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

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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, this project offers high-impact solutions to address health disparities across a wide range of chronic diseases that disproportionately affect underserved populations.