Cited by 229Heart Rate Variability and Autonomic ControlNon-Invasive Vital Sign MonitoringPhysical Activity and Health

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 access

Natarajan, Aravind, Pantelopoulos, Alexandros, Emir-Farinas, Hulya, Natarajan, Pradeep·2020·The Lancet Digital Health

Open this paper in CoChat — free account

Abstract

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.

Integrity signals

Registry

Crossref · 10.1016/s2589-7500(20)30246-6

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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
Twenty-Four Hour Time Domain Heart Rate Variability and Heart Rate: Relations to Age and Gender Over Nine Decades
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
Low Heart Rate Variability in a 2-Minute Rhythm Strip Predicts Risk of Coronary Heart Disease and Mortality From Several Causes
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
A Quantitative Systematic Review of Normal Values for Short-Term Heart Rate Variability in Healthy Adults: REVIEW OF SHORT-TERM HRV VALUES
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
Advances in heart rate variability signal analysis: Joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society
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

Show all 32 references

Research works printed but not linked: 1

Irurzun, 2018, Scaling behaviour in the heart rate variability characteristics with age, arXiv

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-citation
The 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
Wearable Devices for Physical Activity and Healthcare Monitoring in Elderly People: A Critical Review
Teixeira, E., Fonseca, H., Diniz‐Sousa, F., Veras, L., Boppre, G., de Oliveira, J. J. F., Pinto, D., Alves, A. J., Barbosa, A., Mendes, R., et al. (2021) · Geriatrics · doi:10.3390/geriatrics6020038 · Cited by 179
Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet
Charlton, P. H., Paliakaitė, B., Pilt, K., Bachler, M., Zanelli, S., Kulin, D., Allen, J., Hallab, M., Bianchini, E., Mayer, C. C., et al. (2021) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00392.2021 · Cited by 166
Measurement of respiratory rate using wearable devices and applications to COVID-19 detection
Natarajan, A., Su, H.-W., Heneghan, C., Blunt, L., O’Connor, C., & Niehaus, L. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00493-6 · Cited by 99
Self-citation
Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine
Petek, B. J., Al‐Alusi, M. A., Moulson, N., Grant, A. J., Besson, C., Guseh, J. S., Wasfy, M. M., Grémeaux, V., Churchill, T. W., & Baggish, A. L. (2023) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2023.04.054 · Cited by 113
The Potential Role of Digital Health in Obesity Care
Hinchliffe, N., Capehorn, M., Bewick, M., & Feenie, J. (2022) · Advances in Therapy · doi:10.1007/s12325-022-02265-4 · Cited by 100
Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU
Lee, H., Yang, H.-L., Ryu, H. G., Jung, C.-W., Cho, Y. J., Yoon, H., Yoon, H.-K., & Lee, H. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00960-2 · Cited by 73
Moving beyond disciplinary silos towards a transdisciplinary model of wellbeing: an invited review
Mead, J., Fisher, Z., & Kemp, A. H. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.642093 · Cited by 71
Assessing the clinical reliability of short-term heart rate variability: insights from controlled dual-environment and dual-position measurements
Besson, C., Baggish, A. L., Monteventi, P., Schmitt, L., Stucky, F., & Grémeaux, V. (2025) · Scientific Reports · doi:10.1038/s41598-025-89892-3 · Cited by 74
Long-term association of ultra-short heart rate variability with cardiovascular events
Orini, M., van Duijvenboden, S., Young, W. J., Ramírez, J., Jones, A. R. C., Hughes, A. D., Tinker, A., Munroe, P. B., & Lambiase, P. D. (2023) · Scientific Reports · doi:10.1038/s41598-023-45988-2 · Cited by 51
pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis
Goda, M. Á., Charlton, P. H., & Behar, J. A. (2024) · Physiological Measurement · doi:10.1088/1361-6579/ad33a2 · Cited by 59
Unconstrained Piezoelectric Vascular Electronics for Wireless Monitoring of Hemodynamics and Cardiovascular Health
Tang, C., Liu, Z., Hu, Q., Jiang, Z., Zheng, M., Xiong, C., Wang, S., Yao, S., Zhao, Y., Wan, X., et al. (2023) · Small · doi:10.1002/smll.202304752 · Cited by 50
The Impact of Heart Rate Variability Monitoring on Preventing Severe Cardiovascular Events
Turcu, A.-M., Ilie, A. C., Ștefăniu, R., Țăranu, S. M., Sandu, I. A., Alexa‐Stratulat, T., Pîslaru, A. I., & Alexa, I. D. (2023) · Diagnostics · doi:10.3390/diagnostics13142382 · Cited by 46
Historical development of accelerometry measures and methods for physical activity and sedentary behavior research worldwide: A scoping review of observational studies of adults
Evenson, K. R., Scherer, E. M., Peter‐Marske, K. M., Cuthbertson, C. C., & Eckman, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276890 · Cited by 42
HRV Monitoring Using Commercial Wearable Devices as a Health Indicator for Older Persons during the Pandemic
Rodrigues, E., Lima, D., Barbosa, P. E. E. S., Gonzaga, K. D. A., Guerra, R. O., Pimentel, M. M., Barbosa, H. A., & Maciel, Á. C. C. (2022) · Sensors · doi:10.3390/s22052001 · Cited by 38
Heart rate variability during mindful breathing meditation
Natarajan, A. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2022.1017350 · Cited by 37
Self-citation
The emotion paradox in the aging body and brain
Mather, M. (2024) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15138 · Cited by 38
Accurate and early detection of sleepiness, fatigue and stress levels in drivers through Heart Rate Variability parameters: a systematic review
Burlacu, A., Brinza, C., Brezulianu, A., & Covic, A. C. (2021) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2203090 · Cited by 34
On the Reliability of Wearable Technology: A Tutorial on Measuring Heart Rate and Heart Rate Variability in the Wild
Dudarev, V., Barral, O., Zhang, C., Davis, G., & Enns, J. T. (2023) · Sensors · doi:10.3390/s23135863 · Cited by 35
Emotion Recognition: Photoplethysmography and Electrocardiography in Comparison
Rinella, S., Massimino, S., Fallica, P. G., Giacobbe, A., Donato, N., Coco, M., Neri, G., Parenti, R., Perciavalle, V., & Sabrina Conoci, A. (2022) · Biosensors · doi:10.3390/bios12100811 · Cited by 32
Predicting cardiovascular disease risk using photoplethysmography and deep learning
Weng, W., Baur, S., Daswani, M., Chen, C., Harrell, L., Kakarmath, S. S., Jabara, M., Behsaz, B., McLean, C. Y., Matias, Y., et al. (2024) · PLOS Global Public Health · doi:10.1371/journal.pgph.0003204 · Cited by 46
Digital biomarkers of ageing for monitoring physiological systems in community-dwelling adults
Lu, J. K., Wang, W., Mahadzir, M. D. A., Poganik, J. R., Moqri, M., Herzog, C. M. S., Verdin, E. M., Sebastiano, V., Gladyshev, V. N., Maier, A. B., et al. (2025) · The Lancet Healthy Longevity · doi:10.1016/j.lanhl.2025.100725 · Cited by 34
Time-Domain Heart Rate Variability Features for Automatic Congestive Heart Failure Prediction
Moses, J. C., Adibi, S., Angelova, M. N., & Islam, S. M. S. (2023) · ESC Heart Failure · doi:10.1002/ehf2.14593 · Cited by 30
Should We Use Activity Tracker Data From Smartphones and Wearables to Understand Population Physical Activity Patterns?
Mair, J. L., Hayes, L. D., Campbell, A. K., & Sculthorpe, N. F. (2021) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2021-0012 · Cited by 30
Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study
Song, S., Seo, Y., Hwang, S., Kim, H., & Kim, J. (2024) · JMIR mhealth and uhealth · doi:10.2196/55842 · Cited by 33
