The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
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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In the citing paper
“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pace of technological change, these existing studies have largely been completed on devices that would be considered outdated, such as earlier Fitbit models (One, Zip, Flex, Ultra), Jawbone Up, and the Nike FuelBand.”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]
Citations
Cites
Compher, C., Frankenfield, D., Keim, N., & Roth-Yousey, L. (2006) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2006.02.009 · Cited by 923 · cited 1×
“after a 10-h fast, following previously published guidelines (6).”
MethodValidity of four motion sensors in measuring moderate intensity physical activity
Bassett, D. R. (2000) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-200009001-00006 · Cited by 533
Accuracy of Armband Monitors for Measuring Daily Energy Expenditure in Healthy Adults
Johannsen, D. L., Calabro, M. A., Stewart, J. W., Franke, W. D., Rood, J. C., & Welk, G. J. (2010) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3181e0b3ff · Cited by 433
Self-citationReliability of accelerometry-based activity monitors: a generalizability study.
Welk, G. J., Schaben, J. A., & Morrow, J. R. (2004) · PubMed · Cited by 394
Self-citationStaudenmayer, J., Pober, D., Crouter, S., Bassett, D., & Freedson, P. (2009) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00465.2009 · Cited by 361
Validity and Reliability issues in Objective Monitoring of Physical Activity
Bassett, D. R. (2000) · Research Quarterly for Exercise and Sport · doi:10.1080/02701367.2000.11082783 · Cited by 330
Testing Statistical Hypotheses of Equivalence
Wellek, S. (2002) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420035964 · Cited by 323
Predictive Validity of Three ActiGraph Energy Expenditure Equations for Children
Trost, S. G., Way, R., & Okely, A. D. (2006) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000183848.25845.e0 · Cited by 220
A new statistical procedure for testing equivalence in two-group comparative bioavailability trials
Hauck, W. W., & Anderson, S. (1984) · Journal of Pharmacokinetics and Biopharmaceutics · doi:10.1007/bf01063612 · Cited by 209
Comparison of Four ActiGraph Accelerometers during Walking and Running
John, D., Tyo, B. M., & Bassett, D. R. (2010) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3181b3af49 · Cited by 183
Cited by
Sorted by citations
Thomas, D. T., Erdman, K. A., & Burke, L. M. (2016) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2015.12.006 · Cited by 1,359
Nutrition and Athletic Performance
(2016) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000852 · Cited by 1,305
The Rise of Consumer Health Wearables: Promises and Barriers
Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016) · PLoS Medicine · doi:10.1371/journal.pmed.1001953 · Cited by 1,301
Systematic review of the validity and reliability of consumer-wearable activity trackers
Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-015-0314-1 · Cited by 1,232
“One study also reported entering stride length [34], another study input handedness and smoking status [35], and another study used event markers to denote when an activity started and ended [39].”
BackgroundMethodResultInfluentialFuller, D., Colwell, E., Low, J., Orychock, K., Tobin, M. A., Simango, B., Buote, R., Van Heerden, D., Luan, H., Cullen, K., et al. (2020) · JMIR mHealth and uHealth · doi:10.2196/18694 · Cited by 715
“device energy expenditure measurements compared with reference standard criterion measures, including direct calorimetry [86,104] and indirect calorimetry [20,21,29-31,38,39,41-43,53,55,63,66,73,85,87,93,95,97,103, 105,116,117,129,130,142,143,146,148,159,165,166,177], in controlled settings.”
Method
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
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