Early warning of hypoglycemia via sensor-agnostic machine learning: a clinical app design for type 1 diabetes
Publication date
2025
Document type
Konferenzbeitrag
Organisational unit
Conference
22nd International Conference on Applied Computing 2025 and 24th International Conference on WWW/Internet 2025 (AC ICWI 2025) ; Porto, Portugal ; November 1–3, 2025
Publisher
IADIS Press
Book title
International Conferences on Applied Computing 2025 and WWW/Internet 2025 : proceedings
First page
216
Last page
224
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
App development
Diabetes
Diabetes self-management
Hypoglycemia prediction
Personalized healthcare
Wearable health devices
Abstract
Type 1 diabetes is an incurable chronic disease with an increasing incidence, especially in highly developed countries. The main challenge in living with type 1 diabetes is blood glucose management, which requires lifelong insulin treatment, dietary adjustments as well as placing restrictions on physical activities. Incorrect insulin dosages or errors in diet and physical activity can lead blood glucose levels to drop too far, causing hypoglycemia, a severe health risk. In this work we introduce our framework and proof of concept app DiApp, to assist patients with diabetes in managing their blood sugar levels. It does this by merging data from blood glucose and heart rate sensors and using this data to offer an early detection of hypoglycemia. If hypoglycemia is detected to occur in the near future, action recommendations, such as resting and eating carbohydrate-rich food, are suggested as appropriate. This developed proof-of-concept app, DiApp, uses the Apple Health interface to collect sensor data, as this interface is implemented by a wide variety of commercially available sensors. While this implementation still has some limitations, it is capable of evaluating the data from different sensors, and alerting users if hypoglycemia is predicted to occur. In the future, we aim to implement specific glucose and heart rate sensors interfaces and conduct a clinical trial with real patients to investigate how effective the app is at reducing unhealthy glucose levels.
Version
Published version
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