Accuracy of Apple Watch Measurements for Heart Rate and Energy Expenditure in Patients With Cardiovascular Disease: Cross-Sectional Study
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Abstract
BACKGROUND: Wrist-worn tracking devices such as the Apple Watch are becoming more integrated in health care. However, validation studies of these consumer devices remain scarce. OBJECTIVES: This study aimed to assess if mobile health technology can be used for monitoring home-based exercise in future cardiac rehabilitation programs. The purpose was to determine the accuracy of the Apple Watch in measuring heart rate (HR) and estimating energy expenditure (EE) during a cardiopulmonary exercise test (CPET) in patients with cardiovascular disease. METHODS: Forty patients (mean age 61.9 [SD 15.2] yrs, 80% male) with cardiovascular disease (70% ischemic, 22.5% valvular, 7.5% other) completed a graded maximal CPET on a cycle ergometer while wearing an Apple Watch. A 12-lead electrocardiogram (ECG) was used to measure HR; indirect calorimetry was used for EE. HR was analyzed at three levels of intensity (seated rest, HR1; moderate intensity, HR2; maximal performance, HR3) for 30 seconds. The EE of the entire test was used. Bias or mean difference (MD), standard deviation of difference (SDD), limits of agreement (LoA), mean absolute error (MAE), mean absolute percentage error (MAPE), and intraclass correlation coefficients (ICCs) were calculated. Bland-Altman plots and scatterplots were constructed. RESULTS: SDD for HR1, HR2, and HR3 was 12.4, 16.2, and 12.0 bpm, respectively. Bias and LoA (lower, upper LoA) were 3.61 (-20.74, 27.96) for HR1, 0.91 (-30.82, 32.63) for HR2, and -1.82 (-25.27, 21.63) for HR3. MAE was 6.34 for HR1, 7.55 for HR2, and 6.90 for HR3. MAPE was 10.69% for HR1, 9.20% for HR2, and 6.33% for HR3. ICC was 0.729 (P<.001) for HR1, 0.828 (P<.001) for HR2, and 0.958 (P<.001) for HR3. Bland-Altman plots and scatterplots showed good correlation without systematic error when comparing Apple Watch with ECG measurements. SDD for EE was 17.5 kcal. Bias and LoA were 30.47 (-3.80, 64.74). MAE was 30.77; MAPE was 114.72%. ICC for EE was 0.797 (P<.001). The Bland-Altman plot and a scatterplot directly comparing Apple Watch and indirect calorimetry showed systematic bias with an overestimation of EE by the Apple Watch. CONCLUSIONS: In patients with cardiovascular disease, the Apple Watch measures HR with clinically acceptable accuracy during exercise. If confirmed, it might be considered safe to incorporate the Apple Watch in HR-guided training programs in the setting of cardiac rehabilitation. At this moment, however, it is too early to recommend the Apple Watch for cardiac rehabilitation. Also, the Apple Watch systematically overestimates EE in this group of patients. Caution might therefore be warranted when using the Apple Watch for measuring EE.
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PPG sensors have been widely employed on smartwatches, such as the Apple Watch, to monitor cardiovascular health.
