Accuracy of wrist-worn heart rate monitors
Open accessAbstract
Accuracy of Wrist-Worn Heart Rate MonitorsWrist-worn fitness and heart rate (HR) monitors are popular.1,2 While the accuracy of chest strap, electrode-based HR monitors has been confirmed, 3,4 the accuracy of wrist-worn, optically based HR monitors is uncertain.5,6 Assessment of the monitors' accuracy is important for individuals who use them to guide their physical activity and for physicians to whom these individuals report HR readings.The objective of this study was to assess the accuracy of 4 popular wrist-worn HR monitors under conditions of varying physical exertion.Methods | This study recruited 50 healthy adults.The mean (SD) age was 37 (11.3)years; the mean (SD) body mass index (calculated as weight in kilograms divided by height in meters squared) was 23.5 (3.1); 28 participants were women (58%), and 7 participants were African American (14%).Exclusion criteria included cardiovascular disease, pacemakers, and treatment with heart rhythm medications.The protocol was approved by the Cleveland Clinic institutional review board.Participants provided written informed consent.Participants wore standard electrocardiographic limb leads and a Polar H7 chest strap monitor secured tightly to ensure skin contact.Each participant was randomly assigned to wear 2 different wrist-worn HR monitors placed tightly above the ulnar styloid.Four wrist-worn monitors were assessed: Fitbit Charge HR (Fitbit), Apple Watch (Apple), Mio Alpha (Mio Global), and Basis Peak (Basis).Heart rate was assessed with the participant on a treadmill at rest and at 2 mph, 3 mph, 4 mph, 5 mph, and 6 mph.Participants exercised at each setting for 3 minutes to achieve a steady state; HR was recorded instantaneously at the 3-minute point.After completion of the treadmill protocol, HR was recorded at 30, 60, and 90 seconds' recovery.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The Polar H7 uses an ECG-based sensor technique with medical grade accuracy.
“The Polar H7 [17] is a chest strap recording heart beats and interbeat-intervals, using an electrocardiograph (ECG) based sensor technique with medical grade accuracy [83].”
From Sensing Interruptibility in the Office: A Field Study on the Use of Biometric and Computer Interaction Sensors (2018) · cites this paper as [83]
The Methods passage (page unavailable) identifies the Polar H7 as a chest-strap monitor, and the table reports 0.99 concordance with electrocardiogram-measured heart rate. The paper describes chest-strap monitors as electrode-based, but does not explicitly establish the Polar H7’s sensor technique or designate its accuracy as “medical grade.” (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe actigraphy-based sleep tracker has proven to be most accurate when collecting measurements at rest.
“The tracker has proven to be most accurate when collecting measurements at rest [34,35].”
From Reduced cognitive function during a heat wave among residents of non-air-conditioned buildings: An observational study of young adults in the summer of 2016 (2018) · cites this paper as [34]
The Discussion (provided as passage [3] under Abstract; page not given) says wrist-worn heart-rate monitors were generally most accurate at rest. The paper assessed heart-rate readings, not the accuracy of an actigraphy-based sleep tracker or its sleep measurements. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe two devices showed reasonable accuracy in HR and EE estimations under certain exercise conditions.
“Similar to previous studies [13,28,30-32], we found reasonable accuracy in HR and EE estimations for these 2 devices under certain exercise conditions.”
From Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study (2018) · cites this paper as [28]
The Results and Discussion (Abstract; page unavailable) report that wrist-worn monitors’ HR accuracy varied with exercise intensity and was generally best at rest, supporting only the qualified HR portion. The study assessed four monitors, not the citing authors’ two devices, and our copy reports no evaluation of energy-expenditure (EE) accuracy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBecause the Fitbit samples at a coarser rate, it cannot measure heart rate variability.
