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

项目概要/摘要 吸烟是一个主要的可预防的转诊断风险因素，如果更有效地针对， 可以减少广泛的健康差距的患病率，严重程度，治疗效果，死亡率， 许多慢性健康状况（例如，糖尿病、阻塞性睡眠呼吸暂停），降低复杂性/多发病率， 降低高达80%的医疗成本。尤其是东南部地区，迫切需要打破现状 烟草控制（<2% CDC建议拨款;吸烟和死亡率最高）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 少数民族以吸烟和慢性病的形式更大，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，黑人的成功率较低 吸烟者，即使他们以相似的速度和强度吸烟，并做出更多的戒烟尝试。移动的健康 移动健康（mHealth）在解决种族差异方面可能特别有用。黑人吸烟者表现出高参与度 使用智能手机获得医疗保健的比率以及对数字干预的更高依从性，这可能有助于 定制以满足不同的需求。迫切需要克服公平差距，这将需要多样性 并包括来自代表性种族/民族的个体以确定有效的治疗。有必要 对于及时适应性干预措施（JITAI），1）可以快速部署（最好是在渴望发生之前）， 2)有效地防止或迅速减弱渴望，以及3）适合个性化治疗。我们 自动化，但个性化，JITAI应用程序，QuitBuddy，允许患者在高风险情况下做好准备， 它们的出现，有效地促进了戒断和防止复发。我们的总体目标是优化智能 算法，识别个性化的复发风险，并自动提示交付实时，抢先 在接近个性化的高风险地点时。来自NIDA资助的（K23）试点随机 对照试验证明了出色的可用性（超过500个应用程序中的前10%），可接受性（>80%的依从性）， 技术可行性（<10% GPS数据）。我们在这些有希望的数据的基础上， 强大而严格的SMART设计，具有服务不足人群的多样化代表性， 社区需要SDoH干预。目标1和2：评估QuitBuddy和SDoH增强干预 通过务实的远程SMART设计（N= 2，090）戒烟和预防复吸的有效性。我们 预期QuitBuddy和SDoH增强的上级6个月生化验证戒烟率更高 相对于对照组，干预。探索性目的：测试潜在的主持人/调解人。我们的方法 首次整合了复发风险，循证治疗，智能手机/GPS的既定理论 技术和SDoH。因此，该项目提供了高影响力的解决方案，以解决整个国家的健康差距。 广泛的慢性病不成比例地影响到得不到充分服务的人群。

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.