Heart rate variability measurement through a smart wearable device: another breakthrough for personal health monitoring?
Open accessAbstract
Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.
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Red and infrared light wavelengths, which are less absorbed by melanin, have potential to reduce error rates and enhance inclusivity.
“Additionally, Dias et al. (2018) and Castaneda et al. (2018) highlighted the potential of red and infrared light wavelengths, which are less absorbed by melanin, to reduce error rates and enhance inclusivity [16,17].”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [17]
Section 5.2 (PPG; page not provided) says skin color can affect photoplethysmography accuracy. It does not say that red or infrared light is less absorbed by melanin, or that those wavelengths could reduce errors and improve inclusivity. (LLM, conf=0.85)
Experimental — accuracy not yet measuredLi et al. demonstrated reliable HRV tracking overall but did not stratify outcomes by skin tone.
“Li et al. (2023) demonstrated reliable HRV tracking overall but did not stratify outcomes by skin tone [17].”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [17]
Section 7 (page unavailable) reports that verification studies found wearable devices can provide reliable HRV readings compared with clinical ECG. But Li et al. review those studies rather than demonstrate reliability in their own trial, and Section 10 cautions that reliable long-term tracking remains difficult. Section 5.2 notes that skin color can affect PPG accuracy, but the paper does not present HRV outcomes stratified by skin tone. (LLM, conf=0.87)
Experimental — accuracy not yet measuredSocioeconomic disparities in access to smartwatches may interact with skin-tone-related bias, raising additional equity concerns.
“However, regional variations may exist, and socioeconomic disparities in access to these devices may further interact with skin-tone-related bias, raising additional equity concerns [17-23].”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [17]
Section 5.2 (PPG; page not provided) says skin color can affect the accuracy of wearable-device measurements, supporting the skin-tone-related concern. The passages do not address socioeconomic disparities in smartwatch access or say that access and measurement bias interact to create additional equity concerns. (LLM, conf=0.83)
Experimental — accuracy not yet measured
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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 · see citing works
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 · see citing works
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 · see citing works
Thayer, J. F., Åhs, F., Fredrikson, M., Sollers, J. J., & Wager, T. D. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2011.11.009 · Cited by 3,224 · see citing works
Hypothalamic-pituitary-adrenal axis, neuroendocrine factors and stress
Tsigos, C., & Chrousos, G. P. (2002) · Journal of Psychosomatic Research · doi:10.1016/s0022-3999(02)00429-4 · Cited by 2,822 · cited 1× · see citing works
“A meta-analysis showed HRVB improved depression symptoms in several psychophysiological conditions in adults and should be considered a valid technique to increase psychological well-being [84].”
MethodStress and Heart Rate Variability: A Meta-Analysis and Review of the Literature
Kim, H.-G., Cheon, E.-J., Bai, D.-S., Lee, Y. H., & Koo, B.-H. (2018) · Psychiatry Investigation · doi:10.30773/pi.2017.08.17 · Cited by 2,308 · see citing works
Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1901183 · Cited by 1,917 · see citing works
On the Analysis of Fingertip Photoplethysmogram Signals
Elgendi, M. (2012) · Current Cardiology Reviews · doi:10.2174/157340312801215782 · Cited by 1,206 · see citing works
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 · see citing works
Ghamari, M. (2018) · International Journal of Biosensors & Bioelectronics · doi:10.15406/ijbsbe.2018.04.00125 · Cited by 945 · see citing works
La Rovere, M. T., Pinna, G. D., Maestri, R., Mortara, A., Capomolla, S., Febo, O., Ferrari, R., Franchini, M., Gnemmi, M., Opasich, C., et al. (2003) · Circulation · doi:10.1161/01.cir.0000047275.25795.17 · Cited by 917 · see citing works
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 · see citing works
Heart Rate Variability ? A Historical Perspective
Billman, G. E. (2011) · Frontiers in Physiology · doi:10.3389/fphys.2011.00086 · Cited by 786 · see citing works
How accurate is pulse rate variability as an estimate of heart rate variability?
