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Supporting Meal Management in Type 1 Diabetes (SUMMIT1)

Primary Purpose

Type 1 Diabetes

Status
Recruiting
Phase
Not Applicable
Locations
Switzerland
Study Type
Interventional
Intervention
SNAQ app
Traditional carbohydrate counting
Sponsored by
Lia Bally
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional prevention trial for Type 1 Diabetes focused on measuring Carbohydrates, Continuous glucose measurement, Closed-loop systems, Type 1 Diabetes, Hyperglycaemia, Hypoglycaemia

Eligibility Criteria

12 Years - 20 Years (Child, Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria: Written informed consent Adults (aged 18 years or older) Type 1 diabetes (as defined by World Health Organization (WHO) for at least 12 month) Current use of a commercial hybrid closed-loop system HbA1c≤12% (measured within the past 3 months) Willing to use the SNAQ app on a daily basis for over 3 weeks The participant is willing to follow study specific instructions and share their treatment data with the study team Exclusion Criteria: Any physical or psychological disease or condition likely to interfere with the normal conduct of the study and interpretation of the study results Previous use of SNAQ app for more than 5 days within the past 3 months Self-reported pregnancy, planed pregnancy within next 3 months or breast-feeding Severe visual impairment Severe hearing impairment Lack of reliable telephone facility for contact Concomitant participation in another trial that interferes with the normal conduct of the study and interpretation of the study results Participant not proficient in German

Sites / Locations

  • Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism (UDEM), Inselspital, Bern University HospitalRecruiting

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

Active Comparator

Arm Label

Intervention

Control

Arm Description

The intervention group will use SNAQ app for the first 3 weeks (baseline to V1) of the study.

The control group will continue estimating the carbohydrate count using their traditional methods for the first three weeks of the study (baseline to V1).

Outcomes

Primary Outcome Measures

Percentage of time with sensor glucose in the target range
Percentage of time with sensor glucose in the target range between 3.9 to 10.0mmol/L, %

Secondary Outcome Measures

Percentage of time with sensor glucose in hyperglycaemia
Percentage of time with sensor glucose in the target range above 10.0mmol/L, %
Percentage of time with sensor glucose in hypoglycaemia
Percentage of time with sensor glucose in the target range below 3.9 mmol/L, %
Percentage of postprandial time with sensor glucose in target range
Percentage of postprandial time with sensor glucose in target range between 3.9 to 10.0 mmol/L
Percentage of postprandial time with sensor glucose in hyperglycaemia
Percentage of postprandial time with sensor glucose in the target range above 10.0mmol/L, %
Percentage of postprandial time with sensor glucose in hypoglycaemia
Percentage of postprandial time with sensor glucose in the target range below 3.9 mmol/L, %

Full Information

First Posted
December 22, 2022
Last Updated
April 5, 2023
Sponsor
Lia Bally
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1. Study Identification

Unique Protocol Identification Number
NCT05671679
Brief Title
Supporting Meal Management in Type 1 Diabetes
Acronym
SUMMIT1
Official Title
Supporting Meal Management in Type 1 Diabetes
Study Type
Interventional

2. Study Status

Record Verification Date
April 2023
Overall Recruitment Status
Recruiting
Study Start Date
March 27, 2023 (Actual)
Primary Completion Date
July 2024 (Anticipated)
Study Completion Date
September 2024 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor-Investigator
Name of the Sponsor
Lia Bally

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No

5. Study Description

Brief 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.
Detailed 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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Type 1 Diabetes
Keywords
Carbohydrates, Continuous glucose measurement, Closed-loop systems, Type 1 Diabetes, Hyperglycaemia, Hypoglycaemia

