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Article: Comparisons of prediction equations for estimating energy expenditure in youth

TitleComparisons of prediction equations for estimating energy expenditure in youth
Authors
KeywordsPhysical fitness
Validation studies
Public health
Children
Calibration
Accelerometer
Issue Date2016
Citation
Journal of Science and Medicine in Sport, 2016, v. 19, n. 1, p. 35-40 How to Cite?
Abstract© 2014 Sports Medicine Australia. Objectives: The purpose of this study was to compare the validity of Actigraph 2-regression models (2RM) and 1-regression models (1RM) for estimation of EE in children. Design: The study used a cross-sectional design with criterion estimates from a metabolic cart. Methods: A total of 59 children (7-13yrs) performed 12 activities (randomly selected from a set of 24 activities) for 5min each, while being concurrently measured with an Actigraph GT3X and indirect calorimetry. METRMR(MET considering one's resting metabolic rate) for the GT3X was estimated applying 2RM with vector magnitude (VM2RM) and vertical axis (VA2RM), and four standard 1RMs. The validity of the 2RMs and 1RMs was evaluated using 95% equivalence testing and mean absolute percent error (MAPE). Results: For the group-level comparison, equivalence testing revealed that the 90% confidence intervals for all 2RMs and 1RMs were outside of the equivalence zone (range: 3.63, 4.43) for indirect calorimetry. When comparing the individual activities, VM2RM produced smaller MAPEs (range: 14.5-45.3%) than VA2RM (range, 15.5-58.1%) and 1RMs (range, 14.5-75.1%) for most of the light and moderate activities. Conclusions: None of the 2RMs and 1RMs were equivalent to indirect calorimetry. The 2RMs showed smaller individual-level errors than the 1RMs.
Persistent Identifierhttp://hdl.handle.net/10722/267017
ISSN
2023 Impact Factor: 3.0
2023 SCImago Journal Rankings: 1.222
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorKim, Youngwon-
dc.contributor.authorCrouter, Scott E.-
dc.contributor.authorLee, Jung Min-
dc.contributor.authorDixon, Phillip M.-
dc.contributor.authorGaesser, Glenn A.-
dc.contributor.authorWelk, Gregory J.-
dc.date.accessioned2019-01-31T07:20:16Z-
dc.date.available2019-01-31T07:20:16Z-
dc.date.issued2016-
dc.identifier.citationJournal of Science and Medicine in Sport, 2016, v. 19, n. 1, p. 35-40-
dc.identifier.issn1440-2440-
dc.identifier.urihttp://hdl.handle.net/10722/267017-
dc.description.abstract© 2014 Sports Medicine Australia. Objectives: The purpose of this study was to compare the validity of Actigraph 2-regression models (2RM) and 1-regression models (1RM) for estimation of EE in children. Design: The study used a cross-sectional design with criterion estimates from a metabolic cart. Methods: A total of 59 children (7-13yrs) performed 12 activities (randomly selected from a set of 24 activities) for 5min each, while being concurrently measured with an Actigraph GT3X and indirect calorimetry. METRMR(MET considering one's resting metabolic rate) for the GT3X was estimated applying 2RM with vector magnitude (VM2RM) and vertical axis (VA2RM), and four standard 1RMs. The validity of the 2RMs and 1RMs was evaluated using 95% equivalence testing and mean absolute percent error (MAPE). Results: For the group-level comparison, equivalence testing revealed that the 90% confidence intervals for all 2RMs and 1RMs were outside of the equivalence zone (range: 3.63, 4.43) for indirect calorimetry. When comparing the individual activities, VM2RM produced smaller MAPEs (range: 14.5-45.3%) than VA2RM (range, 15.5-58.1%) and 1RMs (range, 14.5-75.1%) for most of the light and moderate activities. Conclusions: None of the 2RMs and 1RMs were equivalent to indirect calorimetry. The 2RMs showed smaller individual-level errors than the 1RMs.-
dc.languageeng-
dc.relation.ispartofJournal of Science and Medicine in Sport-
dc.subjectPhysical fitness-
dc.subjectValidation studies-
dc.subjectPublic health-
dc.subjectChildren-
dc.subjectCalibration-
dc.subjectAccelerometer-
dc.titleComparisons of prediction equations for estimating energy expenditure in youth-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jsams.2014.10.002-
dc.identifier.pmid25459235-
dc.identifier.scopuseid_2-s2.0-84948119782-
dc.identifier.volume19-
dc.identifier.issue1-
dc.identifier.spage35-
dc.identifier.epage40-
dc.identifier.eissn1878-1861-
dc.identifier.isiWOS:000330469702281-
dc.identifier.issnl1878-1861-

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