Innovative Approaches in Diabetes Care

NCT ID: NCT05687968

Last Updated: 2025-07-30

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 39 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-10-19
• Study Completion Date: 2025-10-01

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

Conditions

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

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: SUPPORTIVE_CARE
• Blinding Strategy: NONE

Study Groups

eHealth group

Participants in the eHealth group will receive a multi-component digital health intervention. This includes:

1. Real-time personalized dietary feedback based on weekly food image submissions via the Formosa FoodAPP, delivered initially by trained nutritionists and later by AI.

2. A 10-minute digital food portion size and nutrition education session using the lab-developed "3D/AR MetaFood" platform.

3. Access to educational videos on healthy eating, glycemic index/load, physical activity, and digital food recording.

Group Type: EXPERIMENTAL

Real-Time Personalized Dietary Feedback (via AI and Nutritionist)

Intervention Type: BEHAVIORAL

• Behavioral: "3D/AR MetaFood" Portion Size and Nutrition Education
• Behavioral: Nutrition and Physical Activity Educational Videos

conventional nutrition education by dietitian

Intervention Type: BEHAVIORAL

The participants receive conventional health and nutrition education from state registered dietitian.

Interventions

Real-Time Personalized Dietary Feedback (via AI and Nutritionist)

• Behavioral: "3D/AR MetaFood" Portion Size and Nutrition Education
• Behavioral: Nutrition and Physical Activity Educational Videos

Intervention Type: BEHAVIORAL

conventional nutrition education by dietitian

The participants receive conventional health and nutrition education from state registered dietitian.

Intervention Type: BEHAVIORAL

Eligibility Criteria

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.

• Minimum Eligible Age: 20 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Taipei Medical University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Jung-Su Chang, PhD.

Role: PRINCIPAL_INVESTIGATOR

College of Nutrition, Taipei Medical University

Locations

Jung-Su Chang

Taipei, , Taiwan

Site Status: RECRUITING

Countries

Taiwan

Central Contacts

Jung-Su Chang, PhD.

Role: CONTACT

susanchang@tmu.edu.tw

886-66382736 ext. 6506

Facility Contacts

Jung-Su Chang, PhD

Role: primary

susanchang@tmu.edu.tw

886-66382736 ext. 6564

References

Ho DKN, Chiu WC, Kao JW, Tseng HT, Yao CY, Su HY, Wei PH, Le NQK, Nguyen HT, Chang JS. Mitigating errors in mobile-based dietary assessments: Effects of a data modification process on the validity of an image-assisted food and nutrition app. Nutrition. 2023 Dec;116:112212. doi: 10.1016/j.nut.2023.112212. Epub 2023 Sep 9.

Reference Type: BACKGROUND

PMID: 37776838 (View on PubMed)

Lee YB, Kim G, Jun JE, Park H, Lee WJ, Hwang YC, Kim JH. An Integrated Digital Health Care Platform for Diabetes Management With AI-Based Dietary Management: 48-Week Results From a Randomized Controlled Trial. Diabetes Care. 2023 May 1;46(5):959-966. doi: 10.2337/dc22-1929.

Reference Type: BACKGROUND

PMID: 36821833 (View on PubMed)

Other Identifiers

N202101052

Identifier Type: -

Identifier Source: org_study_id