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Cited by 222Physical Activity and HealthContext-Aware Activity Recognition SystemsCardiovascular and exercise physiology

A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations

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Lyden, Kate, Kozey, Sarah L., Staudenmeyer, John W., Freedson, Patty S.·2011·European Journal of Applied Physiology
DOI 10.1007/s00421-010-1639-8PMC PMC3432480
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Abstract

Numerous accelerometers and prediction methods are used to estimate energy expenditure (EE). Validation studies have been limited to small sample sizes in which participants complete a narrow range of activities and typically validate only one or two prediction models for one particular accelerometer. The purpose of this study was to evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Two hundred and seventy-seven participants completed an average of six treadmill (TRD) (1.34, 1.56, 2.23 ms(-1) each at 0 and 3% grade) and five self-paced activities of daily living (ADLs). EE estimates were compared with indirect calorimetry. Accelerometers were worn while EE was measured using a portable metabolic unit. To estimate EE, 4 ActiGraph prediction models were used, 5 Actical models, and 2 RT3 proprietary models. Across all activities, each equation underestimated EE (bias -0.1 to -1.4 METs and -0.5 to -1.3 kcal, respectively). For ADLs EE was underestimated by all prediction models (bias -0.2 to -2.0 and -0.2 to -2.8, respectively), while TRD activities were underestimated by seven equations, and overestimated by four equations (bias -0.8 to 0.2 METs and -0.4 to 0.5 kcal, respectively). Misclassification rates ranged from 21.7 (95% CI 20.4, 24.2%) to 34.3% (95% CI 32.3, 36.3%), with vigorous intensity activities being most often misclassified. Prediction equations did not yield accurate point estimates of EE across a broad range of activities nor were they accurate at classifying activities across a range of intensities (light <3 METs, moderate 3-5.99 METs, vigorous ≥ 6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE.

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  • Accelerometry alone largely registers walking, running and other horizontal movement, does not adequately capture resistance-based movements, and is expected to underestimate PALs when used alone.

    “Accelerometry largely registers walking, running and other horizontal movement, but does not adequately capture resistance-based movements where the torso does not move sufficiently, and so it is expected that accelerometry underestimates PALs when used alone [32].”

    From Physical Activity and Modernization among Bolivian Amerindians (2013) · cites this paper as [32]

  • The rate of decline in PALs among Dutch adults was 0.060 per decade for men and 0.023 per decade for women.

    “The rate of decline in PALs among Dutch adults was 0.060 per decade for men and 0.023 per decade for women (adapted from Table 1 from 32).”

    From Physical Activity and Modernization among Bolivian Amerindians (2013) · cites this paper as [32]

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Authors
Lyden, K., Kozey, S. L., Staudenmeyer, J. W., & Freedson, P. S.
Affiliations
University of Massachusetts Amherst (US)
Corresponding author
Lyden, Kate
ORCID
Venue
European Journal of Applied Physiology
Publisher
Springer Science and Business Media LLC
Published
2010-09-15
Volume
111
Issue
2
Pages
187-201
ISSN
  • 0020-9376 (ISSN-L)
  • 1439-6319
  • 1439-6327
DOI
10.1007/s00421-010-1639-8
PMC
PMC3432480
OpenAlex
W2092417862
Cited by
222
References
25
Citations by year
2026: 4 · 2025: 5 · 2024: 10 · 2023: 7 · 2022: 11 · 2021: 13 · 2020: 14 · 2019: 25 · 2018: 20 · 2017: 17 · 2016: 18 · 2015: 15 · 2014: 25 · 2013: 18 · 2012: 12
FWCI
8.23
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • European Journal of Applied Physiology (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • Scholarworks (University of Massachusetts Amherst) (submittedVersion)
  • ScholarWorks@UMassAmherst (University of Massachusetts Amherst) (submittedVersion)
Funders
  • National Institutes of Health
  • National Cancer Institute (CA121005, R01CA121005)
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
equation validation, ActiGraph, Actical, reverse T3, activities of daily living, treadmill walking, activity intensity classification, underestimation of energy expenditure, indirect calorimetry
MeSH terms
Acceleration, Adult, Basal Metabolism, Endocrinology, Energy Metabolism, Exercise Test, Female, Humans, Male, Middle Aged, Prognosis, Models, Statistical, Mathematical Concepts, Young Adult, Actigraphy

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