A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study
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 robu...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42138-6 |
| _version_ | 1797453228773736448 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Anastasiya Slyepchenko |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T15:19:46Z |
| format | Article |
| id | doaj.art-a693500a0bcd42ea8003426e263478cf |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:19:46Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-a693500a0bcd42ea8003426e263478cf2023-11-26T12:52:13ZengNature PortfolioScientific Reports2045-23222023-09-0113111310.1038/s41598-023-42138-6A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring StudyAnastasiya Slyepchenko0Rudolf Uher1Keith Ho2Stefanie Hassel3Craig Matthews4Patricia K. Lukus5Alexander R. Daros6Anna Minarik7Franca Placenza8Qingqin S. Li9Susan Rotzinger10Sagar V. Parikh11Jane A. Foster12Gustavo Turecki13Daniel J. Müller14Valerie H. Taylor15Lena C. Quilty16Roumen Milev17Claudio N. Soares18Sidney H. Kennedy19Raymond W. Lam20Benicio N. Frey21Department of Psychiatry and Behavioural Neurosciences, McMaster UniversityDepartment of Psychiatry, Dalhousie UniversityCentre for Depression and Suicide Studies, St. Michael’s HospitalDepartment of Psychiatry, Cumming School of Medicine, and Hotchkiss Brain Institute, University of CalgaryDepartment of Psychiatry and Behavioural Neurosciences, McMaster UniversityMood Disorders Program, St. Joseph’s Healthcare HamiltonCampbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthDepartment of Psychiatry, Dalhousie UniversityUniversity Health Network, University of TorontoNeuroscience, Janssen Research & Development, LLCCentre for Depression and Suicide Studies, St. Michael’s HospitalDepartment of Psychiatry, University of MichiganDepartment of Psychiatry and Behavioural Neurosciences, McMaster UniversityDouglas Institute, Department of Psychiatry, McGill UniversityCampbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthDepartment of Psychiatry, Cumming School of Medicine, and Hotchkiss Brain Institute, University of CalgaryCampbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthDepartment of Psychiatry, Queen’s University and Providence Care HospitalDepartment of Psychiatry, Queen’s University and Providence Care HospitalCentre for Depression and Suicide Studies, St. Michael’s HospitalDepartment of Psychiatry, University of British ColumbiaDepartment of Psychiatry and Behavioural Neurosciences, McMaster UniversityAbstract 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.https://doi.org/10.1038/s41598-023-42138-6 |
| spellingShingle | 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 A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study Scientific Reports |
| title | A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study |
| title_full | A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study |
| title_fullStr | A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study |
| title_full_unstemmed | A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study |
| title_short | A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study |
| title_sort | standardized workflow for long term longitudinal actigraphy data processing using one year of continuous actigraphy from the can bind wellness monitoring study |
| url | https://doi.org/10.1038/s41598-023-42138-6 |
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