ArticleJMIR mHealth and uHealth2017
Mobile Device Accuracy for Step Counting Across Age Groups.
Article in JMIR mHealth and uHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review.JMIR mHealth and uHealth · 2020Pooled it
- Criterion Validity of a Consumer Wearable for Step Counting and Activity Intensity Classification in Adults With Lung Cancer: Laboratory-Based Validation Study.JMIR formative research · 2026Article
- Affordable fitness tracker step count, heart rate and calorie expenditure validation in controlled environment: a cross-sectional study.BMC sports science, medicine & rehabilitation · 2025Article
- Epidemiology and Cardiovascular Benefits of Physical Activity and Exercise.Circulation research · 2025Review
- Effects of Mobile Health Care App "Asmile" on Physical Activity of 80,689 Users in Osaka Prefecture, Japan: Longitudinal Observational Study.Journal of medical Internet research · 2025Observational
- The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program.JMIR mHealth and uHealth · 2023Article
- Physical Activity Over the Lifecourse and Cardiovascular Disease.Circulation research · 2023Review
- Article
- Wearable Leg Movement Monitoring System for High-Precision Real-Time Metabolic Energy Estimation and Motion Recognition.Research (Washington, D.C.) · 2023Article
- Personalized Activity Recognition with Deep Triplet Embeddings.Sensors (Basel, Switzerland) · 2022Article
- [Physical activity, biochemical and somatometric profile's residents physicians during the COVID-19 pandemic].Revista medica del Instituto Mexicano del Seguro Social · 2022Article
- Adherence and retention to the self-managed community-based Step Into Health program in Qatar (2012-2019).Frontiers in public health · 2022Article
- Observational
- Back to living well: community-based management of low back pain: a feasibility study.Pilot and feasibility studies · 2021Article
- Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management.Nature reviews. Cardiology · 2021Review
- Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements.Sensors (Basel, Switzerland) · 2020Article
- Step Count Reliability and Validity of Five Wearable Technology Devices While Walking and Jogging in both a Free Motion Setting and on a Treadmill.International journal of exercise science · 2020Article
- Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials.Statistics in biosciences · 2019Article
- Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors.JMIR mHealth and uHealth · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundOnly one in five American meets the physical activity recommendations of the Department of Health and Human Services. The proliferation of wearable devices and smartphones for physical activity tracking has led to an increasing number of interventions designed to facilitate regular physical activity, in particular to address the obesity epidemic, but also for cardiovascular disease patients, cancer survivors, and older adults. However, the inconsistent findings pertaining to the accuracy of wearable devices for step counting needs to be addressed, as well as factors known to affect gait (and thus potentially impact accuracy) such as age, body mass index (BMI), or leading arm.
objectiveWe aim to assess the accuracy of recent mobile devices for counting steps, across three different age groups.
methodsWe recruited 60 participants in three age groups: 18-39 years, 40-64 years, and 65-84 years, who completed two separate 1000 step walks on a treadmill at a self-selected speed between 2 and 3 miles per hour. We tested two smartphones attached on each side of the waist, and five wrist-based devices worn on both wrists (2 devices on one wrist and 3 devices on the other), as well as the Actigraph wGT3X-BT, and swapped sides between each walk. All devices were swapped dominant-to-nondominant side and vice-versa between the two 1000 step walks. The number of steps was recorded with a tally counter. Age, sex, height, weight, and dominant hand were self-reported by each participant.
resultsAmong the 60 participants, 36 were female (60%) and 54 were right-handed (90%). Median age was 53 years (min=19, max=83), median BMI was 24.1 (min=18.4, max=39.6). There was no significant difference in left- and right-hand step counts by device. Our analyses show that the Fitbit Surge significantly undercounted steps across all age groups. Samsung Gear S2 significantly undercounted steps only for participants among the 40-64 year age group. Finally, the Nexus 6P significantly undercounted steps for the group ranging from 65-84 years.
conclusionsOur analysis shows that apart from the Fitbit Surge, most of the recent mobile devices we tested do not overcount or undercount steps in the 18-39-year-old age group, however some devices undercount steps in older age groups. This finding suggests that accuracy in step counting may be an issue with some popular wearable devices, and that age may be a factor in undercounting. These results are particularly important for clinical interventions using such devices and other activity trackers, in particular to balance energy requirements with energy expenditure in the context of a weight loss intervention program.
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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.