Glucose interpretation meaning and action: enhancing type 1 diabetes decision-making with textual descriptions

Findings from our previous study indicate that people with type 1 diabetes mellitus (T1DM) unknowingly misinterpret data displayed on glucose monitoring systems and make inaccurate treatment decisions, which increases the risk of hospitalisation.

This study aims to assess the effectiveness of incorporating textual descriptions in glucose monitoring systems compared to existing systems. The main goal is to minimise the effort required in glucose data interpretation, facilitating better self-management and ultimately improving haemoglobin A1C levels.

A two-arm and mixed-methods evaluation was conducted. Participants were randomly allocated to the control arm (existing systems) or the experimental arm (newly developed systems incorporating textual descriptions). In the first part, a task-based usability assessment was conducted to compare performance between the two arms. The second part evaluated participant preferences, agreement with textual descriptions and perceptions of the new systems.

A total of 86 participants were recruited. The experimental arm achieved an 85.15% total correctness score, compared to 74.38% in the control arm (p < 0.001). The experimental arm particularly outperformed the control arm in the ambiguous tasks, such as compression low. However, despite a higher performance and greater agreement with the textual descriptions, the experimental group exhibited a less favourable perception compared to the control group.

Incorporating textual description into glucose monitoring systems enhances treatment decision-making for people with T1DM. It suggests that we are on the right path to helping them better understand their glucose data and assist their self-management. Extensive research is required to focus more on the patient-centred approach in information presentation and prioritise it in parallel with other advancements in glucose monitoring technologies.