Innovative Approaches in Diabetes Care

July 24, 2025 updated by: Taipei Medical University

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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 Type

Interventional

Enrollment (Estimated)

39

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Taiwan
      • Taipei, Taiwan, 110
        • Recruiting
        • Jung-Su Chang
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

20 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Supportive Care
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: 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.
Behavioral: conventional nutrition education by dietitian
The participants receive conventional health and nutrition education from state registered dietitian.
Behavioral: 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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
HbA1c
Time Frame: baseline, 3 month, 6 month, 9 month, 12 month
the change of HbA1c
baseline, 3 month, 6 month, 9 month, 12 month
Fasting glucose
Time Frame: baseline, 3 month, 6 month, 9 month, 12 month
the change of Fasting glucose
baseline, 3 month, 6 month, 9 month, 12 month

Secondary Outcome Measures

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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Taipei Medical University

Investigators

  • Principal Investigator: Jung-Su Chang, PhD., College of Nutrition, Taipei Medical University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 19, 2022

Primary Completion (Estimated)

October 1, 2025

Study Completion (Estimated)

October 1, 2025

Study Registration Dates

First Submitted

January 9, 2023

First Submitted That Met QC Criteria

January 9, 2023

First Posted (Actual)

January 18, 2023

Study Record Updates

Last Update Posted (Actual)

July 30, 2025

Last Update Submitted That Met QC Criteria

July 24, 2025

Last Verified

June 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • N202101052

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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