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Open Access Research

Identifying accelerometer nonwear and wear time in older adults

Brent Hutto1, Virginia J Howard2, Steven N Blair3, Natalie Colabianchi4, John E Vena5, David Rhodes6 and Steven P Hooker7*

Author Affiliations

1 Prevention Research Center, Arnold School of Public Health, University of South Carolina, 921 Assembly Street, Columbia, SC 29208, USA

2 Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Ryals Bldg 210 F, 1665 University Blvd, Birmingham, AL 35294-0022, USA

3 Departments of Exercise Science and Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, 921 Assembly Street, Columbia, SC 29208, USA

4 Institute for Social Research, University of Michigan, P.O. Box 2346, 426 Thompson Street, Ann Arbor, MI 48106, USA

5 Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, B.S. Miller Hall Room 105, 101 Buck Road, Athens, GA 30602, USA

6 Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, 912 Building, Suite 200, 1720 2nd Ave. South, Birmingham, AL 35294-0022, USA

7 Exercise and Wellness Program, School of Nutrition and Health Promotion, Arizona State University, 500 North Third Street, MC 3020, Phoenix, AZ 85004, USA

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International Journal of Behavioral Nutrition and Physical Activity 2013, 10:120  doi:10.1186/1479-5868-10-120

Published: 25 October 2013

Abstract

Background

Five accelerometer-derived methods of identifying nonwear and wear time were compared with a self-report criterion in adults ≥ 56 years of age.

Methods

Two hundred participants who reported wearing an Actical™ activity monitor for four to seven consecutive days and provided complete daily log sheet data (i.e., the criterion) were included. Four variables were obtained from log sheets: 1) dates the device was worn; 2) time(s) the participant put the device on each day; 3) time(s) the participant removed the device each day; and 4) duration of self-reported nonwear each day. Estimates of wear and nonwear time using 60, 90, 120, 150 and 180 minutes of consecutive zeroes were compared to estimates derived from log sheets.

Results

Compared with the log sheet, mean daily wear time varied from -84, -43, -24, -14 and -8 min/day for the 60-min, 90-min, 120-min, 150-min and 180-min algorithms, respectively. Daily log sheets indicated 8.5 nonwear bouts per week with 120-min, 150-min and 180-min algorithms estimating 8.2-8.9 nonwear bouts per week. The 60-min and 90-min methods substantially overestimated number of nonwear bouts per week and underestimated time spent in sedentary behavior. Sensitivity (number of compliant days correctly identified as compliant) improved with increasing minutes of consecutive zero counts and stabilized at the 120-min algorithm. The proportion of wear time being sedentary and absolute and proportion of time spent in physical activity of varying intensities were nearly identical for each method.

Conclusions

Utilization of at least 120 minutes of consecutive zero counts will provide dependable population-based estimates of wear and nonwear time, and time spent being sedentary and active in older adults wearing the Actical™ activity monitor.

Keywords:
Activity monitor; Physical activity assessment; Nonwear classification; Sedentary behavior; Aging