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Development and Feasibility Testing of DM-BOOST Intervention. (DM-BOOST)

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ClinicalTrials.gov Identifier: NCT04710940
Recruitment Status : Recruiting
First Posted : January 15, 2021
Last Update Posted : September 14, 2022
Sponsor:
Collaborator:
Worcester Polytechnic Institute
Information provided by (Responsible Party):
Daniel Amante, University of Massachusetts, Worcester

Brief Summary:
DM-BOOST uses clinical informatics tools to identify types of patients with gaps in diabetes care and deploy tailored, proactive outreach methods rooted in behavioral economics to nudge them towards increased engagement with diabetes self-management training and leverage patient-facing technologies to enhance longitudinal patient self-management support.

Condition or disease Intervention/treatment Phase
Diabetes Mellitus, Type 2 Behavioral: Diabetes BOOST Behavioral: Usual Care Not Applicable

Detailed Description:

In DM-BOOST, the Principal investigator will deploy a mixed-methods, patient-centered approach to intervention development and initiate a multiphase optimization strategy (MOST) to learn how to maximize patient engagement and support of self-management training. In this pilot, study team will complete the first phase (Preparation), and initiate feasibility piloting of the second phase (Optimization). Completion of optimization and MOST's final phase (Evaluation), will occur in a subsequent project.

In the preparation phase, Principal investigator will first analyze EHR and claims data in the UMCCTS data lake to identify sociodemographic characteristics associated with gaps in diabetes care to develop patient persona archetypes (Aim 1). Next, Principal investigator will selectively recruit patients of identified persona types as consultants, elicit stakeholder feedback during community engagement studios and conduct usability testing to iteratively design the intervention (Aim 2). Study team will then conduct a feasibility pilot (Aim 3) to assess user experience of the intervention implementation and collect exploratory outcome data to be used to inform a subsequent, complete optimization trial.

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 70 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Intervention Model Description:

The purpose of this study is to develop and usability test a patient-centric intervention designed to improve implementation of diabetes self-management training. To accomplish this, 3 specific aims will be completed.

Aim 1 - Retrospective data from the UMass Medical School EHR data repository will be analyzed to identify different clusters of patients with diabetes.

Aim 2 - To facilitate a patient-centric design of the DM-BOOST intervention, Patient Research Expert Panel (PREP) members (n</=10) will be recruited from various patient types identified in Aim 1 (Aim 2a), participate in Community Engagement Studios to inform intervention conceptualization (Aim 2b) and usability test the intervention (Aim 2c).

Aim 3 - The intervention will be pilot tested in n</=70 patients with type 2 diabetes (T2D). Participants will be randomized to either intervention or comparison groups.

Masking: Double (Participant, Investigator)
Masking Description: After completing the informed consent, study staff will enter the participant's information into pre-populated REDCap identification numbers. This will assign allocation based on the randomization table. Using this technique, participants will be blinded to allocation. However, research staff will not be blinded to provide personalized training for intervention and control. The investigator will be blinded to randomization for all participants during the study.
Primary Purpose: Supportive Care
Official Title: Development and Feasibility Testing of a Diabetes Mellitus Program Using Behavioral Economics to Optimize Outreach and Self-management Support With Technology.
Actual Study Start Date : January 13, 2021
Estimated Primary Completion Date : October 2022
Estimated Study Completion Date : June 2023

Arm Intervention/treatment
Experimental: Intervention - Diabetes BOOST
Intervention group participants will complete a baseline survey, receive a referral to DSMT from the research team, a mailed welcome letter and self-care education sent via a series of personalized patient portal secure messages, text messages, and video call. They will be sent text messages with information about one of the American Association of Diabetes Educators 7 self-care behaviors and will receive encouragement to author their own self-management behavioral goals. Participants will also complete a telehealth training video call with research staff and review the goals that the participant replied with. The participant will then be encouraged to send a patient portal message to their DSMT CDCES that includes their personalized goals prior to their scheduled DSMT session. They will then complete a 3-month follow-up survey and qualitative interview.
Behavioral: Diabetes BOOST
Participants will receive supportive care using technology for DSMT in addition to usual care.

