How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough?
Open accessAbstract
Background: Heart rate (HR) monitors are valuable devices for fitness-orientated individuals. There has been a vast influx of optical sensing blood flow monitors claiming to provide accurate HR during physical activities. These monitors are worn on the arm and wrist to detect HR with photoplethysmography (PPG) techniques. Little is known about the validity of these wearable activity trackers. Aim: Validate the Scosche Rhythm (SR), Mio Alpha (MA), Fitbit Charge HR (FH), Basis Peak (BP), Microsoft Band (MB), and TomTom Runner Cardio (TT) wireless HR monitors. Methods: 50 volunteers (males: n=32, age 19–43 years; females: n=18, age 19–38 years) participated. All monitors were worn simultaneously in a randomised configuration. The Polar RS400 HR chest strap was the criterion measure. A treadmill protocol of one 30 min bout of continuous walking and running at 3.2, 4.8, 6.4, 8.0, and 9.6 km/h (5 min at each protocol speed) with HR manually recorded every minute was completed. Results: For group comparisons, the mean absolute percentage error values were: 3.3%, 3.6%, 4.0%, 4.6%, 4.8% and 6.2% for TT, BP, RH, MA, MB and FH, respectively. Pearson product-moment correlation coefficient (r) was observed: r=0.959 (TT), r=0.956 (MB), r=0.954 (BP), r=0.933 (FH), r=0.930 (RH) and r=0.929 (MA). Results from 95% equivalency testing showed monitors were found to be equivalent to those of the criterion HR (±10% equivalence zone: 98.15–119.96). Conclusions: The results demonstrate that the wearable activity trackers provide an accurate measurement of HR during walking and running activities.
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The 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 [35]
The Discussion (page unavailable) says the tested heart-rate trackers had their lowest measurement error at the highest running speed, not at rest. The paper evaluates heart-rate measurements, not the accuracy of an actigraphy-based sleep tracker. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
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 [31]
The Discussion reports that six monitors measured heart rate comparably to the criterion measure during resting, walking, and running. The Methods describe heart-rate readings and analyses, but the paper does not assess the accuracy of energy-expenditure (EE) estimates. Its introductory mention of EE does not establish that part of the claim. (LLM, conf=0.90)
Experimental — accuracy not yet measuredThe mean absolute percentage error (MAPE) of the Basis Peak and Fitbit Charge HR were 3.6% and 6.2%, respectively.
“These results align with an assessment done by Stahl et al., where they measured the mean absolute percentage error values (MAPE) of BP and the FB to be 3.6% and 6.2%, respectively [166].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [166]
During the high intensity running stage, some of the lowest MAPE were from the Mio Alpha (0.82%), TomTom Runner Cardio (0.97%), and Basis Peak (3.28%).
“They also reported that during the high intensity running stage, they saw some of the lowest MAPE from the MA (0.82%), the TT (0.97%), and the BP (3.28%).”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [166]
When going from a resting phase to a low-intensity walking phase, the correlation coefficient went down but slowly rebounded when moved into a more intense walking phase.
“When going from a resting phase to a low-intensity walking phase, they found that the correlation coefficient went down but had slowly rebounded when moved into a more intense walking phase.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [166]
Other 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 [47]
Fitbit Charge HR has been found to have the greatest error in light to moderate physical activity and the least amount of error in vigorous physical activity.
“with further examination showing the device to have the greatest error in light to moderate physical activity and least amount of error in vigorous physical activity [19].”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [19]
Another study examining heart rate error during treadmill activities found the highest percentage error in light walking and the least error in running for Mio Alpha, Fitbit Charge HR, Basis Peak, Microsoft Band, and TomTom devices.
“Conversely, another study examining heart rate error during treadmill activities found the highest percentage error in light walking and the least error in running for Mio Alpha, Fitbit Charge HR, Basis Peak, Microsoft Band, and TomTom devices [19].”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [19]
Results of the Fitbit Charge HR are similar to previous research, which found the highest percentage error in moderate-intensity treadmill activity and the lowest percentage error during vigorous activity [19].
