# The Digital Phenotype of Bipolar Disorder: Harnessing Technology to Identify Bipolar Mood Symptoms

> **NIH NIH K23** · FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH · 2020 · $195,480

## Abstract

Project Summary
Bipolar disorder (BD) is associated with significant mortality and morbidity. It typically begins in adolescence or
early adulthood, an important developmental period during which higher education, first jobs, and relationships
are pursued. Recurrent mood episodes during this period can have a devastating impact on a young person's
ability to achieve a high quality of life as an adult. A method by which to predict the onset of mood symptoms in
adolescence would create an opportunity to intervene and reduce exposure to the harmful effects of recurrent
episodes. A new approach – digital phenotyping – may make this possible. Digital phenotyping is defined as
the “moment-by-moment quantification of the human phenotype in situ” using data collected from smartphone
sensors (accelerometer, texts, calls, GPS). Digital phenotyping has been used to identify mood changes and
potential signs of relapse in adults with BD, but has not yet been applied to adolescents. We will use Beiwe, a
digital phenotyping application for iOS and Android phones, to collect digital phenotypes from participants
(aged 14-19) over 18-months (N=120; n=70 with BD [I, II, Other Specified], n=50 typically-developing). Over
the follow-up period, participants will complete biweekly mood assessments, and both participants and
caregivers will be interviewed monthly to track changes in mood/behavior. This will allow the phone sensor
data collected with Beiwe to be closely linked to symptom changes. The specific aims of this project are (1) to
characterize the digital phenotype of BD symptoms in adolescents, (2) to describe differences in the digital
phenotypes of the BD and typically developing groups, and (3) to develop a model for predicting mood
symptoms prospectively. The proposed study is consistent with all four NIMH strategic objectives for the future
of mental health research. This K23 Award will provide Anna Van Meter, PhD with the necessary training and
mentorship to (1) gain proficiency in computational psychiatry by learning to analyze longitudinal data using
statistical and machine learning techniques, (2) build expertise in patient-oriented translational research by
designing and conducting a longitudinal study with youth participants; (3) learn to employ state-of-the-art
mobile technology to personalize assessment and intervention using patient data. To accomplish these training
goals, Dr. Van Meter has organized an outstanding mentorship team (Anil Malhotra, MD, Jukka-Pekka Onnela,
DSc, John Kane, MD, Christoph Correll, MD, and Deborah Estrin, PhD), with expertise in patient-oriented
research, technology-based mental health research, computational psychiatry, bipolar disorder in youth, and
computer science. The proposed study will be the first to describe the digital phenotype of BD in adolescents, a
population at great risk for the onset of BD as well as the damaging effects of repeated episodes. The
completion of the proposed K23 Mentored Career Award wil...

## Key facts

- **NIH application ID:** 9996797
- **Project number:** 5K23MH120505-02
- **Recipient organization:** FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
- **Principal Investigator:** Anna Robinson Van Meter
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $195,480
- **Award type:** 5
- **Project period:** 2019-08-15 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9996797

## Citation

> US National Institutes of Health, RePORTER application 9996797, The Digital Phenotype of Bipolar Disorder: Harnessing Technology to Identify Bipolar Mood Symptoms (5K23MH120505-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9996797. Licensed CC0.

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