Congruent Accuracy of Wrist-worn Activity Trackers during Controlled and Free-living Conditions
Open accessAbstract
International Journal of Exercise Science 11(7): 575-584, 2018. To examine activity tracker accuracy for measuring steps, energy expenditure, and heart rate in controlled and free-living conditions. Forty participants performed four, five-minute stages (walking: 53.7 m∙min-1, 80.5 m∙min-1; running: 134.1 m∙min-1, 160.9 m∙min-1) while wearing the Fitbit Charge HR (FB) and the Mio FUSE (MF) activity trackers. Measurements included steps, energy expenditure (kcals), and heart rate (beats∙min-1). In addition to the FB and MF, participants wore the NL-1000 (NL) activity tracker during waking hours of the subsequent day. One way ANOVAs with Tukey’s post hoc analyses were performed to compare mean values for steps, kcals, and mean heart rate between the FB, MF, and criterion measures. Levels of agreement for heart rate with 95% confidence intervals were examined with Bland-Altman plots. Compared to criterion measures, the FB and MF underestimated steps and overestimated kcals at 53.7 m∙min-1 (FB: 12.7% for steps, 89.2% for kcals; MF: 15.8% for steps, 44.9% for kcals, p<.001) and 80.5 m∙min-1 (FB: 9.7% for steps, 69.9% for kcals; MF: 13.4% for steps, 32.0% for kcals, p<.001). During free-living conditions, the MF significantly underestimated steps by 30.0% (p<.05). Increasing exercise intensity is indicative of heightened accuracy for step detection and kcal estimation for the FB and MF, while decreasing heart rate accuracy for the FB. However, the MF performed poorly for estimating total daily activity.
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Dondzila et al found that with aerobic exercise under laboratory conditions, the Fitbit Charge HR underestimated the HR compared with a Polar chest strap, with higher error at slower speeds.
“Dondzila et al [34] also looked at the Fitbit Charge HR and found that with aerobic exercise under laboratory conditions, the Fitbit Charge HR underestimated the HR compared with a Polar chest strap, with higher error at slower speeds.”
From Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study (2018) · cites this paper as [34]
Cites
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“Energy expenditure output from the FB and MF were compared with kcals derived from kcals calculated via MET values of treadmill intensities (1).”
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“ABSTRACT International Journal of Exercise Science 11(7): 575-584, 2018.”
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“com 581 similar to the current study (2, 5, 10, 13, 18).”
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“It is interesting to note the trend for waist-worn Fitbit devices in underestimating kcals (19, 23), with reference to wrist-worn Fitbit devices.”
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“Interestingly, two studies (9, 25) reported MAPE >20% for walking speeds.”
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“7% to 47% (3, 6, 14, 22, 25, 27), influenced by the intensity and volume of activities engaged in.”
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“It is interesting to note the trend for waist-worn Fitbit devices in underestimating kcals (19, 23), with reference to wrist-worn Fitbit devices.”
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“Interestingly, two studies (9, 25) reported MAPE >20% for walking speeds.”
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“com 581 similar to the current study (2, 5, 10, 13, 18).”
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Sorted by citations
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- Authors
- Dondzila, C. J., Lewis, C., Lopez, J. R., & Parker, T.
- ORCID
- Parker, Tonya
- Venue
- International Journal of Exercise Science
- Published
- 2018
- Volume
- 11
- Issue
- 7
- Pages
- 575-584
- ISSN
- 1939-795X (ISSN-L)
- OpenAlex
- W2787184052
- Cited by
- 16
- References
- 28
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 3 · 2020: 3 · 2019: 3 · 2018: 4
- FWCI
- 1.73
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nd
- Locations
- International journal of exercise science (publishedVersion, cc-by-nd)
- TopSCHOLAR (Western Kentucky University) (submittedVersion)
- 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, measurement accuracy, step counting, energy expenditure estimation, heart rate monitoring, walking and running, free-living conditions, exercise intensity
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