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Multi-Modal Home Sleep Monitoring in Older Adults
Published on: January 26, 2019
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A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy
Anastasiya Slyepchenko1, Rudolf Uher2, Keith Ho3
1Department of Psychiatry and Behavioural Neurosciences, McMaster University, 100 West 5th Street, Suite C124, Hamilton, ON, L8N 3K7, Canada.
Scientific Reports
|September 15, 2023
Summary
Longitudinal sleep monitoring using wearable actigraphy requires robust data processing. A novel algorithm improved non-wear detection, crucial for accurate sleep analysis in digital health research.
Area of Science:
- Digital health
- Wearable technology
- Sleep science
Background:
- Long-term sleep and activity monitoring via wearable devices like wrist-worn actigraphs offers personalized, in-home data collection.
- Analyzing extensive longitudinal actigraphy data necessitates standardized pre-processing, data trimming, and advanced algorithms to manage non-wear and missing data.
Purpose of the Study:
- To develop and evaluate a data-driven pipeline for quality control, pre-processing, and analysis of year-long actigraphy data.
- To assess the impact of non-wear and missing data on sleep variables and depressive symptoms through sensitivity analysis.
- To introduce a novel non-wear detection algorithm for improved data quality in digital health studies.
Main Methods:
- Implemented an open-source pipeline for longitudinal actigraphy data processing, including missing data imputation and sleep/wake scoring.
- Conducted a 1-year study with 95 participants, analyzing compliance and missing data patterns over time.
- Developed and validated a novel non-wear algorithm, comparing its performance against existing methods and a capacitive wear sensor.
Main Results:
- Participant compliance with actigraph wear decreased over 12 months, with missing data increasing from 4.8% to 23.6%.
- Sensitivity analyses confirmed that pre-processing thresholds significantly influence the predictive power of variables on sleep outcomes.
- The novel non-wear algorithm demonstrated superior performance in quality control compared to other algorithms and a capacitive wear sensor.
Conclusions:
- Robust data processing and quality control are essential for reliable longitudinal actigraphy studies.
- A novel non-wear detection algorithm enhances data accuracy in digital health research.
- Findings inform the design of future digital health studies utilizing wearable sensor data for sleep and activity monitoring.
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