# Statistical methods in mHealth to signal interventional needs for mental health patients

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2022 · $395,504

## Abstract

Project Summary
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

## Key facts

- **NIH application ID:** 10319183
- **Project number:** 5R01MH116884-04
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Ian James Barnett
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $395,504
- **Award type:** 5
- **Project period:** 2019-03-05 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10319183

## Citation

> US National Institutes of Health, RePORTER application 10319183, Statistical methods in mHealth to signal interventional needs for mental health patients (5R01MH116884-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10319183. Licensed CC0.

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