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Regression equations for RT3 activity monitors to estimate energy expenditure in manual wheelchair users

Hiremath, SV and Ding, D (2011) Regression equations for RT3 activity monitors to estimate energy expenditure in manual wheelchair users. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 7348 - 7351. ISSN 1557-170X

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Abstract

Activity monitors (AMs) can assist persons with Spinal Cord Injury (SCI) who use manual wheelchairs to self-assess regular physical activity to move towards healthier lifestyles. In this study we evaluated the validity of an accelerometer-based RT3 AM in predicting energy expenditure (EE) of manual wheelchair users with SCI. Twenty-four subjects performed four types of physical activities including wheelchair propulsion, arm-ergometry exercise, deskwork, and resting in a laboratory setting. Subjects wore two RT3 AMs: an RT3 around the waist (RT3W) per the manufacturer's instruction and an RT3 on the upper arm (RT3A). Criterion EE was collected by a portable metabolic system. The absolute EE estimation error for the RT3W varied from 21.3%-55.2% for different activities. Two EE prediction equations (general and activity-specific) were developed from 19 randomly selected subjects and validated on the remaining 4 subjects for the RT3A, RT3W, and RT3 AMs combined. The results showed that the general and activity-specific regression equations for the RT3A performed better than the RT3W and similar to the RT3 AMs combined. The general EE equation for RT3A consisted of both the demographic variable weight and accelerometer variables showing it is sensitive to subject parameters and upper extremity movement. The activity-specific EE equations for RT3A showed demographic variable weight to be a significant predictor during resting and deskwork and accelerometer variables along with weight to be significant predictors during propulsion and arm-ergometry. The validation results from the activity-specific equations for the RT3A showed that the absolute EE estimation error varied from 12.2%-38.1%. Future work will recruit more subjects and refine the prediction equations for the RT3 AM to quantify physical activity in MWUs with SCI © 2011 IEEE.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Hiremath, SVsvh4@pitt.eduSVH4
Ding, Ddad5@pitt.eduDAD5
Centers: Other Centers, Institutes, Offices, or Units > Human Engineering Research Laboratories
Date: 26 December 2011
Date Type: Publication
Journal or Publication Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Page Range: 7348 - 7351
DOI or Unique Handle: 10.1109/iembs.2011.6091714
Schools and Programs: School of Health and Rehabilitation Sciences > Rehabilitation Science and Technology
Refereed: No
ISSN: 1557-170X
MeSH Headings: Adolescent; Adult; Energy Metabolism--physiology; Female; Humans; Male; Middle Aged; Models, Biological; Monitoring, Ambulatory--instrumentation; Regression Analysis; Wheelchairs
PubMed ID: 22256036
Date Deposited: 30 Jan 2013 20:58
Last Modified: 31 Jul 2020 16:59
URI: http://d-scholarship.pitt.edu/id/eprint/17161

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