Type 1 Diabetes Mellitus
Conditions
Keywords
Type 1 Diabetes, Carbohydrate Counting, Artificial Intelligence, AI-Powered Education, Digital Health
Brief summary
The goal of this clinical trial is to learn whether an AI-powered carbohydrate counting educational platform can help parents of children with type 1 diabetes improve their carbohydrate counting skills and diabetes management. The study will include parents or primary caregivers of children aged 2-12 years with type 1 diabetes. The main questions it aims to answer are: * Is the AI-powered educational platform feasible, acceptable, and easy for parents to use? * Can the platform improve carbohydrate counting accuracy, parental confidence in diabetes management, and diabetes outcomes compared with usual education alone? Researchers will compare parents who receive access to the AI-powered carbohydrate counting educational platform plus usual diabetes education with parents who receive usual diabetes education alone to see whether the AI-supported approach provides additional benefits. Participants will: * Complete baseline assessments, including questionnaires and a carbohydrate counting test. * Be randomly assigned to either the AI-supported education group or the usual education group. * Use the assigned educational resources for 12 weeks. * Complete a follow-up assessment at 6 weeks and a final assessment at 12 weeks. * Provide information about their child's diabetes management, including HbA1c and glucose monitoring data. * Complete questionnaires about confidence, usability, and satisfaction with the educational support they receive. The AI platform is designed to provide educational support only and does not replace medical advice, insulin dosing decisions, or routine diabetes care provided by healthcare professionals.
Detailed description
This multicentre randomized controlled feasibility trial will evaluate an AI-powered educational platform designed to support carbohydrate counting education for parents of children with type 1 diabetes (T1D). Accurate carbohydrate counting is an essential component of T1D management because insulin dosing is closely linked to carbohydrate intake. However, many parents experience challenges in estimating carbohydrate content accurately, which may affect glycemic control. The intervention uses conversational artificial intelligence to provide personalized educational support, interactive learning opportunities, and practical guidance related to carbohydrate counting. The platform is intended as an educational tool and does not provide medical advice or insulin dosing recommendations. Educational content and safety oversight are provided by pediatric endocrinologists, diabetes educators, and registered dietitians. The primary objective of this feasibility study is to evaluate recruitment, retention, participant engagement, intervention adherence, and data collection procedures to determine whether a future definitive efficacy trial is warranted. Secondary objectives include assessment of participant acceptability and usability, as well as exploration of preliminary effects on carbohydrate counting accuracy, parental diabetes management self-efficacy, and glycemic outcomes. Participants will be recruited from our pediatric diabetes centers, and randomized to receive either access to the AI-powered educational platform in addition to enhanced usual care or enhanced usual care alone. Study findings will inform the development of larger trials evaluating the role of conversational artificial intelligence in diabetes education and chronic disease self-management.
Interventions
BEHAVIORALAI-Powered Carbohydrate Counting Educational Platform
Participants will receive access to an AI-powered carbohydrate counting educational platform designed for parents of children with type 1 diabetes. The platform provides interactive carbohydrate counting education, personalized educational guidance, carbohydrate estimation practice, natural-language question answering, and scenario-based learning. Participants will use the platform for 12 weeks in addition to enhanced usual diabetes care. All educational content is developed and supervised by pediatric endocrinologists, certified diabetes educators, and registered dietitians. The platform functions as an educational support tool only and does not provide medical advice or insulin dosing recommendations.
Sponsors
Sultan Qaboos University
Applied Science Private University
Al Jalila Children's Specialty Hospital
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SUPPORTIVE_CARE
Masking
SINGLE (Outcomes Assessor)
Intervention model description
Participants will be randomized in a 1:1 ratio to either an AI-powered carbohydrate counting educational platform plus enhanced usual care or enhanced usual care alone. The two groups will be followed in parallel for 12 weeks to evaluate feasibility, acceptability, usability, and preliminary effectiveness outcomes
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Primary responsibility for carbohydrate counting and insulin dosing decisions for the child * English-speaking * Access to a smartphone (iOS or Android) with internet connectivity * Willing and able to provide informed consent and complete study procedures * Diagnosis of type 1 diabetes for at least 1 month * Receiving intensive insulin therapy (multiple daily injections or insulin pump) * Using carbohydrate counting for insulin dosing
Exclusion criteria
* Child has significant developmental delay or a medical condition that substantially alters nutritional requirements or carbohydrate metabolism (e.g., celiac disease, cystic fibrosis) * Parent or caregiver has significant cognitive impairment that would preclude participation * Family plans to relocate from the study area during the study period * Participation in another diabetes intervention study
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Recruitment Rate | 12 months | Number and proportion of eligible participants recruited into the study across participating sites. |
| Retention Rate | 12 weeks | Proportion of enrolled participants who complete the 12-week follow-up assessment. |
| Intervention Adherence | 12 weeks | Proportion of participants in the intervention group who engage with the AI-powered carbohydrate counting educational platform for at least 10 sessions during the study period. |
| Data Completeness | 12 weeks | Proportion of participants with complete primary outcome data collected at baseline and 12-week follow-up assessments. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Acceptability of Intervention Measure | 12 weeks | Participant-rated acceptability of the AI-powered carbohydrate counting educational platform using the validated 4-item Acceptability of Intervention Measure. |
| System Usability Scale | 12 weeks | Participant-rated usability of the AI-powered carbohydrate counting educational platform measured using the System Usability Scale (SUS), a validated 10-item questionnaire. The SUS total score ranges from 0 to 100, with higher scores indicating better perceived usability. |
| Net Promoter Score | 12 weeks | Participant likelihood of recommending the AI-powered carbohydrate counting educational platform to other parents of children with type 1 diabetes, measured using the Net Promoter Score (NPS). Participants rate their likelihood of recommending the platform on a scale from 0 (Not at all likely) to 100 (Extremely likely). Higher scores indicate a greater likelihood of recommending the intervention. |
| Carbohydrate Counting Accuracy | Baseline and 12 weeks | Change in carbohydrate counting accuracy assessed using a standardized carbohydrate counting assessment. Accuracy will be defined as the percentage of estimates within 20% of dietitian-calculated carbohydrate values. |
| Time in Range (70-180 mg/dL) | Baseline and 12 weeks | Percentage of time glucose values remain within the target range of 70-180 mg/dL based on continuous glucose monitoring or glucose meter data. |
| Glucose Variability | Baseline and 12 weeks | Change in glucose variability measured by coefficient of variation of glucose values. |
Countries
Oman, United Arab Emirates
Contacts
CONTACTZainab Al-Abadla, BSN, MSc, BC-ADM
CONTACTHussain Alsaffar, FACE, MSc, FRCPCH
STUDY_CHAIRMoez AlIslam Faris, PhD
Applied Science Private University
Outcome results
None listed