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Cited by 310Context-Aware Activity Recognition SystemsPhysical Activity and HealthMobile Health and mHealth Applications

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study

Open access
Xie, Junqing, Wen, Dong, Liang, Lizhong, Jia, Yuxi, Gao, Li, Lei, Jianbo·2018·JMIR mHealth and uHealth
DOI 10.2196/mhealth.9754PMC PMC5920198

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Abstract

BACKGROUND: Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. OBJECTIVE: The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. METHODS: A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). RESULTS: Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). CONCLUSIONS: At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states.

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Crossref · 10.2196/mhealth.9754

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Figure 1 Comprehensive accuracies of indicators by different wearable devices. S=steps, D=distance, and C=calorie.
Figure 1 Comprehensive accuracies of indicators by different wearable devices. S=steps, D=distance, and C=calorie.CC BY · PubMed Central
Figure 2 Distance accuracy for different devices used during three different physical activities.
Figure 2 Distance accuracy for different devices used during three different physical activities.CC BY · PubMed Central
Figure 3 Calorie accuracy for different devices used during three different physical activities.
Figure 3 Calorie accuracy for different devices used during three different physical activities.CC BY · PubMed Central

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Research works printed but not linked: 3

  1. 2010, Global Recommendations on Physical Activity for Health
  2. 10.1093/jamia/ocx006 · DOI
  3. Jo, E, 2016, J Sports Sci Med, 15, 540

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Metadata

Authors
Xie, J., Wen, D., Liang, L., Jia, Y., Gao, L., & Lei, J.
Affiliations
  • Peking University (CN)
  • Guangdong Medical College (CN)
  • Jilin University (CN)
Corresponding author
Lei, Jianbo
ORCID
Venue
JMIR mHealth and uHealth
Publisher
JMIR Publications Inc.
Published
2018-04-12
Volume
6
Issue
4
Pages
e94
ISSN
2291-5222 (ISSN-L)
DOI
10.2196/mhealth.9754
PMC
PMC5920198
OpenAlex
W2798041585
Cited by
310
References
31
Citations by year
2026: 17 · 2025: 31 · 2024: 38 · 2023: 36 · 2022: 35 · 2021: 63 · 2020: 55 · 2019: 27 · 2018: 8
FWCI
19.13
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR mhealth and uhealth (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords
wearable activity trackers, heart rate monitoring, step counting, distance measurement, energy expenditure estimation, Apple Watch, mean absolute percentage error, oxygen consumption, mobile health apps

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