NCT07884526|Not Yet RecruitingDMC
AI-CARE: A Novel AI-Driven Mobile Insulin Calculator Integrating Image- Based Carbohydrate Estimation and CGM
AI-CARE
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AI-CARE
Sponsor study identifier
Secondary identifiers
OTHER · NHG Translational Research Programme Funding
interventional
48
1 country
1
Study Start
October 2026
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Primary Completion
October 2028
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Study Completion
October 2028
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
AI-CARE is an artificial intelligence-enabled clinical decision support mobile application designed to support prandial insulin dosing in individuals receiving MDI, incorporating a "human-in-the-loop" approach to ensure user verification of recommended doses. The application integrates AI-driven image-based carbohydrate estimation tailored to Asian dietary contexts, individualized patient-specific insulin parameters, and real-time CGM data to generate personalized insulin dose recommendations. The primary objective of this study is to evaluate the feasibility, safety, and preliminary effectiveness of the AI-CARE mobile application among adults with diabetes receiving MDI. Feasibility will be assessed in terms of recruitment, retention, participant adherence, and application usability. Safety will be evaluated by monitoring the incidence of hypoglycaemia, hyperglycaemia, and other adverse events throughout the intervention period. The study will also examine the preliminary effectiveness of AI-CARE on glycaemic and patient-reported outcomes compared with usual care. In addition, a qualitative process evaluation will be conducted to explore participants' experiences and perceptions of using AI-CARE, including perceived ease of use, usefulness, acceptability, and barriers and facilitators to real-world implementation. Findings from this phase will inform further refinement and optimization of the AI-CARE system and guide the design of a future large- scale randomized controlled trial.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
October 2028
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
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Some registry-reported names shown below are not yet linked in this graph.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
September 26, 2026
Actual (reported)October 2026
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
October 20, 2026
Actual (reported)October 2028
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
October 2028
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Time to achieve target sample size
Recruitment feasibility will be evaluated by measuring the time (in weeks) required to achieve the target sample size.
From enrollment to the end of follow-up at 12 weeks
Eligibility rate
Recruitment feasibility will also be measured by calculating the eligibility rate, which is the proportion of eligible participants among those screened.
From enrollment to the end of follow-up at 12 weeks
Recruitment rate
Recruitment feasibility will also be measured by calculating the recruitment rate, which is the proportion of enrolled participants among those eligible.
From enrollment to the end of follow-up at 12 weeks
Retention and attrition rates
Retention and attrition rates will also be calculated, with attrition defined as the proportion of participants who withdraw or are lost to follow-up during the 12-week intervention period.
From enrollment to the end of follow-up at 12 weeks
Intervention engagement
Intervention engagement will be assessed through application usage metrics, including the frequency of daily insulin calculator activation and the proportion of meals supported by AI-CARE. Acceptability will be further inferred from app usage patterns.
Changes in glycaemic control (HbA1c)
From enrollment to the end of follow-up at 12 weeks
Continuous glucose monitoring-derived metrics
From enrollment to the end of follow-up at 12 weeks
Body Weight
From enrollment to the end of follow-up at 12 weeks
Body Mass Index (BMI)
From enrollment to the end of follow-up at 12 weeks
Blood Pressure
From enrollment to the end of follow-up at 12 weeks
Intervention Safety
From enrollment to the end of follow-up at 12 weeks
Participants randomized to the AI-CARE intervention arm will receive the intervention over a 12-week period, comprising five study contacts: a baseline visit (Visit 1), nurse-led telephone reviews at Week 1, 2 and 6, and a post-intervention visit (Visit 2) at Week 12. Following completion of the study, participants who provide consent will be invited to participate in semi-structured focus group discussions. Participants will be instructed to use the AI-CARE application prior to each main meal (typically two to three times daily) throughout the intervention period. Application usage data, including the frequency of meal image capture and acceptance of insulin dose recommendations, will be automatically recorded to assess intervention adherence and user engagement. A structured support infrastructure will be provided, including access to a study contact line during office hours and a predefined escalation pathway for urgent clinical concerns.
Participants in the usual care arm will continue to receive standard diabetes self-management practices without access to the AI-CARE application. Prandial insulin dosing will be calculated manually by participants using their individualized ICR and ISF, or according to instructions provided by their treating physician. All participants in this arm will wear a CGM sensor for study measurement purposes throughout the study period. Routine diabetes management and follow-up will continue in accordance with standard outpatient care practices. The study visit schedule includes two in-person visits: Visit 1 at baseline for enrolment, baseline assessments, and CGM sensor application, structured nurse-led telephone reviews at Week 1, 2 and 6, and Visit 2 at Week 12 for post-intervention outcome assessment. Following completion of the study, participants who provided consent will be invited to participate in semi-structured focus group discussions.
