# Ameliorating Social Isolation in Populations Facing Health Disparities: Identifying Social Structural and Person-level Factors that Impede or Facilitate Health-related Social Behavior Change

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $601,236

## Abstract

PROJECT SUMMARY/ABSTRACT
Social isolation and its subjective counterpart loneliness—well established as risk factors for poor physical and
mental health—have been rising at alarming rates in the US, especially among young adults. Mechanistic
understanding of how best to build social connectedness to ameliorate social isolation is sorely needed to
redirect life trajectories toward health and well-being. In creating this foundational knowledge, populations who
face higher disease burden—Black/African American young adults, Latino/Hispanic young adults, and those
with less socioeconomic privilege—merit special focus because persons in these at-risk populations often face
unique challenges in initiating social interactions, including discrimination and economic inequality. The broad,
overarching objective of this work is to conduct basic experimental research on social connectedness to test
whether, how, where, and for whom health communication messages can motivate in-person interactions to
reduce young adults’ social isolation and loneliness. Our multi-disciplinary team brings together expertise in
social psychology, emotion science, communication science, and health disparities and will carry out a 6-week
randomized controlled trial—the Keep Social RCT—using our innovative and ecologically valid simulated social
media platform and a suite of rigorous repeated measures of social behavior, loneliness, and other health-
relevant outcomes. This program of research is designed to meet three specific aims. SPECIFIC AIM 1 is to
optimize health messages about the value of social connectedness for young adults (ages 18-29) from
populations who face higher disease burden and then conduct the Keep Social RCT to build a rich empirical
platform. This aim will be met using a human-centered process to design health communication messages that
include peer imagery and stories for an online experiment with 500 at-risk young adults. Messages that receive
highest ratings for encouraging in-person interactions in this online experiment will be selected for the Keep
Social RCT, which is placebo controlled with behavioral, survey, and implicit assessments repeated over six
weeks. SPECIFIC AIM 2 is to analyze theory-driven mechanisms through which health communication
messages in the Keep Social RCT may reduce young adults’ social isolation and loneliness to identify
intervention targets. This aim will be met with longitudinal statistical modeling to test whether and how the
experimental health communication messages improve social connectedness. SPECIFIC AIM 3 is to extend
data analyses of the Keep Social RCT to identify social structural and person-level moderators of reduced
social isolation and loneliness to identify where and for whom effects are largest. This aim will be met with
advanced statistical modeling to illuminate the conditions under which our health communication messages
most effectively ameliorate social isolation and loneliness in young adults who fa...

## Key facts

- **NIH application ID:** 10839396
- **Project number:** 5R01MD018492-02
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** BARBARA LEE FREDRICKSON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $601,236
- **Award type:** 5
- **Project period:** 2023-05-09 → 2027-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10839396

## Citation

> US National Institutes of Health, RePORTER application 10839396, Ameliorating Social Isolation in Populations Facing Health Disparities: Identifying Social Structural and Person-level Factors that Impede or Facilitate Health-related Social Behavior Change (5R01MD018492-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10839396. Licensed CC0.

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