Differences In Physical Activity Estimates By Wear Time, Body Placement And Data Processing Of Accelerometers

Abstract

PURPOSE: As accelerometer methods advance, researchers may employ different body placements and data processing techniques. This may result in different physical activity (PA) estimates that will make study comparisons challenging. We compared three data processing techniques for hip worn accelerometer data; compared wrist and hip locations; and compared wake-time wrist to a 24-hour wrist protocol. METHODS: 2608 days from 333 women (mean age 55) with matched hip and wrist accelerometer data were compared using generalized estimating equations adjusting for days within individuals. Participants were asked to wear hip accelerometers for waking hours and the wrist accelerometer (on the non-dominant hand) for 24 hours over 7 days. Standard wake wear time criteria (5 days, 600 mins/day) were applied to the hip and wrist. Minute level count (CPM) cut points from the vertical axis were applied to the hip data (1952 cpm (MVPA)). A laboratory developed algorithm (GGIR) for wrist and hip vector magnitude (VM) data was employed to identify MVPA. A free living machine learned (ML) behavioral algorithm was applied to classify walking in the hip and wrist. Meeting guidelines was considered as 30 mins PA per day. RESULTS: Wear time compliance between the hip and wrist only varied by 2%. 25% of days included 30 minutes of PA with the hip cut points, 35% with the GGIR VM at the hip, and 71% with the ML walking algorithm. 54% of days were classified by the wrist GGIR wake time criteria compared to 58% with the 24 hour protocol. The ML algorithm classified 60% of days on the wrist wake time, compared with 61% with the 24 hour protocol. All differences were statistically significant at p<.05. CONCLUSIONS: Different processing methods and body placements appear to significantly affect estimates of PA. These differences could greatly impact population estimates of PA. Differences between methods could have been affected by the validity of the algorithms for this aged population: the CPM and VM algorithms were developed in younger adults in the laboratory, and the ML algorithm was developed in free-living women the same age as this cohort. These findings will inform consensus development for accelerometer wear and data processing protocols in future studies.