Accuracy of optical heart rate sensing technology in wearable fitness trackers for young and older adults: validation and comparison study

Chow, Hsueh-Wen; Yang, Chao-Ching

doi:10.2196/14707

Cited by 140Non-Invasive Vital Sign MonitoringCardiovascular and exercise physiologyHeart Rate Variability and Autonomic Control

Accuracy of optical heart rate sensing technology in wearable fitness trackers for young and older adults: validation and comparison study

Open access

Chow, Hsueh-Wen, Yang, Chao-Ching·2020·JMIR mHealth and uHealth

DOI 10.2196/14707PMC PMC7218601

Published version of a preprint

Open this paper in CoChat — free account Open PDF

Abstract

BACKGROUND: Wearable fitness trackers are devices that can record and enhance physical activity among users. Recently, photoplethysmography (PPG) devices that use optical heart rate sensors to detect heart rate in real time have become popular and help in monitoring and controlling exercise intensity. Although the benefits of using optical heart rate monitors have been highlighted through studies, the accuracy of the readouts these commercial devices generate has not been widely assessed for different age groups, especially for the East Asian population with Fitzpatrick skin type III or IV. OBJECTIVE: This study aimed to examine the accuracy of 2 wearable fitness trackers with PPG to monitor heart rate in real time during moderate exercise in young and older adults. METHODS: A total of 20 young adults and 20 older adults were recruited for this study. All participants were asked to undergo a series of sedentary and moderate physical activities using indoor aerobic exercise equipment. In this study, the Polar H7 chest-strapped heart rate monitor was used as the criterion measure in 2 fitness trackers, namely Xiaomi Mi Band 2 and Garmin Vivosmart HR+. The real-time, second-by-second heart rate data obtained from both devices were recorded using the broadcast heart rate mode. To critically analyze the results, multiple statistical parameters including the mean absolute percentage error (MAPE), Lin concordance correlation coefficient (CCC), intraclass correlation coefficient, the Pearson product moment correlation coefficient, and the Bland-Altman coefficient were determined to examine the performances of the devices. RESULTS: =6.04%). The scores for reliability test of CCC for Garmin were 0.92 (Young) and 0.80 (Senior), whereas those for Xiaomi were 0.76 (Young) and 0.73 (Senior). However, the results obtained using the Bland-Altman analysis indicated that both test optical devices underestimated the average heart rate. More importantly, the study documented some unexpected outlier readings reported by these devices when used on certain participants. CONCLUSIONS: The study reveals that commonly used optical heart rate sensors, such as the ones used herein, generally produce accurate heart rate readings irrespective of the age of the user. However, users should avoid relying entirely on these readings to indicate exercise intensities, as these devices have a tendency to produce erroneous, extreme readings, which might misinterpret the real-time exercise intensity. Future studies should therefore emphasize the occurrence rate of such errors, as this will likely benefit the development of improved models of heart rate sensors.

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

Crossref · 10.2196/14707

Cites

58 of 58 references verified0 citation errors · 0 not checkable

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?

Technological limitations are primarily sensor-related (LED wavelength, light intensity, placement), while algorithmic limitations stem from biased datasets, poor calibration, and underrepresentation of darker skin in training cohorts.
“Technological limitations are primarily sensor-related - such as LED wavelength, light intensity, and placement - while algorithmic limitations stem from biased datasets, poor calibration, and underrepresentation of darker skin in training cohorts.”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [27]
The Introduction (Accuracy of Optical Heart Rate Monitoring; Research Gaps, page unavailable) identifies sensor placement and skin structure as factors in optical heart-rate accuracy. The Discussion (Limitations, page unavailable) says devices may use different PPG modules or algorithms, but the paper does not identify LED wavelength or light intensity as primary limitations, or attribute algorithmic limitations to biased training datasets, poor calibration, or underrepresentation of darker skin in training cohorts. Its observation that earlier validation studies enrolled mostly white participants concerns study samples, not algorithm training cohorts. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Misleading heart rate readings lead to negative consequences such as substandard recovery assessments, improper exercise intensity control, and cardiovascular condition identification, resulting in deteriorated fitness results and healthcare outcomes.
“Misleading heart rate readings lead to several negative consequences, such as substandard recovery assessments and improper exercise intensity control, and cardiovascular condition identification, resulting in deteriorated fitness results and healthcare outcomes [27].”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [27]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
For light skin pigmentation, pulse rate mean bias was -1.24 bpm (95% CI -5.31 to 2.83).
“Mean biases were –1.24 (95% CI –5.31 to 2.83) bpm, –0.89 (95% CI –3.70 to 1.93) bpm, and –0.57 (95% CI –9.44 to 8.29) bpm for the corresponding groups (Table 4 and Figures 5-7 [41,60-62]).”
From Impact of skin pigmentation on pulse oximetry blood oxygenation and wearable pulse rate accuracy: systematic review and meta-analysis (2024) · cites this paper as [62]
The Results (Reliability of Examined Devices, Table 1; page unavailable) report heart-rate mean differences by age group, activity, and device, not a light-pigmentation group. Searches for −1.24, the stated interval, and skin-pigmentation subgroup results found no passage reporting this estimate. The Introduction (Aim of the Study; page unavailable) describes a study of participants with Fitzpatrick skin types III or IV. (LLM, conf=0.77)
Experimental — accuracy not yet measured
For light skin pigmentation, pulse rate 95% limits of agreement were -16.02 to 13.54 bpm.
“Our analysis revealed 95% LoA of –16.02 to 13.54 bpm, –18.62 to 16.84 bpm, and –33.69 to 32.54 bpm for light, medium, and dark skin pigmentation groups, respectively.”
From Impact of skin pigmentation on pulse oximetry blood oxygenation and wearable pulse rate accuracy: systematic review and meta-analysis (2024) · cites this paper as [62]
The Methods and Results report limits of agreement by age group, activity, and device, not by light, medium, and dark skin-pigmentation groups. Searches for “−16.02 to 13.54,” “16.02,” and skin-pigmentation subgroup results found no passage reporting the claimed limits for light skin. (LLM, conf=0.78)
Experimental — accuracy not yet measured
For medium skin pigmentation, pulse rate 95% limits of agreement were -18.62 to 16.84 bpm.
“Our analysis revealed 95% LoA of –16.02 to 13.54 bpm, –18.62 to 16.84 bpm, and –33.69 to 32.54 bpm for light, medium, and dark skin pigmentation groups, respectively.”
From Impact of skin pigmentation on pulse oximetry blood oxygenation and wearable pulse rate accuracy: systematic review and meta-analysis (2024) · cites this paper as [62]
The Results section (Bland-Altman Analysis; page unavailable) reports limits of agreement by age group, device, and activity, not by light, medium, and dark skin pigmentation. A search for the stated limits of −18.62 to 16.84 bpm and for skin-pigmentation subgroup analyses found no passage verifying that figure. The citing sentence also describes the figures as the citing authors’ own analysis. (LLM, conf=0.72)
Experimental — accuracy not yet measured

For dark skin pigmentation, pulse rate mean bias was -0.57 bpm (95% CI -9.44 to 8.29).
“Mean biases were –1.24 (95% CI –5.31 to 2.83) bpm, –0.89 (95% CI –3.70 to 1.93) bpm, and –0.57 (95% CI –9.44 to 8.29) bpm for the corresponding groups (Table 4 and Figures 5-7 [41,60-62]).”
From Impact of skin pigmentation on pulse oximetry blood oxygenation and wearable pulse rate accuracy: systematic review and meta-analysis (2024) · cites this paper as [62]
The Results (Reliability of Examined Devices, Table 1) report heart-rate differences by age group, activity, and device, not a mean bias for dark skin pigmentation. The Introduction (Aim of the Study) describes participants with Fitzpatrick skin types III or IV. Searches for the quoted bias and interval found no matching result in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measured
For medium skin pigmentation, pulse rate mean bias was -0.89 bpm (95% CI -3.70 to 1.93).
“Mean biases were –1.24 (95% CI –5.31 to 2.83) bpm, –0.89 (95% CI –3.70 to 1.93) bpm, and –0.57 (95% CI –9.44 to 8.29) bpm for the corresponding groups (Table 4 and Figures 5-7 [41,60-62]).”
From Impact of skin pigmentation on pulse oximetry blood oxygenation and wearable pulse rate accuracy: systematic review and meta-analysis (2024) · cites this paper as [62]
The Results (Bland-Altman Analysis and Table 1; page unavailable) report heart-rate biases by age group, device, and activity, not a bias for a medium-pigmentation group. Searches for −0.89 bpm and the stated confidence interval found no matching result in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measured
For dark skin pigmentation, pulse rate 95% limits of agreement were -33.69 to 32.54 bpm.
“Our analysis revealed 95% LoA of –16.02 to 13.54 bpm, –18.62 to 16.84 bpm, and –33.69 to 32.54 bpm for light, medium, and dark skin pigmentation groups, respectively.”
From Impact of skin pigmentation on pulse oximetry blood oxygenation and wearable pulse rate accuracy: systematic review and meta-analysis (2024) · cites this paper as [62]
The Results (Bland-Altman Analysis and Table 1; page unavailable) report limits of agreement by age group, device, and activity, not by light, medium, or dark skin pigmentation. Searches for the stated limits (−33.69 to 32.54 bpm) and skin-pigmentation subgroups found no matching result in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

