Comparison of Consumer and Research Monitors under Semistructured Settings
Abstract
PURPOSE: This study evaluated the relative validity of different consumer and research activity monitors during semistructured periods of sedentary activity, aerobic exercise, and resistance exercise. METHODS: Fifty-two (28 male and 24 female) participants age 18-65 yr performed 20 min of self-selected sedentary activity, 25 min of aerobic exercise, and 25 min of resistance exercise, with 5 min of rest between each activity. Each participant wore five wrist-worn consumer monitors [Fitbit Flex, Jawbone Up24, Misfit Shine (MS), Nike+ Fuelband SE (NFS), and Polar Loop] and two research monitors [ActiGraph GT3X+ on the waist and BodyMedia Core (BMC) on the arm] while being concurrently monitored with Oxycon Mobile (OM), a portable metabolic measuring system. Energy expenditure (EE) on different activity sessions was measured by OM and estimated by all monitors. RESULTS: Mean absolute percent error (MAPE) values for the full 80-min protocol ranged from 15.3% (BMC) to 30.4% (MS). EE estimates from ActiGraph GT3X+ were found to be equivalent to those from OM (± 10% equivalence zone, 285.1-348.5). Correlations between OM and the various monitors were generally high (ranged between 0.71 and 0.90). Three monitors had MAPE values lower than 20% for sedentary activity: BMC (15.7%), MS (18.2%), and NFS (20.0%). Two monitors had MAPE values lower than 20% for aerobic exercise: BMC (17.2%) and NFS (18.5%). None of the monitors had MAPE values lower than 25% for resistance exercise. CONCLUSION: Overall, the research monitors and Fitbit Flex, Jawbone Up24, and NFS provided reasonably accurate total EE estimates at the individual level. However, larger error was evident for individual activities, especially resistance exercise. Further research is needed to examine these monitors across various activities and intensities as well as under real-world conditions.
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Presently, there are at least four studies that have investigated the accuracy of wearable devices during resistance exercises and none during high-intensity interval training.
“Presently, there are at least 4 studies [21-24] that have investigated the accuracy of wearable devices during resistance exercises and none during HIIT training.”
From Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study (2018) · cites this paper as [21]
Bai et al reported that energy expenditure measured during an unstructured resistance exercise protocol in which participants selected exercises and loads was inaccurate across numerous devices.
“Bai et al [21] reported that EE measured during an unstructured resistance exercise protocol in which participants selected exercises and loads was inaccurate across numerous devices.”
From Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study (2018) · cites this paper as [21]
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- Authors
- BAI, Y., WELK, G. J., NAM, Y. H., LEE, J. A., LEE, J.-M., KIM, Y., MEIER, N. F., & DIXON, P. M.
- Affiliations
- University of Nebraska at Omaha (US)
- Iowa State University (US)
- ORCID
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2016-01
- Volume
- 48
- Issue
- 1
- Pages
- 151-158
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2346316985
- Cited by
- 149
- References
- 24
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 5 · 2023: 6 · 2022: 9 · 2021: 14 · 2020: 14 · 2019: 21 · 2018: 26 · 2017: 26 · 2016: 20 · 2015: 1
- FWCI
- 17.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council (MC_UU_12015/3)
- 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
- wearable activity trackers, energy expenditure estimation, wrist-worn devices, sedentary activity, aerobic exercise, resistance training, measurement validity, Fitbit, jaw bone, misfit, ActiGraph GT3X+
- MeSH terms
- Adolescent, Adult, Aged, Energy Metabolism, Female, Humans, Male, Middle Aged, Exercise, Resistance Training, Young Adult, Sedentary Behavior, Accelerometry
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