“While the Fitbit's coarser sampling granularity does not allow measuring HRV [83] and tends to overestimate sleep duration, it has a high intra-device reliability [60]”
From Sensing Interruptibility in the Office: A Field Study on the Use of Biometric and Computer Interaction Sensors (2018) · cites this paper as [83]
The Methods passage (page not provided) describes recording Fitbit heart-rate readings at set points during a treadmill test. The Discussion passage (page not provided) says continuous assessment was not feasible with all devices, but neither passage identifies Fitbit’s sampling rate or addresses whether it can measure heart rate variability. Searches for HRV and sampling granularity found no passage establishing the claim. (LLM, conf=0.75)
Experimental — accuracy not yet measuredOther studies have used suboptimal reference methods, such as chest straps or pulse oximeters, which themselves have a degree of error varying between 0 and 40 bpm when compared to electrocardiograms.
“other studies have used suboptimal reference methods, such as the chest straps45–47 or pulse oximeters48, which themselves have a degree of error (varying between 0 and 40 bpm when compared to ECGs44).”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [44]
Some studies have used the gold-standard reference method of electrocardiogram, which directly measures heart rate via electrical impulses from the heart and is used for clinical accuracy.
“Although some studies have attempted to address wearable HR accuracy using the gold-standard reference method of ECG, which directly measures HR via electrical impulses from the heart and is used for clinical accuracy38–44”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [44]
The Apple Watch has slightly greater accuracy than Fitbit devices (Apple Watch: −1.3 bpm, Fitbit: −9.3 bpm).
“with the Apple Watch having slightly greater accuracy than Fitbit devices (Apple Watch: −1.3 bpm, Fitbit: −9.3 bpm42–44)”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [44]
On average, wearables slightly and negligibly underestimate absolute heart rate (−0.9 to −7.2 bpm) compared with gold-standard electrocardiogram.
“Current research verifying wearables to the gold-standard ECG, which uses electrodes to directly measure cardiac muscular contractions from electrical activity of the heart, have shown that on average wearables slightly and negligibly underestimate (−0.9 to −7.2 bpm) absolute HR40,41,44,52”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [44]
Apple Watch has lower mean difference standard deviation and higher agreement with electrocardiogram than Fitbit devices.
“lower mean difference standard deviation43, and higher agreement with ECG than Fitbit devices39,44.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [44]
Larger wrist circumference has been shown to be associated with reduced heart rate accuracy, although these outcomes have not been found in all studies.
“Larger wrist circumference has been shown to be associated with reduced HR accuracy42, although these outcomes have not been found in all studies44,46.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [44]
Higher body mass index, which likely covaries with larger wrist circumference, has been shown to be associated with reduced heart rate accuracy, although these outcomes have not been found in all studies.
“Higher body mass index, which likely covaries with larger wrist circumference as described above, has been shown to be associated with reduced HR accuracy42,62, although again these outcomes have not been found in all studies44,46.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [44]
Wearable devices are more accurate during rest and low intensity exercise compared to higher intensity exercise, when they can begin to deviate more from ECG recordings.
“a substantial amount of research showing that wearable devices are more accurate during rest and low intensity exercise when compared to higher intensity exercise, when wearable devices can begin to deviate more from ECG recordings38,39,44,46,70–73”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [44]
Wrist-worn heart rate monitors, including the Apple Watch, have variable accuracy compared with electrocardiogram measurement.
“Variable accuracy for wrist-worn heart rate monitors, including the Apple Watch, compared with electrocardiogram measurement has also been noted previously in brief comparisons of bouts of exercise [56].”
From Wearable technology for high-frequency cognitive and mood assessment in major depressive disorder: longitudinal observational study (2019) · cites this paper as [56]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015) · JAMA · doi:10.1001/jama.2014.14781 · Cited by 808 · see citing works
Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data
Case, M. A., Burwick, H. A., Volpp, K. G., & Patel, M. S. (2015) · JAMA · doi:10.1001/jama.2014.17841 · Cited by 694 · see citing works
El-Amrawy, F., & Nounou, M. I. (2015) · Healthcare Informatics Research · doi:10.4258/hir.2015.21.4.315 · Cited by 287 · see citing works
Heart rate monitors: State of the art
Laukkanen, R. M. T., & Virtanen, P. K. (1998) · Journal of Sports Sciences · doi:10.1080/026404198366920 · Cited by 219 · see citing works
Murakami, H., Kawakami, R., Nakae, S., Nakata, Y., Ishikawa-Takata, K., Tanaka, S., & Miyachi, M. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.0152 · Cited by 204 · see citing works
Validity of Seven Commercially Available Heart Rate Monitors
Terbizan, D. J., Dolezal, B. A., & Albano, C. B. (2002) · Measurement in Physical Education and Exercise Science · doi:10.1207/s15327841mpee0604_3 · Cited by 147 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [5]5. Nagele P. The case for a revised definition of myocardial infarction: resolving the ambiguity of type 2 myocardial infarction. JAMA Cardiol. 2016;1(3):247-248.
