Digital Phenotyping & Deep Learning: Substance Use Impact on PrEP Adherence among Black Sexual and Gender Minorities

数字表型分析

• 批准号: 10928591
• 负责人: Brenda Curtis
• 金额: $ 128万
• 依托单位: NATIONAL INSTITUTE ON DRUG ABUSE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10928591
• 关键词: Address; Adherence; Affect; Algorithms; Artificial Intelligence; Attitude; Awareness; Behavior; Behavior monitoring; Behavioral; Behavioral Paradigm; Big Data; Biological Markers; Black race; Cannabis; Cellular Phone; Classification; Clinical; Communication; Communities; Complex; Computational algorithm; Consumption; Data; Data Collection; Data Set; Devices; Ecological momentary assessment; Epidemic; Event; Face; Formulation; Goals; Gravitation; HIV; HIV Seropositivity; HIV risk; Health; Incidence; Individual; Intervention; Language; Legal; Location; Measures; Methodology; Modernization; Monitor; Moods; Oral; Outcome; Participant; Pattern; Phenotype; Policies; Prevalence; Research; Risk; Risk Behaviors; Sex Behavior; Sexual Partners; Sexual and Gender Minorities; Social support; Surveys; Techniques; Technology; Time; United States; Unsafe Sex; algorithm training; binge drinking; cannabis craving; clinical practice; comparative; dating behavior; deep learning; demographics; digital; effective intervention; feature selection; gender minority; health record; high risk behavior; high risk sexual behavior; innovation; insight; machine learning algorithm; marijuana legalization; marijuana use; marijuana user; men; phenotypic data; pre-exposure prophylaxis; sex; sexual encounter; sexual minority men; social media; substance use; tailored messaging; tool; transmission process

The United States has grappled with multiple epidemics, notably the persistent HIV epidemic, predominantly affecting Black sexual minority men and gender minorities. These groups register elevated HIV incidence and prevalence. Parallelly, as several states are gravitating towards cannabis legalization, we witness a surge in its usage. Black sexual and gender minorities consuming cannabis have shown heightened levels of high-risk behaviors, including binge drinking, unprotected sex under the influence, and intercourse with partners potentially HIV-positive. Intriguingly, modern geosocial networking and dating applications are incorporating features allowing users to match specifically with cannabis users. This blend of sexualized cannabis use may be amplifying HIV transmission due to heightened engagement in condomless sex and an increased number of sexual partners. With HIV pre-exposure prophylaxis (PrEP) showcasing near 95% efficacy in adherent individuals, it emerges as a potential combatant against this epidemic. Despite having two PrEP formulations availabledaily oral and long-acting injectablethe awareness, usage, and adherence amongst the target demographic remains alarmingly low. Preliminary data suggests that adherence among Black sexual minority men and gender minorities is contingent on their substance use. To address these complex intersections, we use a holistic examination of the relationships among HIV transmission risk, cannabis consumption, and PrEP outcomes. This would harness innovative digital phenotyping, social media language and online behaviors, event-level measures, and critical biomarker data. Given the ubiquity of smartphones (used by over 95% of the target group), these devices were chosen for continuous monitoring and data collection. An application, developed by our lab and previously validated, was deployed to accumulate passive mobile data and administer timely ecological momentary assessments. Employing cutting-edge deep learning techniques, we will synthesize this data into dynamic digital phenotypes, capable of allotting daily risk scores to participants. The overarching objective is to ascertain correlations between cannabis consumption and two pivotal outcomes: risky sexual practices and diminished PrEP adherence. To accomplish the goals listed above, we will follow Black sexual minorities and gender minorities (who have sex with men) who use cannabis longitudinally to generate a dataset that will be used to train the algorithm behind the digital phenotypes. This includes examining online communication regarding sexualized cannabis use and/or PrEP intentions and attitudes. This project will address the following concepts: Advance Computational Algorithms to Identify Cannabis Use and Cravings We will use advanced computational algorithms to predict cannabis use, including sexualized cannabis use, and cannabis cravings. Our first goal will be to carry out a comparative analysis of machine learning algorithms to determine the optimal classifier we can use to provide accurate classifications. Our second goal is to implement a feature selection algorithm that will extract the most relevant features (from clinical, EMA, and digital phenotype data) that provide the best classification of cannabis cravings and use, especially in the context of sexual behavior. Artificial Intelligence to Identify PrEP Non-Adherence and Risky Sexual Behaviors High-risk sexual behaviors and non-adherence to PrEP occur for many reasonsincluding cannabis use. The best approach to tackling these problems will involve big data that is context-aware and individually tailored. Digital phenotype data contains a rich set of information that includes demographics, mood, sexual behavior, social support, dating behavior, substance use, and location. We will also have access to clinical health records. In combination, we will be able to develop and validate a PrEP adherence and HIV risk artificial intelligence (AI) tool to identify participants who are at risk for PrEP non-adherence and risky sexual encounters. This AI tool will be programmed to deliver tailored messaging when elevations in risk are detected. In summary, this project delves into the intricate dynamics of cannabis use, its socio-technological implications, and its intersection with HIV risk and PrEP adherence. The methodologies and results would be invaluable for key stakeholders striving for comprehensive solutions to this multifaceted challenge.

