ArticleJMIR mHealth and uHealth2018
Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study.
Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 121 papers, 8 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Improving Spine Surgical Care With Real-Time Objective Patient Tracking Using the Apple Watch
Validation of the Quality of Sleep Data for Xiaomi Domestic Wristbands
Observational Study About Sleep Quality and Its Impact on Daily Life of Nursing-home Residents, Through the Use of Wearable Technologies
Who cites it
121 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications.Sensors (Basel, Switzerland) · 2022Pooled it
- Wearables for Measuring the Physical Activity and Sedentary Behavior of Patients With Axial Spondyloarthritis: Systematic Review.JMIR mHealth and uHealth · 2022Pooled it
- Recommendations for Determining the Validity of Consumer Wearables and Smartphones for the Estimation of Energy Expenditure: Expert Statement and Checklist of the INTERLIVE Network.Sports medicine (Auckland, N.Z.) · 2022Pooled it
- Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 2022Pooled it
- Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2020Pooled it
- Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review.JMIR mHealth and uHealth · 2020Pooled it
- Mobile Apps to Quantify Aspects of Physical Activity: a Systematic Review on its Reliability and Validity.Journal of medical systems · 2020Pooled it
- Differential benefits of 12-week morning vs. evening aerobic exercise on sleep and cardiometabolic health: a randomized controlled trial.Scientific reports · 2025Trial
- An Evaluation of the Effect of App-Based Exercise Prescription Using Reinforcement Learning on Satisfaction and Exercise Intensity: Randomized Crossover Trial.JMIR mHealth and uHealth · 2024Trial
- Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture.JMIR mHealth and uHealth · 2024Trial
- Wearable Activity Tracking Device Use in an Adolescent Weight Management Clinic: A Randomized Controlled Pilot Trial.Journal of obesity · 2021Trial
- Effect of Evidence-Based Materials and Access to Local Resources on Physical Activity Levels, Beliefs, and Motivation During Pregnancy in a Rural Setting.Journal of physical activity & health · 2020Trial
- Validity and Reliability of Three Commercially Available Smart Sports Bras during Treadmill Walking and Running.Scientific reports · 2020Trial
- Accuracy of consumer-level and research-grade activity trackers in ambulatory settings in older adults.PloS one · 2019Trial
- Sleep Pattern and Structural Damage in Older Adults with Glaucoma: A United States and China Study.Ophthalmology science · 2026Article
- Demographic and Socioeconomic Factors Associated with Fitbit Ownership in the NIHInternational journal of environmental research and public health · 2026Article
- The All of Us Research Program's wearables dataset.Nature medicine · 2026Article
- Bilateral DLPFC transcranial direct current stimulation for mood and motor symptoms in Parkinson's disease: a preliminary study.Acta neuropsychiatrica · 2026Article
- Improvements in blood and fitness tracker biomarkers in a longitudinal real-world cohort of digital health platform users.PLOS digital health · 2026Article
61 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWearable 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.
objectiveThe 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.
methodsA 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).
resultsWearable 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).
conclusionsAt 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.