Estimates of adherence and error analysis of physical activity data collected via accelerometry in a large study of free-living adults

Adherence estimates were generated from a pool of 524 women and men that wore AMs for 13 – 15 consecutive days. To simulate the effect of data loss due to AM removal, a reference dataset was first compiled from a subset consisting of 35 highly adherent subjects (24 HR; minimum of 20 hrs/day for seven consecutive days). AM removals were then simulated during sleep and between one and ten waking hours using this 24 HR dataset. Differences in the mean values for PA and TEE between the 24 HR reference dataset and the different simulations were compared using paired t-tests and/or coefficients of variation.The estimated average adherence of the pool of 524 subjects was 15.8 ± 3.4 hrs/day for approximately 11.7 ± 2.0 days. Simulated data loss due to AM removals during sleeping hours in the 24 HR database (n = 35), resulted in biased estimates of PA (p < 0.05), but not TEE. Losing as little as one hour of data from the 24 HR dataset during waking hours results in significant biases (p < 0.0001) and variability (coefficients of variation between 7 and 21%) in the estimates of PA. Inserting a constant value for sleep and imputing estimates for missing data during waking hours significantly improved the estimates of PA.Although estimated adherence was good, measurements of PA can be improved by relatively simple imputation of missing AM data.The benefits of physical activity (PA) on the reduction of risk of developing many chronic diseases [1-3] have lead to recommendations that the public should increase moderate intensity PA to a minimum of 30 – 60 min/day [2,4,5]. Despite the importance placed on investigating the effects of PA, scientists continue to struggle with the complexities associated with quantifying it [6], particularly using one of the many traditional measurement techniques (such as questionnaires, PA records and recall diaries) [6-10]. As an alternative to traditional survey techniques, activity monitors (AM) have been increasingly utilized by investigators [1