Developing an artificial intelligence-based mHealth intervention to increase HIV testing in Malaysia

开发基于人工智能的移动医疗干预措施，以增加马来西亚的艾滋病毒检测

• 批准号: 10669814
• 负责人: FREDERICK LEWIS ALTICE
• 金额: $ 31.2万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-11 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10669814
• 关键词: AIDS prevention; Acceleration; Algorithms; Artificial Intelligence; Automation; Behavioral; Cellular Phone; Charge; China; Clinic; Communities; Computer Systems; Continuity of Patient Care; Country; Data; Data Analyses; Decision Making; Disclosure; Discrimination; Early Diagnosis; Early treatment; Epidemic; Facebook; Frequencies; General Population; Guidelines; HIV; HIV/STD; Health Personnel; Health Services Accessibility; Human; Human Resources; Human immunodeficiency virus test; Hybrids; Income; Individual; Infection; Injecting drug user; Intelligence; Intervention; Learning; Machine Learning; Malaysia; Malaysian; Manuals; Measures; Medical Students; Methods; Modeling; Motivation; Names; Output; Participant; Patient Self-Report; Patients; Pattern; Persons; Peru; Phase; Physicians; Police; Policies; Population; Prevalence; Prevention strategy; Process; Reach, Effectiveness, Adoption, Implementation, and Maintenance; Recommendation; Research; Research Methodology; Research Personnel; Risk; Same-sex; Sex Behavior; Sexual Partners; Sexual Transmission; Social Network; South Africa; Stigmatization; Subgroup; Substance Use Disorder; Surveys; Target Populations; Television; Testing; Time; Training; Universities; Viral; Volatilization; acceptability and feasibility; biobehavior; chatbot; clinical care; computer program; cost effective; criminal behavior; design; efficacy evaluation; efficacy outcomes; experience; health management; high risk behavior; high risk men; humiliation; implementation outcomes; implementation science; innovation; low and middle-income countries; mHealth; machine learning algorithm; member; men who have sex with men; mobile application; model building; new epidemic; next generation; overtreatment; pilot test; pre-exposure prophylaxis; response; same sex behavior; scale up; simulation; skills; social stigma; sodomy; stem; tailored messaging; theories; transmission process; treatment as usual; web site

Project Summary HIV testing jumpstarts entry into the HIV prevention and treatment cascade. HIV testing levels, however, are especially low in men who have sex with men (MSM), who increasingly contribute to heightened HIV transmission in the presence of high levels of stigma and discrimination. For high risk MSM, new guidelines recommend frequent HIV testing, ranging from every 3 to 6 months. Yet, HIV testing in MSM often occurs less frequently due to individual (e.g., heightened concerns about risk disclosure), clinic (e.g., confidentiality breaches, and discrimination from healthcare providers) and policy (criminalization of same-sex sexual behaviors) barriers. HIV prevalence in MSM in Malaysia has soared to 21.6% nationally, exceeding 40.9% in Kuala Lumpur. While surveillance surveys of MSM in Malaysia who meet criteria for PrEP suggest that ever tested is 70.3%, past- year tested is 40.9%, and only 9.5% were tested more than 1 time per year, despite extraordinary levels of self- reported risk. Once tested, however, MSM with HIV in Malaysia are likely to be treated with ART and achieve viral suppression, making HIV testing a central focus for HIV prevention and treatment. Innovative strategies that motivate and provide guidance for testing among MSM in Malaysia are therefore urgently needed. Intervening using Information-Motivation-Behavioral Skills (IBM) model is ideally suited to overcome barriers to recommended HIV testing in MSM. Moreover, in settings like Malaysia where the HIV epidemic has transitioned from primarily concentrated in PWID to a volatile epidemic in MSM, theory-guided behavioral change strategies that inform, motivate and provide pragmatic skills to more fully engage in recommended HIV testing are poised to accelerate the HIV prevention and care continuum. Given that there are many individual, clinic and policy barriers to HIV testing, mobile health (mHealth) interventions that reduce “in person” contact and offer a menu of behavioral skills is ideally suited to increase access to MSM in highly stigmatized settings and promote recommended HIV testing. Recent studies in the U.S., China, South Africa, and Peru show that mHealth interventions using smartphones and apps have the potential to increase HIV testing while maintaining MSM’s confidentiality. Such mHealth interventions are feasible and acceptable among MSM, including in Malaysia where most MSM find sexual partners using social-networking apps with similar interfaces and functionalities to the proposed intervention. Current mHealth strategies, however, are limited by their lack of automation and need for high-intensity and sustained human inputs, which restricts their scale-up. Artificial intelligence (AI) using machine learning (ML) may overcome such limitations, but has yet to be applied to mHealth-based HIV testing algorithms. We therefore aim to develop and pilot test an AI-chatbot (R21 phase). Findings from the R21 phase will inform a Type 1 Hybrid Implementation Science trial (R33 phase) to evaluate the efficacy and implementation outcomes of the AI-chatbot for HIV testing relative to treatment as usual.

项目摘要 HIV测试Jumpstarts进入HIV预防和治疗级联。但是，HIV测试水平是 与男性发生性关系（MSM）的男性尤其低，他们越来越有助于增加艾滋病毒传播 在高水平的污名和歧视的情况下。对于高风险MSM，新指南建议 经常进行艾滋病毒测试，每3到6个月不等。但是，MSM中的艾滋病毒测试通常频繁发生 对个人（例如，对风险披露的担忧加剧），诊所（例如，机密性违规和 歧视医疗保健提供者）和政策（同性性行为的刑事犯罪）障碍。艾滋病病毒 马来西亚MSM的患病率在全国范围内飙升至21.6％，吉隆坡超过40.9％。尽管 符合PREP标准的马来西亚MSM的监视调查表明，经过测试为70.3％，过去 经过测试的年度为40.9％，每年仅测试超过1个时间，目的地非凡的自我水平 报告的风险。但是，一旦测试，马来西亚的MSM可能会接受艺术和成就治疗 病毒抑制，使HIV检验成为预防HIV和治疗的主要重点。 因此 迫切需要。使用信息动机行为技能（IBM）模型进行干预非常适合 克服在MSM中推荐的HIV测试的障碍。而且，在艾滋病毒的马来西亚等环境中 从PWID中的原发性浓缩到MSM的挥发性流行，理论引入了流行病。 行为改变策略，这些策略为，动机和提供务实的技能，以更充分的参与 建议的HIV检测被中毒以加速HIV预防和护理连续体。鉴于有 许多个人，诊所和政策障碍艾滋病毒测试，移动健康（MHealth）干预措施减少了” 人的联系并提供行为技能菜单非常适合增加高度访问MSM的访问 污名化的设置并促进了建议的HIV测试。美国，中国，南非的最新研究 秘鲁表明，使用智能手机和应用程序的MHealth干预措施有可能增加HIV测试 同时保持MSM的机密性。此类MHealth干预措施在MSM中是可行的，并且可以接受 包括在马来西亚，大多数MSM都使用具有类似界面的社交网络应用程序找到性伴侣 和拟议干预的功能。但是，当前的MHealth策略受到缺乏的限制 自动化和对高强度和持续人类投入的需求，这限制了其规模。人造的 使用机器学习（ML）的智能（AI）可能会克服此类限制，但尚未应用于 基于MHealth的HIV测试算法。因此，我们旨在开发和试点测试AI-Chatbot（R21阶段）。 R21阶段的发现将告知1型混合实施科学试验（R33阶段）以评估 AI-Chatbot的效率和实施结果与往常一样相对于治疗而言。