Ignite Creation Date:
2025-12-24 @ 5:38 PM
Ignite Modification Date:
2025-12-31 @ 9:31 AM
Study NCT ID:
NCT05687968
Status:
RECRUITING
Last Update Posted:
2025-07-30
First Post:
2023-01-09
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Innovative Approaches in Diabetes Care
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003924', 'term': 'Diabetes Mellitus, Type 2'}, {'id': 'D003920', 'term': 'Diabetes Mellitus'}], 'ancestors': [{'id': 'D044882', 'term': 'Glucose Metabolism Disorders'}, {'id': 'D008659', 'term': 'Metabolic Diseases'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D064686', 'term': 'Nutritionists'}], 'ancestors': [{'id': 'D006282', 'term': 'Health Personnel'}, {'id': 'D005159', 'term': 'Health Care Facilities Workforce and Services'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'SUPPORTIVE_CARE', 'interventionModel': 'SINGLE_GROUP'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 39}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2022-10-19', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-06', 'completionDateStruct': {'date': '2025-10-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-07-24', 'studyFirstSubmitDate': '2023-01-09', 'studyFirstSubmitQcDate': '2023-01-09', 'lastUpdatePostDateStruct': {'date': '2025-07-30', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-01-18', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-10-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'HbA1c', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of HbA1c'}, {'measure': 'Fasting glucose', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Fasting glucose'}], 'secondaryOutcomes': [{'measure': 'Triglyceride (TG)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Triglyceride (TG)'}, {'measure': 'Total cholesterol (TC)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of total cholesterol (TC)'}, {'measure': 'Low-density lipoprotein-cholesterol (LDL-C)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of low-density lipoprotein-cholesterol (LDL-C)'}, {'measure': 'Triglyceride-glucose (TyG)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of triglyceride-glucose (TyG)'}, {'measure': 'Estimated Glomerular filtration rate (eGFR)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of estimated Glomerular filtration rate (eGFR)'}, {'measure': 'Creatinine (CRE)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of creatinine (CRE)'}, {'measure': 'Dietary Intake (Food portion)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of food portion'}, {'measure': 'Dietary Intake (Energy)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Energy'}, {'measure': 'Dietary Intake (Carbohydrate)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Carbohydrate'}, {'measure': 'Dietary Intake (Fiber)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Fiber'}, {'measure': 'Dietary Intake (Sugar)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Sugar'}, {'measure': 'Dietary Intake (Protein)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Protein'}, {'measure': 'Dietary Intake (Fat)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Fat'}, {'measure': 'Dietary Intake (Saturated Fat)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Saturated Fat'}, {'measure': 'Dietary Intake (Cholesterol)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Cholesterol'}, {'measure': 'Dietary Intake (Sodium)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Sodium'}, {'measure': 'Dietary Intake (Potassium)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Potassium'}, {'measure': 'Dietary Intake (Calcium)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Calcium'}, {'measure': 'Dietary Intake (Magnesium)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Magnesium'}, {'measure': 'Dietary Intake (Iron)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Iron'}, {'measure': 'Dietary Intake (Vitamin C)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Vitamin C'}, {'measure': 'Dietary Intake (Dietary GI)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Dietary GI'}, {'measure': 'Dietary Intake (Dietary GL)', 'timeFrame': 'baseline, 3 month, 6 month, 9 month, 12 month', 'description': 'the change of Dietary GL'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Diabetes Mellitus', 'AI-supported real-time dietary feedback', 'M-health'], 'conditions': ['Type 2 Diabetes', 'AI-supported Real-time Dietary Feedback', 'M-health']}, 'referencesModule': {'references': [{'pmid': '37776838', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '36821833', 'type': 'BACKGROUND', 'citation': '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.'}]}, 'descriptionModule': {'briefSummary': '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.\n\nThis 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.\n\n• 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.\n\nAnthropometric 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.', 'detailedDescription': 'Objective:\n\nThis study aims to evaluate the effectiveness of eHealth interventions in the care of patients with diabetes.