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Feasibility and Effectiveness of an AI-Powered Carbohydrate Counting Educational Platform to Support Parents of Children With Type 1 Diabetes (CARB-AI)

2026년 6월 26일 업데이트: Dr. Hussain Al Saffar, Sultan Qaboos University

Feasibility and Effectiveness of an AI-Powered Carbohydrate Counting Educational Platform to Support Parents of Children With Type 1 Diabetes: A Multicentre Randomized Controlled Trial

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.

연구 개요

상태

아직 모집하지 않음

상세 설명

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.

연구 유형

중재적

등록 (추정된)

80

단계

  • 해당 없음

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

연구 연락처 백업

  • 이름: Hussain Alsaffar, FACE, MSc, FRCPCH
  • 전화번호: +96896399402
  • 이메일: hussaina@squ.edu.om

연구 장소

      • Dubai, 아랍 에미리트
        • Al Jalila Children's Hospital
      • Sharjah city, 아랍 에미리트
        • Al Qasimi Hospital
      • Muscat, 오만, 123
        • Sultan Qaboos University Hospital

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 어린이
  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

아니

설명

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

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

  • 주 목적: 지지 요법
  • 할당: 무작위
  • 중재 모델: 병렬 할당
  • 마스킹: 하나의

무기와 개입

참가자 그룹 / 팔
개입 / 치료
실험적: AI-Powered Carbohydrate Counting Educational Platform + Enhanced Usual Care
Participants receive access to an AI-powered carbohydrate counting educational platform in addition to enhanced usual diabetes care. The platform provides interactive educational support, carbohydrate counting practice, personalized feedback, scenario-based learning, and educational guidance under dietitian and diabetes specialist oversight. Participants also receive standard diabetes education materials and routine clinical care for 12 weeks.
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.
간섭 없음: Enhanced Usual Care Alone
Participants receive enhanced usual diabetes care consisting of standard diabetes education provided by their clinical care team, printed carbohydrate counting educational materials, portion size reference materials, educational PDF resources, and routine clinical care. Participants do not receive access to the AI-powered carbohydrate counting educational platform

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
Recruitment Rate
기간: 12 months
Number and proportion of eligible participants recruited into the study across participating sites.
12 months
Retention Rate
기간: 12 weeks
Proportion of enrolled participants who complete the 12-week follow-up assessment.
12 weeks
Intervention Adherence
기간: 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.
12 weeks
Data Completeness
기간: 12 weeks
Proportion of participants with complete primary outcome data collected at baseline and 12-week follow-up assessments.
12 weeks

2차 결과 측정

결과 측정
측정값 설명
기간
Acceptability of Intervention Measure
기간: 12 weeks
Participant-rated acceptability of the AI-powered carbohydrate counting educational platform using the validated 4-item Acceptability of Intervention Measure.
12 weeks
Carbohydrate Counting Accuracy
기간: 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.
Baseline and 12 weeks
Time in Range (70-180 mg/dL)
기간: 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.
Baseline and 12 weeks
Glucose Variability
기간: Baseline and 12 weeks
Change in glucose variability measured by coefficient of variation of glucose values.
Baseline and 12 weeks
System Usability Scale
기간: 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.
12 weeks
Net Promoter Score
기간: 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.
12 weeks

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

수사관

  • 연구 의자: Moez AlIslam Faris, PhD, Applied Science Private University

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (추정된)

2026년 9월 1일

기본 완료 (추정된)

2027년 8월 31일

연구 완료 (추정된)

2027년 12월 31일

연구 등록 날짜

최초 제출

2026년 6월 22일

QC 기준을 충족하는 최초 제출

2026년 6월 22일

처음 게시됨 (실제)

2026년 6월 26일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 6월 30일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 6월 26일

마지막으로 확인됨

2026년 6월 1일

추가 정보

이 연구와 관련된 용어

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아니요

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

아니

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