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Cited by 80Physical Activity and HealthObesity, Physical Activity, DietContext-Aware Activity Recognition Systems

Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry

Zakeri, Issa, Adolph, Anne L., Puyau, Maurice R., Vohra, Firoz A., Butte, Nancy F.·2008·Journal of Applied Physiology
DOI 10.1152/japplphysiol.01163.2007
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Abstract

Accurate estimation of energy expenditure (EE) in children and adolescents is required for a better understanding of physiological, behavioral, and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models, which account for correlation structure of repeated observations on the same individual, may be advantageous for prediction of EE. CSTS models for prediction of minute-by-minute EE and, hence, total EE (TEE) from heart rate (HR), physical activity (PA) measured by accelerometry, and observable subject variables were developed in 109 children and adolescents by use of Actiheart and 24-h room respiration calorimetry. CSTS models based on HR, PA, time-invariant covariates, and interactions were developed. These dynamic models involve lagged and lead values of HR and lagged values of PA for better description of the series of minute-by-minute EE. CSTS models with random intercepts and random slopes were investigated. For comparison, likelihood ratio tests were used. Log likelihood increased substantially when random slopes for HR and PA were added. The population-specific model uses HR and 1- and 2-min lagged and lead values of HR, HR 2, and PA and 1- and 2-min lagged values of PA, PA 2, age, age 2, sex, weight, height, minimum HR, sitting HR, HR × height, HR × weight, HR × age, PA × weight, and PA × sex interactions ( P < 0.001). Prediction error for TEE was 0.9 ± 10.3% (mean ± SD). Errors were not correlated with age, weight, height, or body mass index. CSTS modeling provides a useful predictive model for EE and, hence, TEE in children and adolescents on the basis of HR and PA and other observable explanatory subject characteristics of age, sex, weight, and height.

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Authors
Zakeri, I., Adolph, A. L., Puyau, M. R., Vohra, F. A., & Butte, N. F.
Affiliations
United States Department of Agriculture (US)
ORCID
Venue
Journal of Applied Physiology
Publisher
American Physiological Society
Published
2008-06
Volume
104
Issue
6
Pages
1665-1673
ISSN
  • 0161-7567 (ISSN-L)
  • 8750-7587
  • 1522-1601
DOI
10.1152/japplphysiol.01163.2007
OpenAlex
W1985857846
Cited by
80
References
40
Citations by year
2026: 2 · 2025: 3 · 2024: 1 · 2023: 4 · 2022: 5 · 2021: 1 · 2020: 2 · 2019: 5 · 2018: 2 · 2017: 6 · 2016: 4 · 2015: 7 · 2014: 4 · 2013: 4 · 2012: 6
FWCI
5.19
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Applied Physiology (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK074387, DK-74387)
Topics
  • Health Sciences › Medicine › Physiology › Physical Activity and Health
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords
energy expenditure estimation, total energy expenditure, heart rate, accelerometry, physical activity, children and adolescents, energy balance, Actiheart, prediction error
MeSH terms
Acceleration, Adolescent, Age Factors, Body Height, Body Weight, Calorimetry, Indirect, Child, Energy Metabolism, Female, Heart Rate, Humans, Male, Models, Cardiovascular, Sex Factors, Time Factors, Reproducibility of Results, Exercise, Electrocardiography, Ambulatory, Likelihood Functions, Monitoring, Ambulatory

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