Computer Mouse Movements as an Indicator of Work Stress: Longitudinal Observational Field Study
Banholzer, N., Feuerriegel, S., Fleisch, E., Bauer, G. F., & Kowatsch, T. (2021) · Journal of Medical Internet Research · doi:10.2196/27121 · Cited by 29
Validity and Efficacy of the Elite HRV Smartphone Application during Slow-Paced Breathing
Vondrasek, J. D., Riemann, B. L., Grosicki, G. J., & Flatt, A. A. (2023) · Sensors · doi:10.3390/s23239496 · Cited by 39
Change in outdoor walking behavior during the coronavirus disease pandemic in Japan: A longitudinal study
Obuchi, S. P., Kawai, H., Ejiri, M., Ito, K., & Murakawa, K. (2021) · Gait & Posture · doi:10.1016/j.gaitpost.2021.05.005 · Cited by 28
ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects
Parlato, S. J., Centracchio, J., Esposito, D., Bifulco, P., & Andreozzi, E. (2023) · Sensors · doi:10.3390/s23198114 · Cited by 27
Autonomic dysfunction in neurodegenerative disease
Mather, M. (2025) · Nature reviews. Neuroscience · doi:10.1038/s41583-025-00911-8 · Cited by 28
Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review
Lü, L., Zhu, T., Morelli, D., Creagh, A. P., Liu, Z., Yang, J., Liu, F., Zhang, Y., & Clifton, D. A. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3271595 · Cited by 28
Assessment of Heat Exposure and Health Outcomes in Rural Populations of Western Kenya by Using Wearable Devices: Observational Case Study
Matzke, I., Huhn, S., Koch, M., Maggioni, M. A., Munga, S., Muma, J. O., Odhiambo, C. O., Kwaro, D., Obor, D., Bärnighausen, T., et al. (2024) · JMIR mhealth and uhealth · doi:10.2196/54669 · Cited by 22
Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals
Cui, H., Wang, Z., Yu, B., Jiang, F., Geng, N., Li, Y., Xu, L., Zheng, D., Zhang, B., Lu, P., et al. (2022) · Sensors · doi:10.3390/s22062423 · Cited by 24
Self-recorded heart rate variability profiles are associated with health and lifestyle markers in young adults
Grosicki, G. J., Culver, M. N., McMillan, N. K., Cross, B. L., Montoye, A. H. K., Riemann, B. L., & Flatt, A. A. (2022) · Clinical Autonomic Research · doi:10.1007/s10286-022-00884-z · Cited by 23
Association between distinct coping styles and heart rate variability changes to an acute psychosocial stress task
Machado, A. V., Pereira, M. G., Souza, G. G. L., Xavier, M., Aguiar, C., Oliveira, L., & Mocaiber, I. (2021) · Scientific Reports · doi:10.1038/s41598-021-03386-6 · Cited by 25
Multimodal Assessment of the Pulse Rate Variability Analysis Module of a Photoplethysmography-Based Telemedicine System
Antali, F., Kulin, D., Lucz, K. I., Szabó, B. M., Szücs, L., Kulin, S., & Miklós, Z. (2021) · Sensors · doi:10.3390/s21165544 · Cited by 19
Decrease of coherence between the respiration and parasympathetic control of the heart rate with aging
Пономаренко, В. И., Karavaev, A. S., Borovkova, E. I., Hramkov, A. N., Kiselev, A. R., Prokhorov, M. D., & Penzel, T. (2021) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0056624 · Cited by 23
The Complex Construct of Wellbeing and the Role of Vagal Function
Wilkie, L., Fisher, Z., & Kemp, A. H. (2022) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2022.925664 · Cited by 18
“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.”
Background
Heart rate variability in patients with cardiovascular diseases
Khan, M. S., Javaid, S. S., Mentz, R. J., Lindenfeld, J., Wu, H., Prochaska, J. H., Johansen, J. B., Wild, P. S., Linz, D., Dinh, W., et al. (2025) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2025.06.003 · Cited by 20
A Comparative Study Between ECG- and PPG-Based Heart Rate Sensors for Heart Rate Variability Measurements: Influence of Body Position, Duration, Sex, and Age
Coste, A., Millour, G., & Hausswirth, C. (2025) · Sensors · doi:10.3390/s25185745 · Cited by 20
