Advances in large language models (LLMs) have empowered a variety of applications.
However, there is still a significant gap in research when it comes to understanding and …

Background: Unaddressed early-stage mental health issues, including stress, anxiety, and
mild depression, can become a burden for individuals in the long term. Digital phenoty** …

Mental health disorders—including depression, anxiety, trauma-related, and psychotic
conditions—are pervasive and impairing, representing considerable challenges for both …

Recent research has demonstrated the capability of behavior signals captured by
smartphones and wearables for longitudinal behavior modeling. However, there is a lack of …

Understanding the dynamics of mental health among undergraduate students across the
college years is of critical importance, particularly during a global pandemic. In our study, we …

Most studies on machine learning in sensing systems focus on low-level perception tasks
that process raw sensory data within a short time window. However, many practical …

Background: The impact of digital device use on health and well-being is a pressing
question. However, the scientific literature on this topic, to date, is marred by small and …

AI tools intend to transform mental healthcare by providing remote estimates of depression
risk using behavioral data collected by sensors embedded in smartphones. While these …

Background Digital phenoty** offers a novel and cost-efficient approach for managing
depression and anxiety. Previous studies, often limited to small-to-medium or specific …

Early detection of depressive episodes is crucial in managing mental health disorders such
as Major Depressive Disorder (MDD) and Bipolar Disorder. However, existing methods often …