Innovative Approaches in Diabetes Care
- Conditions
- M-healthMobile ApplicationDietary AssessmentDiabetes Mellitus
- Interventions
- Behavioral: "3D/AR MetaFood " nutrition education, and receive instant feedback from the nutritionists or AI after recording their dietary food image.Behavioral: conventional nutrition education by dietitian
- Registration Number
- NCT05687968
- Lead Sponsor
- Taipei Medical University
- Brief Summary
It is estimated that 2.3 million Taiwanese have diabetes and there is a 44% increased rate among young adults and adolescents. Poor dietary habits and sedentary lifestyle are the major risk factors for type 2 diabetes. The growing availability of smartphones has boosted the development of new technologies that incorporate the use of digital food photography as health promotion and individualized nutrition care. Digital health technology is also used to prevent and treat diabetes with good degree of successes in the short term but the long term effect remains unknown. The broad aim of this study is to evaluate the effectiveness of digital technology eHealth care for diabetic patients. A total of 300 diabetic patients will be recruited from Diabetes Shared Care Network and community care center in Taiwan and follow up 12 months. A simple randomization by computer system will be used to randomly allocate subjects into 2 groups: control group and eHealth care. The control group (n=100) of diabetic patients will receive conventional health and nutrition education from state registered dietitian. The eHealth care group (n=200) of diabetic patients will receive a 10 mins of food portion size nutrition education using " 3D/AR MetaFood platform" and is required to record their consume meal by food image once a week using Taiwan FoodAPP. Patients in the eHealth group will receive instant feedback from the nutritionists or artificial intelligence (AI) for the information of glycemic index (GI) and glycemic load (GL), and educational video related to healthy eating or how to select GI/GL food. Anthropometry, and baseline questionnaires will be collected at baseline. Blood biochemistry (e.g. HbA1c) and body weight will be collected at baseline, 3, 6, 9, and 12 months. The collected food image data will be used for AI training to identify the relationship between the patient's diet and blood glucose changes over time.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 500
- ≧20 years old
- pre-diabetes or diabetes
- Taiwan nationality or people who can speak Chinese fluently
- Currently not pregnant or breastfeeding
- Available to use smartphone to take photos and record food
- Eating disorders
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description eHealth care group "3D/AR MetaFood " nutrition education, and receive instant feedback from the nutritionists or AI after recording their dietary food image. The eHealth group will receive instant feedback from the nutritionists and/or AI after recording their dietary food image, and receive "3D/AR MetaFood" food portion and nutrition education. control group conventional nutrition education by dietitian The control receive conventional health and nutrition education from state registered dietitian.
- Primary Outcome Measures
Name Time Method HbA1c baseline, 3 month, 6 month, 9 month, 12 month the change of HbA1c
Fasting glucose baseline, 3 month, 6 month, 9 month, 12 month the change of Fasting glucose
- Secondary Outcome Measures
Name Time Method Body mass index (kg/m2) baseline, 3 month, 6 month, 9 month, 12 month the change of Body mass index
body weight baseline, 3 month, 6 month, 9 month, 12 month the change of body weight
Triglyceride (TG) baseline, 3 month, 6 month, 9 month, 12 month the change of Triglyceride (TG)
Trial Locations
- Locations (1)
Jung-Su Chang
🇨🇳Taipei, Taiwan