# Improving diabetes and depression self-management via adaptive mobile messaging

> **NIH AHRQ R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2021 · $378,686

## Abstract

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.

## Key facts

- **NIH application ID:** 10204099
- **Project number:** 5R01HS025429-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Adrian Aguilera
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2021
- **Award amount:** $378,686
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10204099

## Citation

> US National Institutes of Health, RePORTER application 10204099, Improving diabetes and depression self-management via adaptive mobile messaging (5R01HS025429-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10204099. Licensed CC0.

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