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Scott LaFee - University of California San Diego summarizes, For the past couple of decades, there has been a loneliness pandemic, marked by rising rates of suicides and opioid use, lost productivity, increased health care costs and rising mortality.
Photo: Ellen Lee, MD, assistant professor of psychiatry at UC San Diego School of Medicine.
The COVID-19 pandemic, with its associated social distancing and lockdowns, have only made things worse, say experts.
Accurately assessing the breadth and depth of societal loneliness is daunting, limited by available tools, such as self-reports. In a new proof-of-concept paper, published online September 24, 2020 in the American Journal of Geriatric Psychiatry , a team led by researchers at University of California San Diego School of Medicine used artificial intelligence technologies to analyze natural language patterns (NLP) to discern degrees of loneliness in older adults.
“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 Ellen Lee, MD, assistant professor of psychiatry at UC San Diego School of Medicine...
“NLP and machine learning allow 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 first author Varsha Badal, PhD, a postdoctoral research fellow.
Among the findings:
- Lonely individuals had longer responses in qualitative interview, and more greatly expressed sadness to direct questions about loneliness.
- Women were more likely than men to acknowledge feeling lonely during interviews.
- Men used more fearful and joyful words in their responses compared to women.
Source: UC San Diego Health
