Diabetes Mellitus, Type 1 Clinical Trial
Official title:
Carbohydrate Estimation Supported by the GoCARB System in Individuals With Type 1 Diabetes
Verified date | August 2016 |
Source | University Hospital Inselspital, Berne |
Contact | n/a |
Is FDA regulated | No |
Health authority | Switzerland: Ethikkommission |
Study type | Interventional |
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.
Status | Completed |
Enrollment | 20 |
Est. completion date | December 2015 |
Est. primary completion date | December 2015 |
Accepts healthy volunteers | No |
Gender | Both |
Age group | 18 Years and older |
Eligibility |
Inclusion Criteria: - Type 1 diabetes - Minimum age of 18 years old - Sensor-augmented pump therapy for at least six months - HbA1c levels within the last 4 months = 8.5% - Familiar with carbohydrate (CHO) counting (e.g. CHO counting training in the past) - Normal insulin sensitivity (reflected by a daily insulin requirement of 0.3-1.0 U/kg body weight) - Able to comprehend German or English - Written informed consent Exclusion Criteria - Relevant diabetic complications - Hypoglycemia unawareness - More than one episode of severe hypoglycemia as defined by American Diabetes Association in preceding 12 months - Pregnancy - Relevant psychiatric disorder - Active neoplasia - Participation in another study - Other individuals especially in need of protection (according to the guidelines of the Swiss Academy of Medical Sciences) |
Allocation: Randomized, Endpoint Classification: Efficacy Study, Intervention Model: Crossover Assignment, Masking: Open Label
Country | Name | City | State |
---|---|---|---|
Switzerland | Division of Endocrinology, Diabetes and Clinical Nutrition, Bern University Hospital | Bern |
Lead Sponsor | Collaborator |
---|---|
University Hospital Inselspital, Berne |
Switzerland,
Agianniotis A, Anthimopoulos M, Daskalaki E, Drapela A, Stettler C, Diem P, Mougiakakou S. GoCARB in the Context of an Artificial Pancreas. J Diabetes Sci Technol. 2015 May;9(3):549-55. doi: 10.1177/1932296815583333. Epub 2015 Apr 21. — View Citation
Anthimopoulos M, Dehais J, Shevchik S, Ransford BH, Duke D, Diem P, Mougiakakou S. Computer vision-based carbohydrate estimation for type 1 patients with diabetes using smartphones. J Diabetes Sci Technol. 2015 May;9(3):507-15. doi: 10.1177/19322968155801 — View Citation
Anthimopoulos MM, Gianola L, Scarnato L, Diem P, Mougiakakou SG. A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE J Biomed Health Inform. 2014 Jul;18(4):1261-71. doi: 10.1109/JBHI.2014.2308928. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Average of the postprandial area under the glucose curve (AUC) measured over three hours after each meal's start using Continuous Glucose Monitoring | 14 days | No | |
Secondary | Composite of insulin-related parameters | Calculated from daily bolus, basal and correction insulin | 14 days | No |
Secondary | Glucose-related parameters | Postprandial glucose levels within, below and above target | 14 days | No |
Secondary | Daily nutritional behavior in individuals with T1D | Measured in kcal and documented by photographs | 14 days | No |
Secondary | User satisfaction | Composite measured by questionnaire: Participants will be invited to complete three questionnaires: i) a carbohydrate counting specific questionnaire including questions concerning educational level and former training of carbohydrate counting, and ii) a questionnaire related to GoCARB's features. | 14 days | No |
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