Improving diabetes and depression self-management via adaptive mobile messaging

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Project Summary/Abstract Diabetes and depression are major public health problems that disproportionately affect racial/ethnic minorities and low-income individuals in the US. Efficacious interventions for depression and diabetes exist but are not often combined despite similar treatment recommendations (specifically physical activity) for both conditions. Especially in resource-constrained environments, mobile health (mHealth) technologies are cost effective and feasible methods for delivering self-management support given the more ubiquitous penetration across socioeconomic status. Existing mHealth interventions have shown preliminary success but have had difficulty sustaining engagement. When combined with machine learning algorithms, health messages can be adapted to specifically motivate individuals based on their unique profiles. In Aim 1, we will integrate content from interventions targeting diabetes, depression, and physical activity applying user design methods. We will utilize the existing HealthySMS platform as the basis for this intervention. This will be called the Diabetes and Mental Health Adaptive Notification Tracking and Evaluation (DIAMANTE) study. In Aim 2, we will test an mHealth intervention for diabetes and depression that will generate messages using an adaptive machine learning algorithm that learns from patient step count data (collected passively via a smartphone app) and patient entered blood glucose and mood ratings. We will compare this adaptive, personalized intervention with a static messaging intervention, typical of many existing text messaging interventions. In Aim 3, we will rerandomize non-responsive participants to receiving nurse outreach using a sequential, multiple assignment, randomized trial (SMART) design. We will leverage the SMART design to conserve more expensive one-on-one nurse outreach for the patients who are no longer engaged in the program and need the most support. We will test this intervention with 350 patients from a safety net setting in English and Spanish. The primary outcomes for this study are HbA1c levels and PHQ-9 scores. The results of this study will help us understand the impact of personalizing content utilizing machine learning algorithms as well as the impact of providing clinician support for those receiving mobile health interventions. Since we are testing this intervention in a resource-constrained environment, the results of this study will be relevant for a broader population.