“Due to their non-invasive and easy-to-carry nature, PPG sensors have been widely employed on smartwatches, such as the Apple Watch, to monitor cardiovascular health [169].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [169]
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Shcherbina, A., Mattsson, C., Waggott, D., Salisbury, H., Christle, J., Hastie, T., Wheeler, M., & Ashley, E. (2017) · Journal of Personalized Medicine · doi:10.3390/jpm7020003 · Cited by 625 · see citing works
Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch
Tison, G. H., Sanchez, J. M., Ballinger, B., Singh, A., Olgin, J. E., Pletcher, M. J., Vittinghoff, E., Lee, E. S., Fan, S. M., Gladstone, R. A., et al. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2018.0136 · Cited by 566 · see citing works
Smart wearable body sensors for patient self-assessment and monitoring
Appelboom, G., Camacho, E., Abraham, M. E., Bruce, S. S., Dumont, E. L., Zacharia, B. E., D’Amico, R. S., Slomian, J., Reginster, J., Bruyère, O., et al. (2014) · Archives of Public Health · doi:10.1186/2049-3258-72-28 · Cited by 445 · see citing works
Accuracy of wrist-worn heart rate monitors
Wang, R., Blackburn, G., Desai, M., Phelan, D., Gillinov, L., Houghtaling, P., & Gillinov, M. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2016.3340 · Cited by 386 · see citing works
Reliability and validity of ten consumer activity trackers
Kooiman, T. J. M., Dontje, M. L., Sprenger, S. R., Krijnen, W. P., van der Schans, C. P., & de Groot, M. (2015) · BMC Sports Science, Medicine and Rehabilitation · doi:10.1186/s13102-015-0018-5 · Cited by 360 · see citing works
Variable Accuracy of Wearable Heart Rate Monitors during Aerobic Exercise
GILLINOV, S., ETIWY, M., WANG, R., BLACKBURN, G., PHELAN, D., GILLINOV, A. M., HOUGHTALING, P., JAVADIKASGARI, H., & DESAI, M. Y. (2017) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001284 · Cited by 345 · see citing works
Accuracy of Heart Rate Watches: Implications for Weight Management
Wallen, M. P., Gomersall, S. R., Keating, S. E., Wisløff, U., & Coombes, J. S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0154420 · Cited by 336 · see citing works
Dooley, E. E., Golaszewski, N. M., & Bartholomew, J. B. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.7043 · Cited by 242 · see citing works
Validity of Wearable Activity Monitors during Cycling and Resistance Exercise
BOUDREAUX, B. D., HEBERT, E. P., HOLLANDER, D. B., WILLIAMS, B. M., CORMIER, C. L., NAQUIN, M. R., GILLAN, W. W., GUSEW, E. E., & KRAEMER, R. R. (2018) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001471 · Cited by 205 · see citing works
Validation of Biofeedback Wearables for Photoplethysmographic Heart Rate Tracking.
(2016) · PubMed Central · Cited by 150 · see citing works
Evaluation of wearable consumer heart rate monitors based on photopletysmography
Parak, J., & Korhonen, I. (2014) · 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc.2014.6944419 · Cited by 148 · see citing works
Comparative evaluation of heart rate-based monitors: Apple Watch vs Fitbit Charge HR
Bai, Y., Hibbing, P. R., Mantis, K., & Welk, G. J. (2017) · Journal of Sports Sciences · doi:10.1080/02640414.2017.1412235 · Cited by 147 · see citing works
Chowdhury, E. A., Western, M. J., Nightingale, T. E., Peacock, O. J., & Thompson, D. (2017) · PLOS ONE · doi:10.1371/journal.pone.0171720 · Cited by 118 · see citing works
Cardiac patients show high interest in technology enabled cardiovascular rehabilitation
Buys, R., Claes, J., Walsh, D. M. J., Cornelis, N., Moran, K., Budts, W., Woods, C. B., & Cornelissen, V. (2016) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-016-0329-9 · Cited by 79 · see citing works
Self-citationEvaluation of accuracy and reliability of PulseOn optical heart rate monitoring device
Delgado-Gonzalo, R., Parák, J., Tarniceriu, A., Renevey, P., Bertschi, M., & Korhonen, I. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318391 · Cited by 55 · see citing works
Validity of heart rate measurements by the Garmin Forerunner 225 at different walking intensities
Claes, J., Buys, R., Ávila, A., Finlay, D., Kennedy, A., Guldenring, D., Budts, W., & Cornelissen, V. (2017) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2017.1333166 · Cited by 49 · see citing works
