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: 10662651
• 负责人: FREDERICK LEWIS ALTICE
• 金额: $ 31.33万
• 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/10662651
• 关键词: AIDS prevention; 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; Reporting; 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; base; 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; men who have sex with men; mobile application; model building; next generation; overtreatment; pilot test; pre-exposure prophylaxis; response; same sex behavior; scale up; simulation; skills; smartphone Application; 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 艾滋病毒检测启动了艾滋病毒预防和治疗的进程。然而，艾滋病毒检测水平 尤其是男男性行为者（MSM），他们越来越多地导致艾滋病毒传播的增加 在高度的耻辱和歧视的存在。对于高风险MSM，新指南建议 经常进行艾滋病毒检测，每3至6个月一次。然而，在MSM中进行艾滋病毒检测的频率往往较低， 对于个人（例如，对风险披露的高度关注），诊所（例如，违反保密规定，以及 医疗保健提供者的歧视）和政策（同性性行为的刑事化）障碍。艾滋病毒 在马来西亚，男男性接触者的患病率在全国范围内飙升至21.6%，超过了吉隆坡的40.9%。而 对马来西亚符合PrEP标准的MSM的监测调查表明，曾经接受过测试的比例为70.3%，过去- 每年测试的是40.9%，只有9.5%的人每年测试超过1次，尽管自我测试的水平非常高， 报告风险。然而，一旦测试，马来西亚的艾滋病毒男男性行为者很可能接受抗逆转录病毒治疗， 病毒抑制，使艾滋病毒检测成为艾滋病毒预防和治疗的中心焦点。 因此，激励和指导马来西亚男男性行为者进行测试的创新战略是 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于 克服在男男性行为者中推荐的艾滋病毒检测的障碍。此外，在马来西亚这样的环境中， 流行病已经从主要集中在PWID转变为MSM中的不稳定流行病，理论指导 行为改变策略，告知，激励和提供务实的技能，以更充分地参与 建议的艾滋病毒检测准备加速艾滋病毒预防和护理的连续性。鉴于有 艾滋病毒检测、移动的健康干预措施面临许多个人、诊所和政策障碍， “人”联系人并提供行为技能菜单非常适合增加高度接触MSM的机会 并促进推荐的艾滋病毒检测。最近在美国的研究，中国、南非、 和秘鲁的研究表明，使用智能手机和应用程序的移动医疗干预有可能增加艾滋病毒检测 同时保持MSM的保密性这种移动健康干预措施在男男性行为者中是可行和可接受的， 包括在马来西亚，大多数MSM使用具有类似界面的社交网络应用程序寻找性伴侣 和功能。然而，目前的移动健康战略受到缺乏 自动化和需要高强度和持续的人力投入，这限制了它们的规模扩大。人工 使用机器学习（ML）的人工智能（AI）可以克服这些限制，但尚未应用于 基于mHealth的HIV检测算法。因此，我们的目标是开发和试点测试AI聊天机器人（R21阶段）。 R21阶段的发现将为1型混合实施科学试验（R33阶段）提供信息，以评估 AI聊天机器人用于HIV检测相对于常规治疗的有效性和实施结果。