Mitchell Elliot G, Heitkemper Elizabeth M, Burgermaster Marissa, Levine Matthew E, Miao Yishen, Hwang Maria L, Desai Pooja M, Cassells Andrea, Tobin Jonathan N, Tabak Esteban G, Albers David J, Smaldone Arlene M, Mamykina Lena
Department of Biomedical Informatics, Columbia University.
School of Nursing, The University of Texas at Austin.
Proc SIGCHI Conf Hum Factor Comput Syst. 2021 May;2021. doi: 10.1145/3411764.3445555. Epub 2021 May 7.
Self-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants' self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.
自我追踪有助于为2型糖尿病（T2D）等慢性病的自我管理干预措施实现个性化，但反思个人数据需要动力和知识素养。机器学习（ML）方法可以识别模式，但一个关键挑战是基于个人健康数据提出可操作的建议。我们推出了GlucoGoalie，它将机器学习与专家系统相结合，将机器学习输出转化为针对2型糖尿病患者的个性化营养目标建议。在一项对照实验中，患有2型糖尿病的参与者发现目标建议是可以理解且可操作的。一项为期4周的实地部署研究表明，接收目标建议增强了参与者的自我发现，选择目标突出了个人偏好的多面性，而遵循目标的体验证明了反馈和背景的重要性。然而，我们发现抽象目标与具体饮食体验之间存在矛盾，并且发现静态文本对于复杂概念来说过于模糊。我们讨论了基于机器学习的干预措施的影响以及对提供更多交互性、反馈和协商功能的系统的需求。
