A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study
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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.
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The authors have developed a method for longitudinal long-term tracking of actigraphy data using an open-source data processing pipeline that accounts for missing data, nonwear periods, and sleep-wake scoring, making it suitable for long-term relapse prediction studies.
“We have developed a method for longitudinal long-term tracking of actigraphy data using an open-source data processing pipeline that accounts for issues such as missing data, nonwear periods, and sleep-wake scoring, 44 making it suitable for long-term relapse prediction studies.”
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One-year actigraphy study of sleep and rest-activity rhythms as markers of relapse in depression
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Self-citation
- Authors
- Slyepchenko, A., Uher, R., Ho, K., Hassel, S., Matthews, C., Lukus, P. K., Daros, A. R., Minarik, A., Placenza, F., Li, Q. S., Rotzinger, S., Parikh, S. V., Foster, J. A., Turecki, G., Müller, D. J., Taylor, V. H., Quilty, L. C., Milev, R., Soares, C. N., Kennedy, S. H., Lam, R. W., & Frey, B. N.
- Affiliations
- McMaster University (CA)
- Dalhousie University (CA)
- St. Michael's Hospital (CA)
- University of Calgary (CA)
- St. Joseph’s Healthcare Hamilton (CA)
- Centre for Addiction and Mental Health (CA)
- University Health Network (CA)
- Janssen Research & Development (United States) (US)
- University of Michigan (US)
- The University of Texas Southwestern Medical Center (US)
- McGill University (CA)
- Queen's University (CA)
- University of British Columbia (CA)
- Corresponding author
- Frey, Benicio N.
- ORCID
- Venue
- Scientific Reports
- Published
- 2023-09-15
- Volume
- 13
- Issue
- 1
- Pages
- 15300-15300
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10504311
- OpenAlex
- W4386776738
- Cited by
- 17
- References
- 37
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 1
- FWCI
- 4.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- OBI
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- actigraphy, missing data, wearable devices, depressive symptoms, sleep, data quality control, sensitivity analysis
- MeSH terms
- Algorithms, Data Collection, Humans, Polysomnography, Actigraphy, Workflow
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