A novel hand-held interface supporting the self-management of Type 1 diabetes

TL;DR

This paper presents a new hand-held interface design that leverages context, user focus, and wearable sensors with machine learning to help Type 1 diabetes patients manage their condition more effectively.

Contribution

It introduces a novel interaction design that integrates contextual cues, user attention, and machine learning for improved diabetes self-management tools.

Findings

Design informed by focus groups with patients and experts

Enables exploration of short-term glucose-insulin-food relationships

Implementation underway before clinical testing

Abstract

The paper describes the interaction design of a hand-held interface supporting the self-management of Type 1 diabetes. It addresses well-established clinical and human-computer interaction requirements. The design exploits three opportunities. One is associated with visible context, whether conspicuous or inconspicuous. A second arises from the design freedom made possible by the user's anticipated focus of attention during certain interactions. A third opportunity to provide valuable functionality arises from wearable sensors and machine learning algorithms. The resulting interface permits ``What if?'' questions: it allows a user to dynamically and manually explore predicted short-term (e.g., 2 hours) relationships between an intended meal, blood glucose level and recommended insulin dosage, and thereby readily make informed food and exercise decisions. Design activity has been informed throughout by focus groups comprising people with Type 1 diabetes in addition to experts in diabetes, interaction design and machine learning. The design is being implemented prior to a clinical trial.