Schäfer, A., & Vagedes, J. (2013) · International Journal of Cardiology · doi:10.1016/j.ijcard.2012.03.119 · Cited by 661 · see citing works
The use of photoplethysmography for assessing hypertension
Elgendi, M., Fletcher, R., Liang, Y., Howard, N., Lovell, N. H., Abbott, D., Lim, K., & Ward, R. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0136-7 · Cited by 625 · see citing works
Kemp, A. H., & Quintana, D. S. (2013) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2013.06.018 · Cited by 538 · see citing works
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 · see citing works
Power spectrum analysis of heart rate variability: a tool to explore neural regulatory mechanisms.
Malliani, A., Lombardi, F., & Pagani, M. (1994) · Heart · doi:10.1136/hrt.71.1.1 · Cited by 492 · see citing works
Brosschot, J. F., Van Dijk, E., & Thayer, J. F. (2007) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2006.07.016 · Cited by 474 · see citing works
Hypertension, Blood Pressure, and Heart Rate Variability
Schroeder, E. B., Liao, D., Chambless, L. E., Prineas, R. J., Evans, G. W., & HEISS, G. M. (2003) · Hypertension · doi:10.1161/01.hyp.0000100444.71069.73 · Cited by 452 · see citing works
Goel, K., Lennon, R. J., Tilbury, R. T., Squires, R. W., & Thomas, R. J. (2011) · Circulation · doi:10.1161/circulationaha.110.983536 · Cited by 420 · see citing works
Heart-Rate Variability—More than Heart Beats?
Ernst, G. (2017) · Frontiers in Public Health · doi:10.3389/fpubh.2017.00240 · Cited by 416 · see citing works
Sajadieh, A. (2004) · European Heart Journal · doi:10.1016/j.ehj.2003.12.003 · Cited by 393 · see citing works
Cardiac rehabilitation outcomes following a 6-week program of PCI and CABG Patients
Jelinek, H. F., Huang, Z., Khandoker, A. H., Chang, D., & Kiat, H. (2013) · Frontiers in Physiology · doi:10.3389/fphys.2013.00302 · Cited by 341 · see citing works
Miller, D. J., Sargent, C., & Roach, G. D. (2022) · Sensors · doi:10.3390/s22166317 · Cited by 328 · cited 2× · see citing works
“…differ in the error rates between methodologies [58] Other veri fi cation studies have been conducted for brands such as Oura [59], Whoop [60], and Apple Watch [38], and for assessing the use of di ff erent apps to acquire camera-based HRV, including Well-tory [61] and HRV4 training [62].”
BackgroundPlews, D. J., Scott, B., Altini, M., Wood, M., Kilding, A. E., & Laursen, P. B. (2017) · International Journal of Sports Physiology and Performance · doi:10.1123/ijspp.2016-0668 · Cited by 323 · see citing works
Hellhammer, J., & Schubert, M. (2012) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2011.05.012 · Cited by 304 · see citing works
Alian, A. A., & Shelley, K. H. (2014) · Best Practice & Research Clinical Anaesthesiology · doi:10.1016/j.bpa.2014.08.006 · Cited by 289 · see citing works
Schneider, M., & Schwerdtfeger, A. (2020) · Psychological Medicine · doi:10.1017/s003329172000207x · Cited by 269 · see citing works
Siepmann, M., Aykac, V., Unterdörfer, J., Petrowski, K., & Mueck-Weymann, M. (2008) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-008-9064-z · Cited by 265 · see citing works
Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data
Moshe, I., Terhorst, Y., Opoku Asare, K., Sander, L. B., Ferreira, D., Baumeister, H., Mohr, D. C., & Pulkki-Råback, L. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.625247 · Cited by 261 · see citing works
Can Wearable Devices Accurately Measure Heart Rate Variability? A Systematic Review
Georgiou, K., Larentzakis, A. V., Khamis, N. N., Alsuhaibani, G. I., Alaska, Y. A., & Giallafos, E. J. (2018) · Folia Medica · doi:10.2478/folmed-2018-0012 · Cited by 244 · see citing works
Chan, P., Wong, C., Poh, Y. C., Pun, L., Leung, W. W., Wong, Y., Wong, M. M., Poh, M., Chu, D. W., & Siu, C. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.116.003428 · Cited by 240 · see citing works
Hernando, D., Roca, S., Sancho, J., Alesanco, Á., & Bailón, R. (2018) · Sensors · doi:10.3390/s18082619 · Cited by 225 · see citing works
Protocol for Heart Rate Variability Biofeedback Training