The effect of exercise training on heart rate variability in patients with hypertension: A systematic review and meta-analysis
Deng, Y., Zeng, X., Tang, C., Hou, X., Zhang, Y., & Shi, L. (2024) · Journal of Sports Sciences · doi:10.1080/02640414.2024.2388984 · Cited by 18
A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables
Yaşar, M. N., Sica, M., O’Flynn, B., Tedesco, S. A., & Menolotto, M. (2024) · Scientific Data · doi:10.1038/s41597-024-03254-8 · Cited by 19
Pulse rate variability is not the same as heart rate variability: findings from a large, diverse clinical population study
Kantrowitz, A. B., Ben‐David, K., Morris, M., Wittels, H. L., Wishon, M. J., McDonald, S. M., Renaghan, E., Feigenbaum, L. A., & Wittels, S. H. (2025) · Frontiers in Physiology · doi:10.3389/fphys.2025.1630032 · Cited by 23
Synchronization of the Processes of Autonomic Control of Blood Circulation in Humans Is Different in the Awake State and in Sleep Stages
Karavaev, A. S., Skazkina, V. V., Borovkova, E. I., Prokhorov, M. D., Hramkov, A. N., Пономаренко, В. И., Евгеньевна, Р. А., Gridnev, V. I., Kiselev, A. R., Kuznetsov, N. V., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.791510 · Cited by 16
Analysis of Gender Differences in HRV of Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Mobile-Health Technology
Capdevila, L., Castro‐Marrero, J., Alegre, J., Ramos-Castro, J. J., & Escorihuela, R. M. (2021) · Sensors · doi:10.3390/s21113746 · Cited by 19
Continuous monitoring with wearables in multiple sclerosis reveals an association of cardiac autonomic dysfunction with disease severity
Hilty, M., Oldrati, P., Barrios, L., Müller, T., Blumer, C., Foege, M., Holz, C., & Lutterotti, A. (2022) · Multiple Sclerosis Journal - Experimental Translational and Clinical · doi:10.1177/20552173221103436 · Cited by 15
Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
Zheng, C., Yong, C., Wei, Q., & Qiao, F. (2025) · Sensors · doi:10.3390/s25164913 · Cited by 17
Circadian system and aging: where both times interact
Cobarro, C., Soares, L. I., Kutsenko, Y., & Tomás‐Loba, A. (2025) · Frontiers in Aging · doi:10.3389/fragi.2025.1646794 · Cited by 15
Nitrate-rich beet juice intake on cardiovascular performance in response to exercise in postmenopausal women with arterial hypertension: study protocol for a randomized controlled trial
Benjamim, C. J. R., Sousa, Y. B. A., Porto, A. A., Pontes, Y. M. D. M., Tavares, S. S., Rodrigues, G. D. S., da Silva, L. S. L., Gonçalves, L. D. S., Guimarães, C. S., Rebelo, M. A., et al. (2023) · Trials · doi:10.1186/s13063-023-07117-2 · Cited by 15
Detection of Walk Tests in Free-Living Activities Using a Wrist-Worn Device
Sokas, D., Paliakaitė, B., Rapalis, A., Marozas, V., Bailón, R., & Petrėnas, A. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.706545 · Cited by 15
Circadian rhythm of heart rate and activity: A cross-sectional study
Natarajan, A., Gleichauf, K., Khalid, M., Heneghan, C., & Schneider, L. D. (2025) · Chronobiology International · doi:10.1080/07420528.2024.2446622 · Cited by 13
Self-citation
Four Sessions of Combining Wearable Devices and Heart Rate Variability (HRV) Biofeedback are Needed to Increase HRV Indices and Decrease Breathing Rates
Lin, I., Chen, T., Tsai, H.-Y., & Fan, S. (2022) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-022-09567-x · Cited by 16
Using wearable activity trackers for research in the global south: Lessons learned from adolescent psychotherapy research in Kenya
Johnson, N. E., Venturo‐Conerly, K. E., & Rusch, T. W. (2023) · Cambridge Prisms Global Mental Health · doi:10.1017/gmh.2023.85 · Cited by 13
Prediction of the efficacy of group cognitive behavioral therapy using heart rate variability based smart wearable devices: a randomized controlled study
Lin, Z., Zheng, J., Wang, Y., Su, Z., Zhu, R., Liu, R., Wei, Y., Zhang, X., & Wang, F. (2024) · BMC Psychiatry · doi:10.1186/s12888-024-05638-x · Cited by 16
