As smartphones have grown in prevalence, so too has their potential grown as a scalable health monitoring tool for the treatment of psychiatric disorders. Behavioral warnings signs in individuals with suicidal ideation, bipolar disorder, eating disorders, depression, schizophrenia, and other psychiatric disorders have, until this point, been difficult to identify prior to the occurrence of an adverse event, such as a suicide attempt or relapse. Digital phenotyping, the moment-by-moment quantification of the individual-level human phenotype in situ , has enabled us to quantify these warnings signs and prompt an appropriately-timed intervention. Current published uses of change point and anomaly detection on digital phenotyping data so far have been proof-of-principal studies demonstrating the potential of digital phenotyping for behavioral and health monitoring. The wider goal that this proposal aims to advance can be characterized in three steps, which are ordered according to the following specific aims.
Aim 1 : Develop novel statistical methods for change point and anomaly detection capable of accounting for longitudinal features with widespread and general patterns of missing data.
Aim 2 : Develop dimensional reduction techniques to improve statistical power and reduce noise in digital phenotypes. This will greatly improve the performance of the methods proposed in aim 1. Crucial to both of these aims is the development of computationally efficient software.
Aim 3 : Implement this software on patient populations through our ongoing and new collaborations so as to analyze new digital phenotyping data as it is uploaded and provide clinicians notifications when behavioral warning signs are detected. This final step is the ultimate goal of the proposed work, as successful completion will lead to an immediate impact on patient health, enabling interventions to prevent relapse in a wide variety of addictions and disorders. Using our expertise in statistical methods, digital phenotyping and software development, combined with our wide network of digital phenotyping collaboration, we are well positioned to both develop the statistical methods and software necessary to identify behavioral warnings signs from digital phenotyping data, as well as implement these methods through collaborative studies. Successful completion of this project will have an immediate impact on personalized medicine and mobile health in the treatment of psychiatric disorders. using data from personal digital devices

Public Health Relevance

Many mobile health studies are emerging that aim to monitor patient behavior with smartphone sensor data passively collected through everyday use. Smartphone data can be used to quantify behavioral traits related to mobility, activity, sleep, and sociability over time. This proposal addresses the critical need for statistical methods that are capable of detecting behavioral changes in order to prompt interventions for patients with psychiatric disorders.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
1R01MH116884-01A1
Application #
9738465
Study Section
Mental Health Services Research Committee (SERV)
Program Officer
Rupp, Agnes
Project Start
2019-03-05
Project End
2022-12-31
Budget Start
2019-03-05
Budget End
2019-12-31
Support Year
1
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104