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.

美国一直在努力应对多种流行病，特别是持续的艾滋病毒流行病，主要影响黑人性少数群体男子和性别少数群体。这些群体的艾滋病毒发病率和流行率较高。然而，随着几个州逐渐走向大麻合法化，我们目睹了大麻使用量的激增。吸食大麻的黑人性少数和性别少数群体表现出高风险行为水平，包括酗酒，在影响下无保护的性行为，以及与可能呈艾滋病毒阳性的伴侣性交。有趣的是，现代地理社交网络和约会应用程序正在整合允许用户与大麻用户进行特定匹配的功能。由于无安全套性行为的增加和性伴侣数量的增加，这种混合性大麻使用可能会放大艾滋病毒的传播。 随着艾滋病毒暴露前预防（PrEP）在依从性个体中显示出近95%的疗效，它成为对抗这种流行病的潜在战斗人员。尽管有两种PrEP制剂可供使用-每日口服和长效注射剂，但目标人群的意识，使用和依从性仍然低得惊人。初步数据表明，黑人性少数群体男子和性别少数群体之间的依从性取决于他们的物质使用。 为了解决这些复杂的交叉点，我们对艾滋病毒传播风险，大麻消费和PrEP结果之间的关系进行了全面检查。这将利用创新的数字表型，社交媒体语言和在线行为，事件级措施和关键的生物标志物数据。鉴于智能手机的普及（超过95%的目标群体使用），这些设备被选择用于持续监测和数据收集。一个应用程序，由我们的实验室开发和以前的验证，部署来积累被动的移动的数据和管理及时的生态瞬时评估。利用尖端的深度学习技术，我们将这些数据合成为动态数字表型，能够为参与者分配每日风险评分。总体目标是确定大麻消费与两个关键结果之间的相关性：危险的性行为和减少PrEP依从性。 为了实现上述目标，我们将跟踪纵向使用大麻的黑人性少数群体和性别少数群体（与男性发生性关系），以生成一个数据集，该数据集将用于训练数字表型背后的算法。这包括检查有关性化大麻使用和/或PrEP意图和态度的在线交流。该项目将涉及以下概念： 先进的计算算法来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的使用，包括性化大麻的使用和大麻的渴望。我们的第一个目标是对机器学习算法进行比较分析，以确定我们可以用来提供准确分类的最佳分类器。我们的第二个目标是实现一种特征选择算法，该算法将提取最相关的特征（从临床，EMA和数字表型数据中），这些特征提供了对大麻渴望和使用的最佳分类，特别是在性行为的背景下。 人工智能识别PrEP不依从性和危险性行为 高风险性行为和不遵守PrEP的原因有很多，包括大麻的使用。解决这些问题的最佳方法将涉及情境感知和个性化定制的大数据。数字表型数据包含丰富的信息，包括人口统计学，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PrEP依从性和HIV风险人工智能（AI）工具，以识别处于PrEP不依从性和危险性接触风险的参与者。这个人工智能工具将被编程为在检测到风险升高时提供量身定制的消息传递。 总之，该项目深入研究了大麻使用的复杂动态，其社会技术影响及其与艾滋病毒风险和PrEP依从性的交叉点。这些方法和结果对于努力全面解决这一多方面挑战的主要利益攸关方将是非常宝贵的。