- [6]6. Shah AS, McAllister DA, Mills R, et al. Sensitive troponin assay and the classification of myocardial infarction. Am J Med. 2015;128(5):493-501.e3.
- [7]7. Baron T, Hambraeus K, Sundström J, Erlinge D, Jernberg T, Lindahl B; TOTAL-AMI study group. Type 2 myocardial infarction in clinical practice. Heart. 2015;101(2):101-106.
- [8]8. Saaby L, Poulsen TS, Hosbond S, et al. Classification of myocardial infarction: frequency and features of type 2 myocardial infarction. Am J Med. 2013;126(9): 789-797.
- [9]9. Sandoval Y, Smith SW, Thordsen SE, Apple FS. Supply/demand type 2 myocardial infarction: should we be paying more attention? J Am Coll Cardiol. 2014;63(20):2079-2087.
Cited by
Sorted by citations
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
Investigating sources of inaccuracy in wearable optical heart rate sensors
Bent, B., Goldstein, B. A., Kibbe, W. A., & Dunn, J. P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0226-6 · Cited by 673 · see citing works
“Periodic, rhythmic movement has been cited as a source of error in optical HR measurements in previous studies.(8,44,45) This has been described as a “signal crossover” effect wherein the optical HR sensors on wearables tend to lock on to the periodic signal stemming from the repetitive motion (e.”
BackgroundSelf-powered cardiovascular electronic devices and systems
Zheng, Q., Tang, Q., Wang, Z. L., & Li, Z. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-020-0426-4 · Cited by 348 · 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
Self-citationNelson, B. W., & Allen, N. B. (2019) · JMIR mHealth and uHealth · doi:10.2196/10828 · Cited by 310 · see citing works
Securing internet of medical things systems: limitations, issues and recommendations
Yaacoub, J.-P. A., Noura, M., Noura, H. N., Salman, O., Yaacoub, E., Couturier, R., & Chehab, A. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2019.12.028 · Cited by 308 · see citing works
Wearable Photoplethysmography for Cardiovascular Monitoring
Charlton, P. H., Kyriacou, P. A., Mant, J., Marozas, V., Chowienczyk, P., & Alastruey, J. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3149785 · Cited by 250 · see citing works
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 · see citing works
Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
Rani, S., Kataria, A., Kumar, S., & Tiwari, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110658 · Cited by 228 · see citing works
Cedeño Laurent, J. G., Williams, A., Oulhote, Y., Zanobetti, A., Allen, J. G., & Spengler, J. D. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002605 · Cited by 205 · 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
Quer, G., Gouda, P., Galarnyk, M., Topol, E. J., & Steinhubl, S. R. (2020) · PLOS ONE · doi:10.1371/journal.pone.0227709 · Cited by 185 · see citing works
Clinical Validation of 5 Direct-to-Consumer Wearable Smart Devices to Detect Atrial Fibrillation
Mannhart, D., Lischer, M., Knecht, S., du Fay de Lavallaz, J., Strebel, I., Serban, T., Vögeli, D., Schaer, B., Osswald, S., Mueller, C., et al. (2023) · JACC: Clinical Electrophysiology · doi:10.1016/j.jacep.2022.09.011 · Cited by 175 · see citing works
Reddy, R. K., Pooni, R., Zaharieva, D. P., Senf, B., El Youssef, J., Dassau, E., Doyle III, F. J., Clements, M. A., Rickels, M. R., Patton, S. R., et al. (2018) · JMIR mHealth and uHealth · doi:10.2196/10338 · Cited by 157 · see citing works
A Review of Wearable Multi-Wavelength Photoplethysmography
Ray, D., Collins, T., Woolley, S., & Ponnapalli, P. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3121476 · Cited by 155 · see citing works
Assessment of the Fitbit Charge 2 for monitoring heart rate
Benedetto, S., Caldato, C., Bazzan, E., Greenwood, D. C., Pensabene, V., & Actis, P. (2018) · PLOS ONE · doi:10.1371/journal.pone.0192691 · Cited by 149 · see citing works
Falter, M., Budts, W., Goetschalckx, K., Cornelissen, V., & Buys, R. (2019) · JMIR mHealth and uHealth · doi:10.2196/11889 · Cited by 142 · 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
Self-citationChow, H.-W., & Yang, C.-C. (2020) · JMIR mHealth and uHealth · doi:10.2196/14707 · Cited by 140 · see citing works
“Because these extreme heart rate readings were only observed for a short period, detecting these deviations while examining the heart rate readings every 15 seconds, every minute, or only at the end of the exercise, as in earlier studies, is difficult [11,15,16].”
