• Li, Qingqin S.
• Placenza, Franca
• Uher, Rudolf
• Lukus, Patricia K.
• Parikh, Sagar V.
• Minarik, Anna
• Daros, Alexander R.
• Slyepchenko, Anastasiya
• Hassel, Stefanie
• Turecki, Gustavo
• Ho, Keith
• Soares, Claudio N.
• Milev, Roumen
• Kennedy, Sidney H.
• Müller, Daniel J.
• Quilty, Lena C.
• Taylor, Valerie H.
• Rotzinger, Susan
• Frey, Benicio N.
• Lam, Raymond W.
• Foster, Jane A.
• Matthews, Craig

Abstract

<jats:title>Abstract</jats:title><jats:p>Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual’s own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data.In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants. We implemented a data processing pipeline using open-source packages for longitudinal data thereby providing a framework for treating missing data patterns, non-wear scoring, sleep/wake scoring, and conducted a sensitivity analysis to demonstrate the impact of non-wear and missing data on the relationship between sleep variables and depressive symptoms. Compliance with actigraph wear decreased over time, with missing data proportion increasing from a mean of 4.8% in the first week to 23.6% at the end of the 12 months of data collection. Sensitivity analyses demonstrated the importance of defining a pre-processing threshold, as it substantially impacts the predictive value of variables on sleep-related outcomes. We developed a novel non-wear algorithm which outperformed several other algorithms and a capacitive wear sensor in quality control. These findings provide essential insight informing study design in digital health research.</jats:p>

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