\n\nStudy Design:\n\nAdult participants with diabetes will be recruited from the Diabetes Shared Care Network and community centers for a 12-month intervention study.\n\nEligibility Criteria:\n\nParticipants 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.\n\nIntervention Arms\n\n• 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.\n\nAdditionally, the eHealth group will receive educational materials including videos and digital leaflets on:\n\n1. How to use the Formosa FoodAPP\n2. Introduction to MyPlate: food classification and portion sizes\n3. The impact of food on blood glucose: understanding glycemic index (GI) and glycemic load (GL)\n4. GI/GL values of commonly consumed Taiwanese foods\n5. Interpretation of blood test reports\n6. Making food choices when dining out\n7. Basics of exercise\n8. Eating during festivals\n\nFrom 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.\n\nBiological Measures:\n\nFasting blood glucose and lipid profiles will be collected every three months during clinic visits.\n\nSample Size Justification:\n\nUsing 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.\n\nData Collection:\n\nBaseline 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).\n\nData will include:\n\n* Demographics: Age, sex, education level, disease history\n* Weekly dietary records\n* Anthropometrics: Height, weight, BMI, waist circumference, grip strength, muscle strength\n* Biochemical data: Fasting glucose, lipid profiles, renal function indicators (clinic-based, insurance-covered tests)\n\nStatistical Analysis:\n\nData will be analyzed using GraphPad Prism 5 (La Jolla, CA, USA).\n\n* Normality: Kolmogorov-Smirnov test\n* Continuous variables: Mean ± 95% CI; analyzed via t-tests or ANOVA\n* Categorical variables: Frequencies/percentages; analyzed via Chi-square or Fisher\'s exact test\n* Correlations: Spearman\'s coefficient; logistic linear regression\n* Nonparametric comparisons: Kruskal-Wallis test\n* Longitudinal analysis: Generalized Linear Mixed Model (GLMM) for glycemic changes and comorbidity risks\n* Significance threshold: p \\< 0.05'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '20 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. 20 years old or older\n2. Pre-diabetes or diabetes\n3. Taiwan nationality or fluent in Mandarin or Taiwanese\n4. Not pregnant or breastfeeding\n5. Capable (or assisted by a caregiver) of using a smartphone to photograph and record meals\n\nExclusion Criteria:\n\n1. Eating disorders\n2. Undergoing treatment for severe illnesses that could affect normal dietary intake (e.g., cancer)\n3. Unable to use a smartphone to take photos and record food intake.'}, 'identificationModule': {'nctId': 'NCT05687968', 'briefTitle': 'Innovative Approaches in Diabetes Care', 'organization': {'class': 'OTHER', 'fullName': 'Taipei Medical University'}, 'officialTitle': 'Innovative Approaches in Diabetes Care', 'orgStudyIdInfo': {'id': 'N202101052'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'eHealth group', 'description': 'Participants in the eHealth group will receive a multi-component digital health intervention. This includes:\n\n1. Real-time personalized dietary feedback based on weekly food image submissions via the Formosa FoodAPP, delivered initially by trained nutritionists and later by AI.\n2. A 10-minute digital food portion size and nutrition education session using the lab-developed "3D/AR MetaFood" platform.\n3. Access to educational videos on healthy eating, glycemic index/load, physical activity, and digital food recording.', 'interventionNames': ['Behavioral: Real-Time Personalized Dietary Feedback (via AI and Nutritionist)', 'Behavioral: conventional nutrition education by dietitian']}], 'interventions': [{'name': 'Real-Time Personalized Dietary Feedback (via AI and Nutritionist)', 'type': 'BEHAVIORAL', 'description': '* Behavioral: "3D/AR MetaFood" Portion Size and Nutrition Education\n* Behavioral: Nutrition and Physical Activity Educational Videos', 'armGroupLabels': ['eHealth group']}, {'name': 'conventional nutrition education by dietitian', 'type': 'BEHAVIORAL', 'description': 'The participants receive conventional health and nutrition education from state registered dietitian.', 'armGroupLabels': ['eHealth group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '110', 'city': 'Taipei', 'status': 'RECRUITING', 'country': 'Taiwan', 'contacts': [{'name': 'Jung-Su Chang, PhD', 'role': 'CONTACT', 'email': 'susanchang@tmu.edu.tw', 'phone': '886-66382736', 'phoneExt': '6564'}], 'facility': 'Jung-Su Chang', 'geoPoint': {'lat': 25.05306, 'lon': 121.52639}}], 'centralContacts': [{'name': 'Jung-Su Chang, PhD.', 'role': 'CONTACT', 'email': 'susanchang@tmu.edu.tw', 'phone': '886-66382736', 'phoneExt': '6506'}], 'overallOfficials': [{'name': 'Jung-Su Chang, PhD.', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'College of Nutrition, Taipei Medical University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Taipei Medical University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}