Accuracy of Consumer Wearable Heart Rate Measurement During an Ecologically Valid 24-Hour Period: Intraindividual Validation Study
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Abstract
BACKGROUND: Wrist-worn smart watches and fitness monitors (ie, wearables) have become widely adopted by consumers and are gaining increased attention from researchers for their potential contribution to naturalistic digital measurement of health in a scalable, mobile, and unobtrusive way. Various studies have examined the accuracy of these devices in controlled laboratory settings (eg, treadmill and stationary bike); however, no studies have investigated the heart rate accuracy of wearables during a continuous and ecologically valid 24-hour period of actual consumer device use conditions. OBJECTIVE: The aim of this study was to determine the heart rate accuracy of 2 popular wearable devices, the Apple Watch 3 and Fitbit Charge 2, as compared with the gold standard reference method, an ambulatory electrocardiogram (ECG), during consumer device use conditions in an individual. Data were collected across 5 daily conditions, including sitting, walking, running, activities of daily living (ADL; eg, chores, brushing teeth), and sleeping. METHODS: One participant, (first author; 29-year-old Caucasian male) completed a 24-hour ecologically valid protocol by wearing 2 popular wrist wearable devices (Apple Watch 3 and Fitbit Charge 2). In addition, an ambulatory ECG (Vrije Universiteit Ambulatory Monitoring System) was used as the gold standard reference method, which resulted in the collection of 102,740 individual heartbeats. A single-subject design was used to keep all variables constant except for wearable devices while providing a rapid response design to provide initial assessment of wearable accuracy for allowing the research cycle to keep pace with technological advancements. Accuracy of these devices compared with the gold standard ECG was assessed using mean error, mean absolute error, and mean absolute percent error. These data were supplemented with Bland-Altman analyses and concordance class correlation to assess agreement between devices. RESULTS: The Apple Watch 3 and Fitbit Charge 2 were generally highly accurate across the 24-hour condition. Specifically, the Apple Watch 3 had a mean difference of -1.80 beats per minute (bpm), a mean absolute error percent of 5.86%, and a mean agreement of 95% when compared with the ECG across 24 hours. The Fitbit Charge 2 had a mean difference of -3.47 bpm, a mean absolute error of 5.96%, and a mean agreement of 91% when compared with the ECG across 24 hours. These findings varied by condition. CONCLUSIONS: The Apple Watch 3 and the Fitbit Charge 2 provided acceptable heart rate accuracy (<±10%) across the 24 hour and during each activity, except for the Apple Watch 3 during the daily activities condition. Overall, these findings provide preliminary support that these devices appear to be useful for implementing ambulatory measurement of cardiac activity in research studies, especially those where the specific advantages of these methods (eg, scalability, low participant burden) are particularly suited to the population or research question.
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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 [60]
The Methods (Recruitment; page unavailable) describe a single participant with Fitzpatrick skin tone 2, not a dark-skin group. The Results (Bland-Altman Analysis; page unavailable) report mean errors of −1.80 bpm for Apple Watch 3 and −3.47 bpm for Fitbit Charge 2 across 24 hours, not the claimed −0.57 bpm with a 95% CI of −9.44 to 8.29. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredFor 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 [60]
The Methods > Recruitment section (page unavailable) says the study involved a single participant with Fitzpatrick skin tone 2, not light, medium, and dark pigmentation groups. The Results > Bland-Altman Analysis section (page unavailable) reports limits of agreement by device and activity, not the claimed −18.62 to 16.84 bpm for a medium-pigmentation group. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredFor 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 [60]
The Methods (Recruitment; page unavailable) describe a single participant with Fitzpatrick skin tone 2, not a medium-pigmentation group. The Results (Bland-Altman Analysis; page unavailable) report device- and activity-specific errors, but not a −0.89 bpm mean bias or the stated 95% confidence interval. Searches for medium skin pigmentation and those figures found no supporting passage. (LLM, conf=0.76)
Experimental — accuracy not yet measuredFor 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 [60]
The Results section reports mean errors by device and activity, not by skin pigmentation. A search of our copy for light skin pigmentation and the stated bias and confidence interval found no passage reporting those subgroup results; the paper’s overall device estimates do not resolve the claim. (LLM, conf=0.75)
Experimental — accuracy not yet measuredFor 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 [60]
The Results section, “Bland-Altman Analysis and 95 Percent Limits of Agreement” (page unavailable), reports limits by device and activity, not by skin pigmentation. Searches for the stated −16.02 to 13.54 bpm range and for light, medium, or dark skin groups found no matching result in our copy. (LLM, conf=0.76)
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 [60]
The Results section, ‘Bland-Altman Analysis and 95 Percent Limits of Agreement’ (page unavailable), reports limits by wearable device and activity, not by skin-pigmentation group. Searches for the stated limits and for dark-skin pigmentation found no passage reporting −33.69 to 32.54 bpm for that group. (LLM, conf=0.75)
Experimental — accuracy not yet measuredOver 24 h, the Apple Watch 3 had a mean difference of -1.80 beats per minute and a mean agreement of 95% compared with the gold standard ECG.
