Scientific Reports (Sep 2023)

A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study

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

DOI
https://doi.org/10.1038/s41598-023-42138-6
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 13

Abstract

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Abstract 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.