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Original research
Using the Translating Research into Practice framework to develop a diabetes prevention intervention in primary care: a mixed-methods study
  1. Eva Tseng1,2,
  2. Katherine Smith3,
  3. Jeanne M Clark1,2,4,
  4. Jodi B Segal1,2,5,6,7,
  5. Jill A Marsteller2,6,
  6. Nisa M Maruthur1,2,4
  1. 1Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  2. 2Welch Center for Prevention, Epidemiology, & Clinical Research, Johns Hopkins University, Baltimore, MD, USA
  3. 3Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
  4. 4Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
  5. 5Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
  6. 6Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
  7. 7Center for Drug Safety and Effectiveness, Johns Hopkins University, Baltimore, MD, USA
  1. Correspondence to Dr Eva Tseng; etseng3{at}jhmi.edu

Abstract

Background Pre-diabetes affects one-third of US adults and increases the risk of type 2 diabetes. Effective evidence-based interventions, such as the Diabetes Prevention Program, are available, but a gap remains in effectively translating and increasing uptake of these interventions into routine care.

Methods We applied the Translating Research into Practice (TRiP) framework to guide three phases of intervention design and development for diabetes prevention: (1) summarise the evidence, (2) identify local barriers to implementation and (3) measure performance. In phase 1, we conducted a retrospective cohort analysis of linked electronic health record claims data to evaluate current practices in the management of pre-diabetes. In phase 2, we conducted in-depth interviews of 16 primary care physicians, 7 payor leaders and 31 patients to elicit common barriers and facilitators for diabetes prevention. In phase 3, using findings from phases 1 and 2, we developed the core elements of the intervention and performance measures to evaluate intervention uptake.

Results In phase 1 (retrospective cohort analysis), we found few patients with pre-diabetes received diabetes prevention interventions. In phase 2 (stakeholder engagement), we identified common barriers to include a lack of knowledge about pre-diabetes among patients and about the Diabetes Prevention Program among clinicians. In phase 3 (intervention development), we developed the START Diabetes Prevention Clinical Pathway as a systematic change package to address barriers and facilitators identified in phases 1 and 2, performance measures and a toolkit of resources to support the intervention components.

Conclusions The TRiP framework supported the identification of evidence-based care practices for pre-diabetes and the development of a well-fitted, actionable intervention and implementation plan designed to increase treatment uptake for pre-diabetes in primary care settings. Our change package can be adapted and used by other health systems or clinics to target prevention of diabetes or other related chronic conditions.

  • PRIMARY CARE
  • DIABETES MELLITUS
  • Chronic disease management
  • Decision support, clinical
  • Implementation science

Data availability statement

All data relevant to the study are included in the article or uploaded as supplemental information.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjoq-2024-002752

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    What is already known on this topic

    • Although effective evidence-based interventions for pre-diabetes, such as the Diabetes Prevention Program lifestyle change programme, are available, there remains a gap in effectively translating and increasing uptake of these interventions into routine care.

    What this study adds

    • This study describes how the Translating Research into Practice (TRiP) framework supported our mixed-methods development process to design and implement a systematic approach to diabetes prevention in primary care.

    How this study might affect research, practice or policy

    • The TRiP framework is a suitable approach to supporting the identification of evidence-based care practices and the development of a well-fitted, actionable intervention and implementation plan. This change package can be adapted and used by other health systems or clinics to target prevention of diabetes or other related chronic conditions.

    Background

    Diabetes prevention is crucial for addressing the public health burden of type 2 diabetes in the USA. Pre-diabetes affects 96 million US adults1 and increases the risk of type 2 diabetes with a 5-year risk of up to 50%.2 Although effective evidence-based interventions, such as the year-long intensive Diabetes Prevention Program (DPP) lifestyle change programme, are available, there remains a gap in effectively translating and increasing uptake of these interventions into routine care.3

    The DPP and metformin are effective treatments for preventing type 2 diabetes. In the DPP clinical trial, compared with the placebo group, diabetes incidence was reduced by 58% at 3 years and 27% at 15 years in the lifestyle intervention group and by 31% at 3 years and 18% at 15 years in the metformin group.4 5 Similar results have been observed in lifestyle intervention trials globally.6–8 Individualised medical nutrition therapy (MNT) has also been demonstrated to improve glycaemic control in people with pre-diabetes.9 10 Based on this evidence, the American Diabetes Association recommends adults with overweight or obesity and at high risk of type 2 diabetes be referred to a DPP and/or start metformin11 along with other guidelines also recommending individualised MNT.9 12

