# SCH: INT: Collaborative Research:  Passive sensing of social isolation: A digital phenotying approach

> **NIH NIH R01** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2020 · $289,665

## Abstract

Social isolation-including both the objective phenomenon of 'aloneness' and the subjective experience of loneliness
(perceived isolation)-is a major problem globally. Our goal in the proposed project is to capitalize on statistical
methods for harnessing the power of smartphone-based measurement of continuous, unobtrusive, and real-time
assessment of social isolation. We bring together a team of clinical scientists with expertise in social behavior
dynamics, engineers/computer scientists at the forefront of research on digital signal processing, and biostatisticians
with expert knowledge in passive sensing technology to provide robust methodological rigor needed to execute the
study's aims. Using a digital phenotyping approach (i.e., the moment-by-moment quantification of the individual-level
human phenotype in situ using data from personal smartphones), we will develop and test algorithms that incorporate
both active (ecological momentary assessment) and passive (movement, location, conversation) metrics to improve
characterization and prediction of social isolation. We will then conduct a preliminary evaluation of the promise of a
dynamic network analysis of social isolation transition states, followed by application of this approach to a clinical
sample characterized by social isolation.
RELEVANCE (See instructions):
Findings from this project will have far-reaching application to global health, as our·emerging understanding of social isolation as a
key contributor to early mortality and other significant health problems highlights the need for a scalable, comprehensive, and
personalized assessment and intervention approach. Developing new methods for improving inference of social behavior from
temporally-dense smartphone data will benefit an expanding area of research in digital health. This contribution extends beyond
the applied aims of the project, as the methodological advancements we will develop can be applied to a large corpus of existing
data and future projects. Ultimately, this work will inform the delivery of sustainable interventions targeting social isolation in ieal-
time and in daily contexts.

## Key facts

- **NIH application ID:** 10022338
- **Project number:** 5R01MH122367-02
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Carlos Alberto Busso
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $289,665
- **Award type:** 5
- **Project period:** 2019-09-23 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10022338

## Citation

> US National Institutes of Health, RePORTER application 10022338, SCH: INT: Collaborative Research:  Passive sensing of social isolation: A digital phenotying approach (5R01MH122367-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10022338. Licensed CC0.

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