Literature DB >> 17761786

A new 2-regression model for the Actical accelerometer.

S E Crouter1, D R Bassett.   

Abstract

OBJECTIVE: The objective of this study was to develop a new 2-regression model relating Actical activity counts to METs.
METHODS: Forty-eight participants (mean (SD) age 35 (11.4) years) performed 10 min bouts of various activities ranging from sedentary behaviours to vigorous physical activities. Eighteen activities were split into three routines with each routine being performed by 20 individuals. Forty-five routines were randomly selected for the development of a new 2-regression model and 15 tests were used to cross-validate the new 2-regression model and compare it against existing equations. During each routine, the participant wore an Actical accelerometer on the hip and oxygen consumption was simultaneously measured by a portable metabolic system. The coefficient of variation (CV) of four consecutive 15 s epochs was calculated for each minute. For each activity, the average CV and the counts min(-1) were calculated for minutes 4-9. If the CV was < or =13% a walk/run regression equation was used and if the CV was >13% a lifestyle/leisure time physical activity regression was used.
RESULTS: An exponential regression line (R(2) = 0.912; standard error of the estimate (SEE) = 0.149) was used for activities with a CV< or =13%, and a cubic regression line (R(2) = 0.884, SEE = 0.804) was used for activities with a CV>13%. In the cross-validation group the mean estimates, using the new 2-regression model with an inactivity threshold, were within 0.56 METs of measured METs for each of the activities performed (p> or =0.05), except cycling (p<0.05).
CONCLUSION: For most activities examined the new 2-regression model predicted METs more accurately than currently available equations for the Actical accelerometer.

Entities:  

Mesh:

Year:  2007        PMID: 17761786     DOI: 10.1136/bjsm.2006.033399

Source DB:  PubMed          Journal:  Br J Sports Med        ISSN: 0306-3674            Impact factor:   13.800


  28 in total

1.  Use of a two-regression model for estimating energy expenditure in children.

Authors:  Scott E Crouter; Magdalene Horton; David R Bassett
Journal:  Med Sci Sports Exerc       Date:  2012-06       Impact factor: 5.411

2.  A comparison of energy expenditure estimates from the Actiheart and Actical physical activity monitors during low intensity activities, walking, and jogging.

Authors:  David K Spierer; Marshall Hagins; Andrew Rundle; Evangelos Pappas
Journal:  Eur J Appl Physiol       Date:  2010-10-17       Impact factor: 3.078

3.  Reliability and validity of CHAMPS self-reported sedentary-to-vigorous intensity physical activity in older adults.

Authors:  Eric B Hekler; Matthew P Buman; William L Haskell; Terry L Conway; Kelli L Cain; James F Sallis; Brian E Saelens; Lawrence D Frank; Jacqueline Kerr; Abby C King
Journal:  J Phys Act Health       Date:  2012-02

4.  Validity of ActiGraph 2-regression model, Matthews cut-points, and NHANES cut-points for assessing free-living physical activity.

Authors:  Scott E Crouter; Diane M DellaValle; Jere D Haas; Edward A Frongillo; David R Bassett
Journal:  J Phys Act Health       Date:  2012-09-11

5.  Sustained and shorter bouts of physical activity are related to cardiovascular health.

Authors:  Nicole L Glazer; Asya Lyass; Dale W Esliger; Susan J Blease; Patty S Freedson; Joseph M Massaro; Joanne M Murabito; Ramachandran S Vasan
Journal:  Med Sci Sports Exerc       Date:  2013-01       Impact factor: 5.411

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

Authors:  Kate Lyden; Sarah L Kozey; John W Staudenmeyer; Patty S Freedson
Journal:  Eur J Appl Physiol       Date:  2010-09-15       Impact factor: 3.078

7.  Validity of the Actical for estimating free-living physical activity.

Authors:  Scott E Crouter; Diane M Dellavalle; Magdalene Horton; Jere D Haas; Edward A Frongillo; David R Bassett
Journal:  Eur J Appl Physiol       Date:  2010-12-12       Impact factor: 3.078

8.  Ankle Accelerometry for Assessing Physical Activity Among Adolescent Girls: Threshold Determination, Validity, Reliability, and Feasibility.

Authors:  Erin R Hager; Margarita S Treuth; Candice Gormely; LaShawna Epps; Soren Snitker; Maureen M Black
Journal:  Res Q Exerc Sport       Date:  2015-08-19       Impact factor: 2.500

9.  Validation of the Actical Accelerometer in Multiethnic Preschoolers: The Children's Healthy Living (CHL) Program.

Authors:  Reynolette Ettienne; Claudio R Nigg; Fenfang Li; Yuhua Su; Katalina McGlone; Bret Luick; Alvin Tachibana; Christina Carran; Jobel Mercado; Rachel Novotny
Journal:  Hawaii J Med Public Health       Date:  2016-04

10.  An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer.

Authors:  John Staudenmayer; David Pober; Scott Crouter; David Bassett; Patty Freedson
Journal:  J Appl Physiol (1985)       Date:  2009-07-30
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