Self-citationBuys, R., Coeckelberghs, E., Vanhees, L., & Cornelissen, V. (2014) · European Journal of Preventive Cardiology · doi:10.1177/2047487314547658 · Cited by 40 · see citing works
Self-citationOsborn, C. Y., van Ginkel, J. R., Marrero, D. G., Rodbard, D., Huddleston, B., & Dachis, J. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.8781 · Cited by 33 · see citing works
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Research works printed but not linked: 1
- [18]McArdle W Katch F Katch V McArdle W Katch F Katch V Measurement of human energy expenditure Nutrition, Energy, and Human Performance. 7th edition 2010 Philadelphia Lippincott Williams & Wilkins 178 192
Cited by
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Fuller, D., Colwell, E., Low, J., Orychock, K., Tobin, M. A., Simango, B., Buote, R., Van Heerden, D., Luan, H., Cullen, K., et al. (2020) · JMIR mHealth and uHealth · doi:10.2196/18694 · Cited by 715 · see citing works
Ethical Issues of Digital Twins for Personalized Health Care Service: Preliminary Mapping Study
Huang, P., Kim, K.-H., & Schermer, M. (2021) · Journal of Medical Internet Research · doi:10.2196/33081 · Cited by 158 · see citing works
Mühlen, J. M., Stang, J. S., Skovgaard, E. L., Júdice, P. B., Molina‐García, P., Johnston, W., Sardinha, L. B., Ortega, F. B., Caulfield, B., Bloch, W., et al. (2021) · British Journal of Sports Medicine · doi:10.1136/bjsports-2020-103148 · Cited by 118 · see citing works
Zhang, Y., Weaver, R. G., Armstrong, B., Burkart, S., Zhang, S., & Beets, M. W. (2020) · Journal of Sports Sciences · doi:10.1080/02640414.2020.1767348 · Cited by 88 · see citing works
Textile triboelectric nanogenerators for self-powered biomonitoring
Lama, J. E., Yau, A., Chen, G., Sivakumar, A., Zhao, X., & Chen, J. (2021) · Journal of Materials Chemistry A · doi:10.1039/d1ta02518j · Cited by 88 · see citing works
The hospital at home in the USA: current status and future prospects
Pandit, J. A., Pawelek, J., Leff, B. A., & Topol, E. J. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01040-9 · Cited by 83 · see citing works
Yuan, Z., Xu, T., Cai, J., Zhao, Y., Cao, W., Fichera, A., Liu, X., Yao, J., & Wang, H. (2020) · Annals of Surgery · doi:10.1097/sla.0000000000004229 · Cited by 74 · see citing works
Sanal‐Hayes, N. E. M., Mclaughlin, M., Hayes, L. D., Mair, J. L., Ormerod, J. A., Carless, D., Hilliard, N., Meach, R., Ingram, J., & Sculthorpe, N. F. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04587-5 · Cited by 90 · see citing works
Islam, M. S., Islam, M. N., Hashim, N., Rashid, M., Bari, B. S., & Farid, F. A. (2022) · IEEE Access · doi:10.1109/access.2022.3178710 · Cited by 72 · see citing works
Association of Habitual Physical Activity With Cardiovascular Disease Risk
Lin, H., Sardana, M., Zhang, Y., Liu, C., Trinquart, L., Benjamin, E. J., Manders, E. S., Fusco, K., Kornej, J., Hammond, M. M., et al. (2020) · Circulation Research · doi:10.1161/circresaha.120.317578 · Cited by 67 · see citing works
The Apple Watch for Monitoring Mental Health–Related Physiological Symptoms: Literature Review
Lui, G. Y., Loughnane, D. C., Polley, C., Jayarathna, T., & Breen, P. P. (2022) · JMIR Mental Health · doi:10.2196/37354 · Cited by 65 · see citing works
Deep learning-based ambient assisted living for self-management of cardiovascular conditions
Qureshi, M., Qureshi, K. N., Jeon, G., & Piccialli, F. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05678-w · Cited by 66 · see citing works
Migliaccio, G. M., Padulo, J., & Russo, L. (2024) · Applied Sciences · doi:10.3390/app14156649 · Cited by 71 · see citing works
Golbus, J. R., Pescatore, N. A., Nallamothu, B. K., Shah, N., & Kheterpal, S. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00138-2 · Cited by 57 · see citing works
Horizons in Single-Lead ECG Analysis From Devices to Data
Abdou, A., & Krishnan, S. (2022) · Frontiers in Signal Processing · doi:10.3389/frsip.2022.866047 · Cited by 48 · see citing works
Wojcikowski, M., & Pankiewicz, B. (2020) · Sensors · doi:10.3390/s20061783 · Cited by 47 · see citing works
Recent Advances in the Wearable Devices for Monitoring and Management of Heart Failure
Odeh, V. A., Chen, Y., Wang, W., & Ding, X. (2024) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2510386 · Cited by 51 · see citing works
Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?
