Validation of Accelerometer Wear and Nonwear Time Classification Algorithm
Open accessAbstract
INTRODUCTION: the use of movement monitors (accelerometers) for measuring physical activity (PA) in intervention and population-based studies is becoming a standard methodology for the objective measurement of sedentary and active behaviors and for the validation of subjective PA self-reports. A vital step in PA measurement is the classification of daily time into accelerometer wear and nonwear intervals using its recordings (counts) and an accelerometer-specific algorithm. PURPOSE: the purpose of this study was to validate and improve a commonly used algorithm for classifying accelerometer wear and nonwear time intervals using objective movement data obtained in the whole-room indirect calorimeter. METHODS: we conducted a validation study of a wear or nonwear automatic algorithm using data obtained from 49 adults and 76 youth wearing accelerometers during a strictly monitored 24-h stay in a room calorimeter. The accelerometer wear and nonwear time classified by the algorithm was compared with actual wearing time. Potential improvements to the algorithm were examined using the minimum classification error as an optimization target. RESULTS: the recommended elements in the new algorithm are as follows: 1) zero-count threshold during a nonwear time interval, 2) 90-min time window for consecutive zero or nonzero counts, and 3) allowance of 2-min interval of nonzero counts with the upstream or downstream 30-min consecutive zero-count window for detection of artifactual movements. Compared with the true wearing status, improvements to the algorithm decreased nonwear time misclassification during the waking and the 24-h periods (all P values < 0.001). CONCLUSIONS: the accelerometer wear or nonwear time algorithm improvements may lead to more accurate estimation of time spent in sedentary and active behaviors.
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Automated algorithms for wear-time determination (Troiano and Choi) were developed for protocols where participants received the device directly from investigators, so mail noise is not applicable.
“Automated algorithms are available (e.g., the commonly used Troiano[38] and Choi[39] algorithms) for wear-time determination; however, these were developed for protocols where participants received the device directly from investigators (thus, “mail noise” is not applicable).”
From Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges (2014) · cites this paper as [39]
In the WHS, using logs only to record whether the device was worn or not worn for the day, but not to indicate times, and then applying the Choi algorithm best reflected actual wear-time as assessed by participant detailed logs.
“Based on preliminary data, we have found that using logs only to record whether the device was “worn” or “not worn” for the day, but not to indicate times (minimizing participant burden and likelihood of missing data), and then applying the Choi algorithm best reflected actual wear-time as assessed by participant detailed logs.”
From Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges (2014) · cites this paper as [39]
Valid wear time was ascertained using Choi and colleagues parameters.
“Valid wear time was ascertained using Choi and colleagues [30] parameters (i.e., nonwear is classified as 90 minutes of consecutive zeros, allowing for nonzero counts up to 2 minutes, if no counts are detected during the 30-minute counts before and after this interval).”
From High-Intensity Interval Training as an Efficacious Alternative to Moderate-Intensity Continuous Training for Adults with Prediabetes (2015) · cites this paper as [30]
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Self-citation
- Authors
- CHOI, L., LIU, Z., MATTHEWS, C. E., & BUCHOWSKI, M. S.
- Affiliations
- Vanderbilt University (US)
- National Cancer Institute (US)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2011-02
- Volume
- 43
- Issue
- 2
- Pages
- 357-364
- ISSN
- 0195-9131 (ISSN-L)
- PMC
- PMC3184184
- OpenAlex
- W2074076764
- Cited by
- 1,804
- References
- 32
- Citations by year
- 2026: 139 · 2025: 211 · 2024: 200 · 2023: 131 · 2022: 207 · 2021: 201 · 2020: 184 · 2019: 124 · 2018: 113 · 2017: 89 · 2016: 67 · 2015: 54 · 2014: 42 · 2013: 29 · 2012: 6
- FWCI
- 8.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5p60dk020593-31)
- National Heart, Lung, and Blood Institute (R01HL082988)
- National Institute of Diabetes and Digestive and Kidney Diseases (P60-DK020593, R01DK069465, R01-DK-69465, DK 20593, P30DK020593)
- National Center for Research Resources (KL2RR024977, TL1RR024978, UL1RR024975)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- algorithm validation, physical activity measurement, sedentary behavior, time window optimization, self-report validation
- MeSH terms
- Acceleration, Adult, Aged, Algorithms, Body Weight, Female, Humans, Male, Middle Aged, Motor Activity, Monitoring, Ambulatory, Sports Equipment, Young Adult, Sedentary Behavior
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