Efficacy of the mLab App: a randomized clinical trial for increasing HIV testing uptake using mobile technology.

IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of the American Medical Informatics Association Pub Date : 2024-11-19 DOI:10.1093/jamia/ocae261
Rebecca Schnall, Thomas Foster Scherr, Lisa M Kuhns, Patrick Janulis, Haomiao Jia, Olivia R Wood, Michael Almodovar, Robert Garofalo
{"title":"Efficacy of the mLab App: a randomized clinical trial for increasing HIV testing uptake using mobile technology.","authors":"Rebecca Schnall, Thomas Foster Scherr, Lisa M Kuhns, Patrick Janulis, Haomiao Jia, Olivia R Wood, Michael Almodovar, Robert Garofalo","doi":"10.1093/jamia/ocae261","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To determine the efficacy of the mLab App, a mobile-delivered HIV prevention intervention to increase HIV self-testing in MSM and TGW.</p><p><strong>Materials and methods: </strong>This was a randomized (2:2:1) clinical trial of the efficacy the mLab App as compared to standard of care vs mailed home HIV test arm among 525 MSM and TGW aged 18-29 years to increase HIV testing.</p><p><strong>Results: </strong>The mLab App arm participants demonstrated an increase from 35.1% reporting HIV testing in the prior 6 months compared to 88.5% at 6 months. In contrast, 28.8% of control participants reported an HIV test at baseline, which only increased to 65.1% at 6 months. In a generalized linear mixed model estimating this change and controlling for multiple observations of participants, this equated to control participants reporting a 61.2% smaller increase in HIV testing relative to mLab participants (P = .001) at 6 months. This difference was maintained at 12 months with control participants reporting an 82.6% smaller increase relative to mLab App participants (P < .001) from baseline to 12 months.</p><p><strong>Discussion and conclusion: </strong>Findings suggest that the mLab App is well-supported, evidence-based, behavioral risk-reduction intervention for increasing HIV testing rates as compared to the standard of care, suggesting that this may be a useful behavioral risk-reduction intervention for increasing HIV testing among young MSM.</p><p><strong>Trial registration: </strong>This trial was registered with Clinicaltrials.gov NCT03803683.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Medical Informatics Association","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1093/jamia/ocae261","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Objective: To determine the efficacy of the mLab App, a mobile-delivered HIV prevention intervention to increase HIV self-testing in MSM and TGW.

Materials and methods: This was a randomized (2:2:1) clinical trial of the efficacy the mLab App as compared to standard of care vs mailed home HIV test arm among 525 MSM and TGW aged 18-29 years to increase HIV testing.

Results: The mLab App arm participants demonstrated an increase from 35.1% reporting HIV testing in the prior 6 months compared to 88.5% at 6 months. In contrast, 28.8% of control participants reported an HIV test at baseline, which only increased to 65.1% at 6 months. In a generalized linear mixed model estimating this change and controlling for multiple observations of participants, this equated to control participants reporting a 61.2% smaller increase in HIV testing relative to mLab participants (P = .001) at 6 months. This difference was maintained at 12 months with control participants reporting an 82.6% smaller increase relative to mLab App participants (P < .001) from baseline to 12 months.

Discussion and conclusion: Findings suggest that the mLab App is well-supported, evidence-based, behavioral risk-reduction intervention for increasing HIV testing rates as compared to the standard of care, suggesting that this may be a useful behavioral risk-reduction intervention for increasing HIV testing among young MSM.

Trial registration: This trial was registered with Clinicaltrials.gov NCT03803683.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
mLab 应用程序的功效:利用移动技术提高艾滋病病毒检测接受率的随机临床试验。
目的确定 mLab 应用程序的疗效,该应用程序是一种移动艾滋病毒预防干预措施,旨在提高 MSM 和 TGW 的艾滋病毒自我检测率:这是一项随机(2:2:1)临床试验,目的是在 525 名 18-29 岁的 MSM 和 TGW 中,比较 mLab App 与标准护理和邮寄家庭 HIV 检测工具的效果,以增加 HIV 检测:结果:mLab 应用程序组的参与者在过去 6 个月中报告接受 HIV 检测的比例从 35.1%上升到 6 个月时的 88.5%。相比之下,28.8% 的对照组参与者在基线时报告进行了 HIV 检测,而在 6 个月时仅增加到 65.1%。通过广义线性混合模型对这一变化进行估算,并对参与者的多重观察结果进行控制,结果显示,对照组参与者在 6 个月时报告的 HIV 检测率比 mLab 参与者低 61.2%(P = .001)。这一差异在 12 个月时得以保持,对照组参与者的艾滋病检测率比 mLab 应用程序参与者低 82.6%(P 讨论和结论:研究结果表明,与标准护理相比,mLab 应用程序是得到充分支持的循证行为风险降低干预措施,可提高 HIV 检测率,这表明它可能是提高年轻 MSM HIV 检测率的有效行为风险降低干预措施:该试验已在 Clinicaltrials.gov NCT03803683 上注册。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
期刊最新文献
Efficacy of the mLab App: a randomized clinical trial for increasing HIV testing uptake using mobile technology. Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use. Research for all: building a diverse researcher community for the All of Us Research Program. Learning health system linchpins: information exchange and a common data model. Oncointerpreter.ai enables interactive, personalized summarization of cancer diagnostics data.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1