Lehrer, P. M., Vaschillo, B., Zucker, T., Graves, J., Karavidas, M. K., Aviles‐Velez, M., & Wamboldt, F. S. (2013) · Biofeedback · doi:10.5298/1081-5937-41.3.08 · Cited by 215 · see citing works
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 213 · see citing works
Dobbs, W. C., Fedewa, M. V., MacDonald, H. V., Holmes, C. J., Cicone, Z. S., Plews, D. J., & Esco, M. R. (2019) · Sports Medicine · doi:10.1007/s40279-019-01061-5 · Cited by 194 · see citing works
Kinnunen, H., Rantanen, A., Kenttä, T., & Koskimäki, H. (2020) · Physiological Measurement · doi:10.1088/1361-6579/ab840a · Cited by 184 · see citing works
Bolaños, M. J., Nazeran, H., & Haltiwanger, E. P. (2006) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2006.260607 · Cited by 176 · see citing works
Heart rate variability in patients with anxiety disorders: A systematic review and meta‐analysis
Cheng, Y., Su, M., Liu, C., Huang, Y., & Huang, W. (2022) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13356 · Cited by 175 · see citing works
Hallman, D., Olsson, E. M. G., von Schéele, B., Melin, L., & Lyskov, E. (2011) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-011-9147-0 · Cited by 174 · see citing works
Khan, A. A., Lip, G. Y. H., & Shantsila, A. (2019) · European Journal of Clinical Investigation · doi:10.1111/eci.13174 · Cited by 172 · see citing works
Hinde, K., White, G., & Armstrong, N. C. D. (2021) · Sensors · doi:10.3390/s21041061 · Cited by 172 · see citing works
A meta-analysis on heart rate variability biofeedback and depressive symptoms
Pizzoli, S. F. M., Marzorati, C., Gatti, D., Monzani, D., Mazzocco, K., & Pravettoni, G. (2021) · Scientific Reports · doi:10.1038/s41598-021-86149-7 · Cited by 170 · see citing works
Hirten, R. P., Danieletto, M., Tomalin, L. E., Choi, K. H., Zweig, M., Golden, E., Kaur, S., Helmus, D. S., Biello, A., Pyzik, R., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/26107 · Cited by 161 · see citing works
Mejía‐Mejía, E., May, J. M., Torres, R., & Kyriacou, P. A. (2020) · Physiological Measurement · doi:10.1088/1361-6579/ab998c · Cited by 160 · see citing works
Cao, R., Azimi, I., Sarhaddi, F., Niela-Vilen, H., Axelin, A., Liljeberg, P., & Rahmani, A. M. (2022) · Journal of Medical Internet Research · doi:10.2196/27487 · Cited by 159 · see citing works
Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction
Attia, Z. I., Harmon, D. M., Dugan, J., Manka, L., Lopez-Jimenez, F., Lerman, A., Siontis, K. C., Noseworthy, P. A., Yao, X., Klavetter, E. W., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02053-1 · Cited by 154 · see citing works
Evaluating Training Adaptation With Heart-Rate Measures: A Methodological Comparison
Plews, D. J., Laursen, P. B., Kilding, A. E., & Buchheit, M. (2013) · International Journal of Sports Physiology and Performance · doi:10.1123/ijspp.8.6.688 · Cited by 151 · see citing works
Accuracy of wearable heart rate monitors in cardiac rehabilitation
Etiwy, M., Akhrass, Z., Gillinov, L., Alashi, A., Wang, R., Blackburn, G., Gillinov, S. M., Phelan, D., Gillinov, A. M., Houghtaling, P. L., et al. (2019) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt.2019.04.08 · Cited by 143 · see citing works
Henriques, G. R., Keffer, S. L., Abrahamson, C., & Horst, S. J. (2011) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-011-9151-4 · Cited by 139 · see citing works
Lin, G., Xiang, Q., Fu, X., Wang, S., Wang, S., Chen, S., Shao, L., Zhao, Y., & Wang, T. (2012) · The Journal of Alternative and Complementary Medicine · doi:10.1089/acm.2010.0607 · Cited by 136 · see citing works
Comparison of HRV parameters derived from photoplethysmography and electrocardiography signals
Jeyhani, V., Mahdiani, S., Peltokangas, M., & Vehkaoja, A. T. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319747 · Cited by 136 · see citing works
Comparison of Heart Rate Variability from PPG with That from ECG
Lin, W.-H., Wu, D., Li, C., Zhang, H., & Zhang, Y. (2013) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-03005-0_54 · Cited by 135 · see citing works
