Wearables and mobile health applications are increasingly adopted for self-management of chronic illnesses; yet the data feels overwhelming for older adults with cardiovascular disease (CVD). This study explores how they make sense of self-tracked data and identifies design opportunities for Large Language Model (LLM)-enabled support. We conducted a seven-day diary study and follow-up interviews with eight CVD patients aged 64-82. We identified six themes: navigating emotional complexity, owning health narratives, prioritizing bodily sensations, selective engagement with health metrics, negotiating socio-technical dynamics of sharing, and cautious optimism toward AI. Findings highlight that self-tracking is affective, interpretive, and socially situated. We outline design directions for LLM-enabled data sensemaking systems: supporting emotional engagement, reinforcing patient agency, acknowledging embodied experiences, and prompting dialogue in clinical and social contexts. To support safety, expert-in-the-loop mechanisms are essential. These directions articulate how LLMs can help translate data into narratives and carry implications for human-data interaction and behavior-change support.

翻译：可穿戴设备与移动健康应用程序正越来越多地被用于慢性病的自我管理；然而，对于老年心血管疾病（CVD）患者而言，这些数据令人倍感压力。本研究探讨了患者如何理解自我追踪数据，并确定了可借助大语言模型（LLM）提供支持的设计机会。我们对8名年龄在64至82岁的CVD患者开展了为期七天的日记研究及后续访谈。我们归纳出六个主题：应对情绪复杂性、掌握健康叙事权、优先关注身体感受、有选择地关注健康指标、协商数据共享的社会技术动态，以及对人工智能持谨慎乐观态度。研究结果表明，自我追踪具有情感性、解释性和社会情境性。我们概述了面向LLM数据理解系统的设计方向：支持情感参与、增强患者主体性、承认具身化体验，并在临床及社交语境中促进对话。为确保安全性，专家参与机制不可或缺。这些方向阐明了LLM如何助力将数据转化为叙事，并对人机交互及行为改变支持具有启示意义。