Active Comparator: Usual Care
Comparison Group participants will complete a baseline survey, receive a DSMT referral request from research team to their primary care provider and a mailed welcome letter. The mailed letter will welcome the participant to the study and contain general information about diabetes self-care behaviors and goal setting. They will complete a DSMT session. They will then complete a 3-month follow-up survey and qualitative interview.
Behavioral: Usual Care
Participants will receive usual care for DSMT.




Primary Outcome Measures :
  1. Intervention Acceptability (Aim 2) [ Time Frame: 1 month ]
    Patient-reported assessment of intervention acceptability via usability testing. Qualitative data collection informed by the Technology Acceptance Model with assessment of perceived usefulness, ease of use, behavioral intention to use and external factors. No quantitative data measured.

  2. Completion of diabetes self-management training (Aim 3) [ Time Frame: 9 months ]
    Completion of diabetes self-management training.


Secondary Outcome Measures :
  1. Clinical utilization (Aim 3) [ Time Frame: 9 months ]
    Rate of clinical utilization as measured by number of visits per participant to primary, specialty care, and emergency/hospital care visits measured 6-months after follow-up visit.

  2. Diabetes self-efficacy (Aim 3) [ Time Frame: 3 months ]
    Diabetes self efficacy will be measured at baseline and 3 months after enrolling in the study using the Diabetes Management Self-Efficacy Scale. Participants will provide feedback on set of questions, using a 5-point Likert scale( with 1=Strong Disagree, 2=Somewhat Disagree, 3= Neutral, 4=Somewhat Agree, 5= Strongly Agree)

  3. Diabetes treatment satisfaction (Aim 3) [ Time Frame: 3 months ]
    Diabetes Treatment Satisfaction will be measured at 3 months after enrolling in the study using the Diabetes Treatment Satisfaction Questionnaire Change tool. Participants will be asked to share how their experience of current treatment has changed from their experience of treatment before the study began. They will answer each question by choosing 3 for Much More Satisfied Now up to -3 for Much Less Satisfied Now. (3,2,1,0,-1,-2,-3)

  4. Diabetes self-management skills (Aim 3) [ Time Frame: 3 months ]
    Self-management skills will be measured at 3 months after enrolling in the study. Participant will be asked questions about their diabetes self-care activities during the past seven days using the Summary of Diabetes Self-Care Activities Measure

  5. Patient engagement with Diabetes Self-Management Training (Aim 3) [ Time Frame: 9 months ]
    Engagement data will be collected by research staff. It will be measured by the numbers of patients who request contact, are reached, enrolled in the study and scheduled DSMT appointment.

  6. Hemoglobin A1C (HbA1C) (Aim 3) [ Time Frame: 6 months ]
    Measurement of HbA1c values to determine impact of intervention. HbA1c values at baseline visit will be compared with values at 3-6 months after participant's enrollment. These data will be obtained through EHR chart review.


Other Outcome Measures:
  1. Predictors of guideline-concordant diabetes care (sociodemographic predictors) (Aim 1) [ Time Frame: Assessed at baseline ]

    Retrospective analysis of EHR data to identify clusters of sociodemographic predictors of guideline-concordant of diabetes care will be identified. Retrospective data will be requested from UMMS Data Lake through the Data Science Core. Data requested for adult patients with T2D since Epic EHR roll-out in October 2017 will include:

    • Sociodemographic characteristics (gender, date of birth, race/ethnicity, zip code, language, marital status, insurance type)


  2. Predictors of guideline-concordant diabetes care (HbA1c level) (Aim 1) [ Time Frame: Assessed at baseline ]

    Retrospective analysis of EHR data to identify clusters of clinical predictors of guideline-concordant of diabetes care will be identified. Retrospective data will be requested from UMMS Data Lake through the Data Science Core. Data requested for adult patients with T2D since Epic EHR roll-out in October 2017 will include:

