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

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.

项目概要 电子烟产品（例如电子尼古丁输送系统、电子烟）的使用一直在迅速增加， 尤其是在青少年和年轻人中。关于电子烟长期影响的信息有限 产品方面，有关电子烟的健康信息在相关健康风险方面尚不清楚。 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对电子烟产品的使用情况 相反，转向社交媒体分享和寻找有关电子烟风险和戒烟的信息 支持。鉴于该人群普遍使用社交媒体平台并且有能力 用于分析内容的先进人工智能 (AI) 和自然语言处理 (NLP) 技术 在社交媒体平台上分享，这些信息有很大的潜力被利用和用于 发现并帮助那些最有可能因电子烟造成负面健康后果的人。因此，我们当前的 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 电子烟、使用聊天机器人来筛选合格用户的需求以及使用数字干预系统 （即使用具体的聊天机器人 quitSTART）旨在通过增加风险来支持戒烟工作 意识和减少支持电子烟的态度。在目标#1a中，我们将开发一个智能检测系统 利用最先进的机器学习、深度学习和 NLP 技术来挖掘海量社交数据 有关电子烟与临床输入的媒体数据。该检测系统将实现多种功能 Twitter 和 Reddit 社交媒体平台均会识别有关使用电子烟产品的帖子、负面信息 所经历的健康结果以及对戒烟的兴趣。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、社交媒体内容的数据 关于电子烟的一代人、使用电子烟产品的动机以及对戒烟的兴趣以完善电子烟产品 开发了目标#1b 中的模型。在目标#2中，我们将开发一个聊天机器人来筛选在 社交媒体以及应用内聊天机器人可指导用户定制内容、进行日常评估和 签到、激励和鼓励他们的戒烟努力，并促进用户在一定范围内持续参与 广泛使用的基于证据的移动应用程序（app）干预可燃性吸烟，戒掉START。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，我们的目标是集成开发的检测模型、聊天机器人筛选器、 并将移动应用程序改编成简化的外展和干预系统，并进行随机 对照试验（N = 189）评估用户对数字干预的参与度和初步效果 关于青少年和年轻人的电子烟行为。该集成系统有潜力改善公共 与电子烟相关的健康结果，并告知此类聊天机器人工具维持移动医疗的可行性 干预参与。