Assessment of Depression and Anxiety in Young and Old with a Question Based Computational Language Approach

Abstract

Abstract Older adults experience depression and anxiety differently than younger adults. Age may affect circumstances, depending on accessibility of social connections, jobs, physical health, etc, as these factors influence the prevalence and symptomatology. Depression and anxiety are typically measured using rating scales, however, recent research suggests that such symptoms can be assessed by open-ended questions that are analysed by question-based computational language assessments (QCLA). Here, we study older and younger adults’ responses about their mental health using open-ended questions and rating scales about their mental health. We then analyse their responses with computational methods based on natural language processing (NLP). The results demonstrate that: (1) older adults describe their mental health differently compared to younger adults; (2) where, for example, older adults emphasise depression and loneliness whereas young adults list anxiety and money; (3) different semantic models are warranted for younger and older adults; (4) compared to young participants, the older participants described their mental health more accurately with words; (5) older adults have better mental health than younger adults as measured by semantic measures. In conclusion, NLP combined with machine learning methods may provide new opportunities to identify, model, and describe mental health in older and younger adults. These semantic measures may provide ecological validity and aid the assessment of mental health.