) · American Journal of Epidemiology · doi:10.1093/aje/kwg120 · Cited by 300
Alian, A. A., & Shelley, K. H. (2014) · Best Practice & Research Clinical Anaesthesiology · doi:10.1016/j.bpa.2014.08.006 · Cited by 289
Low Heart Rate Variability and the Effect of Depression on Post–Myocardial Infarction Mortality
Carney, R. M., Blumenthal, J. A., Freedland, K. E., Stein, P. K., Howells, W. B., Berkman, L. F., Watkins, L. L., Czajkowski, S. M., Hayano, J., Domitrovich, P. P., et al. (2005) · Archives of Internal Medicine · doi:10.1001/archinte.165.13.1486 · Cited by 259
Relation of High Heart Rate Variability to Healthy Longevity
Zulfiqar, U., Jurivich, D. A., Gao, W., & Singer, D. H. (2010) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2009.12.022 · Cited by 239
Validity of Consumer-Based Physical Activity Monitors for Specific Activity Types
Nelson, M. B., Kaminsky, L. A., Dickin, D. C., & Montoye, A. H. K. (2016) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000933 · Cited by 212
Sandrone, G., Mortara, A., Torzillo, D., Rovere, M. T. L., Malliani, A., & Lombardi, F. (1994) · The American Journal of Cardiology · doi:10.1016/0002-9149(94)90400-6 · Cited by 202
Effect of Beta-blockade on heart rate variability in patients with coronary artery disease
Niemelä, M. J., Airaksinen, J. K. E., & Huikuri, H. V. (1994) · Journal of the American College of Cardiology · doi:10.1016/0735-1097(94)90379-4 · Cited by 197
Changes in heart rate variability with age
REARDON, M., & MALIK, M. (1996) · Pacing and Clinical Electrophysiology · doi:10.1111/j.1540-8159.1996.tb03241.x · Cited by 180
Vandewalle, G., Middleton, B., Rajaratnam, S. M. W., Stone, B. M., THORLEIFSDOTTIR, B., Arendt, J., & Dijk, D. (2007) · Journal of Sleep Research · doi:10.1111/j.1365-2869.2007.00581.x · Cited by 169
Singh, N., Moneghetti, K. J., Christle, J. W., Hadley, D., Plews, D. J., & Froelicher, V. F. (2018) · Arrhythmia & Electrophysiology Review · doi:10.15420/aer.2018.27.2 · Cited by 140
Malfatto, G., Facchini, M., Sala, L., Branzi, G., Bragato, R., & Leonetti, G. (1998) · The American Journal of Cardiology · doi:10.1016/s0002-9149(98)00021-6 · Cited by 135
Reference values for time- and frequency-domain heart rate variability measures
Sammito, S., & Böckelmann, I. (2016) · Heart Rhythm · doi:10.1016/j.hrthm.2016.02.006 · Cited by 131
Stein, P. K., Barzilay, J. I., Chaves, P. H. M., MISTRETTA, S. Q., Domitrovich, P. P., Gottdiener, J. S., Rich, M. W., & Kleiger, R. E. (2008) · Journal of Cardiovascular Electrophysiology · doi:10.1111/j.1540-8167.2008.01232.x · Cited by 103
Patel, V. N., Pierce, B. R., Bodapati, R. K., Brown, D., Ives, D. G., & Stein, P. K. (2017) · JACC Heart Failure · doi:10.1016/j.jchf.2016.12.015 · Cited by 87
Vigorous physical activity predicts higher heart rate variability among younger adults
May, R. L., McBerty, V., Zaky, A., & Gianotti, M. (2017) · Journal of PHYSIOLOGICAL ANTHROPOLOGY · doi:10.1186/s40101-017-0140-z · Cited by 40
Physical Activity and Heart Rate Variability in Adolescents with Abdominal Obesity
Farah, B. Q., Andrade-Lima, A., Germano-Soares, A. H., Christófaro, D. G. D., de Barros, M. V. G., do Prado, W. L., & Ritti‐Dias, R. M. (2017) · Pediatric Cardiology · doi:10.1007/s00246-017-1775-6 · Cited by 36
Effect of long term physical training on heart rate variability
AUBERT, A. E., Ramaekers, D., Cuche, Y., Lysens, R., Ector, H., & de Werf, F. V. (2002) · Computers in cardiology · doi:10.1109/cic.1996.542462 · Cited by 22
Tarniceriu, A., Harju, J., Vehkaoja, A. T., Parák, J., Delgado-Gonzalo, R., Renevey, P., Yli‐Hankala, A., & Korhonen, I. (2018) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2018.8333387 · Cited by 20
Research works printed but not linked: 1
- Irurzun, 2018, Scaling behaviour in the heart rate variability characteristics with age, arXiv
Cited by
Sorted by citations
The impact of wearable technologies in health research: scoping review
Huhn, S., Axt, M., Gunga, H.-C., Maggioni, M. A., Munga, S., Obor, D., Sié, A., Boudo, V., Bunker, A., Sauerborn, R., et al. (2022) · JMIR mHealth and uHealth · doi:10.2196/34384 · Cited by 379
Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research
Nelson, B. W., Low, C. A., Jacobson, N., Areán, P., Torous, J., & Allen, N. B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0297-4 · Cited by 241
Assessment of physiological signs associated with COVID-19 measured using wearable devices
Natarajan, A., Su, H.-W., & Heneghan, C. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00363-7 · Cited by 220
Self-citationThe 2023 wearable photoplethysmography roadmap
Charlton, P. H., Allen, J., Bailón, R., Baker, S., Behar, J. A., Chen, F., Clifford, G. D., Clifton, D. A., Davies, H. J., Ding, C., et al. (2023) · Physiological Measurement · doi:10.1088/1361-6579/acead2 · Cited by 233
Wearable Devices: Implications for Precision Medicine and the Future of Health Care
Babu, M., Lautman, Z., Lin, X., Sobota, M. H. B., & Snyder, M. P. (2023) · Annual Review of Medicine · doi:10.1146/annurev-med-052422-020437 · Cited by 206
Metadata
- Authors
- Natarajan, A., Pantelopoulos, A., Emir-Farinas, H., & Natarajan, P.
- Affiliations
- Fitbit (United States) (US)
- Broad Institute (US)
- Corresponding author
- Natarajan, Pradeep
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 2
- Issue
- 12
- Pages
- e650-e657
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W3107602031
- Cited by
- 229
- References
- 32
- Citations by year
- 2026: 44 · 2025: 39 · 2024: 41 · 2023: 52 · 2022: 29 · 2021: 22 · 2020: 2
- FWCI
- 16.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Keywords
- heart rate variability, photoplethysmography, Fitbit, autonomic nervous system, parasympathetic function, sympathetic function, diurnal variation, sex differences, age-related decline, physical activity, Poincaré plot, low-frequency power, cardiovascular risk, wearable health devices, RMSSD, high-frequency power, cardiovascular mortality risk factors
- MeSH terms
- Wearable Electronic Devices, Adolescent, Adult, Age Factors, Aged, Cardiovascular Diseases, Cross-Sectional Studies, Female, Heart Rate, Humans, Male, Middle Aged, Parasympathetic Nervous System, Prospective Studies, Sex Factors, Sympathetic Nervous System, Exercise, Photoplethysmography, Telemedicine, Monitoring, Ambulatory, Young Adult
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