# Supporting Tailored Adaptive Change and Reinforcement for Medication Adherence Program (STAR-MAP): Randomized trial of a novel approach to improve adherence in older hypertensive women and men

> **NIH NIH R01** · TULANE UNIVERSITY OF LOUISIANA · 2024 · $652,956

## Abstract

Project summary
There is a fundamental gap in the availability of interventions that 1) sustainably improve adherence to pre-
scribed blood pressure (BP) medications, 2) identify underlying behavior change mechanisms, and 3) demon-
strate efficacy by sex and race in older adults. Accordingly, interventions to improve adherence and BP control
remain minimally effective. The long-term goal is to improve medication adherence, BP control, and healthy
aging in older hypertensive adults. The overall objective for this R01 application is to determine the efficacy of
the Supporting Tailored Adaptive change and Reinforcement for Medication Adherence Program (STAR-
MAP), which integrates the transformative Overcoming Immunity-to-Change (OITC) health coaching process,
on improving adherence, BP control, and quality of life (QOL). The OITC approach that has proven useful in
other arenas was successfully applied to BP management in the research team's recent pilot study. Based on
this prior work, the central hypothesis is that STAR-MAP, designed to help nonadherent older hypertensive
adults to identify and alter implicit attitudes that hinder medication adherence, will change mindsets and im-
prove daily taking of prescribed medications, leading to lowered BP and better QOL. The rationale for the pro-
posed study is that demonstration of the efficacy of STAR-MAP would provide new opportunities to overturn
nonadherent behavior and improve BP control and healthy aging for the growing population of older adults with
hypertension, overall and in sex and race subgroups. The central hypothesis will be tested by pursuing two
specific aims: (1) Determine the efficacy, underlying mechanism, and sustainability of the STAR-MAP interven-
tion, aimed at improving medication-taking behavior, on medication adherence and clinical outcomes; and (2)
Explore the efficacy of STAR-MAP in sex and race subgroups. Under the first aim, a randomized controlled
trial will be conducted in 402 nonadherent (proportion of days covered (PDC) <0.8) adults ≥65 years with un-
controlled BP (201 per arm – intervention versus usual care; 50% women; 50% black) and fully insured by Blue
Cross Blue Shield of Louisiana. The study will have 90% statistical power to detect a 15% difference at 12
months in the proportion of participants with PDC ≥0.8 between those randomized to intervention versus
usual care with attention control. Under the second aim, efficacy of STAR-MAP in sex and race subgroups
will be explored. The approach is innovative because it will be the first large clinical trial to rigorously test a new
intervention that targets negative implicit attitudes toward medications with a goal to improve adherence, BP
control, and QOL. The proposed research is significant because it will represent an important step in a contin-
uum of research that is expected to lead to an efficacious, scalable intervention (with data on the underlying
behavior change mechanism). Finally, the results are expect...

## Key facts

- **NIH application ID:** 10847397
- **Project number:** 5R01HL153750-04
- **Recipient organization:** TULANE UNIVERSITY OF LOUISIANA
- **Principal Investigator:** Marie Krousel-Wood
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $652,956
- **Award type:** 5
- **Project period:** 2021-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10847397

## Citation

> US National Institutes of Health, RePORTER application 10847397, Supporting Tailored Adaptive Change and Reinforcement for Medication Adherence Program (STAR-MAP): Randomized trial of a novel approach to improve adherence in older hypertensive women and men (5R01HL153750-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10847397. Licensed CC0.

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