Modeling Physical Activity Outcomes from Wearable Monitors
Abstract
Although the measurement of physical activity with wearable monitors may be considered objective, consensus guidelines for collecting and processing these objective data are lacking. This article presents an algorithm embodying best practice recommendations for collecting, processing, and reporting physical activity data routinely collected with accelerometry-based activity monitors. This algorithm is proposed as a linear series of seven steps within three successive phases. The Precollection Phase includes two steps. Step 1 defines the population of interest, the type and intensity of physical activity behaviors to be targeted, and the preferred outcome variables, and identifies the epoch duration. In Step 2, the activity monitor is selected, and decisions about how long and where on the body the monitor is to be worn are made. The Data Collection Phase, Step 3, consists of collecting and processing activity monitor data and is dependent on decisions made previously. The Postcollection Phase consists of four steps. Step 4 involves quality and quantity control checks of the activity monitor data. In Step 5, the raw data are transformed into physiologically meaningful units using a calibration algorithm. Step 6 involves summarizing these data according to the target behavior. In Step 7, physical activity outcome variables based on time, energy expenditure, or movement type are generated. Best practice recommendations include the full disclosure of each step within the algorithm when reporting monitor-derived physical activity outcome variables in the research literature. As such, those reading and publishing within the research literature, as well as future users, will have the best chance for understanding the interactions between study methodology and activity monitor selection, as well as the best possibility for relating their own monitor-derived physical activity outcome variables to the research literature.
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While best practices and standards for accelerometer data reduction have been proposed, no consensus has been reached.
“Further, while best practices and standards have been proposed,[35-37] no consensus has been reached.”
From Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges (2014) · cites this paper as [35]
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- Authors
- HEIL, D. P., BRAGE, S., & ROTHNEY, M. P.
- Affiliations
- Montana State University (US)
- GE Global Research (United States) (US)
- ORCID
- HEIL, DANIEL P.
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2012-01
- Volume
- 44
- Issue
- 1S
- Pages
- S50-S60
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2326204356
- Cited by
- 121
- References
- 24
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 4 · 2022: 7 · 2021: 6 · 2020: 10 · 2019: 13 · 2018: 9 · 2017: 14 · 2016: 12 · 2015: 9 · 2014: 8 · 2013: 11 · 2012: 10
- FWCI
- 6.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- 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
- physical activity measurement, accelerometry, wearable activity trackers, data processing algorithms, data quality control, calibration, energy expenditure estimation, reporting guidelines, monitor placement
- MeSH terms
- Algorithms, Calibration, Data Collection, Humans, Motor Activity, Quality Control, Guidelines as Topic, Monitoring, Ambulatory
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