“Over 24 h, the Apple Watch 3 had a mean difference of –1.80 beats per minute and a mean agreement of 95% compared with the gold standard ECG22;”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [22]
In a study, the Apple Watch 3 and Fitbit Charge 2 both provided acceptable PPG sensor heart rate accuracy (<10% mean absolute percent error) across 24 h and during various activities including sitting, walking, running and activities of daily living.
“One study that investigated the accuracy of the Apple Watch 3 (Apple, USA) and the Fitbit Charge 2 (Fitbit, USA) showed that both devices provided an acceptable PPG sensor HR accuracy (<10% mean absolute percent error) across 24 h and during various activities22, including sitting, walking, running and activities of daily living (such as chores or brushing the teeth).”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [22]
Over 24 h, the Fitbit Charge 2 had a mean difference of 3.47 beats per minute and a mean agreement of 91% compared with the gold standard ECG.
“the Fitbit Charge 2 had a mean difference of 3.47 beats per minute and a mean agreement of 91% compared with the gold standard ECG22.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [22]
Current research into heart rate accuracy of wrist-worn wearable devices compares wearable photoplethysmography measurement with a reference method such as electrocardiogram to determine whether measurements between devices are outside clinically important limits of agreement.
“Current research into the HR accuracy of wrist-worn wearable devices attempt to compare the measurement between wearable PPG and a reference method, such as an ECG, in order to determine whether or not measurements between the devices are outside of clinically important limits of agreement, so that devices can supplement, eventually replace, or be used interchangeably37,38.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [38]
Some studies have used the gold-standard reference method of electrocardiogram, which directly measures heart rate via electrical impulses from the heart and is used for clinical accuracy.
“Although some studies have attempted to address wearable HR accuracy using the gold-standard reference method of ECG, which directly measures HR via electrical impulses from the heart and is used for clinical accuracy38–44”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [38]
In that study, any individual heart rate observation could deviate from electrocardiogram by significantly large margins that would likely be problematic in medical settings.
“any individual HR observation could deviate from ECG by significantly large margins that would be likely be problematic in medical settings38.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [38]
In a study comparing ambulatory electrocardiogram, Apple Watch 3, and Fitbit Charge 2 within individuals across 24 h, devices slightly underestimated heart rate when values were aggregated within and across different daily activities, indicating high heart rate accuracy.
“For example, researchers compared the accuracy of ambulatory ECG, Apple Watch 3, and Fitbit Charge 2 data within an individual across 24 h and found that while devices slightly underestimated HR when values were aggregated within and across different daily activities, which indicated high HR accuracy”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [38]
Wearables detect heart rate with acceptable accuracy in both laboratory and real-world settings for research, but importantly not medical settings.
“These findings have led some researchers to conclude that wearables detect HR with acceptable accuracy in both laboratory and real-world settings for research, but importantly not medical settings38,42 as more research needs to be done.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [38]
Wearable devices are more accurate during rest and low intensity exercise compared to higher intensity exercise, when they can begin to deviate more from ECG recordings.
“a substantial amount of research showing that wearable devices are more accurate during rest and low intensity exercise when compared to higher intensity exercise, when wearable devices can begin to deviate more from ECG recordings38,39,44,46,70–73”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [38]
For the validation study, a sample size of n=20 participants is deemed sufficient, as reported in the literature.
“For the validation study, considering as a first main outcome HR, a sample size of n = 20 participants is deemed sufficient, as reported in the literature [50–52].”
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Smart wearable devices in cardiovascular care: Where we are and how to move forward
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“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]).”
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- Authors
- Nelson, B. W., & Allen, N. B.
- Affiliations
- University of Oregon (US)
- Corresponding author
- Nelson, Benjamin W
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2019-03-11
- Volume
- 7
- Issue
- 3
- Pages
- e10828
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC6431828
- OpenAlex
- W2900058591
- Cited by
- 310
- References
- 41
- Citations by year
- 2026: 39 · 2025: 39 · 2024: 45 · 2023: 52 · 2022: 50 · 2021: 49 · 2020: 33 · 2019: 2 · 2018: 1
- FWCI
- 8.49
- 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)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- wearable devices, heart rate monitoring, Holter monitoring, device accuracy, activities of daily living, sleep heart rate, single-case experimental design, Bland-Altman analysis, concordance correlation coefficient
- MeSH terms
- Heart Rate Determination, Fitness Trackers, Wearable Electronic Devices, Activities of Daily Living, Adult, Heart Rate, Humans, Male, Monitoring, Physiologic, Validation Studies as Topic, Accelerometry
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 ›
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