Publications

Number of accelerometer monitoring days needed for stable group-level estimates of activity.

Author(s): Wolff-Hughes DL,  McClain JJ,  Dodd KW,  Berrigan D,  Troiano RP

Journal: Physiol Meas

Date: 2016 Sep

Major Program(s) or Research Group(s): BRG

PubMed ID: 27510765

PMC ID: not available

Abstract: To determine the number and distribution of days required to produce stable group-level estimates of a 7 d mean for common accelerometer-derived activity measures. Data from the 2003-2006 NHANES were used in this analysis. The sample included 986 youth (6-19 year) and 2532 adults (⩾20 year) with 7 d of  ⩾10 h of wear. Accelerometer measures included minutes of inactive, light physical activity, moderate-to-vigorous physical activity (MVPA); and total activity counts/d. Twenty-five alternative protocols were bootstrapped with 50 000 samples drawn for each protocol. Alternative protocols included: 1-6 random days, Saturday plus 1-5 random weekdays (WD), Sunday plus 1-5 random WD, 1 random weekend day (WE) plus 1-5 WD, and both WE plus 1-4 random WD. Relative difference was calculated between the 7 d mean and alternative protocol mean (((alternative protocol mean - 7 d mean)/7 d mean) (*) 100). Adult MVPA is used as an example; however, similar trends were observed across age groups and variables except adult inactive time, which was stable across protocols. The 7 d mean for adult MVPA was 44.1(0.9) min d(-1). The mean bias for any 1-6 random days ranged from  -0.0(0.3) to 0.0(0.2) min d(-1) with a relative difference of  -0.1 to 0.0%. For protocols with non-random components, bias ranged from  -1.4(0.2) to 0.6(0.1) min d(-1) with relative difference ranging from  -7.2 to 3.1%. Simulation data suggest that stable estimates of group-level means can be obtained from as few as one randomly selected monitoring day from a sampled week. On the other hand, estimates using non-random selection of weekend days may be significantly biased. Purposeful sampling that disproportionally forces inclusion of weekend data in analyses should be discouraged.