Cited by 55Physical Activity and HealthContext-Aware Activity Recognition SystemsMobile Health and mHealth Applications

Validation of the Fitbit Charge 2 compared to the ActiGraph GT3X+ in older adults with knee osteoarthritis in free-living conditions

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Collins, Jamie E., Yang, Heidi Y., Trentadue, Taylor P., Gong, Yusi, Losina, Elena·2019·PLOS ONE

Abstract

OBJECTIVE: To evaluate physical activity (PA) and sedentary time in subjects with knee osteoarthritis (OA) measured by the Fitbit Charge 2 (Fitbit) and a wrist-worn ActiGraph GT3X+ (AGW) compared to the hip-worn ActiGraph (AGH). DESIGN: We recruited a cohort of subjects with knee OA from rheumatology clinics. Subjects wore the AGH for four weeks, AGW for two weeks, and Fitbit for two weeks over a four-week study period. We collected accelerometer counts (ActiGraphs) and steps (ActiGraphs, Fitbit) and calculated time spent in sedentary, light, and moderate-to-vigorous activity. We used triaxial PA intensity count cut-points from the literature for ActiGraph and a stride length-based cadence algorithm to categorize Fitbit PA. We compared Fitbit wear times calculated from a step-based algorithm and a novel algorithm that incorporates steps and heart rate (HR). RESULTS: We enrolled 15 subjects (67% female, mean age 68 years). Relative to AGH, Fitbit, on average, overestimated steps by 39% and sedentary time by 37% and underestimated MVPA by 5 minutes. Relative to AGH, AGW overestimated steps 116%, underestimated sedentary time by 66%, and captured 281 additional MVPA minutes. The step-based wear time Fitbit algorithm captured 14% less wear time than the HR-based algorithm. CONCLUSIONS: Fitbit overestimates steps and underestimates MVPA in knee OA subjects. Cut-offs validated for AGW should be developed to support the use of AGW for PA assessment. The HR-based Fitbit algorithm captured more wear time than the step-based algorithm. These data provide critical insight for researchers planning to use commercially-available accelerometers in pragmatic studies.

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Authors
Collins, J. E., Yang, H. Y., Trentadue, T. P., Gong, Y., & Losina, E.
Affiliations
  • Brigham and Women's Hospital (US)
  • Boston University (US)
Corresponding author
Losina, Elena
Venue
PLOS ONE
Published
2019-01-30
Volume
14
Issue
1
Pages
e0211231
ISSN
1932-6203 (ISSN-L)
OpenAlex
W2912360256
Cited by
55
References
3
Citations by year
2026: 4 · 2025: 8 · 2024: 2 · 2023: 12 · 2022: 6 · 2021: 10 · 2020: 11 · 2019: 2
FWCI
4.97
Type
journal article
Language
en
Open access
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
National Institute of Arthritis and Musculoskeletal and Skin Diseases (U34 AR069187 01A1)
Topics
  • Health Sciences › Medicine › Physiology › Physical Activity and Health
  • 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
Keywords
ActiGraph GT3X+, knee osteoarthritis, older adults, physical activity measurement, sedentary time, moderate-to-vigorous physical activity, accelerometer validation, step counting, wrist-worn accelerometers, hip-worn accelerometers, free-living conditions
MeSH terms
Fitness Trackers, Aged, Algorithms, Female, Humans, Male, Middle Aged, Social Conditions, Exercise, Monitoring, Ambulatory, Osteoarthritis, Knee, Actigraphy, Independent Living, Accelerometry

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