Testing the feasibility and acceptability of social media and digital therapeutics to decrease vaping behaviors

项目摘要 使用电子烟产品（例如，电子尼古丁输送系统，电子香烟）已经迅速增加， 尤其是在青少年和年轻人中。关于vaping长期影响的信息有限 关于电子烟的健康信息在相关的健康风险方面有些不清楚。 青少年和年轻人可能不愿意向父母或医疗服务提供者透露他们使用电子烟产品的情况 而是转向社交媒体分享和寻找有关vaping风险和停止的信息 支持.鉴于这一人群中社交媒体平台的普遍使用以及 先进的人工智能（AI）和自然语言处理（NLP）技术来分析内容 在社交媒体平台上共享，这些信息有很大的潜力被利用和用于 检测并接触那些最有可能因vaping而导致负面健康结果的人。因此，我们目前 该提案概述了使用检测模型来识别青少年和年轻人的社交网络， vaping，使用聊天机器人筛选符合条件的用户的需求，以及使用数字干预系统 (i.e., quitSTART与具体的聊天机器人）旨在通过增加风险来支持vaping停止工作 意识和减少亲vaping态度。在目标#1a中，我们将通过以下方式开发智能检测系统： 利用最先进的机器学习、深度学习和NLP技术来挖掘大规模的社交网络， 媒体数据与临床输入。该检测系统将实现多个功能， Twitter和Reddit社交媒体平台都可以识别有关使用vaping产品的帖子， 健康结果的经验，并在vaping停止的兴趣。评价方法的有效性和特异性， 在这两个平台上开发的检测模型中，我们还将对其中的子样本进行调查， 识别（N=100），以排除误报，并收集有关电子烟行为、社交媒体内容 关于vaping的一代，vaping产品使用的动机，以及对vaping停止的兴趣，以完善 在目标#1B中开发模型。在目标#2中，我们将开发一个聊天机器人来筛选在 社交媒体以及应用程序内聊天机器人，以引导用户定制内容，进行日常评估， 签到，激励和鼓励他们的戒烟努力，并在一个 广泛使用的基于证据的移动的应用程序（app）干预可燃性吸烟，quitSTART。我们 将在参与者样本中对聊天机器人的两个级别进行可用性和可接受性测试 （N=30）在目标1b中招募。在目标#3中，我们的目标是整合开发的检测模型，聊天机器人筛选器， 并将移动的应用程序改编成一个精简的外展和干预系统，并进行随机 对照试验（N= 189），以评价用户参与数字干预的情况和初步疗效 关于青少年和年轻人的电子烟行为。这一综合系统有可能改善公共 与vaping相关的健康结果，并告知这种聊天机器人工具维持mHealth的可行性 干预参与。

Project Summary Use of vaping products (e.g., electronic nicotine delivery systems, e-cigarettes) has been increasing rapidly, particularly among teens and young adults. With limited information on the long-term effects of vaping products, health information about vaping has been somewhat unclear in regards to associated health risks. Teens and young adults may be reluctant to disclose their use of vaping products to parents or health providers and instead turn to social media to share and seek out information regarding vaping risks and cessation supports. Given the ubiquitous use of social media platforms among this population and the ability for advanced artificial intelligence (AI) and natural language processing (NLP) technologies to analyze content shared on social media platforms, there is strong potential for this information to be leveraged and used to detect and reach out to those most at risk for negative health outcomes caused by vaping. Thus, our current proposal outlines the use of detection models to identify teens and young adults socially networking about vaping, the use of a chatbot to screen for the needs of eligible users, and the use of a digital intervention system (i.e., quitSTART with an embodied chatbot) aimed to support vaping cessation efforts by increasing risk awareness and decreasing pro-vaping attitudes. In Aim #1a, we will develop an intelligent detection system by leveraging state-of-the-art machine learning, deep learning, and NLP techniques for mining massive social media data on vaping with clinical inputs. This detection system will implement multiple functionalities on both Twitter and Reddit social media platforms to identify posts regarding the use of vaping products, negative health outcomes experienced, and interest in vaping cessation. To evaluate the validity and specificity of the detection model developed on both platforms, we will also conduct surveys among a subsample of those identified (N=100) to rule out false positives and to gather data on vaping behaviors, social media content generation about vaping, motivations for vaping product use, and interest in vaping cessation to refine the developed models in Aim #1b. In Aim #2, we will develop both a chatbot to screen individuals identified on social media as well as an in-app chatbot to guide users to tailored content, conduct daily assessments and check-ins, motivate and encourage their cessation efforts, and promote sustained user engagement within a widely-used evidence-based mobile application (app) intervention for combustible smoking, quitSTART. We will conduct usability and acceptability testing on both levels of the chatbot among a sample of participants (N=30) recruited in Aim #1b. In Aim #3, we aim to integrate the developed detection model, chatbot screener, and adapted mobile app into a streamlined outreach and intervention system, and conduct a randomized controlled trial (N= 189) to evaluate user engagement with and preliminary efficacy of the digital intervention on vaping behaviors among teens and young adults. This integrated system has the potential to improve public health outcomes related to vaping and to inform the feasibility of such chatbot tools to sustain mHealth intervention engagement.