A guideline of selecting and reporting intraclass correlation coefficients for reliability research
Koo, T. K., & Li, M. Y. (2016) · Journal of Chiropractic Medicine · doi:10.1016/j.jcm.2016.02.012 · Cited by 29,639 · cited 1× · see citing works
“Moreover, other studies on applied sports science have proposed a slightly different version of interpreting ICC values: values between 0.50 and 0.75 indicated moderate reliability, whereas other thresholds were the same [52].”
Influential
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 · cited 5× · see citing works
“In addition to activity intensity, several other studies have identified that motion artifacts during exercise were negatively correlated with the accuracy of PPG heart rate–monitoring systems [32,38,46,54-56].”
Influential
ACSM’s New Preparticipation Health Screening Recommendations from ACSM’s Guidelines for Exercise Testing and Prescription, Ninth Edition
Thompson, P. D., Arena, R. A., Riebe, D. A., & Pescatello, L. S. (2013) · Current Sports Medicine Reports · doi:10.1249/jsr.0b013e31829a68cf · Cited by 1,825 · see citing works
Systematic review of the validity and reliability of consumer-wearable activity trackers
Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-015-0314-1 · Cited by 1,232 · cited 2× · see citing works
“The results revealed that most studies validated the tracker accuracy and indicated that it had a higher accuracy for step counts, followed by that for distance and physical activity and finally for energy consumption and sleep [23].”
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
Older adults: Are they ready to adopt health-related ICT?
Heart, T., & Kalderon, E. (2011) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2011.03.002 · Cited by 588 · cited 1× · see citing works
“The adoption of new technology such as using health-related informatics technology (such as apps) or wearable fitness trackers is increasing [20,58,59], and the benefits are also observed in the senior population [20,60].”
HEART RATE AS A PROGNOSTIC FACTOR FOR CORONARY HEART DISEASE AND MORTALITY: FINDINGS IN THREE CHICAGO EPIDEMIOLOGIC STUDIES
Dyer, A. R., Persky, V., Stamler, J., Paul, O., Shekelle, R. B., Berkson, D. M., Lepper, M. M., Schoenberger, J. A., & Lindberg, H. A. (1980) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a113046 · Cited by 553 · cited 1× · see citing works
“…healthy participants, that is, participants without any cardiovascular diseases (eg, coronary artery disease or abnormal heart rhythms) or neurological disorders (eg, Parkinson disease or essential tremor) because the abnormal heart rate might interfere in the accuracy of comparison [63,64].”
Sample size formulas for estimating intraclass correlation coefficients with precision and assurance
Zou, G. (2012) · Statistics in Medicine · doi:10.1002/sim.5466 · Cited by 473 · cited 1× · see citing works
“Based on the formula proposed by Zou [43], the number of participants (n) required for achieving a target power of 0.90 was 8.”
Influential
Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions.
Gil, E., Orini, M., Bailón, R., Vergara, J. M., Mainardi, L., & Laguna, P. (2010) · Physiological Measurement · doi:10.1088/0967-3334/31/9/015 · Cited by 461 · cited 1× · see citing works
“However, the accuracy might vary across brands [16,31] in terms of activity patterns or speed, exercise intensities [10,14,31], skin tone [10], room temperature [32], placement of sensors [29], or compression-induced and motion-induced artifacts [13,32-34].”
Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation
Mercer, K., Giangregorio, L., Schneider, E., Chilana, P., Li, M., & Grindrod, K. (2016) · JMIR mHealth and uHealth · doi:10.2196/mhealth.4225 · Cited by 419 · cited 2× · see citing works
“The adoption of new technology such as using health-related informatics technology (such as apps) or wearable fitness trackers is increasing [20,58,59], and the benefits are also observed in the senior population [20,60].”
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 · cited 9× · 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].”
Influential
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 · cited 2× · see citing works
“Importantly, while generally producing accurate results, these devices did not report reliable step-count readings in certain conditions, such as slow walking or while performing unnatural hand movements [21-24].”
Artifact Removal in Physiological Signals—Practices and Possibilities
Sweeney, K. T., Ward, T. E., & McLoone, S. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2188536 · Cited by 358 · cited 1× · see citing works
“In addition to activity intensity, several other studies have identified that motion artifacts during exercise were negatively correlated with the accuracy of PPG heart rate–monitoring systems [32,38,46,54-56].”
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 · cited 10× · see citing works
“Moreover, potential variables such as age, ethnicity, and gender were not considered in earlier studies [2,14].”
Influential
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 · cited 10× · see citing works
“Similar findings were also reported in previous studies [11,12] and could represent a general characteristic of optical heart rate fitness trackers.”
Influential
Validation of the Fitbit One activity monitor device during treadmill walking
Takacs, J., Pollock, C. L., Guenther, J. R., Bahar, M., Napier, C., & Hunt, M. A. (2013) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2013.10.241 · Cited by 322 · cited 2× · see citing works
“Importantly, while generally producing accurate results, these devices did not report reliable step-count readings in certain conditions, such as slow walking or while performing unnatural hand movements [21-24].”
Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial?
El-Amrawy, F., & Nounou, M. I. (2015) · Healthcare Informatics Research · doi:10.4258/hir.2015.21.4.315 · Cited by 287 · cited 3× · see citing works
“Wearable fitness trackers have gained popularity worldwide, and their annual sales continue to grow [1,2].”
How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough?
Stahl, S. E., An, H.-S., Dinkel, D. M., Noble, J. M., & Lee, J.-M. (2016) · BMJ Open Sport & Exercise Medicine · doi:10.1136/bmjsem-2015-000106 · Cited by 276 · cited 4× · 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…”
Influential
WORLDWIDE SURVEY OF FITNESS TRENDS FOR 2020
Thompson, W. R. (2019) · ACSMʼs Health & Fitness Journal · doi:10.1249/fit.0000000000000526 · Cited by 275 · cited 1× · see citing works
“These trackers were listed as the No. 1 fitness trends in the years 2016, 2017, 2019, and 2020 in a worldwide survey conducted by the American College of Sports and Medicine [3-6].”
Promoting and prescribing exercise for the elderly.
Nied, R. J., & Franklin, B. A. (2002) · PubMed · Cited by 244 · see citing works
Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices
Dooley, E. E., Golaszewski, N. M., & Bartholomew, J. B. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.7043 · Cited by 242 · cited 7× · see citing works
“Moreover, potential variables such as age, ethnicity, and gender were not considered in earlier studies [2,14].”
Influential
Resting Heart Rate: Risk Indicator and Emerging Risk Factor in Cardiovascular Disease
Böhm, M., Reil, J.-C., Deedwania, P., Kim, J. B., & Borer, J. S. (2015) · The American Journal of Medicine · doi:10.1016/j.amjmed.2014.09.016 · Cited by 236 · cited 1× · see citing works
“Second, resting heart rate is also a widely used parameter for general health assessments to detect cardiovascular diseases [8].”
How consumer physical activity monitors could transform human physiology research
Wright, S. P., Hall Brown, T. S., Collier, S. R., & Sandberg, K. (2017) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00349.2016 · Cited by 234 · cited 2× · see citing works
“Various combinations of correlation tests are frequently adopted in evaluating the reliability or validity of examined devices [35].”
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 · cited 1× · see citing works
“First, heart rate is a vital component in cardiovascular fitness assessments and an important parameter in exercise training programs [7].”
Reliability and Validity of Ten Consumer Activity Trackers Depend on Walking Speed
Fokkema, T., Kooiman, T. J. M., Krijnen, W. P., van der Schans, C. P., & de Groot, M. (2016) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001146 · Cited by 212 · cited 2× · see citing works
“However, the accuracy might vary across brands [16,31] in terms of activity patterns or speed, exercise intensities [10,14,31], skin tone [10], room temperature [32], placement of sensors [29], or compression-induced and motion-induced artifacts [13,32-34].”
Validity of Consumer-Based Physical Activity Monitors for Specific Activity Types
Nelson, M. B., Kaminsky, L. A., Dickin, D. C., & Montoye, A. H. K. (2016) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000933 · Cited by 206 · cited 1× · see citing works
“Nevertheless, most studies recommend caution when deriving energy expenditure estimations directly using these readings [11,13,25,26].”
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 · cited 9× · see citing works
“For instance, Gillinov et al [2] set the CCC value greater than 0.80 to represent acceptable reliability, whereas Boudreaux et al [13] set ICC values from 0.60 to 0.75 to represent moderate reliability and from 0.75 to 0.90 to indicate superior reliability.”
Influential
Current State of Commercial Wearable Technology in Physical Activity Monitoring 2015-2017
Bunn, J., Navalta, J. W., Fountaine, C. J., & Reece, J. (2018) · International Journal of Exercise Science · doi:10.70252/njqx2719 · Cited by 194 · cited 3× · see citing works
“This will allow for better cross-study comparisons and more accurate interpretations [51].”
WORLDWIDE SURVEY OF FITNESS TRENDS FOR 2019
Thompson, W. R. (2018) · ACSMʼs Health & Fitness Journal · doi:10.1249/fit.0000000000000438 · Cited by 193 · cited 2× · see citing works
“1 fitness trends in the years 2016, 2017, 2019, and 2020 in a worldwide survey conducted by the American College of Sports and Medicine [3-6].”
Prevention and Treatment of Skin Aging
McCullough, J. L., & Kelly, K. M. (2006) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1354.044 · Cited by 178 · cited 1× · see citing works
“Typically, the skin changes with age, that is, “fine wrinkles, roughness, mottled hyperpigmentation, dilated blood vessels, and loss of skin tone” are observed [40].”
Validity of using tri-axial accelerometers to measure human movement – Part II: Step counts at a wide range of gait velocities
Fortune, E., Lugade, V. A., Morrow, M. M. B., & Kaufman, K. R. (2014) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2014.02.006 · Cited by 150 · cited 1× · see citing works
“In addition, studies have started to examine the validity and reliability of the fitness trackers among older adults instead of young adults because they might present different movements such as gait patterns or speeds [27,28].”
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 · cited 11× · see citing works
“Similar findings were also reported in previous studies [11,12] and could represent a general characteristic of optical heart rate fitness trackers.”
Influential
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 · cited 2× · see citing works
“However, the accuracy might vary across brands [16,31] in terms of activity patterns or speed, exercise intensities [10,14,31], skin tone [10], room temperature [32], placement of sensors [29], or compression-induced and motion-induced artifacts [13,32-34].”
WORLDWIDE SURVEY OF FITNESS TRENDS FOR 2017
Thompson, W. R. (2016) · ACSMʼs Health & Fitness Journal · doi:10.1249/fit.0000000000000252 · Cited by 147 · cited 2× · see citing works
“1 fitness trends in the years 2016, 2017, 2019, and 2020 in a worldwide survey conducted by the American College of Sports and Medicine [3-6].”
A photoelectric plethysmograph for the measurement of cutaneous blood flow
CHALLONER, A. V. J., & Ramsay, C. A. (1974) · Physics in Medicine and Biology · doi:10.1088/0031-9155/19/3/003 · Cited by 145 · cited 1× · see citing works
“PPG measures heart rates based on the changes in vascular blood flow during the cardiac cycle [9].”
Older Adults’ Experiences Using a Commercially Available Monitor to Self-Track Their Physical Activity
McMahon, S., Lewis, B. A., Oakes, J. M., Guan, W., Wyman, J. F., & Rothman, A. J. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.5120 · Cited by 143 · cited 1× · see citing works
“The adoption of new technology such as using health-related informatics technology (such as apps) or wearable fitness trackers is increasing [20,58,59], and the benefits are also observed in the senior population [20,60].”
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 · cited 9× · see citing works
“To compare the accuracy of test devices, various statistical methods were chosen based on recommendations from relevant studies [2,10,26,38,51].”
Influential
Criterion-Validity of Commercially Available Physical Activity Tracker to Estimate Step Count, Covered Distance and Energy Expenditure during Sports Conditions
Wahl, Y., Düking, P., Droszez, A., Wahl, P., & Mester, J. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00725 · Cited by 133 · cited 2× · see citing works
“Nevertheless, most studies recommend caution when deriving energy expenditure estimations directly using these readings [11,13,25,26].”
When fitness trackers don't 'fit'
Yang, R., Shin, E., Newman, M., & Ackerman, M. S. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2804269 · Cited by 119 · see citing works
WORLDWIDE SURVEY OF FITNESS TRENDS FOR 2016
Thompson, W. R. (2015) · ACSMʼs Health & Fitness Journal · doi:10.1249/fit.0000000000000164 · Cited by 108 · cited 1× · see citing works
“These trackers were listed as the No. 1 fitness trends in the years 2016, 2017, 2019, and 2020 in a worldwide survey conducted by the American College of Sports and Medicine [3-6].”
Fitbit Charge HR Wireless Heart Rate Monitor: Validation Study Conducted Under Free-Living Conditions
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 · cited 1× · see citing works
“Moreover, wrist-based fitness trackers were easy to wear and remove and, thus, eased the discomfort of wearing chest straps for monitoring the real-time heart rate during traditional exercise and fitness training programs or interventions [10,45].”
Comparison of ICC and CCC for assessing agreement for data without and with replications
Chen, C., & Barnhart, H. X. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2008.09.026 · Cited by 89 · see citing works
Efficacy of a Peer‐Guided Exercise Programme for Adolescents with Intellectual Disability
Stanish, H. I., & Temple, V. A. (2012) · Journal of Applied Research in Intellectual Disabilities · doi:10.1111/j.1468-3148.2011.00668.x · Cited by 79 · cited 1× · see citing works
“These types of equipment were widely demonstrated in the previous exercise protocols and proved to be ideal and safe for aerobic training [2,10,47-49].”
The effectiveness of a peer‐mentored older adult fitness program on perceived physical, mental, and social function
Dorgo, S., Robinson, K. M., & Bader, J. O. (2009) · Journal of the American Academy of Nurse Practitioners · doi:10.1111/j.1745-7599.2008.00393.x · Cited by 78 · cited 1× · see citing works
“These types of equipment were widely demonstrated in the previous exercise protocols and proved to be ideal and safe for aerobic training [2,10,47-49].”
CNS-disease affecting the heart: Brain–heart disorders
Finsterer, J., & Wahbi, K. (2014) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2014.07.003 · Cited by 70 · cited 1× · see citing works
“…healthy participants, that is, participants without any cardiovascular diseases (eg, coronary artery disease or abnormal heart rhythms) or neurological disorders (eg, Parkinson disease or essential tremor) because the abnormal heart rate might interfere in the accuracy of comparison [63,64].”
Wrist-worn optical and chest strap heart rate comparison in a heterogeneous sample of healthy individuals and in coronary artery disease patients
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 · cited 2× · see citing works
“These include the MAPE test, the Bland-Altman analysis, the correlation PPMCC, ICC, and CCC tests [2,12,13,15,53].”
Comparison of elliptical training, stationary cycling, treadmill walking and overground walking. Electromyographic patterns
Prosser, L. A., Stanley, C. J., Norman, T. L., Park, H. S., & Damiano, D. L. (2011) · Gait & Posture · doi:10.1016/j.gaitpost.2010.11.013 · Cited by 58 · see citing works
Evaluation 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 · cited 1× · see citing works
“PPG technology uses an optical sensor that illuminates light and measures the change in light absorption by the skin, which varies with change in blood volume; thus, the accuracy of heart rate monitoring using PPG is subject to skin structures [39].”
The Usefulness of Activity Trackers in Elderly with Reduced Mobility: A Case Study
Lauritzen, J., Adolfo, M., Sevillano, J. L., & Civit, A. (2013) · Studies in health technology and informatics · doi:10.3233/978-1-61499-289-9-759 · Cited by 53 · cited 1× · see citing works
“In addition, studies have started to examine the validity and reliability of the fitness trackers among older adults instead of young adults because they might present different movements such as gait patterns or speeds [27,28].”
Methodological Shortcomings of Wrist-Worn Heart Rate Monitors Validations
Sartor, F., Papini, G. B., Cox, L. G. E., & Cleland, J. G. F. (2018) · Journal of Medical Internet Research · doi:10.2196/10108 · Cited by 51 · cited 5× · see citing works
“Sartor et al [38] also supported the use of the CCC test for validating wrist-based heart rate monitors.”
Influential
Assessing agreement with intraclass correlation coefficient and concordance correlation coefficient for data with repeated measures
Chen, C., & Barnhart, H. X. (2012) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2012.11.004 · Cited by 50 · cited 1× · see citing works
“That said, ICC and CCC should nonetheless be the preferred tests, as they were initially used to assess the interrater reliability in related validation studies [61,62].”
Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach
Alqaraawi, A., Alwosheel, A., & Alasaad, A. (2016) · Healthcare Technology Letters · doi:10.1049/htl.2016.0006 · Cited by 50 · cited 1× · see citing works
“In addition to activity intensity, several other studies have identified that motion artifacts during exercise were negatively correlated with the accuracy of PPG heart rate–monitoring systems [32,38,46,54-56].”
Aesthetics and Cosmetic Surgery for Darker Skin Types
Lim, A. (2008) · Australasian Journal of Dermatology · doi:10.1111/j.1440-0960.2007.00428.x · Cited by 44 · cited 1× · see citing works
“This study was conducted in Taiwan to validate 2 trackers used by the yellow skin tone population (Fitzpatrick skin type III or IV) [41,42].”
Concurrent Validity of Resting Pulse-Rate Measurements: A Comparison of 2 Smartphone Applications, the Polar H7 Belt Monitor, and a Pulse Oximeter With Bluetooth
Cheatham, S. W., Kolber, M. J., & Ernst, M. P. (2014) · Journal of Sport Rehabilitation · doi:10.1123/jsr.2013-0145 · Cited by 43 · cited 1× · see citing works
“This study used the Polar heart rate strap (H7, Polar Electro Oy), widely used as the criterion for measuring heart rate in sports science studies [2,44].”
Design, development and clinical validation of a wrist-based optical heart rate monitor
Preejith, S. P., Alex, A., Joseph, J., & Sivaprakasam, M. (2016) · 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2016.7533786 · Cited by 41 · cited 1× · see citing works
“In another validation study, the measured heart rate showed a minor deviation compared with the actual heart rate in participants with a dark skin tone [30].”
Heart Rate monitoring during physical exercise using wrist-type photoplethysmographic (PPG) signals
Ahmadi, A. K., Moradi, P., Malihi, M., Karimi, S., & Shamsollahi, M. B. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319800 · Cited by 30 · cited 1× · see citing works
“In addition to activity intensity, several other studies have identified that motion artifacts during exercise were negatively correlated with the accuracy of PPG heart rate–monitoring systems [32,38,46,54-56].”
Describing the User Experience of Wearable Fitness Technology through Online Product Reviews
Michaelis, J. R., Rupp, M. A., Kozachuk, J. R., Ho, B., Zapata-Ocampo, D., McConnell, D. S., & Smither, J. A.-A. (2016) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601248 · Cited by 29 · cited 1× · see citing works
“Inaccurate and inconsistent readings are major reasons for negative user experiences, which discourage the continued use of these devices [17-20].”
Activity Tracker and Elderly
Rasche, P., Wille, M., Theis, S., Schaefer, K., Schlick, C. M., & Mertens, A. (2015) · doi:10.1109/cit/iucc/dasc/picom.2015.211 · Cited by 26 · cited 1× · see citing works
“The adoption of new technology such as using health-related informatics technology (such as apps) or wearable fitness trackers is increasing [20,58,59], and the benefits are also observed in the senior population [20,60].”