A Practical Guide to Resonance Frequency Assessment for Heart Rate Variability Biofeedback
Shaffer, F., & Meehan, Z. M. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.570400 · Cited by 133 · see citing works
Deep learning with wearable based heart rate variability for prediction of mental and general health
Coutts, L. V., Plans, D., Brown, A. W., & Collomosse, J. P. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103610 · Cited by 123 · see citing works
Moya-Ramon, M., Mateo-March, M., Peña-González, I., Zabala, M., & Javaloyes, A. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106696 · Cited by 122 · see citing works
Comparison of finger plethysmograph to ECG in the measurement of heart rate variability
Giardino, N. D., Lehrer, P. M., & Edelberg, R. (2002) · Psychophysiology · doi:10.1111/1469-8986.3920246 · Cited by 119 · see citing works
Elevated hair cortisol levels in chronically stressed dementia caregivers
Tobias Stalder, A., Tietze, A., Steudte‐Schmiedgen, S., Alexander, N., Dettenborn, L., & Kirschbaum, C. (2014) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2014.04.021 · Cited by 115 · see citing works
Goldenberg, I., Goldkorn, R., Nir, S., Einhorn, M., Levitan, J., Kuperstein, R., Klempfner, R., & Johnson, B. D. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.014540 · Cited by 112 · see citing works
Wearables in cardiology: Here to stay
Dagher, L., Shi, H., Zhao, Y., & Marrouche, N. F. (2020) · Heart Rhythm · doi:10.1016/j.hrthm.2020.02.023 · Cited by 107 · see citing works
Factors influencing heart rate variability
Sammito, S., & Böckelmann, I. (2016) · International Cardiovascular Forum Journal · doi:10.17987/icfj.v6i0.242 · Cited by 98 · see citing works
Stone, J. D., Ulman, H. K., Tran, K., Thompson, A. G., Halter, M. D., Ramadan, J. H., Stephenson, M., Finomore, V. S., Galster, S. M., Rezai, A. R., et al. (2021) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2021.585870 · Cited by 95 · see citing works
Lubitz, S. A., Faranesh, A. Z., Atlas, S. J., McManus, D. D., Singer, D. E., Pagoto, S., Pantelopoulos, A., & Foulkes, A. S. (2021) · American Heart Journal · doi:10.1016/j.ahj.2021.04.003 · Cited by 94 · see citing works
Heart Rate Variability as an Index of Resilience
An, E., Nolty, A., Amano, S. S., Rizzo, A., Buckwalter, J. G., & Rensberger, J. K. (2019) · Military Medicine · doi:10.1093/milmed/usz325 · Cited by 93 · see citing works
Pulse rate variability: a new biomarker, not a surrogate for heart rate variability
Yuda, E., Shibata, M., Ogata, Y., Ueda, N., Yambe, T., Yoshizawa, M., & Hayano, J. (2020) · Journal of PHYSIOLOGICAL ANTHROPOLOGY · doi:10.1186/s40101-020-00233-x · Cited by 92 · see citing works
Altini, M., & Plews, D. J. (2021) · Sensors · doi:10.3390/s21237932 · Cited by 88 · see citing works
Goel, K., Pack, Q. R., Lahr, B. D., Greason, K. L., López-Jiménez, F. A., Squires, R. W., Zhang, Z., & Thomas, R. J. (2013) · European Journal of Preventive Cardiology · doi:10.1177/2047487313512219 · Cited by 84 · see citing works
Sharma, V. K. (2009) · The Open Cardiovascular Medicine Journal · doi:10.2174/1874192400903010110 · Cited by 82 · see citing works
Dantas, E. M., Sant'Anna, M. L., Andreão, R. V., Gonçalves, C. P., Morra, E. A., Baldo, M. P., Rodrigues, S. L., & Mill, J. G. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.11.004 · Cited by 82 · see citing works
A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability
Sarhaddi, F., Kazemi, K., Azimi, I., Cao, R., Niela‐Vilén, H., Axelin, A., Liljeberg, P., & Rahmani, A. M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268361 · Cited by 82 · see citing works
Heart Rate Variability Biofeedback Reduces Food Cravings in High Food Cravers
Meule, A., Freund, R., Skirde, A. K., Vögele, C., & Kübler, A. (2012) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-012-9197-y · Cited by 81 · see citing works
Short-term heart rate variability in healthy young adults
