Literature DB >> 20962689

Effects of body mass index and tilt angle on output of two wearable activity monitors.

Yuri Feito1, David R Bassett, Brian Tyo, Dixie L Thompson.   

Abstract

UNLABELLED: Accelerometer-based activity monitors have been used to provide objective measures of physical activity and energy expenditure (EE) in free-living individuals. However, output from these devices has not been compared among normal, overweight, and obese individuals.
PURPOSE: The purpose of this study was to examine the effects of body mass index (BMI) and device tilt angle on activity counts recorded by wearable monitors in a controlled laboratory setting. A secondary aim was to examine the effects of these variables on estimated EE.
METHODS: Seventy-one healthy adults wore an Actical and an ActiGraph GT1M on the right and left hip, respectively, while walking at 40, 67, and 94 m·min. EE was measured by indirect calorimetry and compared with estimated values using published equations. Three-way repeated-measures ANOVA were used to examine differences in outcome variables (activity counts and EE) between speeds, BMI, and tilt angle for each device.
RESULTS: No significant differences in activity counts were observed among BMI categories for either the Actical or ActiGraph (P>0.05). For the Actical, however, among those with an absolute tilt angle <10°, the obese group recorded higher activity counts than the normal weight group (P=0.01). Using the Heil two-regression model, the Actical overestimated EE by up to 35% at the intermediate speed and up to 12% at the fastest speed (P<0.001). The Freedson METs regression equation yielded closer estimates of EE than the Freedson kilocalorie regression equation.
CONCLUSIONS: Our findings indicate that the Actical has limitations when comparing individuals with varying BMI and tilt angles in a controlled laboratory environment. The ActiGraph seems to be a more suitable device for making these comparisons.
© 2011 by the American College of Sports Medicine

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Year:  2011        PMID: 20962689     DOI: 10.1249/MSS.0b013e3181fefd40

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


  13 in total

1.  Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure.

Authors:  John B Correa; John W Apolzan; Desti N Shepard; Daniel P Heil; Jennifer C Rood; Corby K Martin
Journal:  Appl Physiol Nutr Metab       Date:  2016-03-14       Impact factor: 2.665

2.  Self-reported and objectively measured physical activity in adults with systemic lupus erythematosus.

Authors:  Grace E Ahn; Joan S Chmiel; Dorothy D Dunlop; Irene B Helenowski; Pamela A Semanik; Jing Song; Barbara Ainsworth; Rowland W Chang; Rosalind Ramsey-Goldman
Journal:  Arthritis Care Res (Hoboken)       Date:  2015-05       Impact factor: 4.794

3.  Psychometric properties of the modified RESIDE physical activity questionnaire among low-income overweight women.

Authors:  Sydney A Jones; Kelly R Evenson; Larry F Johnston; Stewart G Trost; Carmen Samuel-Hodge; David A Jewell; Jennifer L Kraschnewski; Thomas C Keyserling
Journal:  J Sci Med Sport       Date:  2014-01-01       Impact factor: 4.319

4.  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

5.  Using Activity Monitors to Measure Sit-to-Stand Transitions in Overweight/Obese Youth.

Authors:  Tarrah Mitchell; Kelsey Borner; Jonathan Finch; Jacqueline Kerr; Jordan A Carlson
Journal:  Med Sci Sports Exerc       Date:  2017-08       Impact factor: 5.411

6.  Physical activity monitoring in extremely obese adolescents from the Teen-LABORATORIES study.

Authors:  Renee M Jeffreys; Thomas H Inge; Todd M Jenkins; Wendy C King; Vedran Oruc; Andrew D Douglas; Molly S Bray
Journal:  J Phys Act Health       Date:  2014-09-10

7.  Translation equations to compare ActiGraph GT3X and Actical accelerometers activity counts.

Authors:  Leon Straker; Amity Campbell
Journal:  BMC Med Res Methodol       Date:  2012-04-20       Impact factor: 4.615

8.  The use of individual cut points from treadmill walking to assess free-living moderate to vigorous physical activity in obese subjects by accelerometry: is it useful?

Authors:  Eivind Aadland; Jostein Steene-Johannessen
Journal:  BMC Med Res Methodol       Date:  2012-11-15       Impact factor: 4.615

9.  Treadmill Calibration of the Actigraph GT1M in Young-to-Middle-Aged Obese-to-Severely Obese Subjects.

Authors:  Eivind Aadland; Sigmund Alfred Anderssen
Journal:  J Obes       Date:  2012-11-01

10.  Actical Accelerometry Cut-Points for Quantifying Levels of Exertion: Comparing Normal and Overweight Adults.

Authors:  Jamie Giffuni; Robert G McMurray; Todd Schwartz; Diane Berry
Journal:  Int J Exerc Sci       Date:  2012-04-15
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