# Statistical methods for the assessment of social engagement in psychosis using digital technologies

> **NIH NIH K01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2021 · $141,124

## Abstract

ABSTRACT
This is an application for a K01 award for Dr. Linda Valeri, a biostatistician at McLean Hospital and the Harvard
Medical School. Dr. Valeri is establishing herself as a young investigator in psychiatric biostatistics focusing on
psychotic disorders. This K01 award will provide Dr. Valeri with the support necessary to accomplish the
following goals: (1) to become expert in psychiatric biostatistics focusing on mobile health (mhealth) research
for psychotic disorders (2) to conduct investigations using mhealth technologies in patients with psychosis; (3)
to develop automated software for advanced machine learning methods in mhealth studies; and (4) to develop
an independent research career. To achieve these goals, Dr. Valeri has assembled a team comprised of three
mentors, Dr. Dost Öngür, Chief of the McLean Hospital Psychotic Disorders Division, who leads a
neuroimaging laboratory studying the biology of psychotic illness, and co-mentors Dr. Russell Schutt,
Professor of Sociology at University of Massachusetts in Boston, who has extensive experience in the study of
social interactions in patients with severe mental illness, and Dr. Jukka-Pekka Onnela, Associate Professor of
Biostatistics at Harvard T.H. Chan School of Public Health, who has developed a platform for collection of raw
sensor data from mobile devices, called “Beiwe”, and conducts research in the fields of digital phenotyping and
network science. Dr. Valeri’s research will focus on the development of statistical methods for the analysis of
mhealth data to shed light on the role of social engagement in psychosis. The proposal builds upon the
hypothesis that social interactions captured by passive mobile data streams (call and text logs) and mobile
surveys are potential targets of intervention and could lead to a sustained recovery by promoting perceived
social support and improving psychiatric symptoms. In Aim 1(a) we propose to extend a machine learning
approach, Bayesian Kernel Machine Regression, for the analysis of mobile data streams accounting for time-
varying confounding. The approach will allow in Aim 1(b) to establish (i) reliable links between a high
dimensional time series of passive measures of social and mobility behaviors with self-reported measures of
social interaction and (ii) the effect of social interaction dynamics on perceived social support and psychiatric
symptoms measured in clinical settings. Further, we will extend the approach to correct for selection bias
introduced by missing data in mobile surveys (Aim 2). For both aims, Dr. Valeri will develop software (Aim 3)
and apply the approaches to investigate these scientific questions using data from an ongoing study based at
McLean Hospital Psychotic Disorders Division that employs the smartphone platform for collection of sensor
data developed by Dr. Onnela. Dr. Valeri’s investigation will provide preliminary evidence on features and
timing of social interaction behaviors that can improve psychiatric ...

## Key facts

- **NIH application ID:** 10238134
- **Project number:** 5K01MH118477-04
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Linda Valeri
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $141,124
- **Award type:** 5
- **Project period:** 2018-09-19 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10238134

## Citation

> US National Institutes of Health, RePORTER application 10238134, Statistical methods for the assessment of social engagement in psychosis using digital technologies (5K01MH118477-04). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10238134. Licensed CC0.

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