# Accelerating Health Equity via Just-In-Time Adaptive Interventions (JITAIs): Scalable and High Impact mHealth Precision Smoking Relapse Prevention

> **NIH NIH P50** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2021 · $646,973

## Abstract

PROJECT SUMMARY/ABSTRACT
Tobacco smoking serves as a primary preventable transdiagnostic risk factor that, if targeted more effectively,
could reduce a wide range of health disparities in prevalence, severity, treatment efficacy, and mortality across
many chronic health conditions (e.g., diabetes, obstructive sleep apnea), reduce complexity/multimorbidity, and
reduce healthcare costs by up to 80%. The Southeast, in particular, has an urgent need to disrupt the status quo
of tobacco control (<2% CDC recommend appropriations; highest smoking and mortality) driven in large part
through neglected patterns of SDoH (poverty, access to care) that disproportionately impact racial and ethnic
minorities in the form of greater smoking and chronic diseases, and ultimately nearly a decade of life lost.
Unfortunately, only 5% of smoking cessation attempts last at least one year, with lower success among Black
smokers even though they smoke at similar rates and intensity, and make more quit attempts. Mobile health
(mHealth) may have particular utility in addressing racial disparities. Blacks smokers show high engagement
rates with smartphones to access healthcare and greater adherence to digital interventions, which may facilitate
tailoring to meet distinct needs. There is an urgent need to overcome equity gaps, which will require diversity
and inclusion of individuals from representative races/ethnicities to identify effective treatments. There is a need
for just-in-time adaptive interventions (JITAIs) that 1) can be deployed rapidly (ideally before craving occurs),
2) effectively prevent or attenuate cravings quickly, and 3) are amenable to personalized treatment. Our
automated, yet personalized, JITAI app, QuitBuddy, allows patients to prepare for high-risk situations before
they arise, effectively promoting abstinence and preventing relapse. Our overall goals are to optimize smart
algorithms, identify personalized relapse risk, and automatically prompt delivery a real-time, preemptive
manner, upon approaching personalized high-risk locations. Results from a NIDA-funded (K23) pilot randomized
controlled trial demonstrated outstanding usability (top 10% of over 500 apps), acceptability (>80% compliance),
and technical feasibility (<10% GPS data). We build upon these promising data by testing effectiveness in fully
powered and rigorous SMART design, with diverse representation of underserved populations, and meeting
community needs for SDoH interventions. Aims 1&2: Evaluate QuitBuddy and SDoH augmentation intervention
effectiveness for smoking cessation and relapse prevention via pragmatic remote SMART design (N=2,090). We
expect superior 6-month biochemically verified abstinence rates for QuitBuddy and SDoH augmentation
interventions, relative to controls. Exploratory Aims: Test potential moderators/mediators. Our approach
integrates for the first time established theories of relapse risk, evidence-based treatment, smartphone/GPS
technology, and SDoH. As such...

## Key facts

- **NIH application ID:** 10437313
- **Project number:** 1P50MD017347-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Bryan Wayne Heckman
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $646,973
- **Award type:** 1
- **Project period:** 2021-09-24 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10437313

## Citation

> US National Institutes of Health, RePORTER application 10437313, Accelerating Health Equity via Just-In-Time Adaptive Interventions (JITAIs): Scalable and High Impact mHealth Precision Smoking Relapse Prevention (1P50MD017347-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10437313. Licensed CC0.

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