Assessing state and trait anxiety through digital phenotyping

Abstract

Mental health disorders place an immense burden on individuals and have significant personal and social impacts. This has been worsened by the effects of the COVID-19 pandemic, resulting in remote access to healthcare becoming more extensively studied and enforced. In recent years, digital phenotyping has been utilized in research to analyze and predict mental health outcomes. The aim of this project was to utilize passive mobile data to assess how anxiety impacts activity levels of individuals to help better understand their lived experiences. Potential differences between state and trait anxiety were assessed by comparing tracked and perceived activity levels. The results showed that individuals with low levels of both types of anxiety engaged in more physical activity compared to individuals with high anxiety levels. However, all participants displayed lower physical activity levels when experiencing high levels of state anxiety. Although the findings presented were very limited and lacked significance, if extended using a larger population, they could have the potential to aid in facilitating preventative interventions by detecting early signs of mental illness through evaluating possibly at-risk patients and informing the design of patient-tailored interventions.