美国已经努力处理多种流行病，特别是持续的艾滋病毒流行，主要影响黑人性少数群体和性别少数群体。这些组记录了艾滋病毒的发病率和患病率升高。随着几个州倾向于大麻合法化，我们目睹了它的使用激增。摄入大麻的黑人性和性别少数群体表明，高风险行为的水平提高，包括暴饮暴食，影响下的无保护性，以及与伴侣的性交潜在的HIV阳性。有趣的是，现代的社会网络和约会应用程序正在合并功能，使用户可以专门与大麻用户匹配。性使用大麻的这种混合物可能会由于增加无避孕套性和性伴侣的数量增加而扩大HIV传播。 随着HIV预防前预防（PREP）在附着的个体中表现出95％的疗效，它作为对这种流行病的潜在战斗力。尽管有两种预备表述，每日的口头和长效表明，目标人群中的意识，用法和依从性仍然令人震惊。初步数据表明，黑人性少数群体和性别少数群体之间的遵守取决于他们的物质使用。 为了解决这些复杂的交叉点，我们对HIV传播风险，消费量和准备成果的关系进行整体检查。这将利用创新的数字表型，社交媒体语言和在线行为，事件级别的措施以及关键的生物标志物数据。鉴于智能手机的普遍存在（超过95％的目标组使用），选择了这些设备进行连续监视和数据收集。由我们的实验室开发并已经过验证的应用程序被部署以累积被动移动数据并及时进行生态瞬时评估。采用尖端的深度学习技术，我们将将这些数据综合为动态数字表型，能够为参与者分配每日风险评分。总体目标是确定大麻消耗与两个关键结果之间的相关性：有风险的性行为和依从性降低。 为了实现上面列出的目标，我们将遵循黑人性少数群体和性别少数群体（与男性发生性关系），他们纵向使用大麻来生成一个数据集，该数据集将用于训练数字表型背后的算法。这包括检查有关性使用和/或准备意图和态度的在线沟通。该项目将解决以下概念： 提前计算算法以识别大麻的使用和渴望 我们将使用先进的计算算法来预测大麻使用，包括使用大麻和大麻的渴望。我们的第一个目标是对机器学习算法进行比较分析，以确定我们可以使用的最佳分类器来提供准确的分类。我们的第二个目标是实施一种功能选择算法，该算法将提取最相关的特征（来自临床，EMA和数字表型数据），该功能提供了大麻渴望和使用的最佳分类，尤其是在性行为的背景下。 人工智能确定不遵守和风险的性行为 高风险的性行为和不遵守措施的出现，包括大麻使用包括许多原因。解决这些问题的最佳方法将涉及上下文感知并单独量身定制的大数据。数字表型数据包含一组丰富的信息，其中包括人口统计，情绪，性行为，社会支持，约会行为，物质使用和位置。我们还将获得临床健康记录。结合起来，我们将能够开发和验证预备依从性和艾滋病毒风险人工智能（AI）工具，以识别面临不遵守和风险性的性交风险的参与者。当检测到风险高程时，将对此AI工具进行编程以提供量身定制的消息传递。 总而言之，该项目深入研究了大麻使用的复杂动态，其社会技术含义及其与HIV风险和依从性的相交。对于主要利益相关者努力为这一多方面挑战而综合解决方案，方法和结果将是无价的。