Supporting Meal Management in Type 1 Diabetes
- Conditions
- Type 1 Diabetes
- Interventions
- Other: Traditional carbohydrate countingOther: SNAQ app
- Registration Number
- NCT05671679
- Lead Sponsor
- Lia Bally
- 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.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 44
- 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
- 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 & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Control Traditional carbohydrate counting 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 SNAQ app The intervention group will use SNAQ app for the first 3 weeks (baseline to V1) of the study.
- Primary Outcome Measures
Name Time Method Percentage of time with sensor glucose in the target range 3-week intervention period (Day 1 to Day 21) Percentage of time with sensor glucose in the target range between 3.9 to 10.0mmol/L, %
- Secondary Outcome Measures
Name Time Method Percentage of postprandial time with sensor glucose in hypoglycaemia 3-week intervention period (Day 1 to Day 21) Percentage of postprandial time with sensor glucose in the target range below 3.9 mmol/L, %
Percentage of postprandial time with sensor glucose in target range 3-week intervention period (Day 1 to Day 21) Percentage of postprandial time with sensor glucose in target range between 3.9 to 10.0 mmol/L
Percentage of time with sensor glucose in hyperglycaemia 3-week intervention period (Day 1 to Day 21) Percentage of time with sensor glucose in the target range above 10.0mmol/L, %
Percentage of time with sensor glucose in hypoglycaemia 3-week intervention period (Day 1 to Day 21) Percentage of time with sensor glucose in the target range below 3.9 mmol/L, %
Percentage of postprandial time with sensor glucose in hyperglycaemia 3-week intervention period (Day 1 to Day 21) Percentage of postprandial time with sensor glucose in the target range above 10.0mmol/L, %
Trial Locations
- Locations (1)
Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism (UDEM), Inselspital, Bern University Hospital
🇨🇭Bern, BE, Switzerland