“Results of the Fitbit Charge HR are similar to previous research, which found the highest percentage error in moderate-intensity treadmill activity and the lowest percentage error during vigorous activity [19].”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [19]
Similar to previous heart rate research [19], this study found the highest measurement error for all devices in light and moderate physical activity stages.
“Similar to previous heart rate research [19], this study found the highest measurement error for all devices (Apple Watch, Fitbit Charge HR, Garmin Forerunner 225) in light and moderate physical activity stages.”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [19]
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Cites
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965 · see citing works
Validity of Consumer-Based Physical Activity Monitors
LEE, J.-M., KIM, Y., & WELK, G. J. (2014) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000287 · Cited by 401 · cited 1× · see citing works
“spent on these small, non-invasive, and easy-to-use activity monitors, validated research is needed to ensure the activity monitors accurately project HR under resting, light, moderate, and vigorous intensity conditions.(3) The six newly developed wearable activity trackers that are currently popular in the market and…”
Self-citationGlobal positioning system: a new opportunity in physical activity measurement
Maddison, R., & Mhurchú, C. N. (2009) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/1479-5868-6-73 · Cited by 249 · 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
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
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
Validation of photoplethysmography as a method to detect heart rate during rest and exercise
Spierer, D. K., Rosen, Z., Litman, L. L., & Fujii, K. (2015) · Journal of Medical Engineering & Technology · doi:10.3109/03091902.2015.1047536 · Cited by 139 · see citing works
de Rezende Barbosa, M. P. D. C., Silva, N. T. D., de Azevedo, F. M., Pastre, C. M., & Vanderlei, L. C. M. (2016) · Clinical Physiology and Functional Imaging · doi:10.1111/cpf.12203 · Cited by 129 · see citing works
Brage, S., Brage, N., Ekelund, U., Luan, J., Franks, P. W., Froberg, K., & Wareham, N. J. (2006) · European Journal of Applied Physiology · doi:10.1007/s00421-005-0112-6 · Cited by 127 · see citing works
Validation of Heart Rate Monitor-Based Predictions of Oxygen Uptake and Energy Expenditure
Montgomery, P. G., GREEN, D. J., Etxebarria, N., Pyne, D. B., Saunders, P. U., & MINAHAN, C. L. (2009) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0b013e3181a39277 · Cited by 56 · see citing works
Comparison of heart rate measured by Polar RS400 and ECG, validity and repeatability
Engström, E., Ottosson, E., Wohlfart, B., Grundström, N., & Wisén, A. G. M. (2012) · Advances in Physiotherapy · doi:10.3109/14038196.2012.694118 · Cited by 52 · see citing works
Validity of the Smarthealth Watch to Measure Heart Rate During Rest and Exercise
Lee, C. M., & Gorelick, M. (2011) · Measurement in Physical Education and Exercise Science · doi:10.1080/1091367x.2011.539089 · Cited by 42 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bio-Integrated Wearable Systems: A Comprehensive Review
Ray, T. R., Choi, J., Bandodkar, A. J., Krishnan, S., Gutruf, P., Tian, L., Ghaffari, R., & Rogers, J. A. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00573 · Cited by 1,350 · see citing works
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
Henriksen, A., Haugen Mikalsen, M., Woldaregay, A. Z., Muzny, M., Hartvigsen, G., Hopstock, L. A., & Grimsgaard, S. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9157 · Cited by 542 · see citing works
Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring
Fine, J., Branan, K. L., Rodriguez, A. J., Boonya-ananta, T., Ajmal, Ramella-Roman, J. C., McShane, M. J., & Coté, G. L. (2021) · Biosensors · doi:10.3390/bios11040126 · Cited by 348 · see citing works
Shin, G., Jarrahi, M. H., Fei, Y., Karami, A., Gafinowitz, N., Byun, A., & Lu, X. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103153 · Cited by 326 · see citing works
Xie, J., Wen, D., Liang, L., Jia, Y., Gao, L., & Lei, J. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.9754 · Cited by 310 · see citing works
