Literature DB >> 24496118

Cross-validation of waist-worn GENEA accelerometer cut-points.

Whitney A Welch1, David R Bassett, Patty S Freedson, Dinesh John, Jeremy A Steeves, Scott A Conger, Tyrone G Ceaser, Cheryl A Howe, Jeffer E Sasaki.   

Abstract

PURPOSE: The purpose of this study was to determine the classification accuracy of the waist gravity estimator of normal everyday activity (GENEA) cut-points developed by Esliger et al. for predicting intensity categories across a range of lifestyle activities.
METHODS: Each participant performed one of two routines, consisting of seven lifestyle activities (home/office, ambulatory, and sport). The GENEA was worn on the right waist, and oxygen uptake was continuously measured using the Oxycon mobile. A one-way chi-squared test was used to determine the classification accuracy of the GENEA cut-points. Cross-tabulation tables provided information on under- and overestimations, and sensitivity and specificity analyses of the waist cut-points were also performed.
RESULTS: Spearman rank order correlation for the GENEA gravity-subtracted signal vector magnitude and Oxycon mobile MET values was 0.73. For all activities combined, the GENEA accurately predicted intensity classification 55.3% of the time, and it increased to 58.3% when stationary cycling was removed from the analysis. The sensitivity of the cut-points for the four intensity categories ranged from 0.244 to 0.958, and the specificity ranged from 0.576 to 0.943.
CONCLUSION: In this cross-validation study, the proposed GENEA cut-points had a low overall accuracy rate for classifying intensity (55.3%) when engaging in 14 different lifestyle activities.

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Mesh:

Year:  2014        PMID: 24496118      PMCID: PMC4119860          DOI: 10.1249/MSS.0000000000000283

Source DB:  PubMed          Journal:  Med Sci Sports Exerc        ISSN: 0195-9131            Impact factor:   5.411


  18 in total

1.  Predicting energy expenditure of physical activity using hip- and wrist-worn accelerometers.

Authors:  Kong Y Chen; Sari A Acra; Karen Majchrzak; Candice L Donahue; Lemont Baker; Linda Clemens; Ming Sun; Maciej S Buchowski
Journal:  Diabetes Technol Ther       Date:  2003       Impact factor: 6.118

2.  Self-reported and objectively measured activity related to biomarkers using NHANES.

Authors:  Audie A Atienza; Richard P Moser; Frank Perna; Kevin Dodd; Rachel Ballard-Barbash; Richard P Troiano; David Berrigan
Journal:  Med Sci Sports Exerc       Date:  2011-05       Impact factor: 5.411

Review 3.  The technology of accelerometry-based activity monitors: current and future.

Authors:  Kong Y Chen; David R Bassett
Journal:  Med Sci Sports Exerc       Date:  2005-11       Impact factor: 5.411

4.  Validation of wearable monitors for assessing sedentary behavior.

Authors:  Sarah Kozey-Keadle; Amanda Libertine; Kate Lyden; John Staudenmayer; Patty S Freedson
Journal:  Med Sci Sports Exerc       Date:  2011-08       Impact factor: 5.411

5.  Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample.

Authors:  Patty S Freedson; Kate Lyden; Sarah Kozey-Keadle; John Staudenmayer
Journal:  J Appl Physiol (1985)       Date:  2011-09-01

Review 6.  Best practices for using physical activity monitors in population-based research.

Authors:  Charles E Matthews; Maria Hagströmer; David M Pober; Heather R Bowles
Journal:  Med Sci Sports Exerc       Date:  2012-01       Impact factor: 5.411

7.  Assessment of physical activity using wearable monitors: recommendations for monitor calibration and use in the field.

Authors:  Patty Freedson; Heather R Bowles; Richard Troiano; William Haskell
Journal:  Med Sci Sports Exerc       Date:  2012-01       Impact factor: 5.411

8.  Validation of the GENEA Accelerometer.

Authors:  Dale W Esliger; Ann V Rowlands; Tina L Hurst; Michael Catt; Peter Murray; Roger G Eston
Journal:  Med Sci Sports Exerc       Date:  2011-06       Impact factor: 5.411

Review 9.  Assessing physical activity using wearable monitors: measures of physical activity.

Authors:  Nancy F Butte; Ulf Ekelund; Klaas R Westerterp
Journal:  Med Sci Sports Exerc       Date:  2012-01       Impact factor: 5.411

10.  How many days of monitoring predict physical activity and sedentary behaviour in older adults?

Authors:  Teresa L Hart; Ann M Swartz; Susan E Cashin; Scott J Strath
Journal:  Int J Behav Nutr Phys Act       Date:  2011-06-16       Impact factor: 6.457

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  4 in total

1.  Physical activity measured by accelerometry in paediatric and young adult patients with inflammatory bowel disease.

Authors:  Ken Lund; Michael Due Larsen; Torben Knudsen; Jens Kjeldsen; Rasmus Gaardskær Nielsen; Søren Brage; Bente Mertz Nørgård
Journal:  BMC Gastroenterol       Date:  2022-06-07       Impact factor: 2.847

2.  Population-referenced percentiles for waist-worn accelerometer-derived total activity counts in U.S. youth: 2003 - 2006 NHANES.

Authors:  Dana L Wolff-Hughes; David R Bassett; Eugene C Fitzhugh
Journal:  PLoS One       Date:  2014-12-22       Impact factor: 3.240

3.  Exercise response in Parkinson's disease: insights from a cross-sectional comparison with sedentary controls and a per-protocol analysis of a randomised controlled trial.

Authors:  Foteini Mavrommati; Johnny Collett; Marloes Franssen; Andy Meaney; Claire Sexton; Andrea Dennis-West; Jill F Betts; Hooshang Izadi; Marko Bogdanovic; Martin Tims; Andrew Farmer; Helen Dawes
Journal:  BMJ Open       Date:  2017-12-26       Impact factor: 2.692

4.  Establishing cut-points for physical activity classification using triaxial accelerometer in middle-aged recreational marathoners.

Authors:  Carlos Hernando; Carla Hernando; Eladio Joaquin Collado; Nayara Panizo; Ignacio Martinez-Navarro; Barbara Hernando
Journal:  PLoS One       Date:  2018-08-29       Impact factor: 3.240

  4 in total

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