Heart rate and breathing effects on attention and memory (HeartBEAM): study protocol for a randomized controlled trial in older adults
Nashiro, K., Yoo, H. J., Cho, C., Kim, A. J., Nasseri, P., Min, J., Dahl, M. J., Mercer, N., Choupan, J., Choi, P., et al. (2024) · Trials · doi:10.1186/s13063-024-07943-y · Cited by 12
Accuracy of smartwatches for the remote assessment of exercise capacity
Jamieson, A., Jones, S. H., Chaturvedi, N., Hughes, A. D., & Orini, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-74140-x · Cited by 14
Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical Bayes modeling
Srinivasan, K. K., Currim, F., Lindberg, C. M., Razjouyan, J., Gilligan, B., Lee, H., Canada, K., Goebel, N., Mehl, M. R., Lunden, M. M., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-022-00727-1 · Cited by 14
A PPG Signal Dataset Collected in Semi-Naturalistic Settings Using Galaxy Watch
Park, S., Zheng, D., & Lee, U. (2025) · Scientific Data · doi:10.1038/s41597-025-05152-z · Cited by 12
Smartwatch-derived sleep and heart rate measures complement step counts in explaining established metrics of MS severity
Woelfle, T., Pless, S., Reyes, Ó., Wiencierz, A., Kappos, L., Granziera, C., & Lorscheider, J. (2023) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2023.105104 · Cited by 14
Evaluating a 12-week aerobic exercise intervention in adults with persisting post-concussive symptoms
Mercier, L. J., McIntosh, S. J., Boucher, C., Joyce, J. M., Batycky, J. M., Galarneau, J.-M. F., Burma, J. S., Smirl, J. D., Esser, M. J., Schneider, K., et al. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1482266 · Cited by 13
Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19
Natarajan, A., Su, H.-W., & Heneghan, C. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.898251 · Cited by 12
Self-citation
Humming (Simple Bhramari Pranayama) as a Stress Buster: A Holter-Based Study to Analyze Heart Rate Variability (HRV) Parameters During Bhramari, Physical Activity, Emotional Stress, and Sleep
Trivedi, G. Y., Sharma, K. H., Saboo, B., Kathirvel, S., Konat, A., Zapadia, V., Prajapati, P. J., Benani, U., Patel, K., & Shah, S. D. (2023) · Cureus · doi:10.7759/cureus.37527 · Cited by 20
Exploring the Effect of Long Naps on Handball Performance and Heart Rate Variability
Nishida, M., Yamamoto, K., Murata, Y., Ichinose, A., & Shioda, K. (2021) · Sports Medicine International Open · doi:10.1055/a-1551-8054 · Cited by 11
Forests serve vulnerable groups in times of crises: improved mental health of older adults by individual forest walking during the COVID-19 pandemic
Lamatungga, K. E., Pichlerová, M., Halamová, J., Kanovský, M., Tamatam, D., Ježová, D., & Pichler, V. (2024) · Frontiers in Forests and Global Change · doi:10.3389/ffgc.2024.1287266 · Cited by 10
Heart rate variability: Obtaining the stress score from SDNN values
Salazar-Martı́nez, E., Orellana, J. N., & Cachadiña, E. S. (2024) · Isokinetics and Exercise Science · doi:10.3233/ies-230088 · Cited by 10
Decreased Cardiac Autonomic Function is Associated with Higher Exacerbation Risk and Symptom Burden in Chronic Obstructive Pulmonary Disease
Raju, S., Woo, H., Fawzy, A., Putcha, N., Balasubramanian, A., Mathai, S. C., Berger, R. D., Hansel, N. N., & McCormack, M. C. (2023) · Chronic Obstructive Pulmonary Diseases Journal of the COPD Foundation · doi:10.15326/jcopdf.2023.0410 · Cited by 10
Considerations on How to Prevent Parkinson’s Disease Through Exercise
Oosterhof, T. H., Darweesh, S. K. L., Bloem, B. R., & de Vries, N. M. (2024) · Journal of Parkinson s Disease · doi:10.3233/jpd-240091 · Cited by 13
Changes in Short-Term and Ultra-Short Term Heart Rate, Respiratory Rate, and Time-Domain Heart Rate Variability Parameters during Sympathetic Nervous System Activity Stimulation in Elite Modern Pentathlonists—A Pilot Study
