Development and application of an automated algorithm to identify a window of consecutive days of accelerometer wear for large-scale studies.

ABSTRACT:

Background

Some accelerometer studies ask participants to document in a daily log when the device was worn. These logs are used to inform the window of consecutive days to extract from the accelerometer for analysis. Logs can be missing or inaccurate, which can introduce bias in the data. To mitigate this bias, we developed a simple computer algorithm that used data within the accelerometer to identify the window of consecutive wear days. To evaluate the algorithm's performance, we compared how well it agreed to the window of days identified by visual inspection and participant logs.

Findings

Participants were older women (mean age 79 years) in a cohort study that aimed to examine the relationship of objective physical activity on cardiovascular health. The study protocol re

SUBMITTER: Rillamas-Sun E 

PROVIDER: S-EPMC4482153 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature