We evaluated the accuracy of three automated accelerometer wear-time estimation algorithms against self-report. Direct effects on sedentary time (<100 counts per minute; cpm) and indirect effects on moderate-to-vigorous physical activity time (MVPA, ≥1952 cpm) were examined.
A sub-sample from the 2004/05 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 to one that allows less movement (Algorithm 2), or no movement (Algorithm 3). Implications for population estimates were examined using 2003/04 US National Health and Nutrition Examination Survey data.
Mean difference per day between the criterion and estimated wear time was negligible for all three algorithms (≤11 minutes), but 95% limits of agreement (LOA) were wide (± ≥2 hours). Respectively, the algorithms (1, 2 and 3) misclassified sedentary time as non-wear on 31.9%, 19.4% and 18.0% 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 1) affected population estimates of sedentary time (higher by 20 minutes per 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 minutes), but not for wear time or sedentary time.
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.
Keywords: physical activity, algorithms, validity, AusDiab, NHANES