mHealth Engagement for Antiretroviral Medication Adherence Among People With HIV and Substance Use Disorders: Observational Study.

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Internet Research Pub Date : 2024-12-20 DOI:10.2196/57774
Ranran Z Mi, Ellie Fan Yang, Alexander Tahk, Adati Tarfa, Lynne M Cotter, Linqi Lu, Sijia Yang, David H Gustafson, Ryan Westergaard, Dhavan Shah
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Abstract

Background: Despite the increasing popularity of mobile health (mHealth) technologies, little is known about which types of mHealth system engagement might affect the maintenance of antiretroviral therapy among people with HIV and substance use disorders.

Objective: This study aimed to use longitudinal and detailed system logs and weekly survey data to test a mediation model, where mHealth engagement indicators were treated as predictors, substance use and confidence in HIV management were treated as joint mediators, and antiretroviral therapy adherence was treated as the outcome. We further distinguished the initiation and intensity of system engagement by mode (expression vs reception) and by communication levels (intraindividual vs dyadic vs network).

Methods: Tailored for people with HIV living with substance use disorders, the mHealth app was distributed among 208 participants aged >18 years from 2 US health clinics. Supervised by medical professionals, participants received weekly surveys through the app to report their health status and medication adherence data. System use was passively collected through the app, operationalized as transformed click-level data, aggregated weekly, and connected to survey responses with a 7-day lagged window. Using the weekly check-in record provided by participants as the unit of analysis (N=681), linear regression and structure equation models with cluster-robust SEs were used for analyses, controlling within-person autocorrelation and group-level error correlations. Racial groups were examined as moderators in the structure equation models.

Results: We found that (1) intensity, not initiation, of system use; (2) dyadic message expression and reception; and (3) network expression positively predicted medication adherence through joint mediators (substance use and confidence in HIV management). However, intraindividual reception (ie, rereading saved entries for personal motivation) negatively predicts medication adherence through joint mediators. We also found Black participants have distinct usage patterns, suggesting the need to tailor mHealth interventions for this subgroup.

Conclusions: These findings highlight the importance of considering the intensity of system engagement, rather than initiation alone, when designing mHealth interventions for people with HIV and tailoring these systems to Black communities.

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背景:尽管移动医疗(mHealth)技术越来越受欢迎,但人们对哪种类型的移动医疗系统参与可能会影响艾滋病病毒感染者和药物使用障碍者维持抗逆转录病毒疗法的情况知之甚少:本研究旨在使用详细的纵向系统日志和每周调查数据来测试一个中介模型,其中移动医疗参与指标被视为预测因素,药物使用和对艾滋病管理的信心被视为共同中介因素,而坚持抗逆转录病毒治疗被视为结果。我们还进一步区分了不同模式(表达与接收)和不同交流水平(个人内部与伴侣间交流与网络交流)下系统参与的开始和强度:方法:我们为患有药物使用障碍的艾滋病感染者量身定制了移动医疗应用程序,并向来自美国两家医疗诊所的 208 名年龄大于 18 岁的参与者分发了该应用程序。在医疗专业人员的监督下,参与者每周通过应用程序接受调查,报告他们的健康状况和服药情况。系统使用情况是通过应用程序被动收集的,以转换后的点击级数据进行操作,每周汇总一次,并以 7 天的滞后窗口与调查回复相连接。以参与者提供的每周签到记录为分析单位(N=681),使用线性回归和结构方程模型进行分析,并采用聚类稳健 SE,控制人内自相关性和群体级误差相关性。在结构方程模型中,种族群体被视为调节因素:我们发现:(1) 系统使用的强度(而非开始使用);(2) 二人组信息的表达和接收;(3) 网络表达通过联合中介(药物使用和对艾滋病管理的信心)对坚持服药有积极的预测作用。然而,个体内部的接收(即出于个人动机重读已保存的条目)通过联合中介因素对坚持用药产生负面预测。我们还发现黑人参与者有独特的使用模式,这表明有必要为这一亚群体量身定制移动医疗干预措施:这些发现强调了在为艾滋病毒感染者设计移动医疗干预措施以及为黑人社区量身定制这些系统时,考虑系统参与强度而非仅仅考虑启动的重要性。
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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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