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Abstract Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer depressive mood. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of depressive mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R2≥ 80%) and a 65.7% reduction in the prediction error over a baseline model which predicts future mood based on past depression severity alone. Finally, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.
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MSLife: Digital Behavioral Phenotyping of Multiple Sclerosis Symptoms in the Wild Using Wearables and Graph-Based Statistical Analysis
Treatment for multiple sclerosis (MS) focuses on managing its symptoms (e.g., depression, fatigue, poor sleep quality), varying with specific symptoms experienced. Thus, for optimal treatment, there arises the need to track these symptoms. Towards this goal, there is great interest in finding their relevant phenotypes. Prior research suggests links between activities of daily living (ADLs) and MS symptoms; therefore, we hypothesize that the behavioral phenotype (revealed through ADLs) is closely related to MS symptoms. Traditional approaches to finding behavioral phenotypes which rely on human observation or controlled clinical settings are burdensome and cannot account for all genuine ADLs. Here, we present MSLife, an end-to-end, burden-free approach to digital behavioral phenotyping of MS symptoms in the wild using wearables and graph-based statistical analysis. MSLife is built upon (1) low-cost, unobtrusive wearables (i.e., smartwatches) that can track and quantify ADLs among MS patients in the wild; (2) graph-based statistical analysis that can model the relationships between quantified ADLs (i.e., digital behavioral phenotype) and MS symptoms. We design, implement, and deploy MSLife with 30 MS patients across a one-week home-based IRB-approved clinical pilot study. We use the GENEActiv smartwatch to monitor ADLs and clinical behavioral instruments to collect MS symptoms. Then we develop a graph-based statistical analysis framework to model phenotyping relationships between ADLs and MS symptoms, incorporating confounding demographic factors. We discover 102 significant phenotyping relationships (e.g., later rise times are related to increased levels of depression, history of caffeine consumption is associated with lower fatigue levels, higher relative levels of moderate physical activity are linked with decreased sleep quality). We validate their healthcare implications, using them to track MS symptoms in retrospective analysis. To our best knowledge, this is one of the first practices to digital behavioral phenotyping of MS symptoms in the wild.
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- Award ID(s):
- 2050910
- PAR ID:
- 10399632
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 5
- Issue:
- 4
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 35
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3494970
