Literature DB >> 28282744

Wrist-based cut-points for moderate- and vigorous-intensity physical activity for the Actical accelerometer in adults.

Keith M Diaz1, David J Krupka1, Melinda J Chang1, Ian M Kronish1, Natalie Moise1, Jeff Goldsmith2, Joseph E Schwartz1,3.   

Abstract

Wrist-based accelerometers are increasingly used to assess physical activity (PA) in population-based studies; however, cut-points to translate wrist-based accelerometer counts into PA intensity categories are still needed. The purpose of this study was to determine wrist-based cut-points for moderate- and vigorous-intensity ambulatory PA in adults for the Actical accelerometer. Healthy adults (n = 24) completed a four-phase treadmill exercise protocol (1.9, 3.0, 4.0 and 5.2 mph) while wearing an Actical accelerometer on their wrist. Metabolic equivalent of task (MET) levels were assessed by indirect calorimetry. Receiver operating characteristics (ROC) curves were generated to determine accelerometer counts that maximised sensitivity and specificity for classification of moderate (≥3 METs) and vigorous (>6 METs) ambulatory activity. The area under the ROC curves to discriminate moderate- and vigorous-intensity ambulatory activity were 0.93 (95% confidence interval [CI]: 0.90-0.97; P < 0.001) and 0.96 (95% CI: 0.94-0.99; P < 0.001), respectively. The identified cut-point for moderate-intensity ambulatory activity was 1031 counts per minute, which had a corresponding sensitivity and specificity of 85.6% and 87.5%, respectively. The identified cut-point for vigorous intensity ambulatory activity was 3589 counts per minute, which had a corresponding sensitivity and specificity of 88.0% and 98.7%, respectively. This study established intensity-specific cut-points for wrist-based wear of the Actical accelerometer which are recommended for quantification of moderate- and vigorous-intensity ambulatory activity.

Entities:  

Keywords:  Physical activity; accelerometer; adults; calibration; validation

Mesh:

Year:  2017        PMID: 28282744      PMCID: PMC5568991          DOI: 10.1080/02640414.2017.1293279

Source DB:  PubMed          Journal:  J Sports Sci        ISSN: 0264-0414            Impact factor:   3.337


  38 in total

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

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

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7.  Estimating activity and sedentary behavior from an accelerometer on the hip or wrist.

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Review 8.  Accelerometers and pedometers: methodology and clinical application.

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Authors:  Jamie Giffuni; Robert G McMurray; Todd Schwartz; Diane Berry
Journal:  Int J Exerc Sci       Date:  2012-04-15
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4.  Segmenting accelerometer data from daily life with unsupervised machine learning.

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Journal:  PLoS One       Date:  2019-01-09       Impact factor: 3.240

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Journal:  Prev Med Rep       Date:  2020-12-31
  5 in total

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