A mobile intervention to reduce anxiety among university students, faculty, and staff: Mixed methods study on users' experiences.

Anxiety is highly prevalent among college communities, with significant numbers of students, faculty, and staff experiencing severe anxiety symptoms. Digital mental health interventions (DMHIs), including Cognitive Bias Modification for Interpretation (CBM-I), offer promising solutions to enhance access to mental health care, yet there is a critical need to evaluate user experience and acceptability of DMHIs. CBM-I training targets cognitive biases in threat perception, aiming to increase cognitive flexibility by reducing rigid negative thought patterns and encouraging more benign interpretations of ambiguous situations.

Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.

Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along with sensing features directly associated with mental health, it can be valuable to detect different features of social contexts to learn about social interaction patterns over time and across different environments. This can provide insight into diverse communities' academic, work and social lives, and their social networks.

Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline.

Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing work trains models on an entire group of participants, failing to capture individual differences in their psychological and behavioral responses to social contexts.

Understanding Privacy Risks versus Predictive Benefits in Wearable Sensor-Based Digital Phenotyping: A Quantitative Cost-Benefit Analysis.

Wearable devices with embedded sensors can provide personalized healthcare and wellness benefits in digital phenotyping and adaptive interventions. However, the collection, storage, and transmission of biometric data (including processed features rather than raw signals) from these devices pose significant privacy concerns. This quantitative, data-driven study examines the privacy risks associated with wearable-based digital phenotyping practices, with a focus on user , which is the process of identifying participants' IDs from deidentified digital phenotyping datasets.