Evidence map›Paper›PMID 40957002›Full record

SynthesisJMIR mHealth and uHealth2025

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

Qinglin Cheng, Ping Chen, Ruoqi Dai, Qingjun Jia, Xuexin Bai, Qiancheng Cao, Qingchun Li, Yifei Wu, Yinyan Huang

Abstract readNetwork Meta-AnalysisSystematic Review
In one paragraph

Synthesis in JMIR mHealth and uHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Qinglin Cheng *Department of Tuberculosis Control and Prevention, Hangzhou Center for Disease Control and Prevention (Hangzhou Health Supervision Institution), 568 Mingshi Road, Hangzhou, 310021, China, 86 19033919718.ORCID 0000-0002-5564-911X
Ping Chen *Central Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.ORCID 0009-0007-1932-9366
Ruoqi Dai *Department of Tuberculosis Control and Prevention, Hangzhou Center for Disease Control and Prevention (Hangzhou Health Supervision Institution), 568 Mingshi Road, Hangzhou, 310021, China, 86 19033919718.ORCID 0009-0005-1637-0318
Qingjun JiaDepartment of Tuberculosis Control and Prevention, Hangzhou Center for Disease Control and Prevention (Hangzhou Health Supervision Institution), 568 Mingshi Road, Hangzhou, 310021, China, 86 19033919718.ORCID 0000-0003-0578-5727
Xuexin BaiDepartment of Tuberculosis Control and Prevention, Hangzhou Center for Disease Control and Prevention (Hangzhou Health Supervision Institution), 568 Mingshi Road, Hangzhou, 310021, China, 86 19033919718.ORCID 0000-0003-2676-5792
Qiancheng CaoDepartment of Public Health, School of Public Health and Nursing, Hangzhou Normal University, Hangzhou, China.ORCID 0000-0003-2899-7626
Qingchun LiDepartment of Tuberculosis Control and Prevention, Hangzhou Center for Disease Control and Prevention (Hangzhou Health Supervision Institution), 568 Mingshi Road, Hangzhou, 310021, China, 86 19033919718.ORCID 0000-0001-6823-5074
Yifei WuDepartment of Tuberculosis Control and Prevention, Hangzhou Center for Disease Control and Prevention (Hangzhou Health Supervision Institution), 568 Mingshi Road, Hangzhou, 310021, China, 86 19033919718.ORCID 0000-0002-6572-3862
Yinyan HuangDepartment of Tuberculosis Control and Prevention, Hangzhou Center for Disease Control and Prevention (Hangzhou Health Supervision Institution), 568 Mingshi Road, Hangzhou, 310021, China, 86 19033919718.ORCID 0000-0001-6906-1889

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Biomedical TechnologyDigital TechnologyTuberculosisComparative Effectiveness ResearchDigital HealthHumansRandomized Controlled Trials as Topicdigital health technologiesmobile applicationnetwork meta-analysisrandomized controlled trialstuberculosis interventiontuberculosis treatment

Identifiers

PMID40957002
PMCPMC12440258

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.