Gąsior, J. S., Hoffmann, B., Silva, L. E. V., Małek, Ł. A., Flatt, A. A., Baranowski, R., & Werner, B. (2020) · Diagnostics · doi:10.3390/diagnostics10121104 · Cited by 10
Wearable photoplethysmography devices
Charlton, P. H., & Marozas, V. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823374-0.00011-6 · Cited by 10
Wearable derived cardiovascular responses to stressors in free-living conditions
Presby, D. M., Jasinski, S. R., & Capodilupo, E. R. (2023) · PLoS ONE · doi:10.1371/journal.pone.0285332 · Cited by 10
Continuous Glycemic Monitoring Enabled by A Wi‐Fi Energy‐Harvesting Wearable Sweat‐Sensing Patch
Mirlou, F., Abbasiasl, T., Mirzajani, H., Istif, E., Akhtar, M. J., Cakır, C., & Beker, L. (2024) · Advanced Materials Technologies · doi:10.1002/admt.202301583 · Cited by 9
The Utilization of Heart Rate Variability for Autonomic Nervous System Assessment in Healthy Pregnant Women: Systematic Review
Sharifi-Heris, Z., Rahmani, A. M., Onwuka, J., & Bender, M. (2022) · JMIR Bioinformatics and Biotechnology · doi:10.2196/36791 · Cited by 9
Monitoring Non-dipping Heart Rate by Consumer-Grade Wrist-Worn Devices: An Avenue for Cardiovascular Risk Assessment in Hypertension
Baka, T., & Šimko, F. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.711417 · Cited by 9
The Usefulness of Assessing Heart Rate Variability in Patients with Acute Myocardial Infarction (HeaRt-V-AMI)
Brinza, C., Floria, M., Covic, A. C., Covic, A. S., Scripcariu, D. V., & Burlacu, A. (2022) · Sensors · doi:10.3390/s22093571 · Cited by 9
HRV based feature selection for congestive heart failure and normal sinus rhythm for meticulous presaging of heart disease using machine learning
Aggarwal, R., & Kumar, S. (2022) · Measurement Sensors · doi:10.1016/j.measen.2022.100573 · Cited by 9
Factors Associated with Reduced Heart Rate Variability in the General Japanese Population: The Iwaki Cross-Sectional Research Study
Tsubokawa, M., Nishimura, M., Tamada, Y., & Nakaji, S. (2022) · Healthcare · doi:10.3390/healthcare10050793 · Cited by 9
Multiple Channel Electrocardiogram QRS Detection by Temporal Pattern SearchPreprint
Hopenfeld, B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.15.456413 · Cited by 9
Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features
Moebus, M., & Holz, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305258 · Cited by 9
Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography
Rinkevičius, M., Charlton, P. H., Bailón, R., & Marozas, V. (2023) · Sensors · doi:10.3390/s23042220 · Cited by 11
Advanced cardiovascular physiology in an individual with type 1 diabetes after 10-year ketogenic diet
Watso, J. C., Robinson, A. T., Singar, S., Cuba, J. N., & Koutnik, A. P. (2024) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00694.2023 · Cited by 8
Cardiovascular autonomic dysfunction precedes cardiovascular disease and all‐cause mortality: 11‐year follow‐up in the ADDITION ‐ PRO study
Schaarup, J. R., Bjerg, L., Hansen, C. S., Grove, E. L., Andersen, S. T., Vistisen, D., Brage, S., Sandbæk, A., & Witte, D. R. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16566 · Cited by 7
Contribution of Cardiorespiratory Coupling to the Irregular Dynamics of the Human Cardiovascular System
Ishbulatov, Y. M., Bibicheva, T. S., Gridnev, V. I., Prokhorov, M. D., Ogneva, M. V., Kiselev, A. R., & Karavaev, A. S. (2022) · Mathematics · doi:10.3390/math10071088 · Cited by 7
Efficacy and Validity of the Elite HRV Smartphone Application During Slow-Paced BreathingPreprint
Vondrasek, J. D., Riemann, B. L., Grosicki, G. J., & Flatt, A. A. (2023) · Research Square · doi:10.21203/rs.3.rs-3139466/v1 · Cited by 7
Typical everyday movements cause specific patterns in heart rate