    Despite strong evidence that diabetes can be prevented, less than 5% of eligible persons report being referred to a DPP,13 and the prevalence of metformin use for pre-diabetes has remained low at <5%.14–16 There are a number of factors influencing the low uptake of pre-diabetes treatment, including low knowledge among primary care physicians (PCPs) about the DPP, the lack of availability of DPPs, and low patient awareness and understanding of the importance of pre-diabetes and its management.17–25

    The gap between known effective strategies and successful implementation in practice is explicitly addressed by the Translating Research into Practice (TRiP) framework26 created by Johns Hopkins researchers in 2008 to guide the process of gathering what is known to be best practice and moving this evidence into day-to-day use in patient encounters. The TRiP framework grew out of real-world implementation experience27 28 and was informed by both quality improvement science and implementation science. Although somewhat under-recognised in survey articles from the implementation science literature,29 the TRiP framework is a viable process model that both lays solid groundwork for clinical change and offers detailed steps guiding implementation that is applicable to both inpatient and ambulatory settings.

    Our objective is to describe the mixed-methods development process we undertook to design and develop a systematic intervention for diabetes prevention in primary care using the TRiP framework.26 The three phases described here include the following: (1) summarise existing evidence, (2) identify local barriers to implementation and (3) measure performance.

    Methods

    We used the TRiP framework, an established implementation method for translating evidence into real-world practice (figure 1). The TRiP model features four stages: (1) summarise the evidence, (2) identify local barriers to implementation, (3) measure performance and (4) ensure all patients receive the intervention by implementing the six Es (engage, educate, execute, evaluate, embed and expand). The TRiP framework was originally developed in the hospital setting, but has been successfully applied to the ambulatory setting for conditions like hypertension.30 31

    Figure 1
    Figure 1

    Integration of Translating Evidence into Practice model and implementation framework (blue) into intervention design and development (black). EHR, electronic health record; START, Screen, Test, Act, Refer and Treat.

    The TRiP framework provides actionable steps which align with the three phases of intervention design and development we describe in this paper (figure 1): (1) summarising the existing evidence by conducting a retrospective cohort analysis of linked electronic health record (EHR) claims data from our health system to evaluate current practices in the management of pre-diabetes; (2) identifying local barriers to implementation through stakeholder engagement, including insurance plan leaders, physicians and patients; and (3) measuring performance. These three phases were integral to intervention design and refinement as a systematic change package. We also describe how the six Es approach was used for intervention implementation.32 The results of the effectiveness of the intervention, as tested in the START Diabetes Prevention clinical trial (NCT05265312), will be reported in a separate publication.

    Phase 1: summarise existing evidence

    The first stage of the TRiP framework involves summarising the evidence for interventions to improve pre-diabetes care. To gather this information, we first conducted a retrospective cohort analysis of linked EHR claims data from our health system to evaluate current practices in the management of pre-diabetes. Our objectives were to describe the order rates of pre-diabetes clinical care activities by PCPs and their completion by patients, establish patient factors associated with this care and understand the incidence of diabetes within our cohort. Using the linked EHR claims dataset, we created a cohort of patients aged 18 years and older with ≥2 outpatient primary care encounters during the 5-year window (February 2016–February 2021). Pre-diabetes was defined based on laboratory criteria using haemoglobin A1c or fasting glucose. We conducted descriptive analyses and used multiple logistic regression models for association analyses. The full details regarding inclusion/exclusion criteria, outcome definition and analysis are published elsewhere.33

    Phase 2: identify local barriers to implementation through stakeholder engagement

    In accordance with the second stage of the TRiP framework, we conducted stakeholder engagement including with insurance plan leaders, physicians and patients, to identify barriers and facilitators for implementation of a systematic diabetes prevention intervention in the primary care clinic setting. From May 2020 to February 2022, we conducted remote one-on-one semistructured interviews lasting 0.5–1 hour with several stakeholders: (1) PCPs (defined as internal medicine or family medicine physicians) from primary care practices affiliated with an academic system in the mid-Atlantic region of the USA; (2) payors from regional commercial, Medicare and Medicaid plans; and (3) adult patients with pre-diabetes from the same large academic health system. The full details regarding inclusion/exclusion criteria, methods and analysis from the PCP and payor interviews are published elsewhere.19