Martín-Escudero, P., Cabanas, A. M., Dotor-Castilla, M. L., Galindo-Canales, M., Miguel‐Tobal, F., Fernández, C., Fuentes, M. E., & Giannetti, R. (2023) · Bioengineering · doi:10.3390/bioengineering10020254 · Cited by 40 · see citing works
Digital Health in Cardiac Rehabilitation and Secondary Prevention: A Search for the Ideal Tool
Falter, M., Scherrenberg, M., & Dendale, P. (2020) · Sensors · doi:10.3390/s21010012 · Cited by 45 · see citing works
Self-citationJamieson, A., Chico, T., Jones, S. H., Chaturvedi, N., Hughes, A. D., & Orini, M. (2025) · npj Cardiovascular Health · doi:10.1038/s44325-025-00082-6 · Cited by 38 · see citing works
Helmer, P., Hottenrott, S., Rodemers, P., Leppich, R., Helwich, M., Pryss, R. C., Kranke, P., Meybohm, P., Winkler, B. E., & Sammeth, M. (2022) · Journal of Medical Internet Research · doi:10.2196/42359 · Cited by 33 · see citing works
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
Shiwani, M. A., Chico, T., Ciravegna, F., & Mihaylova, L. S. (2023) · Sensors · doi:10.3390/s23125752 · Cited by 34 · see citing works
A Review of Heartbeat Detection Systems for Automotive Applications
Arakawa, T. (2021) · Sensors · doi:10.3390/s21186112 · Cited by 29 · see citing works
Wang, J., Wu, X., Sun, J., Xu, T., Zhu, T., Fu, Y., Duan, S., Deng, Q., Liu, Z., Guo, F., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1053470 · Cited by 30 · see citing works
Exercise in Obesity—the Role of Technology in Health Services: Can This Approach Work?
Dobbie, L. J., Tahrani, A. A., Alam, U., James, J., Wilding, J. P. H., & Cuthbertson, D. J. (2021) · Current Obesity Reports · doi:10.1007/s13679-021-00461-x · Cited by 26 · see citing works
Montanari, A., Ferlini, A., Balaji, A. N., Cecilia Mascolo, C., & Kawsar, F. (2023) · Scientific Data · doi:10.1038/s41597-023-02762-3 · Cited by 26 · see citing works
Wattanapisit, A., Ng, C. J., Angkurawaranon, C., Wattanapisit, S., Chaovalit, S., & Stoutenberg, M. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e11259 · Cited by 28 · see citing works
Sun, J., & Liu, Y. (2020) · JMIR mhealth and uhealth · doi:10.2196/17699 · Cited by 21 · see citing works
Alfonso, C., Garcia-Gonzalez, M. A., Parrado, E., Gil-Rojas, J., Ramos-Castro, J. J., & Capdevila, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-18356-9 · Cited by 23 · see citing works
Comparison of Apple Watch vs KardiaMobile: A Tale of Two Devices
Lee, C., Lee, C., Fernando, C., & Chow, C.-M. (2022) · CJC Open · doi:10.1016/j.cjco.2022.07.011 · Cited by 20 · see citing works
Measuring Heart Rate During Exercise: From Artery Palpation to Monitors and Apps
de Almeida, M. B., Bottino, A., Ramos, P. S., & de Araújo, C. G. S. (2019) · International Journal of Cardiovascular Sciences · doi:10.5935/2359-4802.20190061 · Cited by 19 · see citing works
Accuracy of wrist-worn wearable devices for determining exercise intensity
Ho, W.-T., Yang, Y.-J., & Li, T.-C. (2022) · Digital Health · doi:10.1177/20552076221124393 · Cited by 18 · see citing works
Gajda, R. (2020) · Diagnostics · doi:10.3390/diagnostics10110867 · Cited by 18 · see citing works
Wearable Devices Beyond Activity Trackers in Youth With Obesity: Summary of Options
Chimatapu, S. N., Mittelman, S. D., Habib, M., Osuna-Garcia, A., & Vidmar, A. P. (2023) · Childhood Obesity · doi:10.1089/chi.2023.0005 · Cited by 17 · see citing works
Ibrahim, N. S., Rampal, S., Lee, W. L., Pek, E. W., & Suhaimi, A. (2023) · Cardiovascular Engineering and Technology · doi:10.1007/s13239-023-00693-z · Cited by 16 · see citing works
Garikapati, K., Turnbull, S., Bennett, R. G., Campbell, T. G., Kanawati, J., Wong, M. S., Thomas, S. P., Chow, C. K., & Kumar, S. (2022) · Heart Lung and Circulation · doi:10.1016/j.hlc.2022.08.001 · Cited by 17 · see citing works
Sardana, M., Lin, H., Zhang, Y., Liu, C., Trinquart, L., Benjamin, E. J., Manders, E. S., Fusco, K., Kornej, J., Hammond, M. M., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/25591 · Cited by 16 · see citing works
The accuracy of Apple Watch measurements: a living systematic review and meta-analysis
Lambe, R., Baldwin, M., O’Grady, B., Schumann, M., Caulfield, B., & Doherty, C. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02238-1 · Cited by 17 · see citing works
Kim, C., Kim, S. H., & Suh, M. R. (2022) · Annals of Rehabilitation Medicine · doi:10.5535/arm.22050 · Cited by 14 · see citing works
Khushhal, A. A., Abdelaal, A. A. M., & Elsayed, M. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s449071 · Cited by 14 · see citing works
Evaluating the Barrier Effects of Charge Point Trauma on UK Electric Vehicle Growth
Chamberlain, K., & Al-Majeed, S. S. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12030152 · Cited by 13 · see citing works
Dandapani, H. G., Davoodi, N. M., Joerg, L., Li, M. M., Strauss, D. H., Fan, K., Massachi, T., & Goldberg, E. M. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.893070 · Cited by 12 · see citing works
Cai, Y., Wang, Z., Zhang, W., Kong, W., Jiang, J., Zhao, R., Wang, D., Feng, L., & Ni, G. (2022) · Sensors · doi:10.3390/s22134661 · Cited by 13 · see citing works
Wang, Z., Zhang, Q., Lan, K., Yang, Z., Gao, X., Wu, A., Yi, X., & Zhang, Z. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.897412 · Cited by 13 · see citing works
Gong, X.-Y., Cheng, J., Wu, Y.-T., He, F., Wang, S.-H., Liu, C., Zhu, Y., & Xu, K.-H. (2024) · Geriatric Nursing · doi:10.1016/j.gerinurse.2024.05.036 · Cited by 11 · see citing works
Sanal‐Hayes, N. E. M., Mclaughlin, M., Mair, J. L., Ormerod, J. A., Carless, D., Meach, R., Hilliard, N., Ingram, J., Sculthorpe, N. F., & Hayes, L. D. (2024) · Fatigue Biomedicine Health & Behavior · doi:10.1080/21641846.2024.2433390 · Cited by 12 · see citing works
On the Impact of Biceps Muscle Fatigue in Human Activity Recognition
Elshafei, M., Costa, D. E., & Shihab, E. (2021) · Sensors · doi:10.3390/s21041070 · Cited by 12 · see citing works
Brandenbarg, P., Hoekstra, F., Barakou, I., Seves, B. L., Hettinga, F. J., Hoekstra, T., van der Woude, L. H. V., Dekker, R., & Krops, L. A. (2023) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-023-00717-0 · Cited by 12 · see citing works
Alessandrini, M., Viziano, A., Pistillo, R., Granito, I., Basso, L., Preziosi, N., & Micarelli, A. (2021) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2021.07.012 · Cited by 11 · see citing works
An Update on the Use of Wearable Devices in Men’s Health
Qureshi, F. M., Golan, R., Ghomeshi, A., & Ramasamy, R. (2023) · The World Journal of Men s Health · doi:10.5534/wjmh.220205 · Cited by 10 · see citing works
Apple watch accuracy in monitoring health metrics: a systematic review and meta-analysis
Choe, J.-P., & Kang, M. (2025) · Physiological Measurement · doi:10.1088/1361-6579/adca82 · Cited by 10 · see citing works
Wearable Devices and AI-Driven Remote Monitoring in Cardiovascular Medicine: A Narrative Review
Gaoudam, N., Sakhamudi, S. K., Kamal, B., Addla, N., Reddy, E. P., Ambala, M., Lavanya, K., Palaparthi, E. C., Bhattam, A., Periasamy, P., et al. (2025) · Cureus · doi:10.7759/cureus.90208 · Cited by 10 · see citing works
