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.

Public Health Relevance

Findings from this project will have far-reaching application to global health, as ouremerging 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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH122367-02
Application #
10022338
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Leitman, David I
Project Start
2019-09-23
Project End
2023-08-31
Budget Start
2020-09-01
Budget End
2021-08-31
Support Year
2
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Boston University
Department
Other Health Professions
Type
Sch Allied Health Professions
DUNS #
049435266
City
Boston
State
MA
Country
United States
Zip Code
02215