AI-CARE is an artificial intelligence-enabled clinical decision support mobile application designed to support prandial insulin dosing in individuals receiving MDI, incorporating a "human-in-the-loop" approach to ensure user verification of recommended doses. The application integrates AI-driven image-based carbohydrate estimation tailored to Asian dietary contexts, individualized patient-specific insulin parameters, and real-time CGM data to generate personalized insulin dose recommendations.
Tan Tock Seng Hospital
Singapore, Singapore, 308433, Singapore
Weisman A, Bai JW, Cardinez M, Kramer CK, Perkins BA. Effect of artificial pancreas systems on glycaemic control in patients with type 1 diabetes: a systematic review and meta-analysis of outpatient randomised controlled trials. Lancet Diabetes Endocrinol. 2017 Jul;5(7):501-512. doi: 10.1016/S2213-8587(17)30167-5. Epub 2017 May 19.
Smart CE, King BR, McElduff P, Collins CE. In children using intensive insulin therapy, a 20-g variation in carbohydrate amount significantly impacts on postprandial glycaemia. Diabet Med. 2012 Jul;29(7):e21-4. doi: 10.1111/j.1464-5491.2012.03595.x.
First Submitted
September 26, 2026
First Posted
October 20, 2026
Study Start
October 2026
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Primary Completion
October 2028
From enrollment to the end of follow-up at 12 weeks
Intervention usability
Usability will be evaluated using the validated mHealth App Usability Questionnaire (MAUQ) for Standalone mHealth Apps Used by Patients, which will be administered at Week 12. In the MAUQ questionnaire, there are 18 statements, for which participants indicate their level of agreement on a 7-point scale (1 - strongly disagree, 2 - disagree, 3 - somewhat disagree, 4 - neither agree nor disagree, 5 - somewhat agree, 6 - agree, 7 - strongly agree). To determine the usability of an app, calculate the total and determine the average of the responses to all statements. The higher the overall average, the higher the usability of the app. There is also a section for participants' written comments. Acceptability will be further inferred from participant feedback.
From enrollment to the end of follow-up at 12 weeks
Participants randomized to the usual care arm will continue to receive standard diabetes self-management practices without access to the AI-CARE application. Usual care includes manual carbohydrate counting using conventional methods, such as estimating carbohydrate content based on food labels, dietary guides, or prior diabetes education. Prandial insulin dosing will be calculated manually by participants using their individualized ICR and ISF, or according to instructions provided by their treating physician. All participants in this arm will wear a CGM sensor for study measurement purposes throughout the study period. Routine diabetes management and follow-up will continue in accordance with standard outpatient care practices. The study visit schedule for the usual care group includes two in-person visits: Visit 1 at baseline for enrolment, baseline assessments, and CGM sensor application, and Visit 2 at Week 12 for post-intervention outcome assessment.
Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.
Contact the study team to confirm eligibility.
Rhyner D, Loher H, Dehais J, Anthimopoulos M, Shevchik S, Botwey RH, Duke D, Stettler C, Diem P, Mougiakakou S. Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study. J Med Internet Res. 2016 May 11;18(5):e101. doi: 10.2196/jmir.5567.
Lian X, Cheng LJ, Teo CJY, Tan IJS, Lim H, Shen L, Chew JHS, Wang W, Dalan R. Impact of real-time continuous glucose monitoring on glycaemic control in adults with type 2 diabetes: systematic review and meta-analysis. Front Endocrinol (Lausanne). 2026 Jan 23;16:1761579. doi: 10.3389/fendo.2025.1761579. eCollection 2025.
Huo L, Harding JL, Peeters A, Shaw JE, Magliano DJ. Life expectancy of type 1 diabetic patients during 1997-2010: a national Australian registry-based cohort study. Diabetologia. 2016 Jun;59(6):1177-85. doi: 10.1007/s00125-015-3857-4. Epub 2016 Jan 21.
American Diabetes Association. (2026). Standards of care in diabetes-2026. Diabetes Care, 49(Suppl. 1), S1-S300.
Anthimopoulos M, Dehais J, Shevchik S, Ransford BH, Duke D, Diem P, Mougiakakou S. Computer vision-based carbohydrate estimation for type 1 patients with diabetes using smartphones. J Diabetes Sci Technol. 2015 May;9(3):507-15. doi: 10.1177/1932296815580159. Epub 2015 Apr 16.
Planned date reported by the registry; this milestone is not confirmed as achieved.
Study Completion
October 2028
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Last Updated
October 20, 2026
Record last verified: 2026-10
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