A wrist biosensor-based mHealth suite to support alcohol intervention in young people living with HIV

基于腕部生物传感器的移动医疗套件，支持对感染艾滋病毒的年轻人进行酒精干预

• 批准号: 10304696
• 负责人: Yan Wang
• 金额: $ 11.94万
• 依托单位: FLORIDA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10304696
• 关键词: Address; Adherence; Adolescent; Affect; Alcohol consumption; Alcohols; Algorithms; Anxiety; Behavioral Research; Behavioral Sciences; Biological Markers; Biosensor; Blood; Blood alcohol level measurement; Caring; Cellular Phone; Chronic Disease; Clinical Trials Design; Cognitive; Computer software; Consumption; Data; Detection; Development; Disease Management; Distal; Ecological momentary assessment; Elements; Ethnic Origin; Feedback; Fostering; Foundations; Funding; Future; Gender Identity; Goals; HIV; HIV Infections; HIV diagnosis; Health; High Prevalence; Hour; Informal Social Control; Intervention; Laboratories; Literature; Machine Learning; Medical; Mental Depression; Mental Health; Modeling; Monitor; National Institute on Alcohol Abuse and Alcoholism; Outcome; Participant; Patient Self-Report; Pattern; Population; Race; Randomized; Reporting; Research; Rewards; Self Management; Subgroup; Substance abuse problem; Symptoms; Testing; Time; Viral; Work; Wrist; Youth; age group; aged; alcohol intervention; base; biological sex; comorbidity; drinking; emerging adult; follow-up; improved; innovation; mHealth; machine learning algorithm; programs; randomized trial; sensor; sensor technology; substance use; time use; tool; transmission process; wearable sensor technology; Address; Adherence; Adolescent; Affect; Alcohol consumption; Alcohols; Algorithms; Anxiety; Behavioral Research; Behavioral Sciences; Biological Markers; Biosensor; Blood; Blood alcohol level measurement; Caring; Cellular Phone; Chronic Disease; Clinical Trials Design; Cognitive; Computer software; Consumption; Data; Detection; Development; Disease Management; Distal; Ecological momentary assessment; Elements; Ethnic Origin; Feedback; Fostering; Foundations; Funding; Future; Gender Identity; Goals; HIV; HIV Infections; HIV diagnosis; Health; High Prevalence; Hour; Informal Social Control; Intervention; Laboratories; Literature; Machine Learning; Medical; Mental Depression; Mental Health; Modeling; Monitor; National Institute on Alcohol Abuse and Alcoholism; Outcome; Participant; Patient Self-Report; Pattern; Population; Race; Randomized; Reporting; Research; Rewards; Self Management; Subgroup; Substance abuse problem; Symptoms; Testing; Time; Viral; Work; Wrist; Youth; age group; aged; alcohol intervention; base; biological sex; comorbidity; drinking; emerging adult; follow-up; improved; innovation; mHealth; machine learning algorithm; programs; randomized trial; sensor; sensor technology; substance use; time use; tool; transmission process; wearable sensor technology

