Literature DB >> 19997000

Comparison of the ActiGraph 7164 and the ActiGraph GT1M during self-paced locomotion.

Sarah L Kozey1, John W Staudenmayer, Richard P Troiano, Patty S Freedson.   

Abstract

PURPOSE: This study compared the ActiGraph accelerometer model 7164 (AM1) with the ActiGraph GT1M (AM2) during self-paced locomotion.
METHODS: Participants (n = 116, aged 18-73 yr, mean body mass index = 26.1 kg x m(-2)) walked at self-selected slow, medium, and fast speeds around an indoor circular hallway (0.47 km). Both activity monitors were attached to a belt secured to the hip and simultaneously collected data in 60-s epochs. To compare differences between monitors, the average difference (bias) in count output and steps output was computed at each speed. Time spent in different activity intensities (light, moderate, and vigorous) based on the cut points of Freedson et al. was compared for each minute.
RESULTS: The mean +/- SD walking speed was 0.7 +/- 0.22 m x s(-1) for the slow speed, 1.3 +/- 0.17 m x s(-1) for medium, and 2.1 +/- 0.61 m x s(-1) for fast speeds. Ninety-five percent confidence intervals (95% CI) were used to determine significance. Across all speeds, step output was significantly higher for the AM1 (bias = 19.8%, 95% CI = -23.2% to -16.4%) because of the large differences in step output at slow speed. The count output from AM2 was a significantly higher (2.7%, 95% CI = 0.8%-4.7%) than that from AM1. Overall, 96.1% of the minutes were classified into the same MET intensity category by both monitors.
CONCLUSIONS: The step output between models was not comparable at slow speeds, and comparisons of step data collected with both models should be interpreted with caution. The count output from AM2 was slightly but significantly higher than that from AM1 during the self-paced locomotion, but this difference did not result in meaningful differences in activity intensity classifications. Thus, data collected with AM1 should be comparable to AM2 across studies for estimating habitual activity levels.

Entities:  

Mesh:

Year:  2010        PMID: 19997000      PMCID: PMC2893387          DOI: 10.1249/MSS.0b013e3181c29e90

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


  22 in total

Review 1.  Validity and reliability issues in objective monitoring of physical activity.

Authors:  D R Bassett
Journal:  Res Q Exerc Sport       Date:  2000-06       Impact factor: 2.500

2.  Estimation of energy expenditure using CSA accelerometers at hip and wrist sites.

Authors:  A M Swartz; S J Strath; D R Bassett; W L O'Brien; G A King; B E Ainsworth
Journal:  Med Sci Sports Exerc       Date:  2000-09       Impact factor: 5.411

3.  Validity of accelerometry for the assessment of moderate intensity physical activity in the field.

Authors:  D Hendelman; K Miller; C Baggett; E Debold; P Freedson
Journal:  Med Sci Sports Exerc       Date:  2000-09       Impact factor: 5.411

4.  Comparison of pedometer and accelerometer accuracy under controlled conditions.

Authors:  Guy C Le Masurier; Catrine Tudor-Locke
Journal:  Med Sci Sports Exerc       Date:  2003-05       Impact factor: 5.411

5.  Accelerometer use in physical activity: best practices and research recommendations.

Authors:  Dianne S Ward; Kelly R Evenson; Amber Vaughn; Anne Brown Rodgers; Richard P Troiano
Journal:  Med Sci Sports Exerc       Date:  2005-11       Impact factor: 5.411

Review 6.  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

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

Authors:  Scott E Crouter; Kurt G Clowers; David R Bassett
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8.  Calibration of the Computer Science and Applications, Inc. accelerometer.

Authors:  P S Freedson; E Melanson; J Sirard
Journal:  Med Sci Sports Exerc       Date:  1998-05       Impact factor: 5.411

9.  Comparison of pedometer and accelerometer measures of free-living physical activity.

Authors:  Catrine Tudor-Locke; Barbara E Ainsworth; Raymond W Thompson; Charles E Matthews
Journal:  Med Sci Sports Exerc       Date:  2002-12       Impact factor: 5.411

10.  Validity of the Computer Science and Applications, Inc. (CSA) activity monitor.

Authors:  E L Melanson; P S Freedson
Journal:  Med Sci Sports Exerc       Date:  1995-06       Impact factor: 5.411

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  41 in total

1.  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

2.  Effects of a randomized exercise trial on physical activity, psychological distress and quality of life in older adults.

Authors:  Elizabeth A Awick; Diane K Ehlers; Susan Aguiñaga; Ana M Daugherty; Arthur F Kramer; Edward McAuley
Journal:  Gen Hosp Psychiatry       Date:  2017-06-15       Impact factor: 3.238

3.  Sustained effects of physical activity on bone health: Iowa Bone Development Study.

Authors:  Shelby L Francis; Elena M Letuchy; Steven M Levy; Kathleen F Janz
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4.  Biomechanical examination of the 'plateau phenomenon' in ActiGraph vertical activity counts.

Authors:  Dinesh John; Ross Miller; Sarah Kozey-Keadle; Graham Caldwell; Patty Freedson
Journal:  Physiol Meas       Date:  2012-01-20       Impact factor: 2.833

5.  Breaks in sedentary time during childhood and adolescence: Iowa bone development study.

Authors:  Soyang Kwon; Trudy L Burns; Steven M Levy; Kathleen F Janz
Journal:  Med Sci Sports Exerc       Date:  2012-06       Impact factor: 5.411

6.  Neighborhood built environment and socioeconomic status in relation to physical activity, sedentary behavior, and weight status of adolescents.

Authors:  James F Sallis; Terry L Conway; Kelli L Cain; Jordan A Carlson; Lawrence D Frank; Jacqueline Kerr; Karen Glanz; James E Chapman; Brian E Saelens
Journal:  Prev Med       Date:  2018-02-09       Impact factor: 4.018

7.  Are Older Adults With Symptomatic Knee Osteoarthritis Less Active Than the General Population? Analysis From the Osteoarthritis Initiative and the National Health and Nutrition Examination Survey.

Authors:  Louise M Thoma; Dorothy Dunlop; Jing Song; Jungwha Lee; Catrine Tudor-Locke; Elroy J Aguiar; Hiral Master; Meredith B Christiansen; Daniel K White
Journal:  Arthritis Care Res (Hoboken)       Date:  2018-10       Impact factor: 4.794

8.  Physical activity and sedentary behavior among adults 60 years and older: New York City residents compared with a national sample.

Authors:  Kelly R Evenson; Kimberly B Morland; Fang Wen; Kathleen Scanlin
Journal:  J Aging Phys Act       Date:  2013-10-23       Impact factor: 1.961

9.  Pattern Analysis of Sedentary Behavior Change after a Walking Intervention.

Authors:  Ann M Swartz; Chi C Cho; Whitney A Welch; Michael E Widlansky; Hotaka Maeda; Scott J Strath
Journal:  Am J Health Behav       Date:  2018-05-01

10.  Physical activity, ambulation, and comorbidities in people with diabetes and lower-limb amputation.

Authors:  Roger J Paxton; Amanda M Murray; Jennifer E Stevens-Lapsley; Kyle A Sherk; Cory L Christiansen
Journal:  J Rehabil Res Dev       Date:  2016
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