Supporting Meal Management in Type 1 Diabetes (SUMMIT1)

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.

Study Overview

• Status: Completed
• Conditions: Type 1 Diabetes
• Intervention / Treatment: Other: SNAQ app; Other: Traditional carbohydrate counting

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.

• Study Type: Interventional
• Enrollment (Actual): 44
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Switzerland
  • BE
    • Bern, BE, Switzerland, 3010
      • Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism (UDEM), Inselspital, Bern University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 12 years to 20 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Intervention; The intervention group will use SNAQ app for the first 3 weeks (baseline to V1) of the study.	Other: SNAQ app; 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.
Active Comparator: Control; The control group will continue estimating the carbohydrate count using their traditional methods for the first three weeks of the study (baseline to V1).	Other: Traditional carbohydrate counting; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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, %	3-week intervention period (Day 1 to Day 21)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Percentage of time with sensor glucose in hyperglycaemia	Percentage of time with sensor glucose in the target range above 10.0mmol/L, %	3-week intervention period (Day 1 to Day 21)
Percentage of time with sensor glucose in hypoglycaemia	Percentage of time with sensor glucose in the target range below 3.9 mmol/L, %	3-week intervention period (Day 1 to Day 21)
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	3-week intervention period (Day 1 to Day 21)
Percentage of postprandial time with sensor glucose in hyperglycaemia	Percentage of postprandial time with sensor glucose in the target range above 10.0mmol/L, %	3-week intervention period (Day 1 to Day 21)
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, %	3-week intervention period (Day 1 to Day 21)

Collaborators and Investigators

• Sponsor: Lia Bally
• Investigators: Principal Investigator: Lia Bally, MD PhD, UDEM Inselspital, University Hospital of Berne, and University Berne

Study record dates

Study Major Dates

• Study Start (Actual): March 27, 2023
• Primary Completion (Actual): May 8, 2024
• Study Completion (Actual): May 8, 2024

Study Registration Dates

• First Submitted: December 22, 2022
• First Submitted That Met QC Criteria: December 22, 2022
• First Posted (Actual): January 5, 2023

Study Record Updates

• Last Update Posted (Estimated): November 13, 2024
• Last Update Submitted That Met QC Criteria: November 11, 2024
• Last Verified: June 1, 2024

More Information

Terms related to this study

• Keywords: Hyperglycaemia; Type 1 Diabetes; Carbohydrates; Hypoglycaemia; Continuous glucose measurement; Closed-loop systems
• Additional Relevant MeSH Terms: Endocrine System Diseases; Metabolic Diseases; Autoimmune Diseases; Immune System Diseases; Glucose Metabolism Disorders; Diabetes Mellitus; Diabetes Mellitus, Type 1
• Other Study ID Numbers: SUMMIT1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD 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.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No