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Validity of Consumer-Based Physical Activity Monitors
Abstract
BACKGROUND: Many consumer-based monitors are marketed to provide personal information on the levels of physical activity and daily energy expenditure (EE), but little or no information is available to substantiate their validity. PURPOSE: This study aimed to examine the validity of EE estimates from a variety of consumer-based, physical activity monitors under free-living conditions. METHODS: Sixty (26.4 ± 5.7 yr) healthy males (n = 30) and females (n = 30) wore eight different types of activity monitors simultaneously while completing a 69-min protocol. The monitors included the BodyMedia FIT armband worn on the left arm, the DirectLife monitor around the neck, the Fitbit One, the Fitbit Zip, and the ActiGraph worn on the belt, as well as the Jawbone Up and Basis B1 Band monitor on the wrist. The validity of the EE estimates from each monitor was evaluated relative to criterion values concurrently obtained from a portable metabolic system (i.e., Oxycon Mobile). Differences from criterion measures were expressed as a mean absolute percent error and were evaluated using 95% equivalence testing. RESULTS: For overall group comparisons, the mean absolute percent error values (computed as the average absolute value of the group-level errors) were 9.3%, 10.1%, 10.4%, 12.2%, 12.6%, 12.8%, 13.0%, and 23.5% for the BodyMedia FIT, Fitbit Zip, Fitbit One, Jawbone Up, ActiGraph, DirectLife, NikeFuel Band, and Basis B1 Band, respectively. The results from the equivalence testing showed that the estimates from the BodyMedia FIT, Fitbit Zip, and NikeFuel Band (90% confidence interval = 341.1-359.4) were each within the 10% equivalence zone around the indirect calorimetry estimate. CONCLUSIONS: The indicators of the agreement clearly favored the BodyMedia FIT armband, but promising preliminary findings were also observed with the Fitbit Zip.
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“However, given the pace of technological change, these existing studies have largely been completed on devices that would be considered outdated, such as earlier Fitbit models (One, Zip, Flex, Ultra), Jawbone Up, and the Nike FuelBand.”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [10]
In the citing paper
“Most research has primarily focused on the accuracy of estimates of EE [7-12].”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [10]
In the citing paper
“One previous study [10] found MAPE for Fitbit Zip (10.1%) and Fitbit One (10.4%) to be much lower than the results from this study, where MAPE was between 16.85% and 84.98%.”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [10]
In the citing paper
“For example, while Fitbit models have changed over time, the basic error rate has been similar across studies and devices [8,10].”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [10]
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BackgroundData from Digital Health Devices Informs Ideal Cardiovascular Health
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Peiris, C. L., Cumming, T. B., Kramer, S. F., Johnson, L. G., Taylor, N. F., & Shields, N. (2016) · Journal of Intellectual & Developmental Disability · doi:10.3109/13668250.2016.1260100 · Cited by 6
Applicability of consumer activity monitor data in marathon events: an exploratory study
Constantinou, V., Felber, A. E., & Chan, J. L. (2017) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2017.1366560 · Cited by 5
Heydenreich, J., Schütz, Y., Melzer, K., & Kayser, B. (2019) · Current Issues in Sport Science (CISS) · doi:10.15203/ciss_2019.003 · Cited by 6
MacDonald, M. J., Fawkner, S. G., Niven, A. G., & Rowe, D. A. (2019) · Pediatric Exercise Science · doi:10.1123/pes.2018-0213 · Cited by 6
Adamakis, M. (2020) · Sport Sciences for Health · doi:10.1007/s11332-020-00654-2 · Cited by 7
Sitthipornvorakul, E., Waongenngarm, P., Lohsoonthorn, V., & Janwantanakul, P. (2020) · Work · doi:10.3233/wor-203206 · Cited by 6
Sports medicine: bespoke player management
Stuart, S., Powell, D., Marshall, S. J., Clark, C. C. T., Martini, D. N., Johnston, W., & Godfrey, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818914-6.00021-1 · Cited by 6
(2019) · CIN Computers Informatics Nursing · doi:10.1097/01.ncn.0000576724.96272.9f · Cited by 6
Salzwedel, A., Rabe, S., Zahn, T., Neuwirth, J., Eichler, S., Haubold, K., Wachholz, A., Reibis, R. K., & Völler, H. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7192 · Cited by 6
“Therefore, validity and reliability of these systems cannot be assumed [17-20].”
Støve, M. P., & Larsen, B. T. (2020) · European Journal of Physiotherapy · doi:10.1080/21679169.2019.1628300 · Cited by 5
Wang, L., Chen, P., Jiang, A., Zhang, F., & Wu, Z. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1539369 · Cited by 6
Karpman, J., Gross, D. P., Manns, P. J., & Tomkins‐Lane, C. (2020) · Work · doi:10.3233/wor-203164 · Cited by 5
“In the past, wearable devices have been used to determine cut points or activity levels on a day-to-day basis [5, 8, 15] or to compare energy expenditure results, [7, 13, 14, 27] but we have not found studies that specifically compared device output and maximal performance.”
An Opinion Regarding Equivalence Testing for Evaluating Measurement Agreement
Adamakis, M. (2019) · Journal of Scientific Research and Reports · doi:10.9734/jsrr/2019/v24i530163 · Cited by 5
“In that initial approach [1], as well as some of the following studies [2-4], equivalence was supported with the use of 90% confidence interval (CI), obtained from a mixed ANOVA.”
MethodKymäläinen, H., Hujala, T., Häggström, C., & Malinen, J. (2023) · International Journal of Forest Engineering · doi:10.1080/14942119.2023.2216113 · Cited by 6
“Accuracy of wearables has shown variation between different devices demonstrating a 9–30 % variation when evaluating accuracy on physical activity, heart rate, and energy consumption (Lee et al. 2014; Yetisen et al. 2018).”
BackgroundValidation Study of Physical Activity and Sedentary Behavior in African-American Men and Women
Hicks, J., Adams-Campbell, L. L., Lu, J., Mills, M. G., & Dash, C. (2023) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-023-01734-y · Cited by 5
Adamakis, M. (2017) · Journal of Mobile Technology in Medicine · doi:10.7309/jmtm.6.1.4 · Cited by 5
Daily Step Count as a Simple Marker of Disease Severity in Hypertrophic Cardiomyopathy
Sweeting, J., Ingles, J., Ball, K., & Semsarian, C. (2018) · Heart Lung and Circulation · doi:10.1016/j.hlc.2017.12.012 · Cited by 5
Edwardson, C. L., Davies, M. J., Khunti, K., Yates, T. E., & Rowlands, A. V. (2018) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2017-0001 · Cited by 6
Assessing the Measurement Properties of the Fitbit Zip® Among Adults Living With HIV
Dagenais, M., Salbach, N. M., Brooks, D., & O’Brien, K. K. (2020) · Journal of Physical Activity and Health · doi:10.1123/jpah.2019-0242 · Cited by 5
Identifying ActiGraph non-wear time in pregnant women with overweight or obesity
Leonard, K. S., Pauley, A. M., Hohman, E. E., Guo, P., Rivera, D. E., Savage, J. S., Buman, M. P., & Downs, D. S. (2020) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2020.08.003 · Cited by 5
Díaz-Quesada, G., Puga-González, E., & Galiano, I. M. M. (2021) · JUMP · doi:10.17561/jump.n3.2 · Cited by 5
Elgendi, M., Wu, W., Guan, C., & Menon, C. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1237043 · Cited by 7
“Researchers could follow previous studies (62, 63) that pre-defined an equivalence zone of +10% of the reference mean, and perform a statistical test to determine whether the 90% confidence interval from the estimations falls within the equivalence zone with 95% precision (a 1⁄4 0:05).”
MethodOutputs Available from Objective Monitors
Tudor‐Locke, C. (2016) · Springer series on epidemiology and public health · doi:10.1007/978-3-319-29577-0_3 · Cited by 5
Miller, T. W. (2017) · Internet Journal of Allied Health Sciences and Practice · doi:10.46743/1540-580x/2017.1604 · Cited by 5
“Results: After the 36-week intervention program, results revealed qualitative improvements in the (1) active minutes, (2) steps taken and (3) miles walked by the subject.”
Result
Metadata
- Authors
- LEE, J.-M., KIM, Y., & WELK, G. J.
- Affiliations
- University of Nebraska at Omaha (US)
- Iowa State University (US)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2014-09
- Volume
- 46
- Issue
- 9
- Pages
- 1840-1848
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2095561638
- Cited by
- 401
- References
- 22
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 10 · 2023: 13 · 2022: 17 · 2021: 19 · 2020: 29 · 2019: 51 · 2018: 68 · 2017: 67 · 2016: 61 · 2015: 39 · 2014: 9
- FWCI
- 39.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- 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
- wearable activity trackers, daily energy expenditure, free-living conditions, Fitbit, ActiGraph, indirect calorimetry, equivalence testing
- MeSH terms
- Adult, Basketball, Energy Metabolism, Exercise Test, Female, Humans, Male, Motor Activity, Oxygen Consumption, Running, Walking, Monitoring, Ambulatory, Young Adult, Accelerometry
About these checks
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