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

Adapt2Quit — 机器学习、自适应激励系统：针对社会经济弱势吸烟者的随机对照试验 —

• 批准号: 10642697
• 负责人: Rajani Sadasivam
• 金额: $ 61.05万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-16 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10642697
• 关键词: Address; Adopted; Award; Behavior Therapy; Behavioral; Belief; Biochemical; Collaborations; Competence; Complex; Computers; Data; Economically Deprived Population; Effectiveness; Engineering; Evaluation; Feedback; Frequencies; Funding; Goals; Health; Healthcare; Heterogeneity; Individual; Intervention; Interview; Lead; Learning; Machine Learning; Measures; Mediating; Mediation; Motivation; Odds Ratio; Pamphlets; Paper; Pattern; Prevalence; Process; Randomized; Readiness; Self Determination; Self Efficacy; Site; Smoke; Smoker; Smoking; Smoking and Health Research; System; Target Populations; Testing; Text Messaging; Time; Urban Health; active control; behavior change; design; digital health; effectiveness evaluation; evidence base; experimental study; health care settings; health disparity; high risk population; improved; innovation; learning progression; machine learning algorithm; multidisciplinary; nicotine replacement; primary outcome; quitline; recruit; rural healthcare; secondary outcome; smoking cessation; smoking-related disease; socioeconomic disadvantage; tailored text messaging; theories

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.

七、项目概要 我们将测试 Adapt2Quit，这是一种创新的机器学习、自适应激励消息传递系统。 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) 吸烟者的个人资料；2) 吸烟者随时间对系统的明确反馈； 3）来自数千名先前吸烟者的资料及其反馈模式的数据 - Adapt2Quit 机器的类型。 学习被称为推荐系统，在医疗保健之外，公司（如亚马逊）使用推荐系统。 系统不断从用户反馈（例如：喜欢的产品、购买的产品）中学习以进行改进，从而 提高个人相关性和客户参与度是数字健康领域的一个巨大挑战。 在计算机定制的健康信息传递领域，Adapt2Quit 是第一个使用机器学习来持续 适应反馈并选择新的个性化消息发送给吸烟者以评估其影响。 推荐系统，Adapt2Quit 将与稳健的主动控制进行比较，这是一个简单但有效的 在我们的试点实验中，Adapt2Quit 的表现优于对照组，尤其是在社交群体中。 经济弱势 (SED) 吸烟者更难参与干预。 除了针对 SED 吸烟者外，Adapt2Quit 增加的参与度也特别重要。 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 增加州戒烟热线的使用。我们将在两个地点招募 700 名 SED 吸烟者。 然后，进行基线访谈并收到一份纸质手册，其中包含有关该州戒烟热线的信息。 被随机化为：Adapt2Quit 或标准消息传递 由于系统旨在增强参与度， 通过参与带来积极行动，目标 1 将侧重于参与 [假设 (H1a) 其中 Adapt2Quit 吸烟者，那些参与度较高（完成更多评分）的人将在以下方面获得更高的分数 感知能力量表 (PCS)] 目标 2 比较（Adapt2Quit 和控制）行为改变过程。 包括感知的戒烟能力和戒烟支持行动（拨打戒烟热线） [H2a：Adapt2Quit 吸烟者在 PCS 上的得分高于对照组吸烟者；H2b：Adapt2Quit 吸烟者 将采取比目标 3 评估的控制吸烟者更多的戒烟支持行动（戒烟热线、NRT）。 系统的有效性[H3a：（主要结果）Adapt2Quit 吸烟者的戒烟效果会更好 H3b：（次要结果） Adapt2Quit 吸烟者首次尝试戒烟的时间比对照吸烟者要短：（中介分析） 测量的内部和外部过程将调节 Adapt2Quit 对戒烟的影响]。 为了实现上述目标，我们聚集了一支具有相关专业知识的多学科团队，以及 良好的合作记录。