A personalized diet and exercise recommender system for type 1 diabetes self-management

An in silico study

Jinyu Xie, Qian Wang

Research output: Contribution to journalArticle

Abstract

Management of diet and exercise levels needs to be personalized for patients with Type 1 Diabetes (T1D) to reduce the number of hypoglycemia events and to achieve a good glycemic control. This study developed a model-based Recommender system that could provide an (optimal) personalized intervention on diet and exercise for T1D patients, which could be potentially implemented as a mobile application (app) for self-management of T1D in the future work. At each intervention time, the Recommender makes prediction of blood glucose based on a patient-specific model of glucose dynamics, and then provides optimal interventions, which could be a meal/snack size or a target heart rate during exercise, by minimizing a risk function with constraints under a future time horizon. Simulations were conducted to evaluate the Recommender through 30 virtual subjects generated from a modified UVa/Padova simulator with an added exercise-glucose subsystem. The performance of the Recommender was compared to two self-management schemes: the Starter scheme and the Skilled scheme, where the Skilled represents an off-line optimal scheme providing a lower bound on the risk index. Compared to the Starter, the Recommender reduced the mean Low Blood Glucose Index by 84% and reduced the Blood Glucose Risk Index by 49% (P < 0.05), and it had comparable performance as the Skilled. The Recommender also reduced the number of hypoglycemia events during and post-exercise compared to the Starter and the Skilled.

Original languageEnglish (US)
Article number100069
JournalSmart Health
Volume13
DOIs
StatePublished - Aug 1 2019

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Recommender systems
Self Care
Nutrition
Medical problems
Type 1 Diabetes Mellitus
Computer Simulation
Glucose
Starters
Exercise
Diet
Blood Glucose
Blood
Hypoglycemia
Mobile Applications
Snacks
Meals
Simulators
Heart Rate

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Computer Science Applications
  • Health Information Management

Cite this

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A personalized diet and exercise recommender system for type 1 diabetes self-management : An in silico study. / Xie, Jinyu; Wang, Qian.

In: Smart Health, Vol. 13, 100069, 01.08.2019.

Research output: Contribution to journalArticle

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