通过Rebite支持个人数据的多重生命周期：大语言模型驱动的食品记录中目标导向的框架构建
Supporting The Many Lives of Personal Data with Rebite: LLM-Powered Goal-Directed Framing in Food Journaling
AI总结：
该研究针对个人信息系统难以适应用户变化目标的问题，提出目标导向的框架构建方法，实现为Rebite系统，经21名参与者一周测试，可优化用户对饮食目标的互动与反思。
AI中文摘要：
人们的健康与追踪目标频繁发生变化，但大多数个人信息系统难以适应，导致人们放弃已有数据并重新开始。我们提出目标导向的框架构建（goal-directed framing），这是一种在个人信息系统中重新定位目标的方法。该方法不在数据采集时固定其含义，而是通过当前目标对已收集的数据进行框架构建，并在目标发生变化时重新框架构建。我们将这一方法实现为Rebite，这是一个基于照片的食品记录系统，它使用大语言模型（LLMs）读取非结构化的餐食照片并生成目标导向的反馈。在为期一周的部署中，21名参与者需管理多个饮食目标，我们发现目标导向的框架构建塑造了参与者与目标互动的方式。将目标转化为指标帮助他们理解其实际意义，确认了现有优先事项，发现了他们忽略的内容，并揭示了指标存在的不足。当目标发生变化时，查看在新目标下重新框架构建的过往餐食会暴露重叠与冲突，促使参与者权衡取舍并完善优先事项。我们探讨了目标导向的框架构建如何在目标变化时支持并复杂化反思，同时为个人信息系统设计提出了支持不断变化目标的启示。
英文摘要：
People's health and tracking goals frequently change, but most personal informatics systems struggle to adapt, leading people to abandon their data and start over. We propose goal-directed framing, an approach that repositions goals within personal informatics systems. Instead of fixing the meaning of data at capture time, the approach frames the collected data through the current goal and reframes it whenever the goal changes. We realize this in Rebite, a photo-based food journaling system that uses LLMs to read unstructured meal photos and produce goal-directed feedback. In a one-week deployment with 21 participants managing multiple dietary goals, we find that goal-directed framing shaped how participants engaged with their goals. Translating a goal into metrics helped them see what it meant in practice, confirming existing priorities, surfacing what they overlooked, and revealing where the metrics fell short. When goals changed, seeing past meals reframed under the new goal exposed overlaps and conflicts, prompting participants to negotiate trade-offs and refine priorities. We discuss how goal-directed framing both supports and complicates reflection as goals change, and offer design implications for personal informatics systems to support evolving goals.
