NEW YORK: Scientists have developed a new personalised app that can allow people with type 2 diabetes to keep a tighter rein on their blood sugar levels – the key to managing the disease.
The app, Glucoracle, comes with an integrated algorithm that predicts the impact of particular foods on an individual’s blood glucose levels.
“While we know the general effect of different types of food on blood glucose, the detailed effects can vary widely from one person to another and for the same person over time,” said David Albers, from Columbia University Medical Centre (CUMC) in the US.
“Even with expert guidance, it is difficult for people to understand the true impact of their dietary choices, particularly on a meal-to-meal basis,” said Albers.
“Our algorithm, integrated into an easy-to-use app, predicts the consequences of eating a specific meal before the food is eaten, allowing individuals to make better nutritional choices during mealtime,” said Albers.
The algorithm uses a technique called data assimilation, in which a mathematical model of a person’s response to glucose is regularly updated with observational data – blood sugar measurements and nutritional information – to improve the model’s predictions.
“The data assimilator is continually updated with the user’s food intake and blood glucose measurements, personalising the model for that individual,” said Lena Mamykina, assistant professor at CUMC, whose team designed and developed the app.
Glucoracle allows the user to upload fingerstick blood measurements and a photo of a particular meal to the app, along with a rough estimate of the nutritional content of the meal.
This estimate provides the user with an immediate prediction of post-meal blood sugar levels.
The researchers initially tested the data assimilator on five individuals using the app, including three with type 2 diabetes and two without the disease.
The app’s predictions were compared with actual post-meal blood glucose measurements and with the predictions of certified diabetes educators.
For the two non-diabetic individuals, the app’s predictions were comparable to the actual glucose measurements.
For the three subjects with diabetes, the app’s forecasts were slightly less accurate, possibly due to fluctuations in the physiology of patients with diabetes or parameter error, but were still comparable to the predictions of the diabetes educators.
The findings were published in the journal PLOS Computational Biology
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