Flagged first, then by citations

Research works printed but not linked: 1

[17]Shih PC Han K Poole ES Rosson MB Carroll JM Use and Adoption Challenges of Wearable Activity Trackers iConference 2015 Proceedings 2015 iConference'15 March 24-27, 2015 Newport Beach, California, USA

Sorted by citations

Wearable Devices for Physical Activity and Healthcare Monitoring in Elderly People: A Critical Review
Teixeira, E., Fonseca, H., Diniz‐Sousa, F., Veras, L., Boppre, G., de Oliveira, J. J. F., Pinto, D., Alves, A. J., Barbosa, A., Mendes, R., et al. (2021) · Geriatrics · doi:10.3390/geriatrics6020038 · Cited by 172 · see citing works
“provide accurate heart rate measurements [46], even in older adults [47], although accuracy varies among different brands over different exercise intensities [48].”
Background
Wearable activity trackers–advanced technology or advanced marketing?
Shei, R., Holder, I. G., Oumsang, A. S., Paris, B. A., & Paris, H. L. R. (2022) · European Journal of Applied Physiology · doi:10.1007/s00421-022-04951-1 · Cited by 169 · see citing works
“2019; Chow and Yang 2020).”
Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network
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
Wearable Skin Sensors and Their Challenges: A Review of Transdermal, Optical, and Mechanical Sensors
Ahmad Tarar, A., Mohammad, U., & K. Srivastava, S. (2020) · Biosensors · doi:10.3390/bios10060056 · Cited by 119 · see citing works
Recent Advances in Wearable Optical Sensor Automation Powered by Battery versus Skin-like Battery-Free Devices for Personal Healthcare—A Review
Kazanskiy, N. L., Butt, M. A., & Khonina, S. N. (2022) · Nanomaterials · doi:10.3390/nano12030334 · Cited by 87 · see citing works
“PPG-based wearable optical heart rate monitors (HRMs) have grown extremely popular, with a slew of tech businesses and products being developed and marketed [80,81].”
Background
Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects
Sunny, J. S., Patro, C. P. K., Karnani, K., Pingle, S. C., Lin, F., Anekoji, M., Jones, L., Kesari, S., & Ashili, S. (2022) · Sensors · doi:10.3390/s22030756 · Cited by 73 · see citing works
Usage Trends and Data Sharing Practices of Healthcare Wearable Devices Among US Adults: Cross-Sectional Study
Chandrasekaran, R., Muhammed Sadiq, T., & Moustakas, E. (2025) · Journal of Medical Internet Research · doi:10.2196/63879 · Cited by 59 · see citing works
“In addition, questions have also been raised about the accuracy and reliability of data captured by wearable devices [14,41,42].”
Is the Xiaomi Mi Band 4 an Accuracy Tool for Measuring Health-Related Parameters in Adults and Older People? An Original Validation Study
Pérez, A. D. L. C., Román, P. Á. L., Jiménez, M. M., Zurita, M. L., Laredo‐Aguilera, J. A., MONTILLA, J. A. P., & Linares, J. C. C. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19031593 · Cited by 53 · see citing works
“On the contrary, previous versions of these wearables showed an underestimation of HR during a running treadmill, jogging, and cycle ergometer protocol [18,27,31].”
Background
Recent advances in skin-like wearable sensors: sensor design, health monitoring, and intelligent auxiliary
Huang, Z., Xu, Y., Cheng, Y., Xue, M., Deng, M., Jaffrezic-Renault, N. J., & Guo, Z. (2022) · Sensors & Diagnostics · doi:10.1039/d2sd00037g · Cited by 51 · see citing works
A Concise Review of the Progress in Photonic Sensing Devices
Shahbaz, M., Butt, M. A., & Piramidowicz, R. (2023) · Photonics · doi:10.3390/photonics10060698 · Cited by 49 · see citing works
Validation of Garmin and Polar Devices for Continuous Heart Rate Monitoring During Common Training Movements in Tactical Populations
Merrigan, J. J., Stovall, J. H., Stone, J. D., Stephenson, M., Finomore, V. S., & Hagen, J. A. (2022) · Measurement in Physical Education and Exercise Science · doi:10.1080/1091367x.2022.2161820 · Cited by 49 · see citing works
“Within an activity, such as treadmill (Pasadyn et al., 2019), cycle ergometer (Chow & Yang, 2020), and resistance training tasks (Boudreaux et al., 2018; Jo et al., 2016; Müller et al., 2019), it is common for wrist-worn device validity to decrease as exercise intensity increases.”
BackgroundMethod
Employing of machine learning and wearable devices in healthcare system: tasks and challenges
Saad, H. S., Zaki, J. F. W., & Abdelsalam, M. M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10197-z · Cited by 51 · see citing works
“Readings from stationary devices in hospitals can be more accurate than these devices but are sometimes considered acceptable depending on the application [10, 11].”
Application and Challenges of the Technology Acceptance Model in Elderly Healthcare: Insights from ChatGPT
Kim, S.-D. (2024) · Technologies · doi:10.3390/technologies12050068 · Cited by 40 · see citing works
“Advocating for processes that collect user feedback, address concerns, and update technologies based on evolving needs contributes to their ongoing relevance and effectiveness.”
BackgroundResult
Measure by measure: Resting heart rate across the 24-hour cycle
Speed, C., Arneil, T., Harle, R. K., Wilson, A., Karthikesalingam, A., McConnell, M. V., & Phillips, J. P. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000236 · Cited by 39 · see citing works
“46% for the Garmin Vivosmart HR+ and Xiaomi Mi Band 2 devices respectively [19].”
Background
Impact of skin pigmentation on pulse oximetry blood oxygenation and wearable pulse rate accuracy: systematic review and meta-analysis
Singh, S., Bennett, M. R., Chen, C., Shin, S., Ghanbari, H., & Nelson, B. W. (2024) · Journal of Medical Internet Research · doi:10.2196/62769 · Cited by 36 · see citing works
“Mean biases were –1.24 (95% CI –5.31 to 2.83) bpm, –0.89 (95% CI –3.70 to 1.93) bpm, and –0.57 (95% CI –9.44 to 8.29) bpm for the corresponding groups (Table 4 and Figures 5-7 [41,60-62]).”
Influential
Technologies for non-invasive physiological sensing: Status, challenges, and future horizons
Yu, Y., Jain, B., Anand, G., Heidarian, M., Lowe, A., & Kalra, A. M. (2023) · Biosensors and Bioelectronics X · doi:10.1016/j.biosx.2023.100420 · Cited by 34 · see citing works
Shedding Light on Nocturnal Movements in Parkinson’s Disease: Evidence from Wearable Technologies
Zampogna, A., Manoni, A., Asci, F., Liguori, C., Irrera, F., & Suppa, A. (2020) · Sensors · doi:10.3390/s20185171 · Cited by 33 · see citing works
Wristbands in Home-Based Rehabilitation—Validation of Heart Rate Measurement
Jachymek, M., Jachymek, M., Kiedrowicz, R., Kaźmierczak, J., Płońska‐Gościniak, E., & Peregud‐Pogorzelska, M. (2021) · Sensors · doi:10.3390/s22010060 · Cited by 31 · see citing works
“We did not identify any co-factors that altered the reliability, such as sex, age, height, weight, and BMI, which is consistent with previous results [8,9,12].”
BackgroundMethodResultInfluential
Heart Rate and Distance Measurement of Two Multisport Activity Trackers and a Cellphone App in Different Sports: A Cross-Sectional Validation and Comparison Field Study
Budig, M., Keiner, M., Stoohs, R. A., Hoffmeister, M., & Höltke, V. (2021) · Sensors · doi:10.3390/s22010180 · Cited by 29 · see citing works
“Chow and Yang [7] reported for resting HR and in series with moderate physical activity high correlation values (r > 0.”
BackgroundResultInfluential
From lab to life: Evaluating the reliability and validity of psychophysiological data from wearable devices in laboratory and ambulatory settings
Hu, X., Sgherza, T. R., Nothrup, J. B., Fresco, D. M., Naragon‐Gainey, K., & Bylsma, L. M. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02387-3 · Cited by 29 · see citing works
“Interestingly, some recent studies also found that PPG-based wrist bands tended to underestimate HR compared to ECG-based chest straps, especially during physical activities (Chow & Yang, 2020; Zhang et al., 2020).”
“Social media, wearables, telemedicine and digital health,”—A Gen Y and Z perspective
Slepian, R. C., Vincent, A. C., Patterson, H. L., & Furman, H. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824010-6.00072-1 · Cited by 27 · see citing works
Advancing Sports Cardiology: Integrating Artificial Intelligence with Wearable Devices for Cardiovascular Health Management
Zheng, X. H., Liu, Z., Liu, J., Hu, C., Du, Y., Li, J., Pan, Z., & Ding, K. (2025) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c22895 · Cited by 29 · see citing works
Wrist-Worn and Arm-Worn Wearables for Monitoring Heart Rate During Sedentary and Light-to-Vigorous Physical Activities: Device Validation Study
Schweizer, T., & Gilgen-Ammann, R. (2025) · JMIR Cardio · doi:10.2196/67110 · Cited by 25 · see citing works
“In previous studies, wrist-worn devices showed similar results: the bias tends to increase with the intensity of activity on a treadmill, while using a cycle ergometer, and during resistance training tasks [19,42,48,49,52,53].”
Sports Heart Monitors as Reliable Diagnostic Tools for Training Control and Detecting Arrhythmias in Professional and Leisure-Time Endurance Athletes: An Expert Consensus Statement
Gajda, R., Gajda, J., Czuba, M., Knechtle, B., & Drygas, W. (2023) · Sports Medicine · doi:10.1007/s40279-023-01948-4 · Cited by 24 · see citing works
“More recent optical HR-measuring devices, in turn, use a light-emitting diode to emit light through the skin and measure how it scatters off blood vessels [6].”
Background
Sensor technology in eating disorders research: A systematic review
Presseller, E. K., Patarinski, A. G. G., Fan, S. C., Lampe, E. W., & Juarascio, A. S. (2022) · International Journal of Eating Disorders · doi:10.1002/eat.23715 · Cited by 23 · see citing works
Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data
Granados, L. H. S., Mendoza-Moreno, J. F., Chantre-Astaiza, Á., Muñoz-Organero, M., & Ramírez-González, G. (2021) · Sensors · doi:10.3390/s21237854 · Cited by 22 · see citing works
Smart cushion-based non-invasive mental fatigue assessment of construction equipment operators: A feasible study
Wang, L., Li, H., Yao, Y., Han, D., Yu, C., Lyu, W., & Wu, H. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102134 · Cited by 23 · see citing works
Exercising with a robotic exoskeleton can improve memory and gait in people with Parkinson’s disease by facilitating progressive exercise intensity
McGibbon, C. A., Sexton, A., & Gryfe, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-54200-y · Cited by 20 · see citing works
Predictive Modeling of Heart Rate Dynamics based on Physical Characteristics and Exercise Parameters: A Machine Learning Approach
Ali, M. A., Abdelsallam, A., Rasslan, A., & Rabee, A. (2024) · International Journal of Physical Education Fitness and Sports · doi:10.54392/ijpefs2421 · Cited by 20 · see citing works
“The advancements in technology and methodology demonstrate an increasing recognition of the practicality of heart rate monitoring in areas beyond high-level athletics, highlighting its significance in the context of public health and well-being (Nelson et al., 2020; Chow, and Yang, 2020).”
Method
Deriving Accurate Nocturnal Heart Rate, rMSSD and Frequency HRV from the Oura Ring
Liang, T., Yılmaz, G., & Soon, C. S. (2024) · Sensors · doi:10.3390/s24237475 · Cited by 19 · see citing works
“…N = 22; 36.4% male) to examine whether Participants were further divided into two age groups: younger (20–44 years old; N = 92; 45.7% male) versus older (45–68 years old; N = 22; 36.4% male) to examine whether HR and HRV measurement accuracies differed between younger and older participants [31].”
Influential
Hybrid sensor integration in wearable devices for improved cardiovascular health monitoring
Khan, B., Khan, W., Masrur, M. A., Khalid, R. T., Awais, M., Khan, B., Khoo, B. L., & Abdullah, S. (2025) · Journal of Science Advanced Materials and Devices · doi:10.1016/j.jsamd.2025.100889 · Cited by 24 · see citing works
Personalized, Naturalistic Virtual Reality Scenarios Coupled With Web-Based Progressive Muscle Relaxation Training for the General Population: Protocol for a Proof-of-Principle Randomized Controlled Trial
Pardini, S., Gabrielli, S., Olivetto, S., Fusina, F., Dianti, M., Forti, S., Lancini, C., & Novara, C. (2023) · JMIR Research Protocols · doi:10.2196/44183 · Cited by 21 · see citing works
“…is being assessed between and within subjects in sense of presence scores related to the VR and guided imagery experiences c Xiaomi Mi Band 2 tool [58,59] Differences between groups and change from baseline to the T1 and T2 phases in heart rate are assessed based on the Xiaomi Mi Band 2 sensor.”
Method
Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance
Ayena, J. C., Bouayed, A., Arous, M. B., Ouakrim, Y., Loulou, K., Ameyed, D., Savard, I., Kamel, L. E., & Mezghani, N. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1581285 · Cited by 27 · see citing works
A Review on Atrial Fibrillation Detection From Ambulatory ECG
Ma, C., Xiao, Z., Zhao, L., Biton, S. B., Behar, J. A., Long, X., Vullings, R., Aarts, R. M., Li, J., & Liu, C. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3321792 · Cited by 20 · see citing works
“The watches such as Apple [31], Huawei [32], Huami [33], and Xiaomi [34] all utilize PPG to screen for AF.”
Background
Evaluation of Wrist-Worn Photoplethysmography Trackers with an Electrocardiogram in Patients with Ischemic Heart Disease: A Validation Study
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
“Further analysis of covariate variables indicates that the PPG HR measurement is not associated with age [49] or atrial fibrillation [26, 31] (Appendix S3C).”
A Systematic Review of Chest-Worn Sensors in Cardiac Assessment: Technologies, Advantages, and Limitations
Machado, A., Ferreira, D. F., Ferreira, S., Almeida‐Antunes, N., Carvalho, P. J., Melo, P. I. D. A., Rocha, N., & Rodrigues, M. A. (2025) · Sensors · doi:10.3390/s25196049 · Cited by 16 · see citing works
“In the studies by Chow and Yang [43], Hoevenaars et al. [46], and Navalta et al. [50], the Polar H7 strap was used.”
Kinetic liquid metal synthesis of flexible 2D conductive oxides for multimodal wearable sensing
Rahman, M. S., Agnew, S. A., Ong, S. W., & Scheideler, W. J. (2024) · npj Flexible Electronics · doi:10.1038/s41528-024-00371-7 · Cited by 15 · see citing works
PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies
Joshi, J., Wang, K., & Cho, Y. (2023) · Sensors · doi:10.3390/s23198244 · Cited by 20 · see citing works
“While validation studies involving physical activity conditions have shown moderate-to-strong agreement for devices, they have consistently revealed a decrease in accuracy as activity levels increase [19, 63].”
Background
Validity of Current Smartwatches for Triathlon Training: How Accurate Are Heart Rate, Distance, and Swimming Readings?
Jacko, T., Bartsch, J., von Diecken, C., & Ueberschär, O. (2024) · Sensors · doi:10.3390/s24144675 · Cited by 15 · see citing works
Accuracy and Validity of Commercial Smart Bands for Heart Rate Measurements During Cardiopulmonary Exercise Test
Kim, C., Kim, S. H., & Suh, M. R. (2022) · Annals of Rehabilitation Medicine · doi:10.5535/arm.22050 · Cited by 14 · see citing works
“org electrodes, and other studies have revealed that chest strap monitoring devices have an acceptable level of accuracy in healthy adults during aerobic exercise [14-17].”
Background
Allostatic Load Is Associated with Overuse Musculoskeletal Injury during US Marine Corps Officer Candidates School
Feigel, E. D., Bird, M. B., Koltun, K. J., Lovalekar, M. T., Forse, J. N., Gage, C. R., Steele, E. J., Kargl, C. K., Martin, B. J., Bannister, A., et al. (2024) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003507 · Cited by 14 · see citing works
Exploring the effects of a wearable biocueing app (Sense-IT) as an addition to aggression regulation therapy in forensic psychiatric outpatients
ter Harmsel, J. F., Noordzij, M. L., van der Pol, T. M., Swinkels, L. T. A., Goudriaan, A. E., & Popma, A. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.983286 · Cited by 13 · see citing works
Using wearable activity trackers for research in the global south: Lessons learned from adolescent psychotherapy research in Kenya
Johnson, N. E., Venturo‐Conerly, K. E., & Rusch, T. W. (2023) · Cambridge Prisms Global Mental Health · doi:10.1017/gmh.2023.85 · Cited by 13 · see citing works
“Moreover, previous research had yielded mixed results regarding the accuracy and validity of the measurements taken with this band (Chow and Yang, 2020; Pino-Ortega et al., 2021; de la Casa Pérez et al., 2022).”
Result
The application of smart wearable devices in the detection of sports energy consumption: A review
Gu, L., & Qian, C. S. (2025) · Intelligent Sports and Health · doi:10.1016/j.ish.2025.04.001 · Cited by 14 · see citing works
Validation and user experience of a dry electrode based Health Patch for heart rate and respiration rate monitoring
Wei, J. C. J., van den Broek, T. J., van Baardewijk, J. U., van Stokkum, R., Kamstra, R. J. M., Rikken, L. F. A., Gijsbertse, K., Uzunbajakava, N. E., & van den Brink, W. J. (2024) · Scientific Reports · doi:10.1038/s41598-024-73557-8 · Cited by 12 · see citing works
Validation of Aerobic Capacity (VO2max) and Pulse Oximetry in Wearable Technology
Carrier, B., Chaves, S. M., & Navalta, J. W. (2025) · Sensors · doi:10.3390/s25010275 · Cited by 16 · see citing works
Validation of an Open-Source Smartwatch for Continuous Monitoring of Physical Activity and Heart Rate in Adults
Ravanelli, N., Lefebvre, K., Brough, A., Paquette, S., & Lin, W. (2025) · Sensors · doi:10.3390/s25092926 · Cited by 9 · see citing works
“Specifically, other low-cost wrist-based heart-rate monitoring devices (e.g., Xiaomi Mi Band) have demonstrated comparable reductions in agreement with increasing activity levels [27].”
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
“Misleading heart rate readings lead to several negative consequences, such as substandard recovery assessments and improper exercise intensity control, and cardiovascular condition identification, resulting in deteriorated fitness results and healthcare outcomes [27].”
Short- and medium-term cumulative effects of traffic-related air pollution on resting heart rate in the elderly: A wearable device study
Chen, C., Lai, F., Huang, L.-Y., & Guo, Y. L. (2024) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.117140 · Cited by 9 · see citing works
An Experimental Feasibility Study Evaluating the Adequacy of a Sportswear-Type Wearable for Recording Exercise Intensity
Marutani, Y., Konda, S., Ogasawara, I., Yamasaki, K., Yokoyama, T., Maeshima, E., & Nakata, K. (2022) · Sensors · doi:10.3390/s22072577 · Cited by 9 · see citing works
Simultaneous measurements of multiple vital signs using non-contact frequency modulated continuous wave radar monitoring
Ahmad, M. A., & Olule, L. J. A. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2023.03.060 · Cited by 8 · see citing works
Accuracy of the Huawei GT2 Smartwatch for Measuring Physical Activity and Sleep Among Adults During Daily Life: Instrument Validation Study
Mei, L., He, Z., & Hu, L. (2024) · JMIR Formative Research · doi:10.2196/59521 · Cited by 9 · see citing works