    • Clinical characteristics as measured by the level of HbA1c


  3. Predictors of guideline-concordant diabetes care (BMI) (Aim 1) [ Time Frame: Assessed at baseline ]

    Retrospective analysis of EHR data to identify clusters of clinical predictors of guideline-concordant of diabetes care will be identified. Retrospective data will be requested from UMMS Data Lake through the Data Science Core. Data requested for adult patients with T2D since Epic EHR roll-out in October 2017 will include:

    • Clinical characteristics as measured by the level of BMI. Weight and height will be combined to report BMI in kg/m^2


  4. Predictors of guideline-concordant diabetes care (Smoking Status) (Aim 1) [ Time Frame: Assessed at baseline ]

    Retrospective analysis of EHR data to identify clusters of clinical predictors of guideline-concordant of diabetes care will be identified. Retrospective data will be requested from UMMS Data Lake through the Data Science Core. Data requested for adult patients with T2D since Epic EHR roll-out in October 2017 will include:

    • Clinical characteristics as measured by the smoking status


  5. Predictors of guideline-concordant diabetes care (Cholesterol level) (Aim 1) [ Time Frame: Assessed at baseline ]

    Retrospective analysis of EHR data to identify clusters of clinical predictors of guideline-concordant of diabetes care will be identified. Retrospective data will be requested from UMMS Data Lake through the Data Science Core. Data requested for adult patients with T2D since Epic EHR roll-out in October 2017 will include:

    • Clinical characteristics as measured by the the level of cholesterol


  6. Predictors of guideline-concordant diabetes care (Clinical utilization) (Aim 1) [ Time Frame: Assessed at baseline ]

    Retrospective analysis of EHR data to identify clusters of clinical predictors of guideline-concordant of diabetes care will be identified. Retrospective data will be requested from UMMS Data Lake through the Data Science Core. Data requested for adult patients with T2D will include:

    • Clinical utilization as measured by number of visits per participant to primary care, specialty visits, emergency room, hospitalizations, education/training, patient portal use, care management engagement since Epic EHR roll-out in October 2017




Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Criteria

Inclusion Criteria:

  • Adults (age 18+)
  • Cognitively able to consent (Aims 2 and 3)
  • Diagnosed with type 2 diabetes (Aims 1-3)
  • Receive primary care at UMMHC in past 12 months at time of initial analysis (Aims 1-3)
  • English speaking (Aims 2 and 3)
  • Have access to patient portal or a smart phone (Aim 3)

Exclusion Criteria:

  • Adults unable to consent (lacking cognitive capacity) (Aims 2 and 3)
  • Individuals who are not yet adults (infants, children, teenagers) (Aims 1-3)
  • Pregnant women (Aims 1-3)
  • Prisoners (Aims 1-3)
  • Non-English speaking (Aims 2 and 3)

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04710940


Contacts
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Contact: Daniel J Amante, PhD, MPH 508-856-8480 daniel.amante@umassmed.edu
Contact: Geraldine Puerto, MPH 508-856-8976 geraldine.puerto@umassmed.edu

Locations
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United States, Massachusetts
University of Massachusetts Medical School Recruiting
Worcester, Massachusetts, United States, 01655
Contact: Daniel Amante, PhD    774-418-3645    Daniel.Amante@umassmed.edu   
Contact: Geraldine Puerto, MPH    508-856-8976    geraldine.puerto@umassmed.edu   
Sponsors and Collaborators
Daniel Amante
Worcester Polytechnic Institute
Investigators
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Principal Investigator: Daniel J Amante, PhD, MPH UMass Medical School
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Responsible Party: Daniel Amante, Assistant Professor, University of Massachusetts, Worcester
ClinicalTrials.gov Identifier: NCT04710940    
Other Study ID Numbers: H00017902
First Posted: January 15, 2021    Key Record Dates
Last Update Posted: September 14, 2022
Last Verified: September 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Diabetes Mellitus
Diabetes Mellitus, Type 2
Glucose Metabolism Disorders
Metabolic Diseases
Endocrine System Diseases