Surveillance of Youth Physical Activity and Sedentary Behavior with Wrist Accelerometry
Open accessAbstract
Introduction Accurate tracking of physical activity (PA) and sedentary behavior (SB) is important to advance public health, but little is known about how to interpret wrist-worn accelerometer data. This study compares youth estimates of SB and moderate to vigorous PA (MVPA) obtained using raw and count-based processing methods.Methods Data were collected between April and October 2014 for the National Cancer Institute's Family Life, Activity, Sun, Health, and Eating Study: a cross-sectional Internet-based study of youth/family cancer prevention behaviors. A subsample of 628 adolescents (aged 12-17 years) wore the ActiGraph GT3X+ on the wrist for 7 days. In 2015-2016, SB and MVPA time were calculated from raw data using R-package GGIR and from activity counts data using published cutpoints (Crouter and Chandler). Estimates were compared across age, sex, and weight status to examine the impact of processing methods on behavioral outcomes.Results ActiGraph data were available for 408 participants. Large differences in SB and MVPA time were observed between processing methods, but age and gender patterns were similar. Younger children (aged 12-14 years) had lower sedentary time and greater MVPA time (p-values <0.05) than older children (aged 15-17 years), consistent across methods. The proportion of youth with ≥60 minutes of MVPA/day was highest with the Crouter methods (~50%) and lowest with GGIR (~0%).Conclusions Conclusions about youth PA and SB are influenced by the wrist-worn accelerometer data processing method. Efforts to harmonize processing methods are needed to promote standardization and facilitate reporting of monitor-based PA data.
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In 52 children, the GGIR algorithm classifies seven distinct types of activities with 91% accuracy for hip-worn accelerometers and 88% accuracy for wrist-worn accelerometers.
“In 52 children, the GGIR algorithm classifies seven distinct types of activities with 91% accuracy for hip-worn accelerometers and 88% accuracy for wrist-worn accelerometers [193].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [193]
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References 40 · Cited by 29
- Authors
- Kim, Y., Hibbing, P., Saint-Maurice, P. F., Ellingson, L. D., Hennessy, E., Wolff-Hughes, D. L., Perna, F. M., & Welk, G. J.
- Affiliations
- Iowa State University (US)
- Frederick National Laboratory for Cancer Research (US)
- National Institutes of Health (US)
- Corresponding author
- Kim, Youngwon
- Venue
- American Journal of Preventive Medicine
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 52
- Issue
- 6
- Pages
- 872-879
- ISSN
- 0749-3797 (ISSN-L)
- PMC
- PMC5497761
- OpenAlex
- W2615290226
- Cited by
- 29
- References
- 40
- Citations by year
- 2024: 3 · 2023: 1 · 2022: 5 · 2021: 5 · 2020: 1 · 2019: 3 · 2018: 5 · 2017: 6
- FWCI
- 3.03
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Preventive Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Iowa State University
- National Institutes of Health (HHSN261200800001C)
- National Cancer Institute (HHSN261201200021I, HHSN261200800001E), HHSN261201200028I, HHSN261201200039C, HHSN261201200081C, HHSN261201200028C, HHSN261201200039I, HHSN261201200021C)
- 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 › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Keywords
- children's physical activity, sedentary behavior, wrist-worn accelerometry, accelerometer data processing, moderate-to-vigorous physical activity, adolescents, ActiGraph GT3X+, data harmonization, cancer prevention
- MeSH terms
- Adolescent, Age Factors, Child, Cross-Sectional Studies, Female, Humans, Male, Wrist, Health Behavior, Exercise, Sedentary Behavior, Accelerometry
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