  In recent years, wearable devices have become more common to capture a wide
range of health behaviors, especially for physical activity and sedentary
behavior. These sensor-based measures are deemed to be objective and thus less
prone to self-reported biases, inherent in questionnaire assessments. While
this is undoubtedly a major advantage, there can still be measurement errors
from the device recordings, which pose serious challenges for conducting
statistical analysis and obtaining unbiased risk estimates. There is a vast
literature proposing statistical methods for adjusting for measurement errors
in self-reported behaviors, such as in dietary intake. However, there is much
less research on error correction for sensor-based device measures, especially
sedentary behavior. In this paper, we address this gap. Exploiting the
excessive multiple-day assessments typically collected when sensor devices are
deployed, we propose a two-stage linear mixed effect model (LME) based approach
to correct bias caused by measurement errors. We provide theoretical proof of
the debiasing process using the Best Linear Unbiased Predictors (BLUP), and use
both simulation and real data from a cohort study to demonstrate the
performance of the proposed approach while comparing to the na\"ive plug-in
approach that directly uses device measures without appropriately adjusting
measurement errors. Our results indicate that employing our easy-to-implement
BLUP correction method can greatly reduce biases in disease risk estimates and
thus enhance the validity of study findings.