InfluentialAccuracy of commercially available heart rate monitors in athletes: a prospective study
Pasadyn, S. R., Soudan, M., Gillinov, M., Houghtaling, P., Phelan, D., Gillinov, N., Bittel, B., & Desai, M. Y. (2019) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt.2019.06.05 · Cited by 135 · see citing works
Self-citationGodino, J. G., Wing, D., de Zambotti, M., Baker, F. C., Bagot, K., Inkelis, S., Pautz, C., Higgins, M., Nichols, J., Brumback, T., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237719 · Cited by 107 · see citing works
Nielsen, J. C., Lin, Y.-J., de Oliveira Figueiredo, M. J., Sepehri Shamloo, A., Alfie, A., Boveda, S., Dagres, N., Di Toro, D., Eckhardt, L. L., Ellenbogen, K., et al. (2020) · EP Europace · doi:10.1093/europace/euaa065 · Cited by 98 · see citing works
Jensen, M. T., Treskes, R. W., Caiani, E. G., Casado-Arroyo, R., Cowie, M. R., Dilaveris, P., Duncker, D., Di Rienzo, M., Frederix, I., De Groot, N., et al. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab011 · Cited by 93 · see citing works
Validity and Reliability of the Apple Watch for Measuring Heart Rate During Exercise
Khushhal, A., Nichols, S., Evans, W., Gleadall-Siddall, D., Page, R., O'Doherty, A., Carroll, S., Ingle, L., & Abt, G. (2017) · Sports Medicine International Open · doi:10.1055/s-0043-120195 · Cited by 92 · 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
Comparison of Polar M600 Optical Heart Rate and ECG Heart Rate during Exercise
HORTON, J. F., STERGIOU, P., FUNG, T. S., & KATZ, L. (2017) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001388 · Cited by 79 · see citing works
Cormack, F., McCue, M., Taptiklis, N., Skirrow, C., Glazer, E., Panagopoulos, E., van Schaik, T. A., Fehnert, B., King, J., & Barnett, J. H. (2019) · JMIR Mental Health · doi:10.2196/12814 · Cited by 79 · see citing works
Züger, M., Müller, S. C., Meyer, A. N., & Fritz, T. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174165 · Cited by 66 · see citing works
Use of wearable devices for post-discharge monitoring of ICU patients: a feasibility study
for the WEARable Information Technology for hospital INpatients (WEARIT-IN) study group, Kroll, R. R., McKenzie, E. D., Boyd, J. G., Sheth, P., Howes, D., Wood, M., & Maslove, D. M. (2017) · Journal of Intensive Care · doi:10.1186/s40560-017-0261-9 · Cited by 69 · see citing works
Sartor, F., Gelissen, J., van Dinther, R., Roovers, D., Papini, G. B., & Coppola, G. (2018) · BMC Sports Science, Medicine and Rehabilitation · doi:10.1186/s13102-018-0098-0 · Cited by 65 · see citing works
Validation of a Photoplethysmographic Heart Rate Monitor: Polar OH1
Hermand, E., Cassirame, J., Ennequin, G., & Hue, O. (2019) · International Journal of Sports Medicine · doi:10.1055/a-0875-4033 · Cited by 59 · see citing works
Meyer, N., Kerz, M., Folarin, A., Joyce, D. W., Jackson, R., Karr, C., Dobson, R., & MacCabe, J. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.8292 · Cited by 58 · see citing works
Zhu, J., Hou, W., Xu, Y., Ji, F., Wang, G., Chen, C., Lin, C., Lin, X., Li, J., Zhuo, C., et al. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.112598 · Cited by 56 · see citing works
Role of wearable rhythm recordings in clinical decision making—The <scp>wEHRAbles</scp> project
Manninger, M., Kosiuk, J., Zweiker, D., Njeim, M., Antolic, B., Kircanski, B., Larsen, J. M., Svennberg, E., Vanduynhoven, P., & Duncker, D. (2020) · Clinical Cardiology · doi:10.1002/clc.23404 · Cited by 50 · see citing works
Noninvasive Continuous Monitoring of Vital Signs With Wearables: Fit for Medical Use?
