SnapDandCGMinType2DM
ConditionType2 Diabetes Mellitus
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18-65
SponsorRamathibodi Hospital
About this trial
Study Title: The Effectiveness of an AI-powered Thai food analysis (SnapD) and Continuous Glucose Monitoring on Glycemic Control in Patients with Type 2 Diabetes and Overweight or Obesity: A Randomized Controlled Pilot Study Rationale: Effective dietary management is the cornerstone of treating Type 2 Diabetes (T2DM) and obesity. However, traditional manual food logging is often inaccurate and burdensome. While digital tools and Continuous Glucose Monitoring (CGM) have shown promise internationally, there is a lack of validated AI-powered tools specifically designed for Thai cuisine. This study introduces SnapD, an AI-powered platform (utilizing Gemini 2.5 Flash) designed to recognize Thai food, estimate nutritional values, and integrate with CGM data to provide personalized feedback.
The primary goal of this pilot study is to evaluate the efficacy of the SnapD application, both as a standalone tool and in combination with CGM, compared to Standard of Care in improving glycemic control (HbA1c) over 8 weeks. Additionally, the study aims to assess the feasibility, participant adherence, and safety of these digital interventions to inform a future, fully powered randomized controlled trial.
Study Design: This is an 8-week, randomized, open-label, parallel-group, superiority pilot study with a 1:1:1 allocation ratio. A total of 45 participants will be enrolled and assigned to one of three arms:
1. Intervention Arm 1: SnapD application + Real-time CGM + Diabetes Self-Management Education and Support (DSMES) 2. Intervention Arm 2: SnapD application standalone + DSMES 3. Control Arm: DSMES alone Inclusion Criteria Highlights: Adults (18-65 years) diagnosed with T2D with BMI \> 23 kg/m² (overweight/obesity) with HbA1c between 6.5% and 9.0% with Must possess a compatible smartphone/tablet Procedures: Baseline (Visit 1): All participants receive 20-30 minutes of DSMES. Intervention groups receive training on SnapD. Arm 1 receives a 15-day CGM sensor.During Study: Intervention arms log meals via SnapD (at least twice daily). Nutritionists conduct bi-weekly follow-up calls to address technical issues and provide support.
End-of-Study (Week 8): Assessment of HbA1c, body weight, waist circumference, lipid profile, and patient-reported outcomes (self-care activities and user satisfaction) Primary Outcome: Mean change in HbA1c from baseline to 8 weeks Secondary Outcomes: Changes in Fasting Plasma Glucose (FPG), body weight, waist circumference, and lipid profiles, Diabetes self-management scores (SDSCA questionnaire), User satisfaction with the SnapD application, Incidence of adverse events (hypoglycemia/hyperglycemia).
Significance: This study will provide preliminary evidence on the synergistic benefits of AI-driven nutritional feedback and CGM in a Thai-specific context, supporting the development of scalable, culturally adapted digital health technologies for diabetes management.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Aged 18 to 65 years, male or female at birth
Diagnosed with type 2 Diabetes Mellitus with overweight or obesity (BMI>23 kg/m²)
Hemoglobin A1c (HbA1c) 6.5-9% measured within 3 months prior to the screening date
Willing to maintain their current antidiabetic medication regimen without dose adjustment for the entire 8-week study duration
Disqualifiers
Currently pregnant, plan pregnancy or breastfeeding during the 8-week study period
Current participation in another interventional clinical trial
Current use of insulin or incretin-based therapies (e.g., GLP-1 Receptor Agonists, GIP/GLP-1 Receptor Agonists)
Presence of severe hearing or visual impairment that, in the investigator's judgment, would preclude the participant from safely and effectively using the SnapD application or the CGM device
Trial design
Design model
Parallel
Treatments tested in this trial
SnapD
DeviceSnapD, developed by the Division of Endocrinology and Metabolism, Department of Medicine, Ramathibodi Hospital, is a Progressive Web App. It is built using React 18.3.1, TypeScript, and Vite for responsive performance on both mobile and desktop platforms. The application utilizes Supabase for database management, which operates on a PostgreSQL backend.
Linx CGM
DeviceThe Linx CGM system, manufactured by Connect Diagnostics, is a real-time device that measures glucose concentrations in the interstitial fluid. It is an all-in-one device, integrating the glucose sensor, applicator, and transmitter into a single unit. The device has a diameter not exceeding 22 mm and a weight not exceeding 2.2 g. The sensor has a maximum operational life (wear time) of 15 days. It demonstrates a Mean Absolute Relative Difference (MARD) not exceeding 8.67%, which meets the standard accuracy requirement of \<10%. This device was registered as a medical device by the Thai Food and Drug Administration (Thai FDA), Ministry of Public Health, in January 2025, for the indication of management of diabetes in adults age 18 and older (as shown in the attached document) .
Diabetes Self-Management Education (DSMES)
Other interventionParticipants will receive 1 single session of DSMES, 20-30 minutes/session. This single session will be delivered at the baseline visit only. All personnel providing DSMES are Certified Dietitians (CD) and/or have passed the Certified Diabetes Educator (Thai CDE) examination
Treatment groups
45 Participants
are divided into 3 treatment groupsGroup A: SnapD + CGMExperimental treatment 3 interventions
Group B: SnapD Application onlyExperimental treatment 2 interventions
Group C: Standard Care (DSMES)Placebo comparator 1 intervention
Trial outcomes
Primary outcomes
1
To evaluate the efficacy of two intervention arms on glycemic control
HbA1c level(%)
Time frame
baseline, 8 weeksSecondary outcomes
1
To assess changes in other glycemic parameter
Fasting plasma glucose(mg/dL)
Time frame
baseline, 8 weeks2
To assess changes in anthropometric measurements
Body Mass Index(Kg/m2)
Time frame
baseline, 8 weeks3
To evaluate changes in metabolic parameters
• change in systolic and diastolic blood pressure (mmHg)
Time frame
baseline, 8 weeks4
To evaluate changes in diabetic self-management activities via questionnaire
A modified version of the SDSCA tool, developed by Rattanaporn Jeerawattana, encompassing domains of diet, exercise, self-monitoring, foot care, and medication. The total score ranges from 0 to 133, where higher scores indicate better diabetes self-care activities.
Time frame
Baseline, 8 weeksSponsors and contacts
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Source ClinicalTrials.gov