Older Adult Misuse of Over-the-Counter Medications: Effectiveness of a Novel Pharmacy-Based Intervention to Improve Patient Safety.

Objectives: Older adults' (ages ≥65) inappropriate over-the-counter medications (OTC) use is prevalent, comprising Drug-Age, Drug-Drug, Drug-Disease, and Drug-Label types. Given that pharmacies sell many OTCs, structurally redesigning pharmacy aisles for improving patient safety (Senior Safe) was conceived to mitigate older adult OTC misuse, using Stop Signs and Behind-the-Counter Signs for high-risk OTCs. This study determined whether Senior Safe reduced high-risk OTCs misuse, while secondarily evaluating misuse changes for all OTCs.

MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences.

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI.

Virtual reality anatomy trainer turns teaching endobronchial ultrasound inside-out.

Background: Traditional approaches for learning anatomy for curvilinear endobronchial ultrasound (EBUS) require learners to mentally visualize structures relative to the position of the bronchoscope. Virtual reality (VR) can demonstrate anatomy from the perspective of bronchoscopic tools.

Research Question: Does the use of a VR anatomy trainer for teaching EBUS-associated anatomy improve procedural performance compared to traditional methods?

Study Design And Methods: In this randomized, cross-over study design, subjects studied EBUS-related anatomy during two sequential sessions using a VR trainer and a traditional modality (two-dimensional pictures (2D) or a three-dimensional model (3D)).

Is Attention All You Need For Actigraphy? Foundation Models of Wearable Accelerometer Data for Mental Health Research.

Wearable accelerometry (actigraphy) has provided valuable data for clinical insights since the 1970s and is increasingly important as wearable devices continue to become widespread. The effectiveness of actigraphy in research and clinical contexts is heavily dependent on the modeling architecture utilized. To address this, we developed the Pretrained Actigraphy Transformer (PAT)-the first pretrained and fully attention-based model designed specifically to handle actigraphy.

Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.

Researchers in ubiquitous computing have long promised that passive sensing will revolutionize mental health measurement by detecting individuals in a population experiencing a mental health disorder or specific symptoms. Recent work suggests that detection tools do not generalize well when trained and tested in more heterogeneous samples. In this work, we contribute a narrative review and findings from two studies with 41 mental health clinicians to understand these generalization challenges.