Jacobsen, M., Dembek, T. A., Kobbe, G., Gaidzik, P. W., & Heinemann, L. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820904947 · Cited by 49 · see citing works
The validity and inter-device variability of the Apple Watch™ for measuring maximal heart rate
Abt, G., Bray, J., & Benson, A. C. (2018) · Journal of Sports Sciences · doi:10.1080/02640414.2017.1397282 · Cited by 41 · see citing works
Garbern, S. C., Mbanjumucyo, G., Umuhoza, C., Sharma, V. K., Mackey, J., Tang, O., Martin, K. D., Twagirumukiza, F. R., Rosman, S. L., McCall, N., et al. (2019) · DIGITAL HEALTH · doi:10.1177/2055207619879349 · Cited by 39 · see citing works
Rozanski, G. M., Aqui, A., Sivakumaran, S., & Mansfield, A. (2018) · JMIR Cardio · doi:10.2196/cardio.8199 · Cited by 36 · see citing works
Continuous heart rhythm monitoring using mobile photoplethysmography in ambulatory patients
Hochstadt, A., Havakuk, O., Chorin, E., Schwartz, A. L., Merdler, I., Laufer, M., Lubman, N., Ghantous, E., Viskin, S., & Rosso, R. (2020) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2020.04.017 · Cited by 18 · see citing works
Nazari, G., MacDermid, J. C., Sinden, K. E., Richardson, J., & Tang, A. (2019) · Physiotherapy Canada · doi:10.3138/ptc.2018-25 · Cited by 15 · see citing works
Comparing the Accuracy of Multiple Commercial Wearable Devices: A Method
Pardamean, B., Soeparno, H., Mahesworo, B., Budiarto, A., & Baurley, J. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.09.015 · Cited by 11 · see citing works
Occupational physical activity in brewery and office workers
Thompson, J. F., Severson, R. L., & Rosecrance, J. C. (2018) · Journal of Occupational and Environmental Hygiene · doi:10.1080/15459624.2018.1492136 · Cited by 9 · see citing works
- Authors
- Wang, R., Blackburn, G., Desai, M., Phelan, D., Gillinov, L., Houghtaling, P., & Gillinov, M.
- Affiliations
- Cleveland Clinic (US)
- Corresponding author
- Gillinov, Marc
- Venue
- JAMA Cardiology
- Publisher
- American Medical Association (AMA)
- Published
- 2017-01-01
- Volume
- 2
- Issue
- 1
- Pages
- 104
- ISSN
- 2380-6583 (ISSN-L)
- OpenAlex
- W2530450109
- Cited by
- 386
- References
- 6
- Citations by year
- 2026: 16 · 2025: 31 · 2024: 29 · 2023: 32 · 2022: 43 · 2021: 40 · 2020: 52 · 2019: 59 · 2018: 57 · 2017: 25 · 2016: 1
- FWCI
- 22.72
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Cleveland Clinic
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- heart rate accuracy, wearable activity trackers, treadmill exercise, exercise intensity, heart rate recovery, Apple Watch
- MeSH terms
- Adult, Heart Rate, Humans, Middle Aged, Wrist, Electrocardiography, Ambulatory, Healthy Volunteers
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