Koskinen, T., Kähönen, M. A. P., Jula, A., Laitinen, T. P., Keltikangas‐Järvinen, L., Viikari, J. S. A., Välimäki, I. A. T., & Raitakari, O. T. (2008) · Autonomic Neuroscience · doi:10.1016/j.autneu.2008.10.011 · Cited by 79 · see citing works
Düking, P., Zinner, C., Trabelsi, K., Reed, J. L., Holmberg, H., Kunz, P., & Sperlich, B. (2021) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2021.04.012 · Cited by 70 · see citing works
Bellenger, C. R., Miller, D., Halson, S. L., Roach, G. D., & Sargent, C. (2021) · Sensors · doi:10.3390/s21103571 · Cited by 69 · see citing works
Manresa‐Rocamora, A., Sarabia, J. M., Javaloyes, A., Flatt, A. A., & Moya‐Ramón, M. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph181910299 · Cited by 65 · see citing works
Jan, H.-Y., Chen, M.-F., Fu, T., Lin, W., Tsai, C.-L., & Lin, K. (2019) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-019-00468-9 · Cited by 64 · see citing works
Shin, H. (2015) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-015-9798-0 · Cited by 62 · see citing works
Azam, M. A., Katz, J. D., Mohabir, V., & Ritvo, P. (2016) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2016.10.011 · Cited by 59 · see citing works
Morgan, C. A., Aikins, D. E., Steffian, G., Coric, V., & Southwick, S. M. (2006) · Psychophysiology · doi:10.1111/j.1469-8986.2006.00475.x · Cited by 58 · see citing works
Olde Bekkink, M., Koeneman, M., de Galan, B. E., & Bredie, S. J. (2019) · Diabetes Care · doi:10.2337/dc18-1843 · Cited by 58 · see citing works
Shah, S. A., Kambur, T. R., Chan, C., Herrington, D. M., Liu, K., & Shah, S. J. (2013) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2013.04.018 · Cited by 55 · see citing works
Shi, M., He, H., Geng, W., et al. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.00118 · Cited by 52 · see citing works
Lin, I., Fan, S., Lu, H.-C., Lin, T., Chu, C., Kuo, H., Lee, C., & Lu, Y. (2015) · Behaviour Research and Therapy · doi:10.1016/j.brat.2015.05.001 · Cited by 51 · see citing works
Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension
Welykholowa, K., Hosanee, M., Chan, G., Cooper, R. E., Kyriacou, P. A., Zheng, D., Allen, J., Abbott, D., Menon, C., Lovell, N. H., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9041203 · Cited by 51 · see citing works
Comparison of finger plethysmograph to ECG in the measurement of heart rate variability
Giardino, N. D., Lehrer, P. M., & Edelberg, R. (2002) · Psychophysiology · doi:10.1017/s0048577202990049 · Cited by 51 · see citing works
Higher heart rate variability as a predictor of atrial fibrillation in patients with hypertension
Kim, S. H., Lim, K. R., Seo, J., Ryu, D., Lee, B., Cho, B. R., & Chun, K. J. (2022) · Scientific Reports · doi:10.1038/s41598-022-07783-3 · Cited by 47 · see citing works
Martínez, G. J., Grover, T., Mattingly, S. M., Mark, G., D'Mello, S. K., Aledavood, T., Akbar, F., Robles-Granda, P., & Striegel, A. (2022) · JMIR Human Factors · doi:10.2196/33754 · Cited by 46 · see citing works
Flatt, A. A., Esco, M. R., Allen, J. R., et al. (2017) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000002241 · Cited by 45 · see citing works
Grant, C. C., van Rensburg, D. C. J., Strydom, N., & Viljoen, M. (2011) · Annals of Noninvasive Electrocardiology · doi:10.1111/j.1542-474x.2011.00422.x · Cited by 44 · see citing works
Monitoring heart and respiratory rates at radial artery based on PPG
Wang, C., Li, Z., & Wei, X. (2013) · Optik · doi:10.1016/j.ijleo.2012.11.044 · Cited by 44 · see citing works
Evaluating the accuracy of heart rate sensors based on photoplethysmography for in-the-wild analysis
Barrios, L., Oldrati, P., Santini, S., & Lutterotti, A. (2019) · Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3329189.3329215 · Cited by 43 · see citing works
Autonomic control is a source of dynamical chaos in the cardiovascular system
Karavaev, A. S., Ishbulatov, Y. M., Пономаренко, В. И., Bezruchko, B. P., Kiselev, A. R., & Prokhorov, M. D. (2019) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5134833 · Cited by 42 · see citing works
Usefulness of heart rate variability as a predictor of sudden cardiac death in muscular dystrophies.