Nelson, B. W., & Allen, N. B. (2019) · JMIR mHealth and uHealth · doi:10.2196/10828 · Cited by 310 · 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
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
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
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
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
Chow, H.-W., & Yang, C.-C. (2020) · JMIR mHealth and uHealth · doi:10.2196/14707 · Cited by 140 · see citing works
“…studies, in which heart rate measurements were less frequent (ie, every 15 seconds/every minute or only at the end of each exercise phase) [11,15,16], the statistical results produced from the current dataset are likely to be more representative because they enabled the researchers to…”
InfluentialTedesco, S., Sica, M., Ancillao, A., Timmons, S., Barton, J., & O’Flynn, B. (2019) · PLOS ONE · doi:10.1371/journal.pone.0216891 · Cited by 116 · see citing works
Gorny, A. W., Liew, S. J., Tan, C. S., & Müller-Riemenschneider, F. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.8233 · Cited by 94 · 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
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
Scardulla, F., D’Acquisto, L., Colombarini, R., Hu, S., Pasta, S., & Bellavia, D. (2020) · Sensors · doi:10.3390/s20185052 · Cited by 72 · see citing works
Rajala, S., Lindholm, H., & Taipalus, T. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aac7ac · Cited by 70 · see citing works
Leth, S., Hansen, J., Nielsen, O., & Dinesen, B. (2017) · Sensors · doi:10.3390/s17010211 · Cited by 64 · 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
Gyroscope vs. accelerometer measurements of motion from wrist PPG during physical exercise
Casson, A. J., Vazquez Galvez, A., & Jarchi, D. (2016) · ICT Express · doi:10.1016/j.icte.2016.11.003 · Cited by 57 · see citing works
Derks, Y. P., Klaassen, R., Westerhof, G. J., Bohlmeijer, E. T., & Noordzij, M. L. (2019) · JMIR mHealth and uHealth · doi:10.2196/13479 · Cited by 45 · see citing works
Sañudo, B., De Hoyo, M., Muñoz-López, A., Perry, J., & Abt, G. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1325-2 · Cited by 48 · see citing works
Béres, S., Holczer, L., & Hejjel, L. (2019) · Measurement Science Review · doi:10.2478/msr-2019-0030 · Cited by 33 · see citing works
Skin tone, confidence, and data quality of heart rate sensing in WearOS smartwatches
Ray, I., Liaqat, D., Gabel, M., & de Lara, E. (2021) · 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) · doi:10.1109/percomworkshops51409.2021.9431120 · Cited by 23 · see citing works
Herkert, C., Kraal, J. J., van Loon, E. M. A., van Hooff, M., & Kemps, H. M. C. (2019) · JMIR mHealth and uHealth · doi:10.2196/15045 · Cited by 19 · see citing works
Smartphones and wearable technology: benefits and concerns in cardiology
Jin, D., Adams, H., Cocco, A. M., Martin, W. G., & Palmer, S. (2020) · Medical Journal of Australia · doi:10.5694/mja2.50446 · Cited by 20 · see citing works
Støve, M. P., & Larsen, B. T. (2020) · European Journal of Physiotherapy · doi:10.1080/21679169.2019.1628300 · Cited by 5 · see citing works
- Authors
- Stahl, S. E., An, H.-S., Dinkel, D. M., Noble, J. M., & Lee, J.-M.
- Affiliations
- University of Nebraska at Omaha (US)
- Corresponding author
- Lee, Jung-Min
- Venue
- BMJ Open Sport & Exercise Medicine
- Publisher
- BMJ
- Published
- 2016-04
- Volume
- 2
- Issue
- 1
- Pages
- e000106
- ISSN
- 2055-7647 (ISSN-L)
- PMC
- PMC5117066
- OpenAlex
- W2343412983
- Cited by
- 276
- References
- 12
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 19 · 2023: 22 · 2022: 25 · 2021: 45 · 2020: 27 · 2019: 56 · 2018: 32 · 2017: 26 · 2016: 2
- FWCI
- 18.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- BMJ Open Sport & Exercise Medicine (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DigitalCommons@UNO (submittedVersion, cc-by-nc)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Complementary and alternative medicine › Cardiovascular and exercise physiology
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- heart rate monitors, wearable activity trackers, photoplethysmography, wrist-worn devices, walking, running, measurement accuracy, treadmill exercise
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