Document Detail


Towards valid estimates of activity energy expenditure using an accelerometer: searching for a proper analytical strategy and big data.
MedLine Citation:
PMID:  24030666     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Estimating energy expenditure and the metabolic response to physical activity is a complex area of research, manifested by the increasingly sophisticated instruments and methodologies being proposed in the literature. Wearable motion sensors such as accelerometers are the most widely studied tools to assess activity-related energy expenditure (AEE) by quantifying body movement. These two fundamental aspects of physical activity, body movement and the metabolic response, although dependent, deserve specific evaluation methods. Body movement has been conventionally represented by the so called activity counts, a summary metric of the acceleration signal variability, while estimates of AEE have been obtained by combining activity counts with subject characteristics using linear regression models. Many consider this approach obsolete or intuitively subject to limitations leading to inevitable methodological error. Indeed, the variability in activity counts between different activities and workloads does not always reflect a corresponding variation in AEE. Thus, interest grew in processing raw accelerometer data to better describe body movement, for example by limiting influences of noise and gravity, gathering advanced statistical features about human motion and discriminating between categories of activities. Concurrently, interpretative algorithms of body movement evolved from simple regression to models developed using machine learning techniques. Among these attempts to accurately estimate AEE, establishing which one yielded the largest improvements has never been an easy mission. Comparing these analytical strategies for estimating AEE was the aim of a study published in the current issue of the Journal of Applied Physiology.
Authors:
Alberto G Bonomi
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-9-12
Journal Detail:
Title:  Journal of applied physiology (Bethesda, Md. : 1985)     Volume:  -     ISSN:  1522-1601     ISO Abbreviation:  J. Appl. Physiol.     Publication Date:  2013 Sep 
Date Detail:
Created Date:  2013-9-13     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8502536     Medline TA:  J Appl Physiol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
1Philipps Research Eindhoven.
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