Politano, L., Palladino, A., Nigro, G., et al. (2008) · PubMed · Cited by 41 · see citing works
Usefulness of heart rhythm complexity in heart failure detection and diagnosis
Tsai, C., Ma, H., Lin, Y.-T., Hung, C., Huang, S.-H., Chuang, B.-L., Lin, C., Lo, M., Peng, C., & Lin, Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-71909-8 · Cited by 41 · see citing works
Low-frequency component of photoplethysmogram reflects the autonomic control of blood pressure
Karavaev, A. S., Боровик, А. С., Borovkova, E. I., Orlova, E. A., Simonyan, M. A., Пономаренко, В. И., Skazkina, V. V., Gridnev, V. I., Bezruchko, B. P., Prokhorov, M. D., et al. (2021) · Biophysical Journal · doi:10.1016/j.bpj.2021.05.020 · Cited by 41 · see citing works
Jiménez-Navarro, M. F., López-Jiménez, F. A., Perez-Belmonte, L. M., Lennon, R. J., Diaz-Melean, C. M., Rodríguez-Escudero, J. P., Goel, K., Crusan, D. J., Prasad, A., Squires, R. W., et al. (2017) · Journal of the American Heart Association · doi:10.1161/jaha.117.006404 · Cited by 38 · see citing works
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 · see citing works
Pradhapan, P., Tarvainen, M. P., Nieminen, T. V. M., Lehtinen, R., Nikus, K. C., Lehtimäki, T. J., Kähönen, M. A. P., & Viik, J. (2014) · Frontiers in Physiology · doi:10.3389/fphys.2014.00208 · Cited by 37 · see citing works
Heart rate variability during mindful breathing meditation
Natarajan, A. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2022.1017350 · Cited by 37 · see citing works
Eysenck, W., Freemantle, N., & Sulke, N. (2019) · Journal of Interventional Cardiac Electrophysiology · doi:10.1007/s10840-019-00515-0 · Cited by 36 · see citing works
Climov, D., Lysy, C., Berteau, S., Dutrannois, J., Dereppe, H., Brohet, C., & Melin, J. (2014) · Acta cardiologica. Supplementum · doi:10.1080/ac.69.3.3027833 · Cited by 34 · see citing works
Kamath, M., Watanabe, M., & Upton, A. R. M. (2012) · CRC Press eBooks · doi:10.1201/b12756-2 · Cited by 32 · see citing works
Reliability of Short-Term Heart Rate Variability Indexes Assessed through Photoplethysmography
Pernice, R., Javorka, M., Krohová, J., Czippelová, B., Turianiková, Z., Busacca, A., & Faes, L. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513634 · Cited by 31 · see citing works
Heart Rate Variability Analysis in Episodic Migraine: A Cross-Sectional Study
Zhang, L., Qiu, S., Zhao, C., et al. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.647092 · Cited by 31 · see citing works
Kamaleswaran, R., Sadan, O., Kandiah, P., Li, Q., Blum, J. M., Coopersmith, C. M., & Buchman, T. G. (2021) · Critical Care Explorations · doi:10.1097/cce.0000000000000570 · Cited by 24 · see citing works
Role of Heart Rate Variability in Predicting the Severity of Severe Acute Pancreatitis
Zhang, L., Zhou, J., Ke, L., Nie, Y., Tong, Z., Li, W., & Li, J. (2014) · Digestive Diseases and Sciences · doi:10.1007/s10620-014-3192-5 · Cited by 21 · see citing works
Criswell, S. R., Sherman, R. A., & Krippner, S. (2018) · The Permanente Journal · doi:10.7812/tpp/17-207 · Cited by 18 · see citing works
Voorhees, E. E. V., Dennis, P. A., Watkins, L. L., Patel, T. A., Calhoun, P. S., Dennis, M. F., & Beckham, J. C. (2021) · Psychosomatic Medicine · doi:10.1097/psy.0000000000001010 · Cited by 18 · see citing works
