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

项目摘要/摘要 吸烟是一个主要的可预防的跨诊断风险因素，如果目标更有效， 可以减少在患病率、严重性、治疗效果和死亡率方面的广泛健康差距 许多慢性健康状况(如糖尿病、阻塞性睡眠呼吸暂停)，降低复杂性/多发病，以及 将医疗成本降低高达80%。尤其是东南部地区，迫切需要打破现状。 控烟(疾控中心建议拨款2%；吸烟率和死亡率最高)在很大程度上是由 通过被忽视的SDOH模式(贫困，获得护理)，不成比例地影响种族和民族 少数族裔吸烟和慢性病的形式更严重，最终失去了近十年的生命。 不幸的是，只有5%的戒烟尝试持续至少一年，而黑人的戒烟成功率较低 吸烟者，即使他们吸烟的速度和强度相似，也会做出更多的戒烟尝试。移动医疗 (MHealth)在解决种族差距方面可能具有特殊的效用。黑人吸烟者表现出高度的参与度 使用智能手机获取医疗保健的费率以及对数字干预的更高遵守，这可能会促进 量身定做以满足不同的需求。迫切需要克服公平差距，这将需要多样性。 以及纳入具有代表性的种族/族裔的个人，以确定有效的治疗方法。有必要 对于1)可以迅速部署的即时适应性干预(JITAI)(理想情况下是在渴望发生之前)， 2)有效地防止或迅速减轻渴求，3)适合个性化治疗。我们的 自动化但个性化的吉泰应用程序QuitBuddy允许患者在之前为高风险情况做准备 它们的出现，有效地促进了禁欲，防止了复发。我们的总体目标是优化智能 算法，识别个性化的复发风险，并自动提示实时、抢先交付 在接近个性化的高风险地点时，采取适当的方式。NIDA资助的(K23)试验结果随机 对照试验显示了卓越的可用性(500多个应用程序中排名前10%的应用程序)、可接受性(80%的合规性)、 和技术可行性(&lt；10%的GPS数据)。我们在这些有希望的数据的基础上，充分测试了 强大而严谨的智能设计，以多样化的方式代表服务不足的人群，并满足 社区对SDoH干预的需求。目标1和2：评估QuitBuddy和SDoH增强干预 通过实用的远程智能设计(N=2,090)实现戒烟和预防复发的有效性。我们 预计QuitBuddy和SDoH增强治疗的6个月生化验证戒毒率会更高 干预措施，相对于对照。探索性目标：测试潜在的主持人/调解人。我们的方法 首次整合了关于复发风险、循证治疗、智能手机/GPS的理论 技术和SDoH。因此，该项目提供了高影响力的解决方案，以解决 广泛的慢性病，对服务不足的人群造成不成比例的影响。

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.