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Completed

NCT Number: NCT04710940

Development and Feasibility Testing of DM-BOOST Intervention.

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.

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Key information

Conditions

Diabetes Mellitus, Type 2 Diabetes Mellitus Endocrine System Diseases Glucose Metabolism Disorders Metabolic Diseases Nutritional and Metabolic Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

University of Massachusetts Medical School

Worcester, Massachusetts, 01655, United States

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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)

Treatment and study plan

Diabetes BOOST

Behavioral

Participants will receive supportive care using technology for DSMT in addition to usual care.

Usual Care

Behavioral

Participants will receive usual care for DSMT.

Primary outcomes

  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 outcomes

  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 outcomes

  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

Sponsors and collaborators

Lead sponsor

Daniel Amante

Other

Collaborators

  • Worcester Polytechnic Institute

Registry information

Official study title

Development and Feasibility Testing of a Diabetes Mellitus Program Using Behavioral Economics to Optimize Outreach and Self-management Support With Technology.

Acronym: DM-BOOST

Important dates

Study start
2021
Primary completion
2022
Study completion
2024
First posted
Jan 15, 2021
Registry last updated
Feb 22, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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