Literature DB >> 27031744

Comparison of Sedentary Estimates between activPAL and Hip- and Wrist-Worn ActiGraph.

Annemarie Koster1, Eric J Shiroma2, Paolo Caserotti3, Charles E Matthews4, Kong Y Chen5, Nancy W Glynn6, Tamara B Harris2.   

Abstract

PURPOSE: Sedentary behavior is an emerging independent health risk factor. The accuracy of measuring sedentary time using accelerometers may depend on the wear location. This study in older adults evaluated the accuracy of various hip- and wrist-worn ActiGraph accelerometer cutoff points to define sedentary time using the activPAL as the reference method.
METHODS: Data from 62 adults (mean age, 78.4 yr) of the Aging Research Evaluating Accelerometry study were used. Participants simultaneously wore an activPAL accelerometer on the thigh and ActiGraph accelerometers on the hip, dominant, and nondominant wrist for 7 d in a free-living environment. Using the activPAL as the reference criteria, we compared classification of sedentary time to hip-worn and wrist-worn ActiGraph accelerometers over a range of cutoff points for both 60-s and 15-s epochs.
RESULTS: The optimal cutoff point for the hip vertical axis was <22 counts per minute with an area under the curve (AUC) of 0.85; the optimal hip vector magnitude cutoff point was <174 counts per minute with an AUC of 0.89. For the dominant wrist, the optimal vector magnitude cutoff point to define sedentary time was <2303 counts per minute (AUC, 0.86) and for the nondominant wrist <1853 counts per minute (AUC, 0.86). The optimal 15-s cutoff points resulted in lower agreements compared with activPAL.
CONCLUSIONS: Hip- and wrist-worn ActiGraph data may be used to define sedentary time with a moderate to high accuracy when compared with activPAL. The observed optimal cutoff point for hip vertical axis <22 counts per minute is substantially lower than the standard <100 counts per minute. It is unknown how these optimal cutoff points perform in different populations. Results on an individual basis should therefore be interpreted with caution.

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Year:  2016        PMID: 27031744      PMCID: PMC4993533          DOI: 10.1249/MSS.0000000000000924

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


  24 in total

Review 1.  Calibration of accelerometer output for adults.

Authors:  Charles E Matthew
Journal:  Med Sci Sports Exerc       Date:  2005-11       Impact factor: 5.411

2.  The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve.

Authors:  Neil J Perkins; Enrique F Schisterman
Journal:  Am J Epidemiol       Date:  2006-01-12       Impact factor: 4.897

3.  Amount of time spent in sedentary behaviors in the United States, 2003-2004.

Authors:  Charles E Matthews; Kong Y Chen; Patty S Freedson; Maciej S Buchowski; Bettina M Beech; Russell R Pate; Richard P Troiano
Journal:  Am J Epidemiol       Date:  2008-02-25       Impact factor: 4.897

4.  ActiGraph GT3X+ cut-points for identifying sedentary behaviour in older adults in free-living environments.

Authors:  Nicolás Aguilar-Farías; Wendy J Brown; G M E E Geeske Peeters
Journal:  J Sci Med Sport       Date:  2013-08-08       Impact factor: 4.319

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6.  Machine learning for activity recognition: hip versus wrist data.

Authors:  Stewart G Trost; Yonglei Zheng; Weng-Keen Wong
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7.  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

8.  Estimating physical activity in youth using a wrist accelerometer.

Authors:  Scott E Crouter; Jennifer I Flynn; David R Bassett
Journal:  Med Sci Sports Exerc       Date:  2015-05       Impact factor: 5.411

Review 9.  Sedentary behavior and health outcomes among older adults: a systematic review.

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Journal:  BMC Public Health       Date:  2014-04-09       Impact factor: 3.295

10.  Calibration and cross-validation of a wrist-worn Actigraph in young preschoolers.

Authors:  E Johansson; U Ekelund; H Nero; C Marcus; M Hagströmer
Journal:  Pediatr Obes       Date:  2014-01-10       Impact factor: 4.000

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  48 in total

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5.  An Evaluation of Accelerometer-derived Metrics to Assess Daily Behavioral Patterns.

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Journal:  Med Sci Sports Exerc       Date:  2017-01       Impact factor: 5.411

6.  Effect of Hospitalizations on Physical Activity Patterns in Mobility-Limited Older Adults.

Authors:  Amal A Wanigatunga; Thomas M Gill; Anthony P Marsh; Fang-Chi Hsu; Lusine Yaghjyan; Adam J Woods; Nancy W Glynn; Abby C King; Robert L Newton; Roger A Fielding; Marco Pahor; Todd M Manini
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7.  The U-Shaped Relationship Between Levels of Bouted Activity and Fall Incidence in Community-Dwelling Older Adults: A Prospective Cohort Study.

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8.  Association Between Brain Volumes and Patterns of Physical Activity in Community-Dwelling Older Adults.

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9.  Visual Impairment and Objectively Measured Physical Activity in Middle-Aged and Older Adults.

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10.  Comparing Methods to Identify Wear-Time Intervals for Physical Activity With the Fitbit Charge 2.

Authors:  Sophie E Claudel; Kosuke Tamura; James Troendle; Marcus R Andrews; Joniqua N Ceasar; Valerie M Mitchell; Nithya Vijayakumar; Tiffany M Powell-Wiley
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