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: 10381513
• 负责人: Rajani Sadasivam
• 金额: $ 62.35万
• 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/10381513
• 关键词: Address; Adopted; Award; Behavior Therapy; Behavioral; Belief; Biochemical; Collaborations; Competence; Complex; Computers; Data; 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; active control; base; behavior change; design; digital health; disadvantaged population; 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 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.

7。项目摘要 我们将测试Adapt2Quit，这是一种创新的机器学习，自适应动机消息传递系统。 Adapt2Quit 使用复杂的机器学习算法来适应为吸烟者选择最佳信息 多个属性，包括：1）吸烟者的个人资料； 2）随着时间的推移，吸烟者对系统的明确反馈； 3）来自成千上万先前吸烟者的资料及其反馈模式的数据。 Adapt2Quit的机器类型 学习称为推荐系统。在医疗保健外，公司（例如亚马逊）使用建议 从用户反馈中不断学习的系统（例如：喜欢的产品，购买的产品）以改进，从而 增强个人相关性和客户参与度。参与是数字健康的巨大挑战。在 Adapt2Quit的计算机量化健康消息领域是第一个使用机器学习的领域 适应反馈并选择新的个性化消息以发送给吸烟者。评估 推荐系统，Adapt2Quit将与强大的，主动的控制，简单但有效 消息系统。在我们的飞行员实验中，Adapt2Quit优于控制，尤其是在社会中 经济困扰（SED）吸烟者。 SED吸烟者很难进行干预。那， Adapt2Quit的提高参与度对于瞄准吸烟者而言尤其重要。此外 Adapt2Quit信息在戒烟中的潜在影响，我们的目标是 增加国家戒烟线的使用。我们将在两个地点招募700名SED吸烟者。所有吸烟者都会完成 基线访谈并收到一本纸手册，其中包含有关该州戒烟线的信息。然后吸烟者会 随机分配为：Adapt2Quit或标准消息传递。由于该系统旨在增强参与度，因此 通过参与导致积极行动，AIM 1将专注于参与[假设（H1A） Adapt2Quit吸烟者，参与度较高的人（完成的评级更多）的分数将更高 感知能力量表（PC）]。 AIM 2比较（Adapt2Quit和Control）行为改变过程 包括感知的戒烟和戒烟的能力支持行动（调用Quitline） [H2A：Adapt2Quit吸烟者在PC上的分数比对照吸烟者的分数更高； H2B：Adapt2Quit吸烟者 与控制吸烟者相比，将采取更多的戒烟支持行动（Quitline，NRT）。 AIM 3将评估 系统的有效性[H3A ：（主要结果）Adapt2Quit吸烟者将戒烟更大 比对照吸烟者（6个月的患病率在生物化学上验证）； H3B ：（次要结果） 与控制吸烟者相比，Adapt2Quit吸烟者将首次退出尝试的时间较低。 H3C ：（调解分析） 测得的内部和外部过程将介导Adapt2Quit对戒烟的影响]。到 完成上述目标，我们将一个多学科团队与相关专家组成 协作的良好记录。