Innovative Approaches in Diabetes Care

Not Applicable

Recruiting

• Conditions: Type 2 Diabetes; AI-supported Real-time Dietary Feedback; M-health

• Registration Number: NCT05687968
• Lead Sponsor: Taipei Medical University

Brief Summary

In Taiwan, an estimated 2.3 million individuals have diabetes, with a 44% increase observed among young adults and adolescents. Poor dietary habits and sedentary lifestyles are major risk factors for type 2 diabetes. The widespread use of smartphones has facilitated the development of digital health technologies, including digital food photography and artificial intelligence (AI), which show promise for personalized nutrition care and health promotion. While such technologies have demonstrated short-term success in diabetes management, their long-term effectiveness remains uncertain.

This study aims to evaluate the effectiveness of a digital eHealth care intervention for individuals with diabetes. Participants will be recruited from the Diabetes Shared Care Network and community care centers in Taiwan and followed for 12 months. Eligible participants will be randomly assigned by computer to either a control or an eHealth care group.

• eHealth Group: Receives a 10-minute digital nutrition education session using the lab-developed "3D/AR MetaFood food portion education platform" (https://sketchfab.com/susanlab108/collections) and is required to submit weekly dietary records through food images using the "Formosa FoodAPP." Participants will receive immediate dietary feedback from nutritionists, followed by AI-generated personalized feedback on the glycemic index (GI) and glycemic load (GL) of their meals. They will also be provided with educational videos on healthy eating, physical activity, and selecting low-GI/GL foods.

Anthropometric measurements and baseline questionnaires will be collected at enrollment. Blood biochemistry, including HbA1c, will be measured at baseline, and at 3, 6, 9, and 12 months. Collected food image data will be used to train AI systems for real-time dietary feedback and to explore the relationship between nutrient intake and long-term glycemic control.

Detailed Description

Objective:

This study aims to evaluate the effectiveness of eHealth interventions in the care of patients with diabetes.

Study Design:

Adult participants with diabetes will be recruited from the Diabetes Shared Care Network and community centers for a 12-month intervention study.

Eligibility Criteria:

Participants must be aged 20 years or older, diagnosed with prediabetes or diabetes, of Taiwanese nationality or fluent in Mandarin or Taiwanese, not pregnant or breastfeeding, and capable (or assisted by a caregiver) of using a smartphone to photograph and record meals. Individuals with diagnosed eating disorders will be excluded.

Intervention Arms

• eHealth Group: Participants will receive 10 minutes of portion size and nutrition education using the lab-developed "MetaFood: 3D/AR Digital Food Education Platform" (https://sketchfab.com/susanlab108/collections). They are required to submit a food image-based dietary record once per week using the lab-developed "Formosa FoodAPP" (1). Trained nutritionists will assess the dietary images using a lab-developed "Digital Photographic Food Atlas" and provide real-time dietary feedback via a LINE social group.

Additionally, the eHealth group will receive educational materials including videos and digital leaflets on:

1. How to use the Formosa FoodAPP

2. Introduction to MyPlate: food classification and portion sizes

3. The impact of food on blood glucose: understanding glycemic index (GI) and glycemic load (GL)

4. GI/GL values of commonly consumed Taiwanese foods

5. Interpretation of blood test reports

6. Making food choices when dining out

7. Basics of exercise

8. Eating during festivals

From the 5th month onward, personalized dietary feedback on the GI/GL values of consumed meals will be provided by lab-developed AI systems, continuing until the end of the study. AI systems for food recognition and the LINE group are managed by lab staff.

Biological Measures:

Fasting blood glucose and lipid profiles will be collected every three months during clinic visits.

Sample Size Justification:

Using G\*Power 3.1.9.7, the primary endpoint is the effect of AI-supported dietary feedback on glycemic control in middle-aged and older adults with type 2 diabetes. Based on Lee et al.'s study on the combination of human and AI-supported nutrition app, the estimated mean HbA1c difference is 0.52% (7.52±0.81 vs. 7.00±0.66) at 12 months. Assuming an effect size of 0.70, 80% power, and 5% significance, 33 participants per group are needed. Accounting for a 10-20% attrition rate, a total sample of 36-40 participants will be recruited.

