# Detecting dynamic fluctuations in emotion, mood, and functioning: A digital phenotyping approach to clinical monitoring in bipolar disorder

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $734,004

## Abstract

Project Summary/Abstract
Digital phenotyping, the in-situ quantification of individual-level phenotypes using data from digital devices, is a
promising new tool for measuring patterns of emotion and mood in the context of individuals lived experience.
Our work using ecological momentary assessment (EMA), an interactive form of digital phenotyping, shows
that patterns in emotional valence and arousal associate with and predict mood symptom severity. Specifically,
mean levels, greater day-to-day fluctuations (instability), and large increases or decreases (anomalies) in
emotion are associated with heightened risk. However, EMA is burdensome if administered frequently,
highlighting the need for complimentary passive digital phenotyping methods that unobtrusively measure
emotion patterns. Our team has developed an app and a series of machine learning algorithms, PRIORI, that
samples the ambient audio every 15 minutes and produces computational estimates of emotional valence and
arousal that correlate with EMA measures. The goal of the present study is to investigate how to mitigate the
burdensome participation requirements of EMA, and substantially improve the ability to predict mood
symptoms using passive (PRIORI) technology. Individuals (n=160) with an established pattern of mood
instability from a prospectively studied cohort of bipolar individuals will enroll into a six-month digital
phenotyping protocol including continuous passive monitoring using PRIORI. In a measurement-burst protocol
one week per month, participants will complete EMA self-reported mood (2xday), self-reported emotion
valence and activation (5xday), and a clinician interview to assess manic and depressive symptoms. Outside of
these bursts, participants will complete weekly EMA ratings of mood. Our central hypothesis is that patterns of
emotional valence and arousal assessed via digital phenotyping will predict mood severity in BD. We will test
whether the prediction of mood severity using EMA data will be enhanced by including measures derived from
PRIORI. Given that EMA data may not always be present (while PRIORI data are), we will also test whether
including emotion estimates from PRIORI during weeks when EMA is not present improves later mood
prediction. Our specific aims test independent hypotheses focused on: (1) mean levels of emotional valence
and arousal, (2) day-to-day fluctuations (instability) in emotional valence and arousal, and (3) large deviations
in emotional valence and arousal from one’s own average (anomalies). For risk mitigation and alternate
strategies, we will examine the extent to which contextual variables (noise from the environment, location,
social factors) and psychosocial functioning influence predictive models. This project will support the
development of fine-grained and quantitative behavioral assessment tools to evaluate dysfunction in the
trajectories of mental illness (NIMH Obj 2.2) and elucidate additive, interactive combinations of data t...

## Key facts

- **NIH application ID:** 10800145
- **Project number:** 1R01MH130411-01A1
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** MELVIN G MCINNIS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $734,004
- **Award type:** 1
- **Project period:** 2023-12-13 → 2028-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10800145

## Citation

> US National Institutes of Health, RePORTER application 10800145, Detecting dynamic fluctuations in emotion, mood, and functioning: A digital phenotyping approach to clinical monitoring in bipolar disorder (1R01MH130411-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10800145. Licensed CC0.

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