Adapt2Quit – A Machine-Learning, Adaptive Motivational System: RCT for Socio-Economically Disadvantaged smokers”

7.项目总结 我们将测试Adapt2Quit，这是一个创新的机器学习、自适应激励消息系统。Adapt2退出 使用复杂的机器学习算法，根据以下信息自适应地为吸烟者选择最佳消息 多个属性，包括：1)吸烟者的简档；2)吸烟者随时间向系统的明确反馈； 3)来自数千名吸烟者的个人资料和他们的反馈模式的数据。Adapt2Quit的机器类型- 学习被称为推荐系统。医疗保健以外的公司(如亚马逊)使用推荐器 系统不断学习用户反馈(例如：喜欢的产品、购买的产品)以进行改进，从而 增强个人相关性和客户参与度。参与度对数字健康来说是一个巨大的挑战。在……里面 在计算机定制的健康消息领域，Adapt2Quit是第一个使用机器学习来连续 适应反馈并选择新的个性化消息发送给吸烟者。评估这一政策的影响 推荐系统中，Adapt2Quit将与健壮、主动控制相比，简单但有效 消息传递系统。在我们的试点实验中，Adapt2Quit的表现优于对照，特别是在社交媒体中 经济困难(SED)吸烟者。SED吸烟者更难参与干预。因此， Adapt2Quit增加的参与度将对瞄准SED吸烟者特别重要。除了……之外 Adapt2Quit信息在诱导和吸引吸烟者戒烟方面的潜在影响，我们的目标是 增加对州Quitline的使用。我们将在两个地点招募700名SED吸烟者。所有吸烟者都将完成一份 基线采访，并收到一本纸质小册子，上面有该州Quitline的信息。那么吸烟者就会 随机分为：Adapt2Quit或标准报文传送。由于该系统被设计成增强参与度， 通过参与导致积极行动，目标1将专注于参与[假设(H1a) Adapt2戒烟者，敬业度较高(完成的评分更多)的吸烟者在以下方面的得分更高 感知能力量表(PCS)]。目标2比较(Adapt2Quit和Control)行为改变过程 包括戒烟感知能力和戒烟支持行动(致电Quitline) [H2 A：Adapt2戒烟者在PCS上的得分将高于对照吸烟者；H2 B：Adapt2戒烟者 将采取比控制吸烟者更多的戒烟支持行动(Quitline，NRT)。目标3将评估 该系统的有效性[H3a：(主要结果)Adapt2戒烟者将有更大的戒烟机会 吸烟率(生化验证的6个月点流行率)比对照吸烟者高；H3B：(第二结果) 与控制吸烟者相比，Adapt2戒烟者首次戒烟尝试的时间更短；H3C：(中介分析) 测量的内部和外部过程将调节Adapt2Quit的戒烟效果]。至 为了实现上述目标，我们组建了一支具有相关专业知识的多学科团队，并建立了 良好的协作记录。

7. Project Summary We will test Adapt2Quit, an innovative Machine-Learning, Adaptive Motivational Messaging System. Adapt2Quit uses complex, machine-learning algorithms to adaptively select the best messages for a smoker, based upon multiple attributes, including: 1) the smoker’s profile; 2) the smoker’s explicit feedback over time to the system; and 3) data from thousands of prior smokers’ profiles and their feedback patterns. Adapt2Quit’s type of machine- learning is called a recommender system. Outside healthcare, companies (like Amazon) use recommender systems to continuously learn from user feedback (e.g.: liked product, products purchased) to improve, thus enhancing personal relevance and customer engagement. Engagement is a huge challenge for digital health. In the field of computer-tailored health messaging, Adapt2Quit is the first to use machine-learning to continuously adapt to feedback and select new personalized messages to send to smokers. To evaluate the impact of the recommender system, Adapt2Quit will be compared with a robust, active control, a simple but effective messaging system. In our pilot experiment, Adapt2Quit outperformed the control, especially among socio- economically disadvantaged (SED) smokers. SED smokers are harder to engage in interventions. Thus, Adapt2Quit’s increased engagement will be of particular importance for targeting SED smokers. In addition to the potential impact of the Adapt2Quit messages in inducing and engaging smokers in cessation, our goal is to increase use of the state Quitline. We will recruit 700 SED smokers at two sites. All smokers will complete a baseline interview and receive a paper brochure with information about the state’s Quitline. Smokers will then be randomized to: Adapt2Quit or the standard messaging. As the system is designed to enhance engagement, and through engagement lead to positive actions, Aim 1 will focus on engagement [Hypothesis (H1a) Among Adapt2Quit smokers, those with higher engagement levels (completed more ratings) will have greater scores on the perceived competence scale (PCS)]. Aim 2 compares (Adapt2Quit and control) behavior change processes including perceived competence for smoking cessation and cessation supporting actions (calling a Quitline) [H2a: Adapt2Quit smokers will have greater scores on the PCS than control smokers; H2b: Adapt2Quit smokers will adopt more cessation supporting actions (Quitline, NRT) than control smokers]. Aim 3 will assess effectiveness of the system [H3a: (primary outcome) Adapt2Quit smokers will have greater smoking cessation rates (6-month point prevalence biochemically verified) than control smokers; H3b: (secondary outcome) Adapt2Quit smokers will have lower time to first quit attempt than control smokers; H3c: (mediation analysis) Measured internal and external processes will mediate the effect of Adapt2Quit on smoking cessation]. To accomplish the above aims, we have brought together a multidisciplinary team with relevant expertise, and a strong track record of collaboration.