Diabetes Mellitus, Type 1 Clinical Trial
Official title:
Carbohydrate Estimation Supported by the GoCARB System in Individuals With Type 1 Diabetes
The standard method for determining the carbohydrate content of a meal in patients with
diabetes mellitus is the weighing of individual foods. However, in daily life, the weighing
is not practical at all times. Inaccurate estimation of meal's CHO content, leads to wrong
insulin doses and consequently to poor postprandial glucose control. Fact is that even well
trained diabetic individuals find it difficult to estimate CHO precisely and that especially
meals served on a plate are prone to false estimations underlining an emergent need for
novel approaches to CHO estimation.
GoCarb is a computer vision-based system for calculating the carbohydrate content of meals.
In a typical scenario, the user places a credit card-sized reference object next to the meal
and acquires two images using his/her smartphone. A series of computer vision modules
follows: the plate is detected and the different food items on the plate are automatically
segmented and recognized, while their 3D shape is reconstructed. On the basis of the shape,
the segmentation results and the reference card, the volume of each item is then estimated.
The CHO content is calculated by combining the food types with its volumes, and by using the
USDA nutritional database. Finally, the results are displayed to the user.
A preclinical study using the GoCarb system indicates that the system is able to estimate
the meal's CHO content with higher accuracy than individuals with T1D. Furthermore, the
feedback gathered by the participants showed that the system is easy to use even for
non-smartphone users.
The aim of this randomized, cross-over pilot study is to investigate the benefits of an
automated determination of the carbohydrate content of meals on glycemic control in subjects
with type 1 diabetes mellitus with sensor-augmented insulin pump therapy.
Background
For individuals with type 1 diabetes (T1D) the current gold standard to evaluate the
carbohydrate (CHO) amount of a meal is by carefully weighing its different components and
calculating the CHO content using reference nutritional tables. The resulting CHO amount is
then used to define the insulin dose needed to avoid an abnormal postprandial glucose
profile. Since this is a cumbersome procedure in real life, diabetic individuals often
estimate the CHO amount based on their personal experience. Especially for food served on a
plate CHO estimates are often significantly over or underestimated leading to high variation
in postprandial blood glucose. Besides the immediate risk of hypoglycaemia, there is
emerging evidence that suboptimal control of postprandial glucose is affiliated with
increased risk for long-term complications (e.g. diabetic micro- and macro-vascular
diseases).
The effect of CHO counting in T1D control has been increasingly recognized and investigated.
A meta-analysis including five studies on individuals with T1D has shown, that improved CHO
counting accuracy reduces the HbA1c significantly (0.64% reduction in HbA1c compared to a
control group). Teaching adult individuals with T1D to count CHO reduces HbA1c significantly
and also leads to an improvement in quality of life. Similar findings are reported in
children, in whom higher CHO counting accuracy is associated with a lower HbA1c. Even a
short educational intervention of 4 weeks can still result in a significant and sustained
effect on HbA1c reduction 9 months after without having an increase in hypoglycaemia. Lower
CHO counting accuracy is a significant predictor of prolonged time in hyperglycaemic state.
In one study with adults only 31% of the participants estimated the CHO content with an
error of less than 20 grams per day and accurate CHO estimation were correlated with the
lowest HbA1c values. In line with these findings another study has shown that individuals on
intensive insulin therapy count CHO content of meals with an average error in the order of
16 grams or 21%. In general there is overestimation of small meals and a substantial
underestimation of large meals. While breakfast (+8.5%) and snacks (-5%) were estimated
fairly accurately, lunch (-28%) and dinner (-23%) are more prone to errors leading to an
underestimation in the order of 30 grams. In children, an inaccuracy of ±10grams does not
deteriorate the postprandial glycaemic control, whereas a ±20 grams variation significantly
impacts the postprandial glycaemia.
The debate how to optimally estimate CHO intake is on-going and controversial. Fact is that
even well trained diabetic individuals find it difficult to estimate CHO precisely and that
especially meals served on a plate are prone to false estimations underlining an emergent
need for novel approaches to CHO estimation. The investigators hypothesize that computer
vision supported CHO estimation can have a beneficial impact on postprandial glucose
control, ultimately leading to reduced episodes of hypoglycaemia and reduction in long term
complications.
The recent advances in smartphone technologies and computer vision permitted the development
of applications for the automatic dietary assessment through meal image analysis. The
applications are using either a number of images or a short video of the upcoming meal, as
captured by the user's smartphone. Although several systems have been proposed in the past
decade, none of them is designed for individuals with diabetes, while they rely on strong
assumptions, which often do not hold in real life, or require too much user input. The
GoCARB system provides CHO estimations to individuals with T1D, by using only two meal
images. The current version GoCARB has been designed to deal with
- elliptical plates with a flat base;
- single-dish images;
- fully visible food items. The system works without assumptions on the food shape, while
it supports the following food classes: Pasta, green salad, meat, breaded food, beans,
carrots, mashed potato, rice, potatoes, bread, cheese, egg, couscous, mushrooms and
spätzli.
Objective
The purpose of this study is to investigate the effect of GoCARB-supported CHO estimation in
the postprandial glucose control of individuals with T1D on sensor augmented insulin pump
therapy.
In a typical scenario, the user places a credit card-sized reference object next to the meal
and acquires two images using her/his smartphone. A graphical user interface guides the user
in choosing the optimal angles for image acquisition based on the smartphone's built-in
sensors. The images are then transmitted to a dedicated server via Wi-Fi or the mobile
network. Then, the following computer vision algorithms are activated: the plate is detected
and the different food items on the plate are automatically segmented and recognized, while
their 3D shape is reconstructed. On the basis of the shape, the segmentation results and the
reference card, the volume and CHO content of each item is then calculated by combining the
food types with its volumes. Finally, the results are displayed to the user.
Methods
This study's overall objective is to perform a prospective randomized controlled clinical
pilot study comparing postprandial glucose control assisted by the GoCARB system against the
usual care approach by the participants. Scope of the study will be to investigate whether
the postprandial glucose profile is improved by using the GoCARB prototype for CHO
estimation. Postprandial glycaemia is evaluated using continuous glucose monitoring.
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Allocation: Randomized, Endpoint Classification: Efficacy Study, Intervention Model: Crossover Assignment, Masking: Open Label
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