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Adapt 2 Quit，这是一个创新的机器学习，自适应激励消息系统。Adapt2Quit 使用复杂的机器学习算法，根据以下因素自适应地为吸烟者选择最佳信息 多个属性，包括：1）吸烟者的概况; 2）吸烟者随时间对系统的明确反馈; 以及3）来自数千名先前吸烟者的概况和他们的反馈模式的数据。Adapt 2 Quit的机器类型- 学习被称为推荐系统。在医疗保健之外，公司（如亚马逊）使用推荐器 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），以改善，因此， 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 在计算机定制健康信息领域，Adapt 2 Quit是第一个使用机器学习不断 适应反馈并选择新的个性化消息发送给吸烟者。的影响进行评估 推荐系统，Adapt 2 Quit将与一个强大的，主动控制，简单而有效的 信息系统。在我们的试点实验中，Adapt 2 Quit的表现优于对照组，尤其是在社会群体中。 经济上处于不利地位的吸烟者。SED吸烟者更难进行干预。因此，在本发明中， Adapt 2 Quit的增加参与对于针对SED吸烟者特别重要。除了 Adapt 2 Quit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 增加使用州戒烟热线。我们将在两个地点招募700名SED吸烟者。所有吸烟者将完成一项 基线采访，并收到一份关于该州戒烟热线信息的纸质小册子。吸烟者将 随机分配到：Adapt 2 Quit或标准消息传递。由于该系统旨在加强参与， 并通过参与导致积极的行动，目标1将侧重于参与[假设（H1 a） Adapt 2戒烟者，那些参与度较高的人（完成更多评级）将在 感知能力量表（PCS）。目标2比较（Adapt 2 Quit和对照）行为改变过程 包括感知戒烟能力和戒烟支持行动（拨打戒烟热线） [H2a：Adapt 2 Quit吸烟者的PCS评分高于对照吸烟者; H2 b：Adapt 2 Quit吸烟者 将比对照吸烟者采取更多的戒烟支持行动（戒烟热线，NRT）。目标3将评估 系统的有效性[H3 a：（主要结局）Adapt 2 Quit吸烟者的戒烟效果更好 比对照组吸烟者的患病率（经生化验证的6个月时点患病率）; H3 b：（次要结局） Adapt 2 Quit吸烟者首次尝试戒烟的时间低于对照吸烟者; H3c：（中介分析） 测量的内部和外部过程将介导Adapt 2 Quit对戒烟的影响。到 为了实现上述目标，我们汇集了一个具有相关专业知识的多学科团队， 良好的合作记录。