In a paper published Thursday, a team led by researchers at UC San Diego School of Medicine used artificial intelligence to analyze language patterns of older adults to discern degrees of loneliness, which could help blunt rising rates of suicides and opioid use.
According to the report, published in the American Journal of Geriatric Psychiatry, accurately assessing the breadth and depth of societal loneliness is daunting being limited by available tools such as self-reports.
"Most studies use either a direct question of 'How often do you feel lonely?' which can lead to biased responses due to stigma associated with loneliness or the UCLA Loneliness Scale, which does not explicitly use the word 'lonely,'" said senior author Dr. Ellen Lee, assistant professor of psychiatry at UCSD School of Medicine. "For this project, we used natural language processing or NLP, an unbiased quantitative assessment of expressed emotion and sentiment, in concert with the usual loneliness measurement tools."
In recent years, numerous studies have documented rising rates of loneliness in various populations of people, particularly vulnerable populations such as older adults, the researchers said. For example, a UC San Diego study published earlier this year found that 85% of residents living in an independent senior housing community reported moderate to severe levels of loneliness. The COVID-19 pandemic, with its associated social distancing and lockdowns, have only made things worse, they said.
The new study also focused on independent senior living residents: 80 participants aged 66 to 94, with a mean age of 83 years. Participants were interviewed by trained study staff in unstructured conversations that were analyzed using NLP-understanding software, plus other machine-learning tools.
"NLP and machine learning allows us to systematically examine long interviews from many individuals and explore how subtle speech features like emotions may indicate loneliness. Similar emotion analyses by humans would be open to bias, lack consistency, and require extensive training to standardize," said Varsha Badal, a postdoctoral research fellow and first author of the report.
Among the findings:
- Lonely individuals had longer responses in the qualitative interview,
and more greatly expressed sadness to direct questions about loneliness;
- Women were more likely than men to acknowledge feeling lonely during interviews; and
- Men used more fearful and joyful words in their responses compared to women.
The authors said the study highlights the discrepancies between research assessments for loneliness and an individual's subjective experience of loneliness, which NLP-based tools could help to reconcile. The early findings reflect how there may be "lonely speech" that could be used to detect loneliness in older adults, improving how clinicians and families assess and treat loneliness, especially during times of physical distancing and social isolation.
The study, said the authors, demonstrates the feasibility of using natural language pattern analyses of transcribed speech to better parse and understand complex emotions like loneliness. The researchers said the machine- learning models predicted qualitative loneliness with 94% accuracy.