Understanding Reflection Needs for Personal Health Data in Diabetes

TL;DR

This study explores user needs for reflection tools in diabetes management, developing a visualization that helps users identify patterns in blood glucose data and highlighting features for effective data interpretation.

Contribution

It introduces PixelGrid, a matrix-based visualization for blood glucose data, and provides insights into user preferences for effective reflection tools in diabetes care.

Findings

84% of users found PixelGrid useful for pattern identification

Users prefer visualizations with textual descriptors and concise presentation

Future tools should automate pattern detection and provide actionable insights

Abstract

To empower users of wearable medical devices, it is important to enable methods that facilitate reflection on previous care to improve future outcomes. In this work, we conducted a two-phase user-study involving patients, caregivers, and clinicians to understand gaps in current approaches that support reflection and user needs for new solutions. Our results show that users desire to have specific summarization metrics, solutions that minimize cognitive effort, and solutions that enable data integration to support meaningful reflection on diabetes management. In addition, we developed and evaluated a visualization called PixelGrid that presents key metrics in a matrix-based plot. Majority of users (84%) found the matrix-based approach to be useful for identifying salient patterns related to certain times and days in blood glucose data. Through our evaluation we identified that users desire data visualization solutions with complementary textual descriptors, concise and flexible presentation, contextually-fitting content, and informative and actionable insights. Directions for future research on tools that automate pattern discovery, detect abnormalities, and provide recommendations to improve care were also identified.