Elshafei, M., Costa, D. E., & Shihab, E. (2022) · Sensors · doi:10.3390/s22041454 · Cited by 10 · see citing works
Ye, X., Sun, M., Yu, S., Yang, J., Liu, Z., Lv, H., Wu, B., He, J., Wang, X., & Huang, L. (2023) · JMIR mhealth and uhealth · doi:10.2196/43340 · Cited by 10 · see citing works
Realize, Analyze, Engage (RAE): A Digital Tool to Support Recovery from Substance Use Disorder
Carreiro, S., Taylor, M., Shrestha, S., Reinhardt, M., Gilbertson, N., & Indic, P. (2021) · Journal of Psychiatry and Brain Science · doi:10.20900/jpbs.20210002 · Cited by 11 · see citing works
Weng, D. Y., Ding, J., Sharma, A., Yanek, L. R., Xun, H., Spaulding, E. M., Osuji, N., Huynh, P. P., Ogunmoroti, O., Lee, M. A., et al. (2021) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2021.05.003 · Cited by 9 · see citing works
Alqahtani, A. S. (2023) · Applied Artificial Intelligence · doi:10.1080/08839514.2023.2175111 · Cited by 10 · see citing works
Mei, L., He, Z., & Hu, L. (2024) · JMIR Formative Research · doi:10.2196/59521 · Cited by 9 · see citing works
Gielen, J., Oost, N. C. V., Debard, G., Sels, R., Witte, N. A. J. D., Colman, T., Bonroy, B., & Aerts, J. (2026) · JMIR Formative Research · doi:10.2196/85186 · Cited by 8 · see citing works
Shaik, J., & Bhavanam, S. N. (2023) · SN Computer Science · doi:10.1007/s42979-023-01970-3 · Cited by 8 · see citing works
Effectiveness of the Apple Watch as a mental health tracker
Adil, M., Atiq, I., & Younus, S. (2024) · Journal of Global Health · doi:10.7189/jogh.14.03010 · Cited by 8 · see citing works
Andersen, M. Ø., & Carlsen, J. (2024) · Contemporary Clinical Trials · doi:10.1016/j.cct.2024.107548 · Cited by 8 · see citing works
Huynh, P. P., Shan, R., Osuji, N., Ding, J., Isakadze, N., Marvel, F. A., Sharma, G. V., & Martin, S. S. (2020) · JMIR Cardio · doi:10.2196/18050 · Cited by 8 · see citing works
An Apple Watch a day keeps the doctor away?
Basza, M., Krzowski, B., Balsam, P., Grabowski, M., Opolski, G., & Kołtowski, L. (2021) · Cardiology Journal · doi:10.5603/cj.2021.0140 · Cited by 8 · see citing works
Artificial Intelligence in Cardiovascular Diseases
Bashir, S., Ahanger, A. N., Assad, A., Bhat, M. R., & Macha, M. A. (2025) · Springer eBooks · doi:10.1007/978-981-96-8176-1_10 · Cited by 7 · see citing works
Dunne, D. M., Lefevre, C. E., Cunniffe, B., Impey, S. G., Tod, D., Close, G. L., Morton, J. P., & Murphy, R. (2022) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2022.911412 · Cited by 7 · see citing works
Deep Learning-Based Arrhythmia Detection in Electrocardiograph
Meng, Y., Liang, G., & Yue, M. (2021) · Scientific Programming · doi:10.1155/2021/9926769 · Cited by 7 · see citing works
Vermunicht, P., Makayed, K., Buyck, C., Knaepen, L., Giraldo, J. S. P., Naessens, S., Hens, W., Craenenbroeck, E. M. V., Laukens, K., Desteghe, L., et al. (2025) · Digital Health · doi:10.1177/20552076251337598 · Cited by 5 · see citing works
(Cardiovascular telerehabilitation: remotely monitored physical exercise)
Baťalík, L. (2021) · Cor et Vasa · doi:10.33678/cor.2020.083 · Cited by 5 · see citing works
Wang, T. Y., Wu, H.-Y., Wang, W., Chen, C., Chien, W., Chu, C., & Wu, Y. (2023) · JMIR Formative Research · doi:10.2196/52519 · Cited by 6 · see citing works
The Energy a Plastic Surgeon Expends during Liposuction
Vega, H. C. D., Echaury, A. L., & Flores, E. (2023) · Plastic & Reconstructive Surgery Global Open · doi:10.1097/gox.0000000000005001 · Cited by 6 · see citing works
