Comparative Effectiveness of Digital Health Technologies in Tuberculosis Treatment: Systematic Review and Network Meta-Analysis of Randomized Controlled Trials.

IF 6.2 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Qinglin Cheng, Ping Chen, Ruoqi Dai, Qingjun Jia, Xuexin Bai, Qiancheng Cao, Qingchun Li, Yifei Wu, Yinyan Huang
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引用次数: 0

Abstract

Background: Tuberculosis (TB) treatment remains a critical global health challenge, as traditional standard of care (SoC) approaches face limitations in accessibility and efficacy. While digital health technologies (DHTs) offer promising solutions to address these gaps, limited evidence exists on their comparative effectiveness.

Objective: This study systematically evaluates and compares the impact of diverse DHTs on improving TB treatment outcomes and adherence, aiming to identify optimal strategies across different patient populations.

Methods: A systematic search was conducted across PubMed, Cochrane Library, Embase, and Web of Science from database inception through February 28, 2025, with no language restrictions. Eligible studies included randomized controlled trials comparing DHTs with SoC for TB treatment. The primary outcome was treatment success, defined as completion or cure. A random-effects network meta-analysis was performed, calculating odds ratios (OR) and 95% credibility intervals (CrI) to assess treatment effects. Surface under the cumulative ranking curve (SUCRA) values were used to rank intervention effectiveness. This study is registered with PROSPERO (International Prospective Register of Systematic Reviews; CRD42025601199).

Results: From 2420 screened studies, 27 randomized controlled trials involving 23,283 patients and eight DHT interventions were included. The network meta-analysis revealed that digital health platforms showed marginal improvements in treatment success (OR=3.44; 95% CrI 0.95-11.67; SUCRA=0.913; P=.05). Compared with SoC, video directly observed treatment (VDOT) significantly improved treatment success (OR=2.39; 95% CrI 1.18-4.75; SUCRA=0.848; P=.01). Medication event reminder monitors significantly enhanced treatment adherence (OR=3.13; 95% CrI 1.55-7.05; SUCRA=0.891; P=.003).

Conclusions: Results underscore the significant potential of DHTs to improve TB treatment management. VDOT emerged as the most effective intervention for enhancing treatment success, while medication event reminder monitors demonstrated efficacy in sustaining adherence. Digital health platforms showed promise but require additional validation. Caution is warranted due to potential heterogeneity across studies, which may affect generalizability. This research offers actionable insights for stakeholders aiming to optimize TB management through strategic DHT integration.

数字健康技术在结核病治疗中的比较效果:随机对照试验的系统评价和网络荟萃分析。
背景:结核病(TB)治疗仍然是一个关键的全球卫生挑战,因为传统的标准护理(SoC)方法在可及性和有效性方面面临局限性。虽然数字卫生技术为解决这些差距提供了有希望的解决方案,但关于其相对有效性的证据有限。目的:本研究系统地评估和比较了不同dht对改善结核病治疗结果和依从性的影响,旨在确定不同患者群体的最佳策略。方法:系统检索PubMed、Cochrane Library、Embase和Web of Science,从数据库建立到2025年2月28日,无语言限制。符合条件的研究包括比较dht与SoC治疗结核病的随机对照试验。主要结果是治疗成功,定义为完成或治愈。进行随机效应网络荟萃分析,计算优势比(OR)和95%可信区间(CrI)来评估治疗效果。采用累积排序曲线下曲面(SUCRA)值对干预效果进行排序。本研究已在PROSPERO (International Prospective Register of Systematic Reviews; CRD42025601199)注册。结果:从2420项筛选研究中,纳入了27项随机对照试验,涉及23283例患者和8项DHT干预。网络meta分析显示,数字健康平台在治疗成功率方面有边际改善(OR=3.44; 95% CrI 0.95-11.67; SUCRA=0.913; P= 0.05)。与SoC相比,视频直接观察治疗(VDOT)显著提高了治疗成功率(OR=2.39; 95% CrI 1.18-4.75; SUCRA=0.848; P= 0.01)。用药事件提醒监测显著提高了治疗依从性(OR=3.13; 95% CrI 1.55-7.05; SUCRA=0.891; P= 0.003)。结论:结果强调了dht在改善结核病治疗管理方面的巨大潜力。VDOT是提高治疗成功率的最有效干预措施,而药物事件提醒监视器在维持依从性方面表现出有效性。数字医疗平台显示了前景,但需要进一步验证。由于研究的潜在异质性,这可能会影响通用性,因此需要谨慎。本研究为旨在通过战略性DHT整合优化结核病管理的利益相关者提供了可操作的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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