Data Collection:

Baseline sociodemographic and anthropometric data will be collected by state-registered dietitians. Standard biochemical test results, available from Taiwan's National Health Insurance, will be collected every three months. Nutrition knowledge, and perceptions and usage of digital food technologies, will be assessed via an online questionnaire developed from the theoretical framework, literature review, and validated by experts. Weekly dietary records will be logged via the Formosa FoodAPP (1).

Data will include:

* Demographics: Age, sex, education level, disease history

* Weekly dietary records

* Anthropometrics: Height, weight, BMI, waist circumference, grip strength, muscle strength

* Biochemical data: Fasting glucose, lipid profiles, renal function indicators (clinic-based, insurance-covered tests)

Statistical Analysis:

Data will be analyzed using GraphPad Prism 5 (La Jolla, CA, USA).

* Normality: Kolmogorov-Smirnov test

* Continuous variables: Mean ± 95% CI; analyzed via t-tests or ANOVA

* Categorical variables: Frequencies/percentages; analyzed via Chi-square or Fisher's exact test

* Correlations: Spearman's coefficient; logistic linear regression

* Nonparametric comparisons: Kruskal-Wallis test

* Longitudinal analysis: Generalized Linear Mixed Model (GLMM) for glycemic changes and comorbidity risks

* Significance threshold: p \< 0.05

Study Timeline

• Study Start: 2022-10-19
• Study First Submitted: 2023-01-09
• Study First Submitted QC: 2023-01-09
• Study First Posted: 2023-01-18
• Status Verified: 2025-06
• Last Update Submitted: 2025-07-24
• Last Update Posted: 2025-07-30
• Primary Completion: 2025-10-01
• Study Completion: 2025-10-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 39

Inclusion Criteria

1. 20 years old or older
2. Pre-diabetes or diabetes
3. Taiwan nationality or fluent in Mandarin or Taiwanese
4. Not pregnant or breastfeeding
5. Capable (or assisted by a caregiver) of using a smartphone to photograph and record meals

Exclusion Criteria

1. Eating disorders
2. Undergoing treatment for severe illnesses that could affect normal dietary intake (e.g., cancer)
3. Unable to use a smartphone to take photos and record food intake.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

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
Triglyceride (TG)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Triglyceride (TG)
Dietary Intake (Food portion)	baseline, 3 month, 6 month, 9 month, 12 month	the change of food portion
Dietary Intake (Sodium)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Sodium
Dietary Intake (Magnesium)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Magnesium
Dietary Intake (Dietary GI)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Dietary GI
Dietary Intake (Dietary GL)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Dietary GL
Total cholesterol (TC)	baseline, 3 month, 6 month, 9 month, 12 month	the change of total cholesterol (TC)
Low-density lipoprotein-cholesterol (LDL-C)	baseline, 3 month, 6 month, 9 month, 12 month	the change of low-density lipoprotein-cholesterol (LDL-C)
Dietary Intake (Energy)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Energy
Dietary Intake (Fiber)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Fiber
Dietary Intake (Fat)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Fat
Dietary Intake (Calcium)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Calcium
Dietary Intake (Iron)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Iron
Triglyceride-glucose (TyG)	baseline, 3 month, 6 month, 9 month, 12 month	the change of triglyceride-glucose (TyG)
Estimated Glomerular filtration rate (eGFR)	baseline, 3 month, 6 month, 9 month, 12 month	the change of estimated Glomerular filtration rate (eGFR)
Creatinine (CRE)	baseline, 3 month, 6 month, 9 month, 12 month	the change of creatinine (CRE)
Dietary Intake (Carbohydrate)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Carbohydrate
Dietary Intake (Sugar)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Sugar
Dietary Intake (Cholesterol)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Cholesterol
Dietary Intake (Potassium)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Potassium
Dietary Intake (Vitamin C)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Vitamin C
Dietary Intake (Protein)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Protein
Dietary Intake (Saturated Fat)	baseline, 3 month, 6 month, 9 month, 12 month	the change of Saturated Fat

Trial Locations

Locations (1)

Jung-Su Chang; 🇨🇳 Taipei, Taiwan