Different Location of Triaxial Accelerometer and Different Energy Expenditures
Open accessAbstract
PURPOSE: We performed a study to determine the best appropriate wearing site of a triaxial accelerometer at different exercise speeds. MATERIALS AND METHODS: We conducted an observational study with 66 healthy Korean adults (26 men and 40 women). Resting metabolic rate (RMR) before exercise, physical activity-related energy expenditure (PAEE) by cardiorespiratory gas analyzer and Signal Vector Magnitude (SVM) were measured while wearing four triaxial accelerometers on four different sites (wrist, waist, upper arm, and ankle) at exercise speeds from 2-10 km/h. RESULTS: The mean RMR was 4.03 mL/kg/min and Actual METs (oxygen consumption at different exercise speeds divided by individual RMR) compared with the calculated METs (oxygen consumption divided by 3.5 mL/kg/min) showed relatively low value. The overall correlation between PAEE and SVM was highest when the accelerometer was worn on the wrist at low exercise speed (r=0.751, p<0.001), waist at a moderate speed (r=0.821, p<0.001), and ankle at a high speed (r=0.559, p<0.001). Using regression analysis, it was shown that the ankle at a low speed (R²=0.564, p<0.001), high speed (R²=0.559, p<0.001), and the waist at a moderate speed (R²=0.821, p<0.001) were the best appropriate sites. CONCLUSION: When measuring the PAEE and SVM at different exercise speeds, the ankle in low and high exercise speed, and waist in moderate speed are the most appropriate sites for an accelerometer.
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There is currently limited reliability and validity data relating to the Fitmeter; however, it has been reported that the Fitmeter does correlate with gas analyzer measures for energy expenditure (r2=0.82).
“There is currently limited reliability and validity data relating to the Fitmeter; however, it has been reported that the Fitmeter does correlate with gas analyzer measures for energy expenditure (r2=0.82) [85].”
From Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis (2019) · cites this paper as [85]
The Methods and Results report an association between Fitmeter acceleration measurements and energy expenditure measured using oxygen consumption from a gas analyzer. But the Results give r=0.821 for the waist at moderate exercise intensity, not r²=0.82; the regression Results give R²=0.674 for that condition. The cited paper does not establish the broader claim that Fitmeter reliability and validity data are limited. (LLM, conf=0.88)
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Cites
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Brickwood, K.-J., Watson, G., O'Brien, J., & Williams, A. D. (2019) · JMIR mHealth and uHealth · doi:10.2196/11819 · Cited by 690 · see citing works
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“(Kim et al., 2014; Wetten et al., 2014) An explanation for the lower mean bias of EE estimations associated with wearing the ActiGraph GT3X+ accelerometer on the waist when walking could be the lower ranges of waist movements compared with the wrist and the * statistically significant ICC, Intra…”
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“When an accelerometer is positioned somewhere closer to the extremities, such as the wrist, only a small mass is being moved, and hence the relation between recorded accelerations and EE will be different (19).”
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“Previous studies have explored the use of the ankle site in adults (Heil, 2006; Hibbing et al., 2017; Kim et al., 2014; Leenders et al., 2003; Melanson & Freedson, 1995) and youth (De Vries et al., 2011; Hager et al., 2015; Heil, 2006) and have shown conflicting results.”
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“, 2011), whilst others have shown the use of the ankle location was similar or better than waist or wrist worn locations for estimating energy expenditure (Kim et al., 2014; Hibbing et al., 2018).”
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MethodObesity Prevention Behaviors in Asian Indian Adolescent Girls: A Pilot Study
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“Energy expenditure was calculated by removing gravitational acceleration and calibrating single-vector magnitude (by summing acceleration in the 3 axes) [30]: Single-vector magnitude 1⁄4 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi x(2)i þ y(2)i þ z(2)i q .”
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Energy Expenditure Estimation of Tabata by Combining Acceleration and Heart Rate
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Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia
Park, B., Choi, B., Lee, H., Jang, J.-H., Roh, H. W., Kim, E. Y., Hong, C. H., Son, S. J., & Yoon, D. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.795171 · Cited by 8 · see citing works
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“821) had been previously found between SVM value and oxygen consumption (Kim et al., 2014).”
InfluentialValidity of Fitbit Flex® consumer device on the sleep evaluation
López-Flores, M., Fernandez, A. R., Villa-Vicente, J., Iglesias, D. S., & Marroyo, J. A. R. (2020) · Cultura Ciencia y Deporte · doi:10.12800/ccd.v15i43.1397 · Cited by 2 · see citing works
Medición del gasto energético en pruebas submáximas de valoración funcional mediante acelerometría
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“[22, 44] Bei Patienten, die für einen längeren Zeitraum ein Wearable zu Screening- oder Monitoringzwecken tragen, sollte darauf geachtet werden, dass es durch das Device nicht zu Druckstellen kommen kann.”
Background
- Authors
- Kim, D. Y., Jung, Y.-S., Park, R.-W., & Joo, N.-S.
- Affiliations
- Fit. Life Inc., Suwon, Korea.
- Ajou University (KR)
- Corresponding author
- Joo, Nam-Seok
- ORCID
- Joo, Nam-Seok
- Venue
- Yonsei Medical Journal
- Published
- 2014
- Volume
- 55
- Issue
- 4
- Pages
- 1145
- ISSN
- 0513-5796 (ISSN-L)
- 1976-2437
- PMC
- PMC4075379
- OpenAlex
- W1964081832
- Cited by
- 34
- References
- 21
- Citations by year
- 2022: 3 · 2021: 3 · 2020: 5 · 2019: 4 · 2018: 5 · 2017: 5 · 2016: 6 · 2015: 3
- FWCI
- 3.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Yonsei Medical Journal (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- AJOU Open Repository (Ajou University) (submittedVersion, cc-by-nc)
- Funders
- Korea Healthcare technology R&D Project, Ministry for Health, Welfare & Family Affairs, Republic of Korea (A084120)
- 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 › Medicine › Complementary and alternative medicine › Cardiovascular and exercise physiology
- Keywords
- triaxial accelerometer, physical activity energy expenditure, exercise speed, resting metabolic rate, oxygen consumption, wrist, waist, ankle joint, upper arm, activity monitoring
- MeSH terms
- Adult, Anthropometry, Basal Metabolism, Energy Metabolism, Female, Humans, Male, Middle Aged, Oxygen Consumption, Exercise, Young Adult
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