ABSTRACT 1 Despite the overall decline in new HIV infections in U.S., adolescents and emerging adults aged 13 to 29 2 remain disproportionately affected by HIV who account for 37% of all new HIV diagnoses. Alcohol use is more 3 common among young people living with HIV (YPLWH) than other age groups, creating a significant barrier for 4 mitigating transmission, achieving viral suppression, and reducing comorbidities. Despite the high prevalence 5 of alcohol use and its detrimental consequences in this population, developmentally-tailored intervention 6 targeting alcohol use in YPLWH is rare. Self-management emerged as an important strategy in the context of 7 medical adherence and chronic disease management, with self-monitoring and personalized feedback being 8 two key elements to improve health outcomes including alcohol use. Recent development of wrist alcohol 9 sensors provides a promising tool for self-monitoring and feedback based on objective data. However, two 10 critical challenges must be addressed to leverage biosensors in alcohol intervention for YPLWH. First, without 11 existing analysis software, accurate detection of alcohol use (e.g., start time, consumption level) based wrist 12 sensor data remains a significant barrier. Some literature also suggests that HIV may impact blood alcohol 13 concentration, so a detection algorithm specific to this population is needed. Second, engagement in mobile 14 health (mHealth), including wearables in youth, is suboptimal. To address these challenges and to achieve the 15 overall P01 goal to improve self-management of alcohol use and HIV care among YPLWH, we propose to 16 develop the first wrist biosensor-based mHealth suite for alcohol use monitoring with tailored detection 17 algorithem and optimized engagement strategies for YPLWH. We will conduct a 30-day micro-randomized trial 18 (MRT)--a clinical trial design for developing and optimizing mHealth interventions, to innovatively and efficiently 19 address the challenges. YPLWH will be asked to wear the Skyn biosensor for 30 days and report alcohol use 20 via smartphone-based ecological momentary assessment (EMA). They will also be micro-randomized twice 21 daily to test two empirically-based engagement strategies – reciprocity and personalized feedback. Data from 22 the 30-day MRT will be used to (a) develop and validate machine learning algorithm for alcohol detection 23 based on Skyn data; and (b) test which engagement strategy works better, for whom and under what 24 conditions in facilitating proximal engagement with the sensor. We will also collect alcohol biomarker (i.e., 25 PEth) at 1-month follow up, which will allow us to (c) explore the potential impact of sensor engagement (and 26 thus self-monitoring) on distal alcohol use. By trial end, we will have the first integrated, optimized, and 27 developmentally appropriate biosensor-based mHealth suite for alcohol self-monitoring that can be used as the 28 foundation for future alcohol interventions in YPLWH. Findings from this project may also inform the 29 development of new translational behavioral research model to guide adolescent and YPLWH research 30 agenda by leveraging powerful sensing technology to improve self-management in an engaging manner.

抽象的 1尽管美国的新艾滋病毒感染总体下降，但青少年和新兴的成年人年龄在13至29岁之间 2仍然受HIV的影响不成比例，占所有新艾滋病毒诊断的37％。饮酒更多 3与其他年龄段相比，在艾滋病毒（yplwh）的年轻人中常见，为 4缓解传播，实现病毒抑制和减少合并症。尽管患病率很高 饮酒的5及其在该人群中的有害后果，开发了限制的干预措施 6在YPLWH中靶向饮酒很少。在 7医学依从性和慢性病管理，自我监控和个性化反馈是 8两个关键要素，以改善包括饮酒在内的健康结果。手腕酒精的最新发展 9传感器为基于客观数据提供了自我监控和反馈的承诺工具。但是，两个 必须提出10个关键挑战，以利用YPLWH的酒精干预中的生物传感器。首先，没有 11现有的分析软件，基于饮酒的准确检测（例如，开始时间，消费水平）作者 12个传感器数据仍然是一个重大障碍。一些文献还表明艾滋病毒可能影响血液酒精 13浓度，因此需要针对该人群的检测算法。第二，参与手机 14 Health（MHealth），包括青年人的可穿戴设备，是最佳选择。解决这些挑战并实现 15总体P01目标是改善YPLWH中酒精使用和艾滋病毒护理的自我管理，我们建议 16开发首个基于腕生物传感器的MHealth Suite，用于饮酒监控，并量身定制检测 17算法和YPLWH的优化参与策略。我们将进行30天的微型试验 18（MRT） - 一种用于开发和优化MHealth干预措施的临床试验设计，以创新有效 19应对挑战。 YPLWH将被要求佩戴Skyn BioSensor 30天，并报告使用酒精 20通过基于智能手机的生态瞬时评估（EMA）。他们也将两次是微型的 21每天测试两种基于经验的参与策略 - 互惠和个性化反馈。来自 22 30天MRT将用于（a）开发和验证机器学习算法用于酒精检测 23基于Skyn数据； （b）测试哪种参与策略效果更好，为谁和下 24条条件促进传感器近端参与。我们还将收集酒精生物标志物（即 25 Peth）在1个月的随访中，这将使我们能够（c）探索传感器参与的潜在影响（和 26因此自我监控）关于远端酒精的使用。在试用期间，我们将进行第一个集成，优化，并且 27开发了适当的基于生物传感器的MHealth套件，用于酒精自我监控，可以用作 28 YPLWH未来酒精干预的基础。该项目的发现也可能告知 29开发新的翻译行为研究模型，以指导青少年和YPLWH研究 30 Agernda通过利用强大的传感技术来以引人入胜的方式提高自我管理。