28.1HCOct 8, 2023
MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' JournalingTaewan Kim, Seolyeong Bae, Hyun Ah Kim et al.
In the mental health domain, Large Language Models (LLMs) offer promising new opportunities, though their inherent complexity and low controllability have raised questions about their suitability in clinical settings. We present MindfulDiary, a mobile journaling app incorporating an LLM to help psychiatric patients document daily experiences through conversation. Designed in collaboration with mental health professionals (MHPs), MindfulDiary takes a state-based approach to safely comply with the experts' guidelines while carrying on free-form conversations. Through a four-week field study involving 28 patients with major depressive disorder and five psychiatrists, we found that MindfulDiary supported patients in consistently enriching their daily records and helped psychiatrists better empathize with their patients through an understanding of their thoughts and daily contexts. Drawing on these findings, we discuss the implications of leveraging LLMs in the mental health domain, bridging the technical feasibility and their integration into clinical settings.
12.4HCFeb 21, 2022
Making Data Tangible: A Cross-disciplinary Design Space for Data PhysicalizationS. Sandra Bae, Clement Zheng, Mary Etta West et al.
Designing a data physicalization requires a myriad of different considerations. Despite the cross-disciplinary nature of these considerations, research currently lacks a synthesis across the different communities data physicalization sits upon, including their approaches, theories, and even terminologies. To bridge these communities synergistically, we present a design space that describes and analyzes physicalizations according to three facets: context (end-user considerations), structure (the physical structure of the artifact), and interactions (interactions with both the artifact and data). We construct this design space through a systematic review of 47 physicalizations and analyze the interrelationships of key factors when designing a physicalization. This design space cross-pollinates knowledge from relevant HCI communities, providing a cohesive overview of what designers should consider when creating a data physicalization while suggesting new design possibilities. We analyze the design decisions present in current physicalizations, discuss emerging trends, and identify underlying open challenges.