Using machine learning to understand social isolation and loneliness in schizophrenia, bipolar disorder, and the community

Samuel J Abplanalp; Michael F Green; Jonathan K Wynn; Naomi I Eisenberger; William P Horan; Junghee Lee; Amanda McCleery; David J Miklowitz; L. Felice Reddy; Eric A Reavis

doi:10.1038/s41537-024-00511-y

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Using machine learning to understand social isolation and loneliness in schizophrenia, bipolar disorder, and the community

Journal article

Open access

Using machine learning to understand social isolation and loneliness in schizophrenia, bipolar disorder, and the community

Samuel J Abplanalp, Michael F Green, Jonathan K Wynn, Naomi I Eisenberger, William P Horan, Junghee Lee, Amanda McCleery, David J Miklowitz, L. Felice Reddy and Eric A Reavis

Schizophrenia, Vol.10(1), 88

01/01/2024

DOI: 10.1038/s41537-024-00511-y

PMCID: PMC11455897

PMID: 39368972

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https://doi.org/10.1038/s41537-024-00511-yView

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Abstract

Social disconnection, including objective social isolation and subjective loneliness, is linked to substantial health risks. Yet, little is known about the predictors of social disconnection in individuals with mental illness. Here, we used machine learning to identify predictors of social isolation and loneliness in schizophrenia (N = 72), a psychiatric condition associated with social disconnection. For comparison, we also included two other groups: a psychiatric comparison sample of bipolar disorder (N = 48) and a community sample enriched for social isolation (N = 151). We fitted statistical models of social isolation and loneliness within and across groups. Each model included five candidate predictors: social avoidance motivation, depression, nonsocial cognition, social anhedonia, and social cognition. The results showed that social anhedonia explained unique variance in social isolation and loneliness in all samples, suggesting that it contributes to social isolation and loneliness broadly. However, nonsocial cognition explained unique variance in social isolation only within schizophrenia. Thus, social anhedonia could be a potential intervention target across populations, whereas nonsocial cognition may play a unique role in determining social disconnection in schizophrenia.

Machine Learning

Schizophrenia

Bipolar disorder

Cognition & reasoning

Loneliness

Social isolation

Details

Title: Subtitle: Using machine learning to understand social isolation and loneliness in schizophrenia, bipolar disorder, and the community
Creators: Samuel J Abplanalp - VA Greater Los Angeles Healthcare System
Michael F Green - VA Greater Los Angeles Healthcare System
Jonathan K Wynn - VA Greater Los Angeles Healthcare System
Naomi I Eisenberger
William P Horan
Junghee Lee - University of Alabama at Birmingham
Amanda McCleery - University of Iowa
David J Miklowitz
L. Felice Reddy - Rehabilitation Research and Development Service
Eric A Reavis - VA Greater Los Angeles Healthcare System
Resource Type: Journal article
Publication Details: Schizophrenia, Vol.10(1), 88
Publisher: Nature Publishing Group
DOI: 10.1038/s41537-024-00511-y
PMID: 39368972
PMCID: PMC11455897
ISSN: 2754-6993
eISSN: 2334-265X
Language: English
Date published: 01/01/2024
Academic Unit: Psychiatry; Psychological and Brain Sciences; Iowa Neuroscience Institute
Record Identifier: 9984721138202771

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