Heidelbach, M. J., Cysarz, D., & Edelhäuser, F. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2024.1379739 · Cited by 5
A bibliometric analysis of wearable device research in sleep science: Trends and hotspots
Lin, X. H., Guo-qing, Z., Cao, S., Wang, S., Wang, F., & Xu, M. (2025) · Medicine · doi:10.1097/md.0000000000042853 · Cited by 7
Heart rate variability and its modulation by nutrients: a narrative review on implications for cardiovascular aging
Zhao, Y., Chen, P., Zhang, Y., Huo, S., Yu, D., Zeng, X., & Zhang, W. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1654796 · Cited by 6
Reliable wrist PPG monitoring by mitigating poor skin sensor contact
Pham, H. M., Ho, M. Y., Zhang, Y., Spathis, D., Saeed, A., & Ma, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-31883-5 · Cited by 13
Frequency-Domain Features and Low-Frequency Synchronization of Photoplethysmographic Waveform Variability and Heart Rate Variability with Increasing Severity of Cardiovascular Diseases
Kiselev, A. R., Posnenkova, O. M., Karavaev, A. S., Shvartz, V. A., Novikov, M. Y., & Gridnev, V. I. (2024) · Biomedicines · doi:10.3390/biomedicines12092088 · Cited by 6
Diffuse transmittance visible spectroscopy using smartphone flashlight for photoplethysmography and vital signs measurements
Bachir, W. (2023) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2023.123181 · Cited by 5
Age-related variations in heart rate variability profiles among patients with schizophrenia and major depressive disorder
Gao, Y., Zhou, L., Wu, H., Wei, Y., Tang, X., Xu, L., Hu, Y., Hu, Q., Liu, H., Wang, Z., et al. (2024) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-024-01942-5 · Cited by 5
Implications of heart rate variability measured using wearable electrocardiogram devices in diagnosing Parkinson’s disease and its association with neuroimaging biomarkers: a case-control study
Park, M., Kim, S.-W., Hong, J. Y., Cho, S. P., Park, J. H., Urtnasan, E., & Baek, M. S. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1530240 · Cited by 5
Influences of Intense Physical Effort on the Activity of the Autonomous Nervous System and Stress, as Measured with Photoplethysmography
González-Moro, I. M., Albertus-Cámara, I., & Paredes-Ruiz, M.-J. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192316066 · Cited by 5
Beat-to-beat finger photoplethysmography in atrial fibrillation patients undergoing electrical cardioversion
Saglietto, A., Scarsoglio, S., Canova, D., Ferrari, G. M. D., Ridolfi, L., & Anselmino, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-33952-z · Cited by 5
Real‐time heart rate variability biofeedback amplitude during a large‐scale digital mental health intervention differed by age, gender, and mental and physical health
Aschbacher, K., Mather, M., Lehrer, P. M., Gevirtz, R., Epel, E. S., & Peiper, N. C. (2024) · Psychophysiology · doi:10.1111/psyp.14533 · Cited by 5
Specificity of cranial cutaneous manipulations in modulating autonomic nervous system responses and physiological oscillations: A controlled study
Keller, M., Perlitz, V., Pelz, H., Borik, Š., Repik, I., Geilgens, A., Çotuk, H. B., Müller, G., Mathiak, K., & Mayer, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0317300 · Cited by 5
A fast permutation entropy for pulse rate variability online analysis with one-sample recursion
Zhan, J., Gan, Z., Chou, L., Hu, L., Zhou, Y., Yang, H., & Chou, Y. (2023) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2023.104050 · Cited by 5

Authors: Natarajan, A., Pantelopoulos, A., Emir-Farinas, H., & Natarajan, P.
Affiliations: Fitbit (United States) (US)
Broad Institute (US)
Corresponding author: Natarajan, Pradeep
ORCID: Natarajan, Aravind
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)
DOI: 10.1016/s2589-7500(20)30246-6
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: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks