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Energy expenditure prediction via a footwear-based physical activity monitor: accuracy and comparison to other devices

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dc.contributor.authorDannecker, Kathryn, author
dc.contributor.authorBrowning, Raymond, advisor
dc.contributor.authorHickey, Matthew S., committee member
dc.contributor.authorBellows, Laura, committee member
dc.date.accessioned2007-01-03T05:00:48Z
dc.date.available2007-01-03T05:00:48Z
dc.date.issued2011
dc.description.abstractAccurately estimating free-living energy expenditure (EE) is important for monitoring or altering energy balance and quantifying levels of physical activity. The use of accelerometers to monitor physical activity and estimate physical activity EE is common in both research and consumer settings. Recent advances in physical activity monitors include the ability to identify specific activities (e.g. stand vs. walk) which has resulted in improved EE estimation accuracy. Recently, a multi]sensor footwear-based physical activity monitor that is capable of achieving 98% activity identification accuracy has been developed. However, no study has compared the EE estimation accuracy for this monitor and compared this accuracy to other similar devices. PURPOSE: To determine the accuracy of physical activity EE estimation of a footwear-based physical activity monitor that uses an embedded accelerometer and insole pressure sensors and to compare this accuracy against a variety of research and consumer physical activity monitors. METHODS: Nineteen adults (10 male, 9 female), mass: 75.14 (17.1) kg, BMI: 25.07(4.6) kg/m2 (mean (SD)), completed a four hour stay in a room calorimeter. Participants wore a footwear-based physical activity monitor, as well as three physical activity monitoring devices used in research: hip]mounted Actical and Actigraph accelerometers and a multi-accelerometer IDEEA device with sensors secured to the limb and chest. In addition, participants wore two consumer devices: Philips DirectLife and Fitbit. Each individual performed a series of randomly assigned and ordered postures/activities including lying, sitting (quietly and using a computer), standing, walking, stepping, cycling, sweeping, as well as a period of self-selected activities. We developed branched (i.e. activity specific) linear regression models to estimate EE from the footwear-based device, and we used the manufacturer's software to estimate EE for all other devices. RESULTS: The shoe-based device was not significantly different than the mean measured EE (476(20) vs. 478(18) kcal) (Mean(SE)), respectively, and had the lowest root mean square error (RMSE) by two]fold (29.6 kcal (6.19%)). The IDEEA (445(23) kcal) and DirecLlife (449(13) kcal) estimates of EE were also not different than the measured EE. The Actigraph, Fitbit and Actical devices significantly underestimated EE (339 (19) kcal, 363(18) kcal and 383(17) kcal, respectively (p<.05)). Root mean square errors were 62.1 kcal (14%), 88.2 kcal(18%), 122.2 kcal (27%), 130.1 kcal (26%), and 143.2 kcal (28%) for DirectLife, IDEEA, Actigraph, Actical and Fitbit respectively. CONCLUSIONS: The shoe based physical activity monitor was able to accurately estimate EE. The research and consumer physical activity monitors tested have a wide range of accuracy when estimating EE. Given the similar hardware of these devices, these results suggest that the algorithms used to estimate EE are primarily responsible for their accuracy, particularly the ability of the shoe-based device to estimate EE based on activity classifications.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierDannecker_colostate_0053N_10687.pdf
dc.identifier.urihttp://hdl.handle.net/10217/46899
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectactivity monitor
dc.subjectposture allocation
dc.subjectphysical activity
dc.subjectfootwear
dc.subjectenergy expenditure
dc.titleEnergy expenditure prediction via a footwear-based physical activity monitor: accuracy and comparison to other devices
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineHealth and Exercise Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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