Validation of a Wearable Sensor Prototype for Measuring Heart Rate to Prescribe Physical Activity: Cross-Sectional Exploratory Study
Loro, F. L., Martins, R., Ferreira, J. B., de Araújo, C. L. P., Prade, L. R., Both, C. B., Nobre, J. C., Monteiro, M. B., & Lago, P. D. (2024) · JMIR Biomedical Engineering · doi:10.2196/57373 · Cited by 10 · see citing works
Remote Monitoring of Critically-Ill Post-Surgical Patients: Lessons from a Biosensor Implementation Trial
Restrepo, M., Huffenberger, A. M., Hanson, C. W., Draugelis, M. E., & Laudański, K. (2021) · Healthcare · doi:10.3390/healthcare9030343 · Cited by 8 · see citing works
“Furthermore, recordings from the research-grade biosensors proved less accurate than those intended for consumers [6,8,9].”
Background
The quality of health and wellness self‐tracking data: A consumer perspective
Zhang, Y., & Trace, C. B. (2021) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24591 · Cited by 8 · see citing works
“Comparing two wearable fitness trackers with a validated heart rate monitor, Chow and Yang (2020) found that both devices had acceptable levels of accuracy; however, they also tended to demonstrate erroneous, extreme readings.”
Method
The effect of the COVID ‐19 pandemic on substance use patterns and physiological dysregulation in pregnant and postpartum women
Ruyak, S. L., Roberts, M. H., Chambers, S., Ma, X., DiDomenico, J., Garza, R. D. L., & Bakhireva, L. N. (2023) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.15077 · Cited by 8 · see citing works
Immersive innovations: an examination of the efficacy and evolution of virtual reality in human movement training
Deng, Y. J., Wang, Z., Dong, L., Lei, Y., & Dong, Y. (2023) · Robotic Intelligence and Automation · doi:10.1108/ria-05-2023-0072 · Cited by 8 · see citing works
Empowering Participatory Research in Urban Health: Wearable Biometric and Environmental Sensors for Activity Recognition
Novak, R., Robinson, J. A., Kanduč, T., Sarigiannis, D. A., Džeroski, S., & Kocman, D. (2023) · Sensors · doi:10.3390/s23249890 · Cited by 7 · see citing works
“Validation studies confirm the Garmin Vivosmart’s accuracy in capturing relevant data, including in older adults [61–63], though caution is advised for energy expenditure and high-intensity activities [64,65].”
Why do generation X customers use wearable fitness technology equipment after recovering from coronavirus? The role of perceived health risks
Khoa, B. T., & Huỳnh, T. T. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32978 · Cited by 7 · see citing works
“Wearable fitness trackers with optical heart rate sensing technology have been validated for accuracy in monitoring heart rate during exercise in different age groups [15].”
Method
The Potential of Wearable Sensor Technologies in Enhancing Personalized Health Monitoring and Management
Mishra, D. P., & Sahu, P. K. (2025) · Biomedical Materials & Devices · doi:10.1007/s44174-025-00472-5 · Cited by 8 · see citing works
Complete Cardiorespiratory Monitoring via Wearable Ultra Low Power Ultrasound
Vostrikov, S., Luca Benini, C., & Cossettini, A. (2023) · IEEE International Ultrasonics Symposium · doi:10.1109/ius51837.2023.10307398 · Cited by 7 · see citing works
“73% [17]), a similar or higher accuracy is achieved for all FFT windows.”
Background
PhysioKit: Open-source, Low-cost Physiological Computing Toolkit for Single and Multi-user StudiesPreprint
Joshi, J., Wang, K., & Cho, Y. (2023) · Preprints.org · doi:10.20944/preprints202308.0545.v1 · Cited by 7 · see citing works
Heart rate responses, agreement and accuracy among persons with severe disabilities participating in the indirect movement program: Team Twin—an observational study
Jørgensen, A., Toftager, M., Eghøj, M., Ried‐Larsen, M., & Petersen, C. B. (2023) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2023.1213655 · Cited by 5 · see citing works
Continuous heart rate measurements in patients with cardiac disease: Device comparison and development of a novel artefact removal procedure
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
“17,28,29 This 10% threshold was also used for labelling artefacts.”
Influential
Estimating core body temperature from heart rate using a residual-compensated adaptive Kalman filter
Zhao, Y., & Bergmann, J. (2025) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2025.10.001 · Cited by 6 · see citing works
Assessment of Heart Rate Monitoring During Exercise With Smart Wristbands and a Heart Rhythm Patch: Validation and Comparison Study
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
“These findings suggest that the MobileAction Q-Band Q-69 smart wristband tends to underestimate heart rates, a trend consistent with previous research findings [13,29,30].”
BackgroundMethodResultInfluential
Assessing the Validity of Several Heart Rate Monitors in Wearable Technology While Mountain Biking
Carrier, B., Salatto, R. W., Davis, D. W., Sertic, J. V. L., Barrios, B., McGinnis, G. R., Girouard, T. J., Burroughs, B., & Navalta, J. W. (2023) · International journal of exercise science · doi:10.70252/pmwe7524 · Cited by 6 · see citing works
“We have used the relatively liberal thresholds of a MAPE < 10% and CCC > 0.7 for the current study, but as has been established, this is not a universally agreed upon threshold for validity (9, 31).”
BackgroundMethodInfluential
Embracing Change: Learnings From Implementing Multidimensional Digital Remote Monitoring in Oncology Patients at a District General Hospital During the COVID-19 Pandemic
Komarzynski, S., Wreglesworth, N. I., Griffiths, D., Pecchia, L., Subbe, C. P., Hughes, S. F., Davies, E. H., & Innominato, P. F. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00136 · Cited by 6 · see citing works
“The performance of this device has been compared with similar consumer activity trackers in studies assessing outcomes related to physical activity, sleep, and heart rate, with overall satisfactory results.(14-16) Nonetheless, Vı́vosmart 4 used here is one of the few armbands equipped with a pulse oximeter, which we r…”
Result
Optimization and pre-use suitability selection for wrist photoplethysmography-based heart rate monitoring in patients with cardiac disease
Vermunicht, P., Buyck, C., Naessens, S., Hens, W., Verberckt, C., Craenenbroeck, E. M. V., Laukens, K., Desteghe, L., & Heidbüchel, H. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf084 · Cited by 5 · see citing works
“24 Less expectedly, the high-accuracy group had a significantly higher weight, while height and BMI were also higher, albeit not statistically significant.”
Influential
An App-Based Remote Patient Monitoring System With Wrist and In-Ear Wearables in Gastrointestinal Oncology: Prospective Feasibility Pilot Study
Kohn, L., Scheble, V. J., Storz, P., Müller, A., Ulas, S. B., Schmitt, F., Thies, C. J., & Malek, N. P. (2025) · JMIR Cancer · doi:10.2196/64184 · Cited by 5 · see citing works
“Despite the necessity of filtering the heart rate values, the proportion of excluded data points was consistent with the range observed with other wearables that have been reported to achieve acceptable measurement accuracy [30].”
What is my heart rate right now? Comparing data from different devices
Cosoli, G., Poli, A., Antognoli, L., Spinsante, S., & Scalise, L. (2022) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc48687.2022.9806601 · Cited by 5 · see citing works
“fit Nonin 9560 Onyx II Red (660 nm), infrared (910 nm) LEDs (pulse-oximeter) Measurement range: [20,250] bpm Precision: ± 3% Manually recorded data TD-3128 Measurement range (HR): [40,119] bpm (BP automatic meter) Precision: ± 4% Manually recorded data”
Running for two (or three!): the journey of an ultramarathoner across two pregnancies
Bains, G., Carter, S., Hayman, M., & Davenport, M. H. (2024) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00488.2024 · Cited by 4 · see citing works
EgoPPG: Heart Rate Estimation From Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks
Braun, B., Armani, R., Meier, M., Moebus, M., & Holz, C. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00529 · Cited by 5 · see citing works
“The sensing configuration of our task can be considered a hybrid between typical contact-based BVP sensors (e.g., those in smartwatches [5, 13, 21, 31, 61, 63]) and rPPG methods that aim to extract HR from a person’s face using a camera [39, 66, 82].”
Comparison of Fitness Tracking Using Three Different Smartwatches during Free Activities in Daily Life
Terasawa, E., Asano, Y., Makiko, A. O. K. I., & Okayama, H. (2023) · Open Journal of Nursing · doi:10.4236/ojn.2023.1310041 · Cited by 4 · see citing works
Validity of Optical Heart Rate Measurement in Commercially Available Wearable Fitness Tracking DevicesPreprint
Thomas, J., Doyle, P., & Doyle, J. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.29.510075 · Cited by 4 · see citing works
Identifying a digital phenotype of allostatic load: association between allostatic load index score and wearable physiological response during military training
Feigel, E. D., Koltun, K. J., Lovalekar, M. T., Bird, M. B., Kargl, C. K., Forse, J. N., Martin, B. J., Nagle, E. F., Steele, E. J., Doyle, T. L. A., et al. (2025) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00216.2025 · Cited by 3 · see citing works
An Adjustable Smart Ring to Monitor Pulse Rate and Peripheral Blood Oxygen Saturation
Montenegro, M., Aliverti, A., & Angelucci, A. (2025) · Annals of Biomedical Engineering · doi:10.1007/s10439-025-03936-3 · Cited by 4 · see citing works
