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Cited by 170Physical Activity and HealthContext-Aware Activity Recognition SystemsCardiovascular and exercise physiology

The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.

Comparison of PAEE from Combined and Separate Heart Rate and Movement Models in Children

Corder, Kirsten, Brage, Søren, Wareham, Nicholas J., Ekelund, Ulf·2005·Medicine & Science in Sports & Exercise
DOI 10.1249/01.mss.0000176466.78408.cc
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Abstract

PURPOSE: Accurate measurement of physical activity in children is a challenge. Combining physiological (e.g., heart rate (HR)) and body movement registration (e.g., accelerometry) may overcome limitations with either method used alone. This study aimed to compare the estimated physical activity energy expenditure (PAEE) from hip- and ankle-mounted MTI Actigraphs, a hip-mounted Actical, and a new combined HR and movement sensor, the Actiheart (Cambridge Neurotechnology, Papworth, UK). METHODS: Resting EE and submaximal EE (treadmill walking and running) were measured in 39 children (13.2 +/- 0.3 yr) by indirect calorimetry during a progressive treadmill exercise bout. Associations between monitor outputs (activity counts, HR, and activity counts + HR) and the criterion were examined by linear regression models. The agreement between measured and predicted PAEE was examined by modified Bland-Altman plots in a subsample of participants. RESULTS: The combined Actiheart model (activity counts + HR) had the strongest relationship with PAEE (R2 = 0.86), compared with those from the single-measure models (R2 = 0.69 and 0.82 for the activity model and HR model). The explained variances from the other activity monitors were lower (R2 = 0.50, 0.37, and 0.67) for the hip MTI, ankle MTI, and Actical, respectively. In cross-validation analyses, significant correlations were observed between estimation errors of the methods with the criterion (r = 0.49 to 0.90) in all models using only activity counts indicating a large systematic error. The HR and combined models indicated less systematic error (r = 0.41 and 0.33, respectively). CONCLUSIONS: Of the techniques considered, combined HR and movement sensing is the most valid for estimating PAEE in children during treadmill walking and running, compared with movement or HR alone. It also has the lowest level of systematic error.

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Metadata

Authors
Corder, K., Brage, S., Wareham, N. J., & Ekelund, U.
Affiliations
  • University of Cambridge (GB)
  • MRC Elsie Widdowson Laboratory (GB)
Corresponding author
Ekelund, Ulf
ORCID
Venue
Medicine & Science in Sports & Exercise
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2005-10
Volume
37
Issue
10
Pages
1761-1767
ISSN
0195-9131 (ISSN-L)
DOI
10.1249/01.mss.0000176466.78408.cc
OpenAlex
W1995743593
Cited by
170
References
39
Citations by year
2025: 1 · 2024: 3 · 2023: 5 · 2022: 3 · 2021: 5 · 2020: 4 · 2019: 7 · 2018: 4 · 2017: 9 · 2016: 9 · 2015: 8 · 2014: 13 · 2013: 22 · 2012: 12
FWCI
4.41
Type
journal article
Language
en
Open access
closed
Locations
  • Medicine & Science in Sports & Exercise (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Health Sciences › Medicine › Physiology (medical) › Cardiovascular and exercise physiology
Keywords
physical activity energy expenditure, children, heart rate monitoring, accelerometry, Actiheart, ActiGraph, Actical, treadmill walking and running, measurement validity, systematic errors
MeSH terms
Adolescent, Calorimetry, Indirect, Child, Energy Metabolism, Exercise Test, Female, Heart Rate, Humans, Male, Movement, Running, Exercise, Walking

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