Digital scenography: 30 years of experimentation and innovation in performance and interactive media
Zezulka, K. (2022) · International Journal of Performance Arts and Digital Media · doi:10.1080/14794713.2022.2078583 · Cited by 6 · see citing works
Tran, T., Steiner, J. M., Venkateswaran, A., & Buber, J. (2023) · Heart · doi:10.1136/heartjnl-2023-322989 · Cited by 5 · see citing works
Agbohessou, K. G., Sahuguede, S., Lacroix, J., Hamdan, F., Conchon, E., Dumas, Y., Julien‐Vergonjanne, A., & Mandigout, S. (2023) · Sensors · doi:10.3390/s23125742 · Cited by 5 · see citing works
Shrestha, A. B., Khanal, B., Mainali, N., Shrestha, S., Chapagain, S., Umar, T. P., & Jaiswal, V. (2023) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2023.102073 · Cited by 5 · see citing works
Sanal‐Hayes, N. E. M., Mclaughlin, M., Hayes, L. D., Mair, J. L., Ormerod, J. A., Carless, D., Hilliard, N., Meach, R., Ingram, J., & Sculthorpe, N. F. (2023) · medRxiv · doi:10.1101/2023.08.10.23293935 · Cited by 5 · see citing works
Exploring the effects of aerobic and resistance exercise on mood-related symptoms and EEG activity
Yuk, K., Lim, J., Kim, S., Kim, T. Y., & Moon, H. Y. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1562702 · Cited by 4 · see citing works
Yusuf, A., & Muhammad, A. Y. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241270707 · Cited by 4 · see citing works
Xu, A., Chen, E., & Chen, J. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3144961 · Cited by 4 · see citing works
Society 5.0 Healthcare: Ethics, Legal Rights, Human Rights, Safety and Security
Blenman, K., Hull, R., Maimela, C., Molefi, T., Khanyile, R., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-36461-7_12 · Cited by 5 · see citing works
Photoplethysmogram (PPG) Signal Analysis for the Heart Rate Estimation Using Shannon Entropy
Suman, Suguna, M., Kumar, R., & Gupta, S. (2025) · IEEE International Colloquium on Signal Processing & Its Applications (CSPA) · doi:10.1109/cspa64953.2025.10933069 · Cited by 4 · see citing works
Relative utility of portable ECG devices in capturing arrhythmias in athletes
Davis, A. J., Driscoll, T., Orchard, J. W., Raju, H., Gray, B. R., & Orchard, J. J. (2024) · Expert Review of Medical Devices · doi:10.1080/17434440.2024.2438313 · Cited by 3 · see citing works
Rosinhas, J., Malheiro, R., Pimenta, J. T., Sá, R., Serdoura, F., & Paiva, J.-A. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1605020 · Cited by 3 · see citing works
Jiang, W., Zhang, W., Li, H., Ngoie, J., Li, W., & Huang, Z. (2025) · Digital Health · doi:10.1177/20552076251377934 · Cited by 3 · see citing works
Ruiz‐Malagón, E. J., Castro-Infantes, S., Real, M. R., & Soto-Hermoso, V. M. (2023) · Proceedings of the Institution of Mechanical Engineers Part P Journal of Sports Engineering and Technology · doi:10.1177/17543371231165102 · Cited by 3 · see citing works
Rockette‐Wagner, B., & Aggarwal, R. (2024) · Rheumatology · doi:10.1093/rheumatology/keae004 · Cited by 4 · see citing works
Wearable Devices for Hemodynamic Assessment in Cardiovascular Disease: A Short Literature Review
Singh, J., Ellingson, C. J., Gagarinova, M., Thakkar, R., Mehta, N., Ahmad, K., Sultana, S., Bhat, S., & Dehghani, P. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000000875 · Cited by 3 · see citing works
Multimodal Data Streaming using Visual IoTs and Wearables for Telerehabilitation and Teletreatment
Guzman, H., Joshi, R., Guzman, V., Kilger, M., & Desai, K. (2021) · World Automation Congress (WAC) · doi:10.23919/wac50355.2021.9559516 · Cited by 3 · see citing works
