Title: impRoving testing for cardiometabolic diseases in women with previous gestational diabetes mellitus: an exemplar study on implementation and evaluation of a novel dAta-DrIven rANdomised clinical Trial platform in primary care (RADIANT)
Funder: Department of Health and Social Care

Abstract: Research question Does a SMS and animation reminder sent to women with previous gestational diabetes mellitus (GDM) increase testing for type 2 diabetes mellitus (T2DM) and cardiometabolic disease risk factors in primary care compared to usual care? Background Women with a history of GDM are at increased risk of T2DM and cardiometabolic disorders. The National Institute for Health and Care Excellence (NICE) guidelines states these women should be tested annually for T2DM; however, many of these women do not get tested. SMS sent to women are effective at increasing screening for breast and cervical cancer in the UK and a SMS reminder may also improve testing of T2DM in women with previous GDM. Moreover, by adding a link in the SMS to a short animation, woman can be educated about the importance of regular testing and the procedures involved. We would like to test this SMS and animation in a data-driven randomised control trial (RCT) platform where electronic health records are used to identify eligible patients and capture health outcomes. This can provide evidence that informs clinical decision-making by capitalising on routinely collected data while maintaining the robustness of an RCT through randomisation. Aims and objectives We aim to conduct a data-driven RCT to demonstrate the capacity of NHS data to answer an important clinical question that benefits patients. We will test this by developing a SMS and animation intervention for women with previous GDM to assess whether it increases testing for T2DM and cardiometabolic disease risk factors in primary care compared to usual care. Methods Work in four conceptually linked but distinct work packages (WP) will underlie the development of a SMS and animation that will be tested using a data-driven RCT platform. WP1 involves co-developing the SMS and animation with women with previous GDM to assess whether it increases testing for T2DM and cardiometabolic disease risk factors among women with previous GDM in a data-driven RCT in WP2. WP3 will evaluate the procedures used in the data-driven platform and explore perspectives from women and GP practices on the trial to explain any variation in effectiveness. WP4 will determine the cost-consequences of the intervention on the number of women tested. Timelines for delivery 0-6 months Develop the SMS and animation (WP1) Develop and test procedures for conducting the data-driven RCT (WP1) 7-15 months Recruitment of GP practices (WP2) Conducting the trial (WP2) Near real-time capture of outcome data and analysis of results (WP2) 15-24 months Recruiting women and GP practice staff to be interviewed (WP3) Semi-structured interviews and thematic analysis (WP3) Cost-consequences analysis (WP4) Anticipated impact and dissemination If the SMS-animation are effective at increasing testing for T2DM this can be rolled out to other GP practices with minimal input from practices. By identifying T2DM earlier, these women can have defined care pathways and reduced complications from T2DM, which will benefit women and the NHS. This study will also demonstrate the capacity and potential of this data-driven RCT platform for future, large clinical trials that will benefit patients.

Lay abstract: The Research Problem We normally test new interventions to improve health using randomised controlled trials (RCTs). These divide people into two groups, one gets the intervention and the other does not. We compare the health effects between the two groups to see if the intervention worked. Finding and including patients is usually done manually and few of those who are suitable actually take part. This makes RCTs time-consuming, and it is hard to know if the intervention will work in everyone. Data-driven trials are RCTs within electronic medical records. This simplifies methods by automatically finding and inviting suitable patients, checking they have given their consent to take part, then randomly dividing them into two groups and giving one group the intervention. We can also reduce the burden of taking part for both patients and health workers by using data recorded in usual clinical practice to find out whether the intervention works. Our Aim We want to show we can answer clinical questions that benefit patients by running a data-driven RCT. We have found a current health problem: lack of testing for diabetes in women who developed diabetes during pregnancy. The Health Problem Each year, 30,000 UK women develop diabetes during pregnancy (we call this gestational diabetes mellitus or GDM for short). It is common in ethnic minority women. Often GDM goes away after giving birth but women who had GDM are more likely to develop diabetes in the next few years. Therefore, women are advised to have yearly diabetes tests to find out and manage diabetes early before problems occur. But many women are not tested yearly so we need ways to help more get tested. How will we do this? In collaboration with patients, we will develop a short animation explaining why it is important for women who had GDM to have yearly diabetes tests. We will send a text message with a link to the animation to women who had GDM, encouraging them to book a diabetes test with their GP. This way we can inform and empower women to request a diabetes test. We will automatically find women who have had GDM and who already receive text messages from their GP and randomly divide them into two groups. One group will receive the text message and animation, the other will not. We will collect anonymous information from electronic medical records to find out if more women who received the text message had diabetes tests. We will find out whether the text message and animation are acceptable and good value for money. We have developed this study in close collaboration with women who have had GDM who think this intervention will inform, remind, and empower women to self-manage.

Research activity codes: 4.3 INFLUENCES AND IMPACT
Health categories: METABOLIC AND ENDOCRINE