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. 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. “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 those most vulnerable, such as older adults. For example, a UC San Diego study published earlier this year found that 85 percent of residents living in an independent senior housing community reported moderate to severe levels of loneliness.
The new study also focused on independent senior living residents: 80 participants aged 66 to 94, with a mean age of 83 years. But, rather than simply asking and documenting answers to questions from the UCLA Loneliness Scale, participants were also interviewed