    A common interview guide was developed by the research team with key themes for all three stakeholders: (1) barriers to diabetes prevention, (2) facilitators for diabetes prevention and (3) potential intervention components for a systematic intervention directed at diabetes prevention. Modifications were made to the guide to accommodate different foci of the three groups. All participants provided verbal informed consent and were offered a $50 gift card for their participation. Each interview was audio-recorded using Zoom or digital recorder then professionally transcribed. Two reviewers cleaned and double-coded the transcripts using the framework analytical approach.34 We created a coding framework based on the main questions from the interview guide and later refined by the reviewers through a consensus process. Both reviewers compared their coding and differences in coding were resolved through consensus-focused discussions. All interviews were organised and analysed using MAXQDA V.2020.

    Phase 3: measure performance

    Intervention design and development began during phases 1 and 2 and were finalised in phase 3 where we developed performance measures to evaluate the effectiveness of the intervention. Based on the Centers for Disease Control and Prevention (CDC) guidelines for addressing pre-diabetes (Screen, Test, Refer),35 we developed a diabetes prevention clinical pathway called START (Screen, Test, Act, Refer and Treat) (table 1). To ensure successful implementation of the clinical pathway, we developed and evaluated a toolkit of resources to support the intervention components and to address findings from phases 1 and 2. During regular team meetings (ET, JMC, JAM and NMM) that occurred during 2021–2022, we discussed, developed and finalised the START Diabetes Prevention Clinical Pathway and toolkit. Then, we developed performance measures to evaluate our intervention, focusing on both process and implementation outcomes.

    View this table:
    Table 1

    Process of translating findings from phases 1 and 2 into action using the Translating Research into Practice (TRiP) framework resulting in the START Diabetes Prevention Clinical Pathway

    Patient and public involvement

    Patients and members of the public were not involved in the design, conduct, reporting or dissemination plans of our research.

    Results

    Phase 1: summarise existing evidence

    Our retrospective cohort analysis summarised the evidence in our population concerning treatment and referral for pre-diabetes care.33 Our cohort included 3888 patients with a laboratory diagnosis of pre-diabetes. The mean age was 63 years with the majority of patients of female sex (65%) and white (55%) or black (35%). Mean body mass index (BMI) at baseline was 30 kg/m² with nearly half of the patients having a haemoglobin A1c of 6–6.4%. Major findings and how they directly informed the overall design and development of the START Diabetes Prevention Clinical Pathway during phase 3 are elaborated in table 1. Other detailed findings are reported elsewhere.33

    In the 12 months after cohort entry, 63% had repeat glycaemic testing ordered, but only 13% with a visit had a diagnosis of pre-diabetes coded. Only 1% of patients were referred to nutrition services and 5% were prescribed metformin. These findings highlighted the need for increased clinician education about pre-diabetes management, which led to the development of a printed clinician treatment algorithm that summarises guideline recommendations on the treatment and follow-up of pre-diabetes (online supplemental appendix 1). In response to the findings, we also developed an EHR clinical decision support tool, specifically a pre-diabetes orders smartset which included specific instructions, such as criteria for the DPP and dosing instructions on metformin, orders (metformin, laboratory tests) and referrals (DPP, MNT) relevant to pre-diabetes.

    Supplemental material

    In our cohort, most patients (80%) had at least one visit with their PCP within 12 months of cohort entry, but only 50% of patients completed repeat glycaemic testing in that period. To address the low follow-up testing, we emphasised in the clinician treatment algorithm the importance of interval follow-up visits and testing to monitor for progression to type 2 diabetes. We also designed the pre-diabetes orders smartset to offer laboratory test orders with specified frequencies of testing (ie, 3 months, 6 months, 12 months).

