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

2026-03-24

AI Summary

This study addresses the challenges older adults with cardiovascular disease (CVD) face in effectively utilizing health data from wearable devices due to information overload and difficulties in interpretation. Through a seven-day diary study and semi-structured interviews with participants aged 64 to 82, the research explores how older CVD patients construct meaning from their health data. It uniquely integrates patients’ embodied experiences, emotional engagement, and clinical dialogue to propose an expert-in-the-loop, large language model (LLM)-driven framework for narrativizing health data. The work identifies six key design themes that offer concrete principles for enhancing older users’ data comprehension, sense of control, and efficacy in clinical communication, thereby advancing human-centered innovation in intelligent health interventions.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Intelligent Robots: Human-Robot Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• self-tracking
• older adults
• cardiovascular disease
• health data sensemaking
• chronic illness management

Innovation

• LLM-enabled sensemaking
• self-tracking
• older adults
• cardiovascular disease
• human-data interaction