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

测试社交媒体和数字疗法减少电子烟行为的可行性和可接受性

• 批准号: 10527045
• 负责人: Patricia A Cavazos-Rehg
• 金额: $ 17.99万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10527045
• 关键词: Acute; Adolescent and Young Adult; Age; Artificial Intelligence; Attitude; Awareness; Behavior; Cessation of life; Chronic; Clinical; Control Groups; Data; Detection; Electronic Nicotine Delivery Systems; Electronic cigarette; Eligibility Determination; Ensure; Generations; Health; Health Personnel; Individual; Intelligence; Intervention; Long-Term Effects; Lung infections; Machine Learning; Mining; Modeling; Motivation; Natural Language Processing; Outcome; Parents; Participant; Perception; Population; Public Health; Randomized; Randomized Controlled Trials; Reporting; Risk; Sampling; Smoker; Smoking; Social Network; Source; Specificity; Surveys; System; Techniques; Technology; Teenagers; Testing; Time; Tobacco use; Twitter; Waiting Lists; Youth; acceptability and feasibility; chatbot; combustible cigarette; deep learning; design; detection platform; digital; digital intervention; digital treatment; eligible participant; evidence base; experience; feasibility testing; improved; improved outcome; interest; lung injury; mHealth; mobile application; outreach; primary outcome; recruit; respiratory; risk perception; secondary outcome; social media; support tools; tobacco advertising; tool; usability; vaping; young adult; Acute; Adolescent and Young Adult; Age; Artificial Intelligence; Attitude; Awareness; Behavior; Cessation of life; Chronic; Clinical; Control Groups; Data; Detection; Electronic Nicotine Delivery Systems; Electronic cigarette; Eligibility Determination; Ensure; Generations; Health; Health Personnel; Individual; Intelligence; Intervention; Long-Term Effects; Lung infections; Machine Learning; Mining; Modeling; Motivation; Natural Language Processing; Outcome; Parents; Participant; Perception; Population; Public Health; Randomized; Randomized Controlled Trials; Reporting; Risk; Sampling; Smoker; Smoking; Social Network; Source; Specificity; Surveys; System; Techniques; Technology; Teenagers; Testing; Time; Tobacco use; Twitter; Waiting Lists; Youth; acceptability and feasibility; chatbot; combustible cigarette; deep learning; design; detection platform; digital; digital intervention; digital treatment; eligible participant; evidence base; experience; feasibility testing; improved; improved outcome; interest; lung injury; mHealth; mobile application; outreach; primary outcome; recruit; respiratory; risk perception; secondary outcome; social media; support tools; tobacco advertising; tool; usability; vaping; young adult

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.

项目摘要 使用蒸发产品（例如电子尼古丁输送系统，电子烟）的使用迅速增加， 特别是在青少年和年轻人中。关于烟的长期影响的信息有限 在相关的健康风险方面，产品，有关烟的健康信息尚不清楚。 青少年和年轻人可能不愿意向父母或健康提供者披露他们对烟产品的使用 而是转向社交媒体分享并寻找有关烟风险和戒烟的信息 支持。考虑到该人群中无处不在的社交媒体平台以及 高级人工智能（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，将这些信息杠杆和习惯具有强大的潜力 检测并接触那些因烟雾而导致的负面健康结果风险的人。因此，我们的当前 提案概述了使用检测模型来识别有关青少年和年轻人的社交网络 Vaping，使用聊天机器人来筛选合格用户的需求，并使用数字干预系统 （即带有具体聊天机器人的戒烟）旨在通过增加风险来支持戒烟工作 意识和降低的宣传态度。在AIM＃1A中，我们将通过 利用最先进的机器学习，深度学习和NLP技术，用于采矿大规模的社交 有关临床输入烟雾烟的媒体数据。该检测系统将在 Twitter和Reddit社交媒体平台都可以识别有关使用Vaping产品的帖子，负面 健康成果经历了，并对戒烟的兴趣。评估有效性和特异性 在两个平台上开发的检测模型，我们还将在这些平台的子样本中进行调查 确定（n = 100）排除误报并收集有关烟行为，社交媒体内容的数据 关于烟，使用产品的动机以及对戒烟的兴趣以完善烟的产生 AIM＃1B开发了模型。在AIM＃2中，我们将开发两个聊天机器人来筛选个人 社交媒体以及应用内聊天机器人，可指导用户量身定制内容，进行日常评估和 签到，激励和鼓励他们的停止工作，并促进持续的用户参与 广泛使用的基于证据的移动应用（APP）干预可燃吸烟，戒烟。我们 将对参与者样本的两个级别级别进行可用性和可接受性测试 （n = 30）在AIM＃1B中招募。在AIM＃3中，我们旨在集成开发的检测模型，Chatbot Screener， 并将移动应用程序调整为简化的外展和干预系统，并进行随机进行 对照试验（n = 189）评估数字干预的用户参与和初步疗效 关于青少年和年轻人的烟行为。该集成系统有可能改善公众 与烟有关的健康结果并告知这种聊天机器人工具以维持MHealth的可行性 干预参与。