Area:
Clinical Psychology
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High-probability grants
According to our matching algorithm, Adrian Aguilera is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2012 — 2016 |
Aguilera, Adrian |
K23Activity Code Description: To provide support for the career development of investigators who have made a commitment of focus their research endeavors on patient-oriented research. This mechanism provides support for a 3 year minimum up to 5 year period of supervised study and research for clinically trained professionals who have the potential to develop into productive, clinical investigators. |
Automated Text Messaging to Improve Depression Treatment in Low-Income Settings @ University of California Berkeley
DESCRIPTION (provided by applicant): Poor adherence to depression treatments (psychotherapy and pharmacotherapy) limits their effectiveness in community settings. Problems with adherence are especially pronounced in low-income settings. Innovative and cost-effective methods are needed to improve adherence to treatments and maximize mental health resources. Mobile phone based text messaging (or short messaging service: SMS) is a ubiquitous technology that has been used in various health applications across socioeconomic status. This technology has the potential to increase the fidelity of mental health treatments via increased adherence. The proposed research project will test whether adding an automated SMS adjunct to group cognitive behavioral therapy (CBT) for depression can increase adherence (homework adherence, attendance, medication adherence) and further reduce depression symptoms. The SMS adjunct will 1) prompt patients to monitor mood, thoughts and behaviors, 2) will provide medication and appointment reminders and 3) will send personalized CBT based tips. The information that patients provide will be used within the clinical setting to highlight interrelations between thoughts, behaviors and symptoms. The results of the research project will inform an R01 to do further testing of health information technology (HIT) applications in low-income settings. The experience gained through this award will complement previous training and prepare me for a successful clinical research career in the application of health information technologies to mental health services in low-income communities. This K23 (Mentored Patient-Oriented Career Development Award) application delineates a training and research plan seeking to improve depression treatment in low-income communities through the use of text messaging as an adjunct to psychotherapy. The applicant is seeking advanced training in 1) community based mental health services research, 2) health information technology and 3) mixed methods research via mentorship from Kurt C. Organista, Ricardo F. Mu¿oz, and Patricia A. Arean. To achieve further expertise in these areas, various training experiences are proposed with a research trial serving as the core of the career development plan. PUBLIC HEALTH RELEVANCE: This training proposal and research plan addresses NIMH strategic objective #3 to improve and personalize mental health treatment and the NIMH Road Ahead recommendation #3C to support research on the effective deployment of health information technology in underserved communities. Improving adherence to depression treatment with a low cost adjunct such as text messaging has the potential to reduce symptoms, sustain treatment gains and make treatments more efficient.
|
0.976 |
2017 — 2021 |
Aguilera, Adrian |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Improving Diabetes and Depression Self-Management Via Adaptive Mobile Messaging @ University of California Berkeley
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.
|
0.976 |