    Phase 2: identify local barriers to implementation through stakeholder engagement

    We held in-depth interviews of 16 PCPs (56% female, 56% white, aged 35–67 years, in practice for 2–27 years) representing 13 community-based primary care clinics. We also interviewed seven payor leaders representing six insurance plans (commercial, Medicare and Medicaid). Finally, we interviewed 31 patients (55% female, 45% black, 35% white, aged 31–74 years). Table 1 highlights common barriers and facilitators identified during stakeholder engagement and the core intervention elements that we selected to address these barriers and facilitators. Major themes and how they directly informed the overall design and development of the START Diabetes Prevention Clinical Pathway during phase 3 are elaborated below and in table 1. Other detailed findings are reported elsewhere.19

    A theme that emerged from the patient interviews was a lack of knowledge about pre-diabetes. While discussing their diagnosis of pre-diabetes, some patients expressed that they had not received much information about pre-diabetes, and few knew much about the DPP and/or metformin. Many participants wanted more detailed information about recommended nutrition changes to prevent diabetes and how to get medical nutrition therapy covered for patients with pre-diabetes. Therefore, we developed a print and digital patient handout with information about what is pre-diabetes, how to prevent diabetes through lifestyle change, and what the DPP and metformin are (online supplemental appendix 2). We also included links to the American Diabetes Association website, which provides information on recipes and healthy food choices to prevent diabetes. Finally, we gathered information from our nutrition department to understand how MNT visits may be covered for individuals with pre-diabetes and provided it to clinicians during the training session and in the treatment algorithm (online supplemental appendix 1) described in phase 3 below.

    Supplemental material

    A theme arising from PCP participants was a lack of knowledge about the DPP in general, insurance coverage of the DPP and the referral process for the DPP. Based on this discussion, we included in the treatment algorithm for PCPs (online supplemental appendix 1) details on the insurance coverage of and referral process for the DPP. Additionally, we developed and refined a DPP referral order in the EHR. The referral order includes instructions on who is eligible for the DPP (ie, laboratory and BMI criteria) and automatically pulls in the patient’s most recent laboratory results and BMI to facilitate clinicians in determining their patient’s eligibility. To encourage PCPs to engage with their patients in discussions about pre-diabetes and joining the DPP, we promoted an online module, developed by a clinical psychologist, that teaches clinicians how to use motivational interviewing techniques to facilitate these discussions.36

    Phase 3: measure performance

    Using our evidence summary from phase 1 to identify gaps in pre-diabetes care and stakeholder engagement about barriers and facilitators in phase 2 to identify sticking points in the implementation process, we shaped the CDC guidelines for addressing pre-diabetes35 into an actionable clinical pathway called START Diabetes Prevention (table 1). To ensure successful implementation of the clinical pathway, we developed and evaluated a toolkit of resources to support the intervention components and address learning from phases 1 and 2. Then, we developed performance measures to evaluate our intervention as described further below.

    The START Diabetes Prevention Clinical Pathway steps and toolkit included:

    1. Screen: identify patients at risk and eligible for screening for pre-diabetes/diabetes. We developed an EHR clinical decision support tool (‘Diabetes Screening Best Practice Advisory’ alert) that flags eligible patients using criteria from the 2021 US Preventive Services Task Force guidelines.37

    2. Test: screen and test eligible patients for pre-diabetes/diabetes. The ‘Diabetes Screening Best Practice Advisory’ prompts clinicians to order one of several laboratory test options (fasting glucose, haemoglobin A1c or point-of-care haemoglobin A1c).

    3. Act: educate clinicians and patients on the diagnosis and prompt shared decision-making through use of a pre-diabetes decision aid. We developed a clinician treatment algorithm (online supplemental appendix 1) and patient handout (online supplemental appendix 2), each containing information about pre-diabetes and treatment options. Both materials included the pre-diabetes decision aid developed by O’Brien et al which was designed to facilitate discussion of treatment options for pre-diabetes, specifically usual care versus starting metformin versus participating in a DPP.38 Additionally, we promoted an online educational module for clinicians on how to engage with patients in discussions about diabetes prevention using established motivational interviewing techniques.36 Recognising that a single conversation may not be sufficient, especially if the patient is not ready to discuss how to manage pre-diabetes, we encouraged clinicians to have regular follow-up with their patients (see Follow-up step).

    4. Refer/test: based on the shared decision-making conversation, clinicians may refer patients to evidence-based diabetes prevention interventions (DPP and/or MNT) and/or prescribe metformin. These orders are streamlined through the EHR orders set (‘pre-diabetes orders smartset’) we developed.