7. Study Design

Primary Purpose
Prevention
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Model Description
The study will follow a randomized two-arm parallel design. Study visits will be done remotely via video calls or in-clinic when coinciding with usual care appointments. Following a baseline visit and before randomization, baseline characteristics and medical history of the participants will be collected (as detailed in section 4.3). Following randomization, the intervention group will use SNAQ app for the first 3 weeks while the control group will proceed without any modification/intervention by the study team. After the first 3 weeks, the control group will undergo 3 weeks of SNAQ app use (weeks 4-6). At the end of their respective SNAQ app periods (weeks 4-6 for the intervention group and weeks 7-9 for the control group), both groups will discontinue the use of SNAQ app for 3 weeks to assess sustainability of potential effects. Finally, both groups will be offered to use SNAQ app for 3 additional weeks as per their preference (follow-up period).
Masking
None (Open Label)
Allocation
Randomized
Enrollment
44 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
Intervention
Arm Type
Experimental
Arm Description
The intervention group will use SNAQ app for the first 3 weeks (baseline to V1) of the study.
Arm Title
Control
Arm Type
Active Comparator
Arm Description
The control group will continue estimating the carbohydrate count using their traditional methods for the first three weeks of the study (baseline to V1).
Intervention Type
Other
Intervention Name(s)
SNAQ app
Intervention Description
SNAQ is a smartphone food analysis app that estimates the macronutrient content of a meal, based on a single image. The app first determines meal content in terms of food components with input from the user to correct or add further components (e.g. foods, ingredients, sauces, herbs or seasonings). Then, the total macronutrient and energy content of the meal is determined based on the estimated volume and information from a nutritional database. Of note, the application also allows for assessing nutritional content of packaged foods by means of a barcode scanning function. The user can always adapt proposed nutritional contents at their own discretion. Meal macronutrients alongside the food pictures are collected in a detailed log which allows users to review their dietary choices. The product is not conceived by its manufacturer to be used for medical purposes and can thus not be considered a medical device.
Intervention Type
Other
Intervention Name(s)
Traditional carbohydrate counting
Intervention Description
Patients will follow their traditional methods of carbohydrate counting during the control period. In addition to assess sustainability of the intervention, following the control period, the control group will also go an intervention period of 3 weeks using the SNAQ App.
Primary Outcome Measure Information:
Title
Percentage of time with sensor glucose in the target range
Description
Percentage of time with sensor glucose in the target range between 3.9 to 10.0mmol/L, %
Time Frame
3-week intervention period (Day 1 to Day 21)
Secondary Outcome Measure Information:
Title
Percentage of time with sensor glucose in hyperglycaemia
Description
Percentage of time with sensor glucose in the target range above 10.0mmol/L, %
Time Frame
3-week intervention period (Day 1 to Day 21)
Title
Percentage of time with sensor glucose in hypoglycaemia
Description
Percentage of time with sensor glucose in the target range below 3.9 mmol/L, %
Time Frame
3-week intervention period (Day 1 to Day 21)
Title
Percentage of postprandial time with sensor glucose in target range
Description
Percentage of postprandial time with sensor glucose in target range between 3.9 to 10.0 mmol/L
Time Frame
3-week intervention period (Day 1 to Day 21)
Title
Percentage of postprandial time with sensor glucose in hyperglycaemia
Description
Percentage of postprandial time with sensor glucose in the target range above 10.0mmol/L, %
Time Frame
3-week intervention period (Day 1 to Day 21)
Title
Percentage of postprandial time with sensor glucose in hypoglycaemia
Description
Percentage of postprandial time with sensor glucose in the target range below 3.9 mmol/L, %
Time Frame
3-week intervention period (Day 1 to Day 21)

10. Eligibility

Sex
All
Minimum Age & Unit of Time
12 Years
Maximum Age & Unit of Time
20 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria: Written informed consent Adults (aged 18 years or older) Type 1 diabetes (as defined by World Health Organization (WHO) for at least 12 month) Current use of a commercial hybrid closed-loop system HbA1c≤12% (measured within the past 3 months) Willing to use the SNAQ app on a daily basis for over 3 weeks The participant is willing to follow study specific instructions and share their treatment data with the study team Exclusion Criteria: Any physical or psychological disease or condition likely to interfere with the normal conduct of the study and interpretation of the study results Previous use of SNAQ app for more than 5 days within the past 3 months Self-reported pregnancy, planed pregnancy within next 3 months or breast-feeding Severe visual impairment Severe hearing impairment Lack of reliable telephone facility for contact Concomitant participation in another trial that interferes with the normal conduct of the study and interpretation of the study results Participant not proficient in German
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Lia Bally, MD PhD
Phone
+41 31 63 2 36 77
Email
lia.bally@insel.ch
First Name & Middle Initial & Last Name or Official Title & Degree
David Herzig, PhD
Phone
+41 31 66 4 29 47
Email
david.herzig@extern.insel.ch
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Lia Bally, MD PhD
Organizational Affiliation
UDEM Inselspital, University Hospital of Berne, and University Berne
Official's Role
Principal Investigator
Facility Information:
Facility Name
Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism (UDEM), Inselspital, Bern University Hospital
City
Bern
State/Province
BE
ZIP/Postal Code
3010
Country
Switzerland
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Lia Bally, Prof.
Phone
+41 31 632 36 77
Email
lia.bally@insel.ch

12. IPD Sharing Statement

Plan to Share IPD
Yes
IPD Sharing Plan Description
Anonymised individual participant data will be shared after inquiry via a validated sharing platform (yet to be defined). Anonymised data packages will be available once the final study results are published in a peer-reviewed journal.
IPD Sharing Time Frame
After publication of the study results in a peer-reviewed journal.
IPD Sharing Access Criteria
Contact with an approval by the corresponding author.

Learn more about this trial

Supporting Meal Management in Type 1 Diabetes

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