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Nonlinear modification to Hildebrand equation for accelerometry assessment of physical activity: improved accuracy for low activity levels
Journal article   Open access

Nonlinear modification to Hildebrand equation for accelerometry assessment of physical activity: improved accuracy for low activity levels

Laura A. Frey-Law, Shannon L. Merkle, Dana Dailey and Kathleen A. Sluka
Exploration of Musculoskeletal Diseases, Vol.2(5), pp.408-422
09/13/2024
DOI: 10.37349/emd.2024.00066
url
https://doi.org/10.37349/emd.2024.00066View
Published (Version of record) Open Access

Abstract

Aim: Physical activity (PA) is increasingly used as a patient-centered means to treat and/or cope with pain and other symptomology resulting from clinical health conditions. Despite the increasing use of wearable sensors to track PA in healthy and patient cohorts, few algorithms are equally accurate in assessing sedentary and light PA as moderate and vigorous. Given that many older adults and patient cohorts are less active, there is a need for simple algorithms that are easily implemented and valid for the assessment of even low activity levels. Thus, the purpose of this study was to test a simple nonlinear modification to a validated linear algorithm for hip- and wrist-worn accelerometry to measure human PA energy expenditure. Methods: Triaxial accelerometers were worn on the wrist and hip during 14 standardized laboratory-based activities in 37 healthy adults across the lifespan [19–65 years, 19 females (F)]. Combined with previously reported energy expenditure data, linear and power equations transforming accelerations to estimates of oxygen consumption (VO2) were compared. Results: The nonlinear algorithm provided equally accurate measures of PA energy expenditure as linear approaches, with the added advantage of being able to estimate even low energy expenditure, a necessary outcome to differentiate sedentary and light PA. Further, the nonlinear algorithm produced a slightly better estimate of PA when using wrist than hip accelerometry. Conclusions: A simple nonlinear algorithm provides a better means for monitoring PA in populations with low activity levels due to its improved ability to discern sedentary from light PA. This is particularly relevant for older and clinical populations as even light levels of PA may provide therapeutic benefits.

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