    5. Follow-up: ensure regular follow-up to reassess treatment and evaluate progress. In the PCP treatment algorithm, we provided a suggested time frame of PCP visits every 3–6 months to continue discussing pre-diabetes, recognising that there may be insufficient time for this topic to be addressed at all visits. We also recommended clinicians recheck laboratory tests at least every 12 months consistent with guidelines,11 and we built these standing orders into the pre-diabetes orders smartset.

    We developed several performance measures to evaluate our intervention. Our primary outcome was referral to a DPP, referral to MNT and/or metformin prescription. Secondary outcomes included enrolment in a DPP, MNT visit completion, follow-up PCP visits, glycaemic laboratory testing and weight loss. Additional process and implementation outcomes are described below where we elaborate on the six Es approach from the TRiP framework32 to ensure implementation of the intervention as follows:

    1. Engage: we engaged clinicians and staff in our intervention clinic by describing the high prevalence of pre-diabetes and showing them evidence of current practices, including external data on screening rates39–41 and local data from phases 1 and 2.19 33 We provided an in-person training session that was attended by 4 out of the 16 physicians in the clinic. The training slides and recording of the training session were shared with all clinicians.

    2. Educate: during the training session, we educated clinicians and staff of the scientific literature supporting the proposed intervention. The training slides described above reviewed the evidence behind the DPPs, MNT and metformin for preventing progression of pre-diabetes to type 2 diabetes.

    3. Execute: we designed an implementation toolkit to address learning from phases 1 and 2 as described above. During the training session, we described to clinicians the resources in our implementation toolkit (table 1 and online supplemental appendices 1 and 2).

    4. Evaluate: we evaluated whether the intervention was successful by examining several primary and secondary outcomes collected during and after implementation of the intervention. Additionally, we collected several patient outcomes, including satisfaction with PCP discussion about pre-diabetes, confidence with and motivation for managing pre-diabetes and engagement in lifestyle change. Among patients who reported that their PCP discussed pre-diabetes with them, more patients in the intervention clinic versus control clinic felt they understood what their doctor was telling them about pre-diabetes (76% vs 62%, p=0.04) and felt their opinion was valued (78% vs 69%, p=0.13).

    5. Embed: we elicited feedback from clinicians to ensure the intervention is sustainable. We surveyed clinicians at baseline, 6 months and 12 months after the intervention to gather data on several implementation outcomes (eg, acceptability, actionability and adoption). At 12 months, 9 out of 11 PCPs agreed that the START Diabetes Prevention Clinical Pathway was acceptable to their patients and 10 out of 11 PCPs agreed that the pathway is actionable and has encouraged them to implement changes in their practices with patients with pre-diabetes. Regarding adoption of the pathway components, all PCPs reported use of the pre-diabetes decision aid and patient education materials on pre-diabetes.

    6. Expand: we created a plan to disseminate our intervention to the other primary care practices within our academic network while also incorporating changes based on clinician feedback.

    We tested the effectiveness of the START Diabetes Prevention Clinical Pathway in a clinical trial (NCT05265312) comparing an intervention clinic with a control clinic providing usual care. Full results will be reported in a separate dedicated publication.

    Discussion

    This paper summarises the mixed-methods development process we undertook using the TRiP framework to develop and implement a systematic approach to diabetes prevention in primary care. This design enabled us to develop an intervention from the existing evidence of current practices that is tailored to the needs of the patient population being served, thus facilitating the translation of research into routine clinical care with the goal of improving treatment uptake for pre-diabetes.

    Despite the large number of diabetes prevention trials demonstrating efficacy of evidence-based interventions such as the DPP for preventing diabetes, there remains a gap in effectively translating these interventions into routine care.3 There have only been several studies examining the systematic identification and referral of patients with pre-diabetes to treatment, including uptake of the DPP in primary care settings. At Intermountain Health, quality improvement work was conducted to create a process for identifying patients with pre-diabetes using the EHR and the creation of an education and referral process to diabetes prevention interventions (2-hour introductory class called Prediabetes 101, MNT, 6-month Weigh to Health behavioural programme or online DPP).42 Although the authors mentioned in their paper the use of plan–do–study–act cycles of the quality improvement process, it was not otherwise well described. In another study, the American Medical Association collaborated with Henry Ford Health System to pilot a pre-diabetes clinical programme designed to include clinical decision support, patient engagement and population health management tools to increase DPP referrals.43 The authors also did not describe any systematic process for engaging stakeholders and for intervention development.

