• Nutter, Paul W.
• Harper, Simon
• Worth, Chris
• Banerjee, Indraneel
• Stevens, Adam
• Auckburally, Sameera
• Estebanez, Maria Salomon

Abstract

Background<br/>Children with hypoglycaemia disorders, such as Congenital Hyperinsulinism (CHI), are at constant risk of hypoglycaemia (low blood sugars) with the attendant risk of brain injury. Current approaches to hypoglycaemia detection and prevention vary from fingerprick glucose testing to provision of continuous glucose monitoring (CGM) to machine learning (ML) driven glucose forecasting. <br/>Recent trends for ML have had limited success in preventing free-living hypoglycaemia, due to a focus on increasingly accurate glucose forecasts and a failure to acknowledge the human in the loop and the essential step of changing behaviour. The wealth of evidence from the fields of Behaviour Change and Persuasive Technology allows for the creation of a theory-informed and technologically considered approach. <br/>Objectives<br/>We aimed to create a persuasive technology that would overcome the identified barriers to hypoglycaemia prevention for those with CHI to focus on proactive prevention rather than commonly used reactive approaches. <br/>Methods<br/>We used the Behaviour Change Technique Taxonomy and Persuasive-Systems-Design models to create HYPO-CHEAT (HYpoglycaemia-Prevention-thrOugh-Cgm-HEatmap-Assisted-Technology): a novel approach that presents aggregated CGM data in simple visualisations. The resultant ease of data interpretation is intended to facilitate behaviour change and subsequently reduce hypoglycaemia. <br/>Results<br/>HYPO-CHEAT was piloted in 10 patients with CHI over 12 weeks and successfully identified weekly patterns of hypoglycaemia. These patterns consistently correlated with identifiable behaviours and were translated into both a change in proximal fingerprick behaviour and ultimately, a significant reduction in aggregated hypoglycaemia from 7.1% to 5.4% with 4 out of 5 patients showing clinically meaningful reductions in hypoglycaemia. <br/>Conclusions<br/>We have provided pilot data of a new approach to hypoglycaemia prevention that focuses on proactive prevention and behaviour change. This approach is personalised for individual patients with CHI and is a first step in changing our approach to hypoglycaemia prevention in this group.<br/>

Topics

• impedance spectroscopy
• theory
• reactive
• machine learning