Clinical Trial Details
— Status: Active, not recruiting
Administrative data
NCT number |
NCT05671679 |
Other study ID # |
SUMMIT1 |
Secondary ID |
|
Status |
Active, not recruiting |
Phase |
N/A
|
First received |
|
Last updated |
|
Start date |
March 27, 2023 |
Est. completion date |
September 2024 |
Study information
Verified date |
April 2023 |
Source |
Insel Gruppe AG, University Hospital Bern |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Interventional
|
Clinical Trial Summary
Carbohydrate count marks the cornerstone of Type 1 Diabetes management. Eventhough it is a
crucial task, it is burdensome and prone to error. Therefore, the investigators want to
explore the effect that SNAQ, a food analyser app would have in glycaemic control by
facilitating the task of carbohydrate estimation.
Description:
Diet and physical activity are critically important in the lifestyle of people with type 1
diabetes. When diagnosed with the disease, people with type 1 diabetes are educated about
nutritional goals and how to estimate nutritional content of food. Carbohydrates are the food
component with the greatest impact on blood glucose levels and typical sources in the diet
include starches, some vegetables, fruits, dairy products and sugars . Thus, people with type
1 diabetes are primarily being trained to estimate the carbohydrate content of food, a task
that is also referred to as carbohydrate counting. Different methods can be used to count
carbohydrate in food and drink. These include reading the nutritional labels, consulting
reference books or websites, carrying a database on a personal digital assistant or using
exchange tables which provides the carbohydrate content for typical serving sizes (e.g. 1
slice of bread). While nutritional information can be accessed through the above mentioned
methods, the quantification of the portion sizes (if not indicated on the food package)
requires the additional use of scale or measuring vessel. Given the required effort and time
investment related to these methods, the great majority of people with type 1 diabetes count
carbohydrates by visual estimation and experience. As a consequence, people's estimate often
deviate substantially from ground truth values and average carbohydrate estimation errors
reported in the literature are 20% or higher.
Of note, more than 60% of individuals with diabetes report having trouble with carbohydrate
counting, despite their awareness on its importance . Even in patients who are confident in
applying carbohydrate counting, the daily task is perceived as major burden of diabetes
self-management.
Since carbohydrate counting is particularly demanding when eating fresh, non-packaged foods,
a concerning trend towards unhealthy dietary choices with preference of prepackaged foods
(with accessible nutrition facts) over whole foods is increasingly observed in people with
type 1 diabetes. This is paralleled by an increasing prevalence of overweight and obesity in
the type 1 diabetes population.
Thus, even with the latest hybrid closed-loop insulin delivery technologies, adequate
nutrition knowledge remains a cornerstone for satisfactory glucose control, metabolic health,
and prevention of diabetes-related complications and comorbidities.
With the development of new technologies embedded in modern smartphones (i.e. depth sensors),
image-based methods to support food assessment have become widely available. Of particular
use is the employment of well-established computer vision methodologies to estimate the
quantity of food. When combined with food-recognition technologies and information from
nutritional databases, a proposition of the nutritional content (e.g. carbohydrates, fat,
proteins, fibres) can be made to the user on the basis of captured images and obviates the
need for error prone visual estimations and mental calculations. Several such applications
have become available and can support monitoring the diet as part of lifestyle management.
Insights from a recent online survey suggest that a high proportion of people with type 1
diabetes believe that such new technologies for meal management could facilitate their daily
self-management and would be interested in using such technology. Moreover, according to a
recent study, such digital tools may promote diabetes education and food literacy which may
particularly benefit those with a lower education level and with a history of depression.
Amongst several options (e.g. Foodvisor, Calorie-Mamma, Lifesum) for image-based food
tracking and analysis, SNAQ is one of the most commonly used app in people with type 1
diabetes. Up to date, more than 40000 users have downloaded the SNAQ app in their phones, of
which 2,500 are living in Switzerland.
The investigators have previously demonstrated that the system estimates the macronutrient
content of real meals with satisfying accuracy.
However, evidence with regards to the effect of the food analysis on daily self-management of
people with type 1 diabetes (e.g. glucose control, meal patterns, perceived benefits) is
currently lacking. The investigators therefore aim to address these aspects in a
randomized-controlled study contrasting the use of the SNAQ app with people's traditional
meal management techniques.