    There are several advantages to using the TRiP framework for translating evidence into real-world practice.26 First, it provides several straightforward, actionable procedures. Using this framework, we undertook three clear phases of intervention design and development. Second, the model focuses on systems and not just on individuals, which is important since clinicians work within healthcare teams. Our intervention was not focused on just changing clinician behaviours, but understanding how we can organise care through a clinical pathway supported by a toolkit of resources. Third, the model promotes a sustainable approach since it focuses on engagement of the local team in adapting the intervention to fit local work processes. We engaged input from a wide range of stakeholders relevant to diabetes prevention to guide intervention design and development to ensure acceptability, appropriateness and feasibility of our intervention.

    Challenges with using the TRiP framework, which also apply to other implementation science models, include the time and resources required to develop, implement and evaluate programmes. The time needed to design and develop an effective intervention must be balanced by the fact that clinical changes are occurring simultaneously in the health system that may affect the intervention or outcomes. As we started this study, there was a new health system initiative focused on diabetes prevention that ultimately impacted some of the intervention components. For example, one area of focus of this health system initiative was on clinician education about pre-diabetes and a push to increase the referral of patients to the DPP, a primary outcome of the aforementioned clinical trial testing the START Diabetes Prevention Clinical Pathway. Nonetheless, the intervention arising from this work addresses many of the barriers that have arisen with the health system initiative. Future consideration might be a hybrid effectiveness–implementation design44 that would allow timely assessment of both effectiveness and implementation outcomes for this type of work.

    Limitations to our approach include the potential lack of generalisability of our findings from phase 1 (summarising existing evidence) and 2 (identify local barriers to implementation through stakeholder engagement). The intervention developed over the course of the three phases may not be applicable to all clinical settings, particularly ones caring for populations experiencing a large burden of adverse social determinants of health. We were fortunate to be able to widely offer the DPP to all our patients due to grant funding supporting the DPP, which may not be possible in many settings. In general, the DPP is covered by Medicare, Medicaid in certain states and some commercial plans.

    We anticipate our future research will include adaptation and dissemination of our approach to populations where there may be barriers to transportation, healthy food, accessing regular medical care and engaging in healthy behaviours. Our multistakeholder engagement process will be particularly important to determine how to adapt our diabetes prevention intervention to address common barriers and social determinants of health. To that end, we may need to involve and use other clinical staff members in our intervention recognising that PCPs have many issues to address in a single visit. During the design and development of the START Diabetes Prevention Clinical Pathway, we engaged stakeholders about using other clinical staff members besides PCPs to deliver the intervention, but we did not ultimately include them because of significant short-staffing of nurses and medical assistants in the clinic as a result of the COVID-19 pandemic. We recognise that other diabetes prevention interventions have focused on the use of pharmacists45 or medical assistants25 to lead shared decision-making treatment discussions with primary care patients, and this could be incorporated in the future if clinic staffing is sufficient.

    Conclusions

    The TRiP framework provided a suitable approach to support the identification of evidence-based care practices for pre-diabetes and the development of a well-fitted, actionable intervention and implementation plan designed to increase treatment uptake for pre-diabetes in primary care. This approach addresses the current gap in effectively translating diabetes prevention interventions into routine care, and our change package can be adapted and used by other health systems or clinics to target prevention of diabetes or other related chronic conditions.

    Data availability statement

    All data relevant to the study are included in the article or uploaded as supplemental information.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study involves human participants and was approved by the Johns Hopkins School of Medicine Institutional Review Board (IRB). Each phase had a separate IRB review (phase 1: IRB00203278; phase 2: IRB00232415; phase 3: IRB00313490). Participants gave informed consent to participate in the study before taking part.

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    Footnotes

    • X @evtseng

    • Contributors ET—study concept and design; oversight of recruitment and data collection; data analysis; manuscript writing; guarantor. KS, JMC, JAM and NMM—study concept and design, advise and review of data analysis, manuscript writing. All authors have commented and approved the manuscript.

    • Funding ET is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (K23DK118205).

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.