Exploring Self-Tracking Practices of Older Adults with CVD to Inform the Design of LLM-Enabled Health Data Sensemaking

TL;DR

This study investigates how older adults with CVD interpret self-tracked health data and proposes design strategies for LLM-enabled systems to enhance understanding, emotional support, and social interaction while ensuring safety.

Contribution

It provides empirical insights into older adults' data sensemaking and offers novel design directions for integrating LLMs into health data support systems.

Findings

Self-tracking is emotionally complex and socially influenced.

Design should support emotional engagement and patient agency.

Expert-in-the-loop mechanisms are crucial for safety.

Abstract

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.