Literature DB >> 21153659

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

Scott E Crouter1, Diane M Dellavalle, Magdalene Horton, Jere D Haas, Edward A Frongillo, David R Bassett.   

Abstract

The purpose of this study was twofold: (1) develop a refined 2-regression model for the Actical which predicts METs every 15 s, and (2) compare the refined and 2008 Crouter 2-regression models and the Klippel and Heil equations during free-living activity. To develop the refined 2-regression model, 48 participants (mean ± SD; age 35 ± 11.4 years) performed 10-min bouts of various activities ranging from sedentary to vigorous intensity. An Actical accelerometer was worn on the left hip, and a Cosmed K4b(2) was used to measure oxygen consumption. For the free-living measurements, 29 participants (age, 38 ± 11.7 years; BMI, 25.0 ± 4.6 kg m(-2)) were monitored for approximately 6 h during work (N = 23) or leisure time (N = 9) while wearing an Actical and Cosmed. Actical prediction equations were compared against the Cosmed for METs and time spent in sedentary behaviors, light physical activity (LPA), moderate PA (MPA), vigorous PA (VPA), and moderate and vigorous PA (MVPA). The refined 2-regression model developed used an exponential regression equation and a linear equation to predict METs every 15 s for walking/running and intermittent lifestyle activities, respectively. Based on the free-living measurement, the refined 2-regression model was the only method that was not significantly different from the Cosmed for estimating time spent in sedentary behaviors, LPA, and MVPA (P > 0.05). On average, compared to the Cosmed, the refined 2-regression model and the Klippel and Heil equations had similar mean errors for average METs.

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Year:  2010        PMID: 21153659      PMCID: PMC3110990          DOI: 10.1007/s00421-010-1758-2

Source DB:  PubMed          Journal:  Eur J Appl Physiol        ISSN: 1439-6319            Impact factor:   3.078


  18 in total

1.  Validation of the COSMED K4 b2 portable metabolic system.

Authors:  J E McLaughlin; G A King; E T Howley; D R Bassett; B E Ainsworth
Journal:  Int J Sports Med       Date:  2001-05       Impact factor: 3.118

2.  Comparison of MTI accelerometer cut-points for predicting time spent in physical activity.

Authors:  S J Strath; D R Bassett; A M Swartz
Journal:  Int J Sports Med       Date:  2003-05       Impact factor: 3.118

3.  Prediction of activity energy expenditure using accelerometers in children.

Authors:  Maurice R Puyau; Anne L Adolph; Firoz A Vohra; Issa Zakeri; Nancy F Butte
Journal:  Med Sci Sports Exerc       Date:  2004-09       Impact factor: 5.411

4.  Validation and calibration of the Actical accelerometer in preschool children.

Authors:  Karin A Pfeiffer; Kerry L McIver; Marsha Dowda; Maria J C A Almeida; Russell R Pate
Journal:  Med Sci Sports Exerc       Date:  2006-01       Impact factor: 5.411

5.  A novel method for using accelerometer data to predict energy expenditure.

Authors:  Scott E Crouter; Kurt G Clowers; David R Bassett
Journal:  J Appl Physiol (1985)       Date:  2005-12-01

6.  Predicting activity energy expenditure using the Actical activity monitor.

Authors:  Daniel P Heil
Journal:  Res Q Exerc Sport       Date:  2006-03       Impact factor: 2.500

7.  Metabolic equivalent: one size does not fit all.

Authors:  Nuala M Byrne; Andrew P Hills; Gary R Hunter; Roland L Weinsier; Yves Schutz
Journal:  J Appl Physiol (1985)       Date:  2005-04-14

8.  Estimating energy expenditure using accelerometers.

Authors:  Scott E Crouter; James R Churilla; David R Bassett
Journal:  Eur J Appl Physiol       Date:  2006-10-21       Impact factor: 3.078

9.  Comparison of PAEE from combined and separate heart rate and movement models in children.

Authors:  Kirsten Corder; Søren Brage; Nicholas J Wareham; Ulf Ekelund
Journal:  Med Sci Sports Exerc       Date:  2005-10       Impact factor: 5.411

10.  Physical activity in the United States measured by accelerometer.

Authors:  Richard P Troiano; David Berrigan; Kevin W Dodd; Louise C Mâsse; Timothy Tilert; Margaret McDowell
Journal:  Med Sci Sports Exerc       Date:  2008-01       Impact factor: 5.411

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  18 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.  Accelerometer Physical Activity is Associated with Greater Gray Matter Volumes in Older Adults Without Dementia or Mild Cognitive Impairment.

Authors:  Shannon Halloway; Konstantinos Arfanakis; JoEllen Wilbur; Michael E Schoeny; Susan J Pressler
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  2019-09-15       Impact factor: 4.077

3.  Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.

Authors:  Thomas Beltrame; Robert Amelard; Alexander Wong; Richard L Hughson
Journal:  J Appl Physiol (1985)       Date:  2017-06-08

4.  Increased de novo lipogenesis is a distinct characteristic of individuals with nonalcoholic fatty liver disease.

Authors:  Jennifer E Lambert; Maria A Ramos-Roman; Jeffrey D Browning; Elizabeth J Parks
Journal:  Gastroenterology       Date:  2013-12-04       Impact factor: 22.682

5.  Palmitoleic acid is elevated in fatty liver disease and reflects hepatic lipogenesis.

Authors:  Joseph J Lee; Jennifer E Lambert; Yelena Hovhannisyan; Maria A Ramos-Roman; Justin R Trombold; David A Wagner; Elizabeth J Parks
Journal:  Am J Clin Nutr       Date:  2014-11-19       Impact factor: 7.045

6.  Accelerometer adherence and performance in a cohort study of US Hispanic adults.

Authors:  Kelly R Evenson; Daniela Sotres-Alvarez; Y U Deng; Simon J Marshall; Carmen R Isasi; Dale W Esliger; Sonia Davis
Journal:  Med Sci Sports Exerc       Date:  2015-04       Impact factor: 5.411

7.  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

8.  Changes in physical activity among postpartum overweight and obese women: results from the KAN-DO Study.

Authors:  Kelly R Evenson; Rebecca J N Brouwer; Truls Østbye
Journal:  Women Health       Date:  2013

9.  Validation of the mywellness key in walking and running speeds.

Authors:  Marco Bergamin; Andrea Ermolao; John C Sieverdes; Marco Zaccaria; Silvano Zanuso
Journal:  J Sports Sci Med       Date:  2012-03-01       Impact factor: 2.988

Review 10.  Validity of activity monitors in health and chronic disease: a systematic review.

Authors:  Hans Van Remoortel; Santiago Giavedoni; Yogini Raste; Chris Burtin; Zafeiris Louvaris; Elena Gimeno-Santos; Daniel Langer; Alastair Glendenning; Nicholas S Hopkinson; Ioannis Vogiatzis; Barry T Peterson; Frederick Wilson; Bridget Mann; Roberto Rabinovich; Milo A Puhan; Thierry Troosters
Journal:  Int J Behav Nutr Phys Act       Date:  2012-07-09       Impact factor: 6.457

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