AI-Enabled Conversational Journaling for Advancing Parkinson's Disease Symptom Tracking

要旨

Journaling plays a crucial role in managing chronic conditions by allowing patients to document symptoms and medication intake, providing essential data for long-term care. While valuable, traditional journaling methods often rely on static, self-directed entries, lacking interactive feedback and real-time guidance. This gap can result in incomplete or imprecise information, limiting its usefulness for effective treatment. To address this gap, we introduce PATRIKA, an AI-enabled prototype designed specifically for people with Parkinson's disease (PwPD). The system incorporates cooperative conversation principles, clinical interview simulations, and personalization to create a more effective and user-friendly journaling experience. Through two user studies with PwPD and iterative refinement of PATRIKA, we demonstrate conversational journaling's significant potential in patient engagement and collecting clinically valuable information. Our results showed that generating probing questions PATRIKA turned journaling into a bi-directional interaction. Additionally, we offer insights for designing journaling systems for healthcare and future directions for promoting sustained journaling.

受賞
Honorable Mention
著者
Mashrur Rashik
University of Massachusetts Amherst, Amherst, Massachusetts, United States
Shilpa Sweth
University of Massachusetts Amherst, Amherst, Massachusetts, United States
Nishtha Agrawal
New York University , New York City , New York, United States
Saiyyam Kochar
University of Massachusetts, Amherst , Amherst , Massachusetts, United States
Kara M. Smith
University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States
Fateme Rajabiyazdi
Carleton University, Ottawa, Ontario, Canada
Vidya Setlur
Tableau Research, Palo Alto, California, United States
Narges Mahyar
University of Massachusetts Amherst, Amherst, Massachusetts, United States
Ali Sarvghad
University of Massachusetts Amherst, Amherst, Massachusetts, United States
DOI

10.1145/3706598.3714280

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714280

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Technologies for Elderly

G316+G317
7 件の発表
2025-04-29 01:20:00
2025-04-29 02:50:00
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