Bolin, L. P., Saul, A. D., Scroggs, L. L. B., & Horne, C. E. (2022) · Clinical Hypertension · doi:10.1186/s40885-021-00185-z · Cited by 14 · see citing works
A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variabilityPreprint
Sarhaddi, F., Kazemi, K., Azimi, I., et al. (2022) · medRxiv · doi:10.1101/2022.04.29.22274461 · Cited by 14 · see citing works
Wearable activity trackers and health awareness: Nursing implications
Edward, K., Garvey, L., & Rahman, M. A. (2020) · International Journal of Nursing Sciences · doi:10.1016/j.ijnss.2020.03.006 · Cited by 13 · see citing works
Koeneman, M., Koch, R. M., van Goor, H., Pickkers, P., Kox, M., & Bredie, S. J. H. (2021) · Shock · doi:10.1097/shk.0000000000001827 · Cited by 13 · see citing works
Lin, I., Lin, P., & Fan, S. (2022) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-022-09539-1 · Cited by 10 · see citing works
Rinne, J. K. A., Miri, S., Oksala, N., Vehkaoja, A. T., & Kössi, J. A. O. (2022) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-022-00854-w · Cited by 10 · see citing works
Reproducibility of 24-h ambulatory blood pressure and measures of autonomic function
Morrin, N. M., Stone, M. R., & Henderson, K. J. (2017) · Blood Pressure Monitoring · doi:10.1097/mbp.0000000000000241 · Cited by 8 · see citing works
S87. Toward Emotion Prosthetics: Emotion Regulation Through Wearable Vibroacoustic Stimulation
Rabin, D., & Siegle, G. J. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.02.978 · Cited by 4 · see citing works
Szmigielska, K., & Jegier, A. (2021) · Diabetes and Vascular Disease Research · doi:10.1177/14791641211020184 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [15]Iannaccone P.M. Khokha M. Fractal Geometry in Biological Systems: An Analytical Approach 1st ed. CRC Press Boca Raton, FL, USA 1996
Cited by
Sorted by citations
Palantzas, A., & Anagnostouli, M. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26178217 · Cited by 11 · see citing works
Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring
Asif, S., AlSaafeen, A., Nadar, S., Nambiar, S., Dannawi, J., Korrapati, H., & Wilkhoo, H. S. (2025) · Cureus · doi:10.7759/cureus.94074 · Cited by 9 · see citing works
- Authors
- Li, K., Cardoso, C., Moctezuma-Ramirez, A., Elgalad, A., & Perin, E.
- Affiliations
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
- Center for Clinical Research, The Texas Heart Institute, Houston, TX 77030, USA
- Corresponding author
- Elgalad, Abdelmotagaly
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2023-12-06
- Volume
- 20
- Issue
- 24
- Pages
- 7146
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC10742885
- OpenAlex
- W4389392255
- Cited by
- 160
- References
- 119
- Citations by year
- 2026: 76 · 2025: 59 · 2024: 24 · 2023: 1
- FWCI
- 3.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Texas Heart Institute
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- heart rate variability, wearable devices, personal health monitoring, autonomic nervous system, disease diagnosis, device comparison
- MeSH terms
- Heart Rate Determination, Wearable Electronic Devices, Autonomic Nervous System, Heart Rate, Monitoring, Physiologic
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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.
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