“Stationary cycling was selected to safely elicit a broad physiological change in pulse rate under controlled laboratory conditions and to maintain a reliable fingertip reference measurement, in contrast with treadmill/running activities, which introduce larger motion artifacts [58].”
Case example of a female solo sailor competing in the Vendée Globe reveals extraordinary biological demands
Komarzynski, S., Adams, L., & Davies, E. H. (2024) · Journal of Sports Sciences · doi:10.1080/02640414.2024.2365011 · Cited by 3 · see citing works
“Studies demonstrated that wrist-worn consumer-grade devices provide heart rate measurements that are within an acceptable error range of 5% as compare to the gold-standard electrocardiogram (Chow & Yang, 2020; Shcherbina et al., 2017), which we considered reasonable for the purpose of this study.”
Method
Selecting a smartwatch for trials involving older adults with neurodegenerative diseases: A researcher’s framework to avoid hidden pitfalls
Boyle, L. D., Marty, B., Haugarvoll, K., Steihaug, O. M., Pătraşcu, M., & Husebo, B. S. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104781 · Cited by 4 · see citing works
Mother’s heart rate: a valid way to measure physical activity during pregnancy? Data from the FitMum RCT
Jensen, I. K. B., Jessen, A. D., Gundersen, S. V., Roland, C. B., Knudsen, S. D. P., Alomairah, S. A., Bendix, J. M., Clausen, T. D., Mortensen, O. H., Teilmann, G., et al. (2025) · BMJ Open Sport & Exercise Medicine · doi:10.1136/bmjsem-2025-002458 · Cited by 3 · see citing works
Wearable technology in healthcare engineering
Mehta, S., & Sharma, D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91182-5.00005-x · Cited by 3 · see citing works
Cardiorespiratory Assessments in Panic Disorder Facilitated by Wearable Devices: A Systematic Review and Brief Comparison of the Wearable Zephyr BioPatch with the Quark-b2 Stationary Testing System
Caldirola, D., Daccò, S., Grassi, M., Alciati, A., Sbabo, W. M., Donatis, D. D., Martinotti, G., Berardis, D. D., & Perna, G. (2023) · Brain Sciences · doi:10.3390/brainsci13030502 · Cited by 5 · see citing works
“When compared to a reference-standard chest-strapped monitor during a series of sedentary and moderate physical activities in the laboratory, HR detection based on the Garmin Vivosmart wrist-worn optical HR sensors (PPG) was found to underestimate the average HR and produced some unexpected outlier readings, despite a…”
Result
A Deeper Look into Exercise Intensity Tracking through Mobile Applications: A Brief Report
Elder, A., Guillen, G., Isip, R., Zepeda, R., & Lewis, Z. H. (2023) · Technologies · doi:10.3390/technologies11030066 · Cited by 3 · see citing works
“As a solution to this, apps have been created that pair with wearable activity trackers and smartwatches to objectively measure heart rates (HR) [12].”
Background
Provoking Reflection in Action in Experienced Practitioners: An Educational Intervention
Regehr, C., Bogo, M., Paterson, J., Birze, A., Sewell, K. M., Fallon, B., & Regehr, G. (2023) · Journal of Social Work Education · doi:10.1080/10437797.2023.2279775 · Cited by 3 · see citing works
“Garmin’s optical heart rate sensor used to detect heart rate in real time has demonstrated acceptable overall accuracy as a heart rate sensor (Chow & Yang, 2020; Fuller et al., 2020).”
Investigating Aesthetic Preferences in Wearable Devices via the Unified Model of Aesthetics: A Review
Ma, J., Yahaya, M. F., Tai, L., Shahbudin, S. A. B., & Gao, Y. (2023) · International Journal of Academic Research in Business and Social Sciences · doi:10.6007/ijarbss/v13-i11/19259 · Cited by 2 · see citing works
A U-shaped dose–response of carbohydrate–protein supplementation on rowing performance
Wang, X., & Wu, H. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1651457 · Cited by 2 · see citing works
“The test was to be stopped if a participant’s heart rate (HR) dropped below 40% of their HR reserve on three separate occasions (24, 25).”
Spatially Adaptive Arch‐Cantilever Hybridized Triboelectric Nanogenerator for Self‐Powered Sensing in AI‐Enhanced Wireless Arrhythmia Monitoring
Cho, H., Yun, J., Goh, T. S., Lee, J. S., & Kim, D. (2026) · Advanced Functional Materials · doi:10.1002/adfm.75823 · Cited by 2 · see citing works
“These devices offer the advantages of simple detachment and the elimination of risks associated with surgical interventions [5, 6].”
CogTrack: A Proof of Concept for Cognition Tracker
Bhargava, Y., Kattupalli, K. K., & Baths, V. (2024) · IEEE Access · doi:10.1109/access.2024.3519220 · Cited by 2 · see citing works
WristSense framework: Exploring the forensic potential of wrist-wear devices through case studies
Almubairik, N. A., Khan, F. A., Mohammad, R. M. A., & Alshahrani, M. (2025) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2025.301862 · Cited by 2 · see citing works
Assessing Minority Stress and Physiological Response Through Ecological Momentary Assessment and Sensors: Protocol for a Feasibility and Acceptability of the Stress and Heart Pilot Study
Tapia, D. U., Corliss, H. L., Lee, K. H., Calzo, J. P., & Jun, H. (2025) · JMIR Formative Research · doi:10.2196/68733 · Cited by 1 · see citing works
“However, studies to test the validity of the measurement of heart rate with wearables, including previous versions of Garmin devices, have shown acceptable readings [87-89].”
Laboratory-Based Wearable Heart Rate Data Efficacies for Informing Real-world Department of Defense Wearables Decisions
Knodel, N. B., McLean, S. G., DiCesare, C. A., & Lu, E. (2025) · Military Medicine · doi:10.1093/milmed/usaf267 · Cited by 1 · see citing works
Target size, self-efficacy, and stress as determinants of precision in a marksmanship task
Ibrahim, F., Wittig, K., & Herzberg, P. Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1710147 · Cited by 1 · see citing works
“For example, elite archers exhibited lower mean heart rates and higher HRV than intermediate shooters (Mat Salleh et al., 2023).”
Influential
Cross-Wavelet Time-Frequency Analysis of Low-Signal Multi-Spectral rPPG
Berezhnyi, I., & Nakonechnyi, A. (2025) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs68557.2025.11322283 · Cited by 1 · see citing works
“This performance gap is driven by three intrinsic issues: quantization artifacts in 8-bit video streams destroy subtle pulsatile components [2]; Fixed-pattern noise in CMOS-sensors introduces spatiotemporal distortions that are not correlated with physiological signals [3]; spectral crosstalk in…”
A novel machine learning procedure to detect and remove artefacts in heart rate data obtained from photoplethysmography wearables: A prospective cohort study
Vermunicht, P., Buyck, C., Naessens, S., Hens, W., Craenenbroeck, E. M. V., Giraldo, J. S. P., Makayed, K., Herman, S., Laukens, K., Roeykens, J., et al. (2026) · Digital Health · doi:10.1177/20552076261426622 · Cited by 2 · see citing works
Assessing the agreement and reliability of Magene ® for heart rate monitoring
Rodrigues, M. A. D. M., da Silva, T. D., Santos, J. T., de Oliveira, V. N., Marques, N. E., de Freitas, J. V. R., Andrade, M. R., Vancini, R. L., Knechtle, B., Weiss, K., et al. (2026) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/09544119261477973 · Cited by 1 · see citing works
“Furthermore, studies in more dynamic environments, such as mountain biking, 31,32 show that accuracy can deteriorate substantially in field conditions, even with chest straps, highlighting the need for context-specific validation.”
Innovative and disruptive technologies to prescribe, encourage, and evaluate physical exercise in healthy adults: a protocol of exploratory study followed by a noninferiority, investigator-blinded randomized clinical trial
Loro, F. L., Martins, R., de Araújo, C. L. P., Prade, L. R., do Rosário, D. L., Seruffo, M. C. D. R., de Freitas, I. A. M., Nobre, J. C., Both, C. B., & Lago, P. D. (2023) · Trials · doi:10.1186/s13063-023-07747-6 · Cited by 5 · see citing works
“There are several brands of wearable devices that measure HR; however, they are expensive for the population [14, 15].”
Background
Gaming Under Stress: Sensor-Monitored Physiological Responses to a Horror Gameplay
Németh, K., & Szűcs, J. (2024) · IEEE International Conference on Intelligent Engineering Systems (INES) · doi:10.1109/ines63318.2024.10629131 · Cited by 1 · see citing works
Digital positive affect intervention (PAI) versus self-monitoring placebo in the treatment of anxiety and depression: a two-arm randomized controlled trial (RCT)
Zainal, N. H., Rajendra, S. J., Doren, N. V., Salamanca-Sanabria, A., Curtiss, J. E., & Keng, S. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-07372-4 · Cited by 2 · see citing works
“The wearable will capture the following dimensions of data during the 6-week intervention phase: geolocation [103], HRV-inter-beat intervals (IBIS) [104], HRV-root mean square of successive differences (RMSSD) [105], HRV-spectrum of frequencies [106], wear-time markers [107], and sleep actigraphy…”
Who is missing? A scoping review on the representation of different populations in studies assessing the validity of consumer wearable PPG-based measurements (Preprint)Preprint
Schipper, R. M., Djibrillah, F. O., Roosink, M., Winkens, L. H. H., John, A., Hazebroek, E. J., Witteveen, A., & Berendsen, A. A. M. (2025) · doi:10.2196/preprints.87324 · Cited by 1 · see citing works
How Do We Best Measure Outcomes Following Cartilage Repair Surgery?
Wolfe, I. A., Burge, A. J., Hidaka, C., & Lyman, S. L. (2021) · Springer eBooks · doi:10.1007/978-3-030-78051-7_3

