Choi Soyoung, Seo JooYoung, Krishnan Ashwath, Kamath Sanchita, Kitsiou Spyros, Haegele Justin
Department of Health and Kinesiology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA.
Mhealth. 2025 Jul 10;11:29. doi: 10.21037/mhealth-24-60. eCollection 2025.
With the advancements in mobile health (mHealth) technologies, sighted individuals can benefit from mobile apps and wearable devices to more easily manage their physical activity (PA) and wellness data through intuitive touch gestures and effective data visualizations. However, for blind and low-vision (BLV) individuals, these conventional interaction methods are often challenging, not only limiting their ability to use these technologies but also potentially diminishing their motivation to adopt them to support health-promoting behaviors. We aimed to develop a health monitoring application called Personalized and Conversational Health Agent (PCHA) that supports BLV individuals with self-monitoring and management of their PA and wellness data (e.g., step count, exercise duration, calories burned, heart rate).
Drawing on social cognitive theory and insights from prior needs assessment research, five key design goals were established to guide the development of the app's core features and functionalities. PCHA leverages a large language model (LLM) to enable a conversational health agent that can be installed on iPhone and Apple Watch devices. This conversational interface is designed to ensure accessibility and inclusivity, offering PA management tools through a voice user interface (VUI) that minimizes the navigation challenges often associated with traditional touchscreen-based systems. To ensure evidence-based PA guidance, a thorough review of scientific literature and published PA guidelines was conducted. Finally, two blind accessibility experts conducted the accessibility testing.
Accessible user interface (UI) designs, featuring high color contrast, large buttons, and a simple layout, were created using Figma. The main features and functionalities include: (I) a voice health interview to assess users' basic health information; (II) PA recommendations to guide users toward achieving their PA goals; (III) a chat feature enabling human-like conversations with the app; (IV) a PA scheduling and reminder feature with haptic feedback on the Apple Watch; and (V) an in-exercise mode that provides audible updates on heart rate, PA duration, and walking speed. The app's mobile accessibility was found to be satisfactory.
A follow-up study involving BLV research participants will be conducted to improve the app's accessibility and usability, and to update its features and functionalities. More research is needed to fully harness the potential of LLMs in the new mHealth system to motivate PA behaviors for BLV populations. To deliver truly personalized PA feedback for BLV individuals, mHealth app developer should incorporate PA and wellness data specific to the BLV population, along with their unique personal and contextual factors that influence PA behaviors.
随着移动健康（mHealth）技术的进步，视力正常的人可以受益于移动应用程序和可穿戴设备，通过直观的触摸手势和有效的数据可视化，更轻松地管理他们的身体活动（PA）和健康数据。然而，对于盲人和低视力（BLV）个体来说，这些传统的交互方法往往具有挑战性，不仅限制了他们使用这些技术的能力，还可能削弱他们采用这些技术来支持健康促进行为的积极性。我们旨在开发一款名为个性化对话健康助手（PCHA）的健康监测应用程序，以支持BLV个体自我监测和管理他们的PA和健康数据（例如，步数、运动时长、燃烧的卡路里、心率）。
借鉴社会认知理论和先前需求评估研究的见解，确立了五个关键设计目标，以指导该应用程序核心功能的开发。PCHA利用大语言模型（LLM）实现一个可以安装在iPhone和Apple Watch设备上的对话健康助手。这个对话界面旨在确保可访问性和包容性，通过语音用户界面（VUI）提供PA管理工具，最大限度地减少通常与传统基于触摸屏的系统相关的导航挑战。为确保基于证据的PA指导，对科学文献和已发布的PA指南进行了全面审查。最后，两名盲人无障碍专家进行了无障碍测试。
使用Figma创建了具有高颜色对比度、大按钮和简单布局的无障碍用户界面（UI）设计。主要功能包括：（I）语音健康访谈，以评估用户的基本健康信息；（II）PA建议，指导用户实现他们的PA目标；（III）聊天功能，实现与应用程序的类人对话；（IV）PA计划和提醒功能，在Apple Watch上提供触觉反馈；（V）运动中模式，提供心率、PA持续时间和步行速度的语音更新。该应用程序的移动无障碍性被认为是令人满意的。
将开展一项涉及BLV研究参与者的后续研究，以提高该应用程序的无障碍性和可用性，并更新其功能。需要更多研究来充分利用LLM在新的mHealth系统中的潜力，以激发BLV人群的PA行为。为了为BLV个体提供真正个性化的PA反馈，mHealth应用程序开发者应纳入特定于BLV人群的PA和健康数据，以及影响PA行为的独特个人和情境因素。
