Walk this way: Validity evidence of iphone health application step count in laboratory and free-living conditions
Abstract
Several attempts have been made to demonstrate the accuracy of the iPhone pedometer function in laboratory test conditions. However, no studies have attempted to evaluate evidence of convergent validity of the iPhone step counts as a surveillance tool in the field. This study takes a pragmatic approach to evaluating Health application derived iPhone step counts by measuring accuracy of a standardized criterion iPhone SE and a heterogeneous sample of participant owned iPhones (6 or newer) in a laboratory condition, as well as comparing personal iPhones to accelerometer derived steps in a free-living test. During lab tests, criterion and personal iPhones differed from manually counted steps by a mean bias of less than ±5% when walking at 5km/h, 7.5km/h and 10km/h on a treadmill, which is generally considered acceptable for pedometers. In the free-living condition steps differed by a mean bias of 21.5% or 1340 steps/day when averaged across observation days. Researchers should be cautioned in considering the use of iPhone models as a research grade pedometer for physical activity surveillance or evaluation, likely due to the iPhone not being continually carried by participants; if compliance can be maximized then the iPhone might be suitable.
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Smartphones may underestimate absolute step counts in free-living conditions because they are not always continuously carried.
“but smartphones may underestimate absolute step counts in free-living conditions because they are not always continuously carried (31).”
From Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial (2020) · cites this paper as [31]
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Ren, S., Boikova, M., McAvoy, C. R., Bucko, A., Begum, T., Dulin, M. W., Ingoglia, S., Raya, M., Patterson, L. M., Barr‐Anderson, D. J., et al. (2026) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2025-0035
Systematic underestimation of daily steps by smartphones: a free-living comparison
Júdice, P. B., Espin, A., & Rodrigues, B. (2026) · Sport Sciences for Health · doi:10.1007/s11332-026-01900-9
- Authors
- Duncan, M. J., Wunderlich, K., Zhao, Y., & Faulkner, G.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Duncan, Markus J.
- Venue
- Journal of Sports Sciences
- Publisher
- Informa UK Limited
- Published
- 2017-11-28
- Volume
- 36
- Issue
- 15
- Pages
- 1695-1704
- ISSN
- 0264-0414 (ISSN-L)
- 1466-447X
- OpenAlex
- W2770928294
- Cited by
- 76
- References
- 27
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 7 · 2023: 14 · 2022: 12 · 2021: 12 · 2020: 10 · 2019: 9 · 2018: 7
- FWCI
- 5.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Sports Sciences (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Applied Public Health Chair (APHC 201405CPP-329463)
- Institute of Population and Public Health (APHC 201405CPP-329463)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- step count accuracy, convergent validity, physical activity surveillance, accelerometer, free-living conditions, treadmill walking, mobile health apps, physical activity monitoring
- MeSH terms
- Smartphone, Fitness Trackers, Activities of Daily Living, Adolescent, Adult, Female, Humans, Male, Walking, Monitoring, Ambulatory, Young Adult, Actigraphy, Mobile Applications
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