Zhang, Y., Ye, Z., Liu, K., Chai, Y., Fen, L., Zhan, H., Zheng, Y., & Yuan, W.-A. (2022) · BMJ Open · doi:10.1136/bmjopen-2022-063576 · Cited by 3 · see citing works
Yamada, K., Enokida, Y., Kato, R., Imaizumi, J., Takada, T., & Ojima, H. (2023) · JMIR Human Factors · doi:10.2196/50891 · Cited by 3 · see citing works
Investigating Human Physiological Responses to Work-Related Stress
Uba, J., & Nuamah, J. K. (2023) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/21695067231192596 · Cited by 3 · see citing works
Data-driven approach to quantify trust in medical devices using Bayesian networks
Thomas, M., Boursalie, O., Samavi, R., & Doyle, T. E. (2023) · Experimental Biology and Medicine · doi:10.1177/15353702231215893 · Cited by 3 · see citing works
Galindo, C. M. E., Quispe, M. P. A., Casas, C. H. A. D. L., & López, J. M. T. (2025) · Ergonomics · doi:10.1080/00140139.2025.2496981 · Cited by 2 · see citing works
Ferreira, A. R. P., Inoue, A., Barbosa, R., Hayek, C., Reis, M., Alcântara, J. A., Shigueoka, A. H., & Santos, M. (2026) · JMIR Formative Research · doi:10.2196/83090 · Cited by 2 · see citing works
observational study on detection of atrial and ventricular arrhythmias with smartphone-based ECG
Chandola, N., Singh, Y., Mahajan, S., Garg, S., & Bansal, B. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns4.10087 · Cited by 2 · see citing works
Sekhar, M., Rudinsky, A. J., Cashman, C., Parker, V. J., Kieves, N. R., Howard, J., & Winston, J. A. (2023) · American Journal of Veterinary Research · doi:10.2460/ajvr.23.10.0242 · Cited by 2 · see citing works
Imai, H., & Shimada, J. (2025) · Cureus · doi:10.7759/cureus.80232 · Cited by 1 · see citing works
Investigation and Validation of New Heart Rate Measurement Sites for Wearable Technologies
Ma’touq, J., AlSaaideh, I., Hatahet, O., & Pott, P. P. (2025) · Sensors · doi:10.3390/s25072069 · Cited by 2 · see citing works
A Low-Cost Cognitive Assistant
Costa, Â., Rincon, J. A., Julián, V., Nováis, P., & Carrascosa, C. (2020) · Electronics · doi:10.3390/electronics9020310 · Cited by 2 · see citing works
- Authors
- Falter, M., Budts, W., Goetschalckx, K., Cornelissen, V., & Buys, R.
- Affiliations
- Universitair Ziekenhuis Leuven (BE)
- KU Leuven (BE)
- Corresponding author
- Buys, Roselien
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2019-03-19
- Volume
- 7
- Issue
- 3
- Pages
- e11889
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC6444219
- OpenAlex
- W2904020811
- Cited by
- 142
- References
- 18
- Citations by year
- 2026: 10 · 2025: 24 · 2024: 18 · 2023: 30 · 2022: 26 · 2021: 19 · 2020: 13 · 2019: 2
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Complementary and alternative medicine › Cardiovascular and exercise physiology
- Keywords
- Apple Watch, heart rate measurement, energy expenditure estimation, cardiovascular disease, cardiopulmonary exercise testing, wearable devices, device validation, mobile health, cardiac rehabilitation, indirect calorimetry, electrocardiogram, ischemic heart disease, valvular heart disease, exercise intensity, measurement accuracy, limits of agreement, intraclass correlation coefficient, mean absolute error, mean absolute percentage error
- MeSH terms
- Heart Rate Determination, Wearable Electronic Devices, Aged, Aged, 80 and over, Cardiovascular Diseases, Cross-Sectional Studies, Electrocardiography, Energy Metabolism, Exercise Test, Female, Heart Rate, Humans, Male, Middle Aged, Monitoring, Physiologic
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