Diabetes Mellitus, Type 2
Conditions
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.
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.
Interventions
BEHAVIORALDiabetes BOOST
Participants will receive supportive care using technology for DSMT in addition to usual care.
BEHAVIORALUsual Care
Participants will receive usual care for DSMT.
Sponsors
Worcester Polytechnic Institute
Daniel Amante
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SUPPORTIVE_CARE
Masking
DOUBLE (Subject, 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.
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.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
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)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Intervention Acceptability (Aim 2) | 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. |
| Completion of diabetes self-management training (Aim 3) | 9 months | Completion of diabetes self-management training. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Diabetes self-efficacy (Aim 3) | 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) |
| Clinical utilization (Aim 3) | 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. |
| Diabetes treatment satisfaction (Aim 3) | 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) |
| Diabetes self-management skills (Aim 3) | 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 |
| Patient engagement with Diabetes Self-Management Training (Aim 3) | 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. |
| Hemoglobin A1C (HbA1C) (Aim 3) | 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
| Measure | Time frame | Description |
|---|---|---|
| Predictors of guideline-concordant diabetes care (BMI) (Aim 1) | 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 |
| Predictors of guideline-concordant diabetes care (Smoking Status) (Aim 1) | 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 |
| Predictors of guideline-concordant diabetes care (Cholesterol level) (Aim 1) | 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 |
| Predictors of guideline-concordant diabetes care (Clinical utilization) (Aim 1) | 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 |
| Predictors of guideline-concordant diabetes care (HbA1c level) (Aim 1) | 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 |
| Predictors of guideline-concordant diabetes care (sociodemographic predictors) (Aim 1) | 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) |
Countries
United States
Outcome results
None listed