Validity Of Optical Heart Rate Measurement In Commercially Available Wearable Fitness Devices
Thomas, J. W., Doyle, P., Tran, P., Tippett, R., Kulkarni, S. M., Rogers, J., & Doyle, J. A. (2020) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000678188.44622.34
Validity of Optical Heart Rate Measurement in Commercially Available Wearable Fitness Tracking DevicesPreprint
Thomas, J., Doyle, P., & Doyle, J. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.29.510075 · Cited by 4 · see citing works
Heart Rate Measurement Using Optical Technology on the Arm and Wrist during Different Activities at Different Intensities: An Analytical Validation Study (Preprint)Preprint
Schweizer, T., & Gilgen-Ammann, R. (2024) · doi:10.2196/preprints.67110
Comparison of heart rate measurement accuracy among commercially available photoplethysmography-based wearable devices across exercise intensities
Jeong, Y., Yoon, S., Lee, J., Jeong, W.-M., & Ha, M. (2026) · Physical Activity and Nutrition · doi:10.20463/pan.2026.0032
Validity of the Polar Vantage M watch when measuring heart rate at different exercise intensities
Shumate, T., Link, M., Furness, J. W., Kemp‐Smith, K., Simas, V., & Climstein, M. (2021) · PeerJ · doi:10.7717/peerj.10893 · Cited by 18 · see citing works

