Using fitness trackers and smartwatches to measure physical activity in research: analysis of consumer wrist-worn wearables
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Abstract
BACKGROUND: New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. OBJECTIVE: The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. METHODS: We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. RESULTS: We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. CONCLUSIONS: The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.
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Errors during exercise monitoring may result in inappropriate training intensities or misleading recovery assessments.
“Errors during exercise monitoring may result in inappropriate training intensities or misleading recovery assessments [7,8,9].”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [8]
The Introduction (Sensors; Wearable Usage Scenario, pages unavailable) says wearable measurements vary in accuracy and that researchers can use continuous data to adjust physical-activity interventions. It does not specifically say that monitoring errors may lead to inappropriate training intensities or misleading recovery assessments. (LLM, conf=0.78)
Experimental — accuracy not yet measuredReduced accuracy in darker skin tones has been linked to missed detection of atrial fibrillation when irregular pulse notifications fail to capture abnormal rhythms.
“Reduced accuracy in darker skin tones has been linked to missed detection of atrial fibrillation when irregular pulse notifications fail to capture abnormal rhythms.”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [8]
The Introduction’s Sensors section describes how wearable optical heart-rate measurements can be affected by movement, ambient light, and tissue compression. Our copy does not address reduced accuracy in darker skin tones or missed atrial-fibrillation detection through irregular-pulse notifications (searched for those terms and for skin pigmentation). (LLM, conf=0.76)
Experimental — accuracy not yet measuredDemand for wearable devices has risen substantially in recent years.
“Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen considerably in the last few years [1-3].”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [2]
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- Authors
- Henriksen, A., Haugen Mikalsen, M., Woldaregay, A. Z., Muzny, M., Hartvigsen, G., Hopstock, L. A., & Grimsgaard, S.
- Affiliations
- UiT The Arctic University of Norway (NO)
- Corresponding author
- Henriksen, André
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2018-03-22
- Volume
- 20
- Issue
- 3
- Pages
- e110
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5887043
- OpenAlex
- W2792479193
- Cited by
- 542
- References
- 95
- Citations by year
- 2026: 33 · 2025: 72 · 2024: 68 · 2023: 71 · 2022: 93 · 2021: 89 · 2020: 63 · 2019: 44 · 2018: 9
- FWCI
- 25.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- wearable activity trackers, smartwatches, physical activity measurement, accelerometer, photoplethysmography, health data access, wearable device validation, Fitbit, Garmin, misfit, Apple Inc., polars, consumer wearable devices
- MeSH terms
- Fitness Trackers, Wearable Electronic Devices, Female, Heart Rate, Humans, Male, Wrist, Exercise, Photoplethysmography, Mobile Applications
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.
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