Development and Feasibility Testing of DM-BOOST Intervention.

Diabetes Mellitus, Type 2 Clinical Trial

— DM-BOOST  

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04710940
• Other study ID #: H00017902
• Status: Completed
• Phase: N/A
• Start date: January 13, 2021
• Est. completion date: January 1, 2024

Study information

• Verified date: February 2024
• Source: University of Massachusetts, Worcester
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 66
• Est. completion date: January 1, 2024
• Est. primary completion date: November 1, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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)

Related Conditions & MeSH terms

• Diabetes Mellitus
• Diabetes Mellitus, Type 2

Intervention

Behavioral:
• Diabetes BOOST
Participants will receive supportive care using technology for DSMT in addition to usual care.
• Usual Care
Participants will receive usual care for DSMT.

Locations

Country	Name	City	State
United States	University of Massachusetts Medical School	Worcester	Massachusetts

Sponsors (2)

Lead Sponsor	Collaborator
Daniel Amante	Worcester Polytechnic Institute

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Predictors of guideline-concordant diabetes care (sociodemographic predictors) (Aim 1)	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)	Assessed at baseline
Other	Predictors of guideline-concordant diabetes care (HbA1c level) (Aim 1)	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	Assessed at baseline
Other	Predictors of guideline-concordant diabetes care (BMI) (Aim 1)	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	Assessed at baseline
Other	Predictors of guideline-concordant diabetes care (Smoking Status) (Aim 1)	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	Assessed at baseline
Other	Predictors of guideline-concordant diabetes care (Cholesterol level) (Aim 1)	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	Assessed at baseline
Other	Predictors of guideline-concordant diabetes care (Clinical utilization) (Aim 1)	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	Assessed at baseline
Primary	Intervention Acceptability (Aim 2)	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.	1 month
Primary	Completion of diabetes self-management training (Aim 3)	Completion of diabetes self-management training.	9 months
Secondary	Clinical utilization (Aim 3)	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.	9 months
Secondary	Diabetes self-efficacy (Aim 3)	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 months
Secondary	Diabetes treatment satisfaction (Aim 3)	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)	3 months
Secondary	Diabetes self-management skills (Aim 3)	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	3 months
Secondary	Patient engagement with Diabetes Self-Management Training (Aim 3)	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.	9 months
Secondary	Hemoglobin A1C (HbA1C) (Aim 3)	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.	6 months