Authors: Chow, H.-W., & Yang, C.-C.
Affiliations: National Cheng Kung University (TW)
Corresponding author: Chow, Hsueh-Wen
ORCID: Chow, Hsueh-Wen
Yang, Chao-Ching
Venue: JMIR mHealth and uHealth
Publisher: JMIR Publications Inc.
Published: 2020-04-28
Volume: 8
Issue: 4
Pages: e14707
ISSN: 2291-5222 (ISSN-L)
DOI: 10.2196/14707
PMC: PMC7218601
OpenAlex: W3004958388
Cited by: 140
References: 58
Citations by year: 2026: 12 · 2025: 38 · 2024: 26 · 2023: 40 · 2022: 12 · 2021: 10 · 2020: 2
FWCI: 5.02
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)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Ministry of Science and Technology, Taiwan (MOST108-2410-H006-099)
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 › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
Keywords: wearable activity trackers, photoplethysmography, heart rate accuracy, older adults, young adults, exercise intensity monitoring, Fitzpatrick skin type, device validation
MeSH terms: Fitness Trackers, Wearable Electronic Devices, Aged, Heart Rate, Humans, Technology, Reproducibility of Results, Optics and Photonics, Young Adult

About these checks

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

Statistics and arithmetic are recomputed, not judged.

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

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

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

Accuracy of optical heart rate sensing technology in wearable fitness trackers for young and older adults: validation and comparison study

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks