Identifying sedentary time using automated estimates of accelerometer wear time
MetadataShow full item record
Purpose: The authors evaluated the accuracy of three automated accelerometer wear-time estimation algorithms against self-report. Direct effects on sedentary time (<100 cpm) and indirect effects on moderate-to-vigorous physical activity (MVPA, =1952 cpm) time were examined. Methods: A subsample from the 2004/2005 Australian Diabetes, Obesity and Lifestyle Study (n=148) completed activity logs and wore accelerometers for a total of 987 days. A published algorithm that allows movement within non-wear periods (Algorithm 1) was compared with one that allows less movement (Algorithm 2) or no movement (Algorithm 3). Implications for population estimates were examined using 2003/2004 US National Health and Nutrition Examination Survey data. Results: Mean difference per day between the criterion and estimated wear time was negligible for all three algorithms (=11 min), but 95% limits of agreement (LOA) were wide (±=2 h). Respectively, the algorithms (1, 2 and 3) misclassified sedentary time as non-wear on 31.9%, 19.4% and 18% of days and misclassified non-wear time as sedentary on 42.8%, 43.7% and 51.3% of days. Use of Algorithm 2 (compared with Algorithm 1) affected population estimates of sedentary time (higher by 20 min/day) but not MVPA time. Agreement between Algorithms 1 and 2 was good for MVPA time (mean difference -0.08, LOA: -2.08, 1.91 min), but not for wear time or sedentary time. Conclusion: Accelerometer wear time can be estimated accurately on average; however, misclassification can be substantial for individuals. Algorithm choice affects estimates of sedentary time. Allowing very limited movement within non-wear periods can improve accuracy.
Showing items related by title, author, creator and subject.
Identifying adults' valid waking wear time by automated estimation in activPAL data collected with a 24 h wear protocolWinkler, E.; Bodicoat, D.; Healy, Genevieve; Bakrania, K.; Yates, T.; Owen, N.; Dunstan, D.; Edwardson, C. (2016)© 2016 Institute of Physics and Engineering in Medicine.The activPAL monitor, often worn 24 h d-1, provides accurate classification of sitting/reclining posture. Without validated automated methods, diaries-burdensome to ...
Validity of an automated algorithm to identify waking and in-bed wear time in hip-worn accelerometer data collected with a 24 h wear protocol in young adultsMcVeigh, Joanne; Winkler, E.; Healy, Genevieve; Slater, J.; Eastwood, Peter; Straker, Leon (2016)Researchers are increasingly using 24 h accelerometer wear protocols. No automated method has been published that accurately distinguishes 'waking' wear time from other data ('in-bed', non-wear, invalid days) in young ...
Demographic and clinical correlates of accelerometer assessed physical activity and sedentary time in lung cancer survivorsD'Silva, A.; Bebb, G.; Boyle, Terry; Johnson, S.; Vallance, J. (2018)Copyright © 2017 John Wiley & Sons, Ltd. Objective: To determine demographic and clinical correlates of accelerometer assessed physical activity and sedentary time among a population-based sample of lung cancer survivors. ...