Decision Support Tool for Prediabetes

Overseen By Marielle Bugayong, MSPH

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Johns Hopkins University

Disqualifiers: Diabetes, Non-English, Severe psychiatric, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 4

100 Participants Needed

Trial Summary

What is the purpose of this trial?

The investigators overarching goal is to increase the percentage of patients engaging in diabetes prevention activities to reduce the incidence of diabetes. The investigators objective is to design and pilot test a prediabetes clinical decision support (CDS) tool in the electronic health record (EHR) that will assess the patient's activation level based on responses to a questionnaire. Based on the patient's assessed level of activation, the tool will generate several communication recommendations to guide clinicians in conversations related to prediabetes/lifestyle change and tailor recommendations about available resources (e.g., care manager, health coach, DPP) to support patient activation.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the Diabetes Prevention Patient Activation Clinical Decision Support Tool treatment?

The use of the Prediabetes Clinical Decision Support tool was associated with higher rates of important diabetes-related tests, like hemoglobin A1c orders, which suggests it may help improve diabetes prevention efforts by encouraging necessary medical evaluations. ^{1 2 3 4 5}

Is the Decision Support Tool for Prediabetes safe for humans?

The available research does not provide specific safety data for the Decision Support Tool for Prediabetes, but it focuses on improving patient engagement and decision-making in diabetes prevention, which suggests it is designed to be safe for use in humans. ^{5 6 7 8 9}

How is the Diabetes Prevention Patient Activation Clinical Decision Support Tool different from other treatments for prediabetes?

This tool is unique because it integrates into electronic health records to streamline prediabetes management, making it easier for healthcare providers to order necessary tests and refer patients to lifestyle change programs, which can improve patient outcomes. ^{1 5 10 11 12}

Research Team

Eva Tseng, MD

Principal Investigator

Johns Hopkins University

Eligibility Criteria

This trial is for patients with prediabetes who have a MyChart account and are seen at the Johns Hopkins General Internal Medicine clinic at Green Spring Station. It aims to help them engage in activities that could prevent diabetes.

Inclusion Criteria

Patient at Johns Hopkins General Internal Medicine clinic at Green Spring Station

MyChart account

I have been diagnosed with prediabetes.

Exclusion Criteria

I have a history of diabetes.

Non-English speaker

Severe intellectual disability or psychiatric comorbidities

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Participants complete a patient activation survey before each visit. Results are shared with the primary care doctor to use the Diabetes Prevention Patient Activation Clinical Decision Support Tool during the visit.

6 months

At least 2 visits with primary care doctor

Control

Participants receive a handout on pre-diabetes and complete a patient activation survey before each visit, but results are not shared with the doctor.

6 months

At least 2 visits with primary care doctor

Follow-up

Participants are monitored for changes in patient activation, diet, physical activity, and referrals to Diabetes Prevention Program.

4 weeks

Treatment Details

Interventions

• Diabetes Prevention Patient Activation Clinical Decision Support Tool

Trial Overview The study is testing a new tool within the electronic health record system designed to activate patients' involvement in their own care. The tool provides tailored communication recommendations for clinicians based on patient responses.

Participant Groups

Group I: Intervention Arm (DPACT) Active Control 1 Intervention

In this arm, patients will have at least 2 visits with the participants primary care doctor. Before each visit, participants will complete a patient activation survey. Results of the survey will be shared with the primary care doctor and entered into the medical record so that the Diabetes Prevention Patient Activation Clinical Decision Support Tool can be used by the participants doctor during the visit to discuss lifestyle management.

Group II: Control Arm (Usual Care) Active Control 1 Intervention

Patients in the control arm will follow the same visit schedule as the intervention arm. At the beginning of the study, participants will receive a handout on pre-diabetes which will include information on lifestyle changes and the benefits of joining a Diabetes Prevention Program. Before each visit, participants will complete a patient activation survey but the results will not be shared with the participants primary care doctor

Who Is Running the Clinical Trial?

Johns Hopkins University

Lead Sponsor

Trials

2,366

Recruited

15,160,000+

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

Collaborator

Trials

2,513

Recruited

4,366,000+

Findings from Research

In a study of 352 adults participating in the Diabetes Prevention Program (DPP), baseline patient activation did not predict attendance or uptake of the program, indicating that initial engagement levels may not influence participation.

However, attending the DPP was linked to a significant increase in patient activation scores, suggesting that the program not only helps with weight loss and health improvements but also empowers participants to take a more active role in their health decisions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Diabetes Prevention Program attendance is associated with improved patient activation: Results from the Prediabetes Informed Decisions and Education (PRIDE) study. Skrine Jeffers, K., Castellon-Lopez, Y., Grotts, J., et al.[2020]

References

1. United States pubmed.ncbi.nlm.nih.gov

Personalized decision support in type 2 diabetes mellitus: current evidence and future directions. [2022]

2. England pubmed.ncbi.nlm.nih.gov

Impact of a Diabetes Risk Score on Lifestyle Education and Patient Adherence (IDEA) in Prediabetes: A Multisite Randomized Controlled Trial. [2017]

3. United States pubmed.ncbi.nlm.nih.gov

Preventing Diabetes in Primary Care: Providers' Perspectives About Diagnosing and Treating Prediabetes. [2020]

4. England pubmed.ncbi.nlm.nih.gov

Outpatient diabetes clinical decision support: current status and future directions. [2022]

5. United States pubmed.ncbi.nlm.nih.gov

Development of a novel clinical decision support tool for diabetes prevention and feasibility of its implementation in primary care. [2023]

6. United States pubmed.ncbi.nlm.nih.gov

Diabetes Prevention Program attendance is associated with improved patient activation: Results from the Prediabetes Informed Decisions and Education (PRIDE) study. [2020]

7. United States pubmed.ncbi.nlm.nih.gov

Digital Coaching Strategies to Facilitate Behavioral Change in Type 2 Diabetes: A Systematic Review. [2021]

8. United States pubmed.ncbi.nlm.nih.gov

Effectiveness of Shared Decision-making for Diabetes Prevention: 12-Month Results from the Prediabetes Informed Decision and Education (PRIDE) Trial. [2022]

9. England pubmed.ncbi.nlm.nih.gov

The effect of a patient-oriented treatment decision aid for risk factor management in patients with diabetes (PORTDA-diab): study protocol for a randomised controlled trial. [2021]

10. United States pubmed.ncbi.nlm.nih.gov

Using a certified electronic health record technology platform to screen, test and refer patients with prediabetes. [2023]

11. United States pubmed.ncbi.nlm.nih.gov

Implementing a prediabetes clinical decision support system in a large primary care system: Design, methods, and pre-implementation results. [2022]

12. United States pubmed.ncbi.nlm.nih.gov

Computerised decision-support tools in diabetes care: hurdles to implementation. [2005]