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Assessing Physical Activity Using Wearable Monitors: Measures of Physical Activity
Open accessAbstract
BACKGROUND: Physical activity may be defined broadly as "all bodily actions produced by the contraction of skeletal muscle that increase energy expenditure above basal level." Physical activity is a complex construct that can be classified into major categories qualitatively, quantitatively, or contextually. The quantitative assessment of physical activity using wearable monitors is grounded in the measurement of energy expenditure. Six main categories of wearable monitors are currently available to investigators: pedometers, load transducers/foot-contact monitors, accelerometers, HR monitors, combined accelerometer and HR monitors, and multiple sensor systems. BEST PRACTICES: Currently available monitors are capable of measuring total physical activity as well as components of physical activity that play important roles in human health. The selection of wearable monitors for measuring physical activity will depend on the physical activity component of interest, study objectives, characteristics of the target population, and study feasibility in terms of cost and logistics. FUTURE DIRECTIONS: Future development of sensors and analytical techniques for assessing physical activity should focus on the dynamic ranges of sensors, comparability for sensor output across manufacturers, and the application of advanced modeling techniques to predict energy expenditure and classify physical activities. New approaches for qualitatively classifying physical activity should be validated using direct observation or recording. New sensors and methods for quantitatively assessing physical activity should be validated in laboratory and free-living populations using criterion methods of calorimetry or doubly labeled water.
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“It has been demonstrated that triaxial accelerometers (5), multiple monitors worn in various locations (51), and sophisticated pattern-recognition techniques (21,35,39,46) can enhance PA characterization and lead to more accurate assessment of PA.”
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MethodCombatting type 2 diabetes by turning up the heat
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“It should be noted, however, that although HRMs may allow precise measures of heart rate throughout the day, heart rate may be affected by variables other than activity (Butte et al., 2012).”
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A practical guidance for assessments of sedentary behavior at work: A PEROSH initiative
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“Additional assessments of physiological data, like heart rate and ventilationmakes the estimation of activity intensitymore accurate than just interpreting data derived from accelerometers (Butte et al., 2012).”
BackgroundPolar Vantage and Oura Physical Activity and Sleep Trackers: Validation and Comparison Study
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Assessment of walking, running, and jumping movement features by using the inertial measurement unit
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“Because accelerometers are portable and can facilitate extended data collection, they have been applied in physical activity (PA) measurements or used to develop commercial wearable device [1–3].”
MethodHuman Performance Optimization Metrics
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Akzelerometrie zur Erfassung körperlicher Aktivität
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Accelerometer Cut Points for Physical Activity Assessment of Older Adults with Parkinson’s Disease
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“5 cm, 19 g) easy-to-wear device that yields valid estimates of physical activity in controlled and free-living environments (20, 21).”
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Real-Time Monitoring in Home-Based Cardiac Rehabilitation Using Wrist-Worn Heart Rate Devices
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BackgroundThe examination of mental toughness, sleep, mood and injury rates in an Arctic ultra‐marathon
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“This approach is in concordance with best practise recommendations (e.g., Bastian et al., 2015; Butte et al., 2012).”
ResultA Comparison of Accelerometer Accuracy in Older Adults
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Reliable assessment of physical activity in disease
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“Wearable health devices that monitor PA can take the form of an accelerometer, a pedometer, a heart rate monitor, or a combination of these mechanisms [9-12].”
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Dynamic assessment of inhaled air pollution using GPS and accelerometer data
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“For example, the total average times reported for bicycling, swimming and weight lifting (activities not well captured by the accelerometer) (23) was 9 min ( 15 min), with 38% of participants reporting no time spent in these activities over the last year.”
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Metadata
- Authors
- BUTTE, N. F., EKELUND, U., & WESTERTERP, K. R.
- Affiliations
- Agricultural Research Service (US)
- University of Cambridge (GB)
- Maastricht University (NL)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2012-01
- Volume
- 44
- Issue
- 1S
- Pages
- S5-S12
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2329242152
- Cited by
- 359
- References
- 51
- Citations by year
- 2026: 10 · 2025: 18 · 2024: 14 · 2023: 14 · 2022: 14 · 2021: 16 · 2020: 29 · 2019: 23 · 2018: 26 · 2017: 39 · 2016: 38 · 2015: 32 · 2014: 39 · 2013: 29 · 2012: 16
- FWCI
- 2.88
- Type
- journal article
- Language
- en
- Open access
- Licence
- other-oa
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › 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, wearable monitors, accelerometer, pedometers, heart rate monitors, energy expenditure, physical activity classification, sensor validation, doubly labeled water, calorimetry
- MeSH terms
- Energy Metabolism, Humans, Motor Activity, Exercise, Monitoring, Ambulatory
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