ArticleTuberculosis and respiratory diseases2023
Improving Tuberculosis Medication Adherence: The Potential of Integrating Digital Technology and Health Belief Model.
Article in Tuberculosis and respiratory diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.
What it found
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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.
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.
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Who cites it
37 citing papers in PubMed, 4 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- Effectiveness and safety of the available preventive tuberculosis treatment regimens for children and adolescents: systematic review and network meta-analysis.BMJ paediatrics open · 2026Pooled it
- Effective mHealth interventions to improve tuberculosis treatment adherence in Asia: a systematic review and meta-analysis.BMC public health · 2026Pooled it
- Digital adherence technology to improve medication adherence in tuberculosis patients: a systematic review and meta-analysis randomized control trials.NPJ primary care respiratory medicine · 2025Pooled it
- Efficacy of eHealth Interventions for Hemodialysis Patients: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Non-Adherence to Anti-Tuberculosis Treatment and Its Determinants Among Tuberculosis Patients in Tanzania.Journal of community health · 2026Article
- A Decade of Unfavorable Outcomes in Pulmonary Tuberculosis: Proportion and Factors Associated in Kelantan, Malaysia.Infectious diseases & clinical microbiology · 2026Article
- Adaptation of a Smartphone-Based Mobile Health Program to Support Person-Centered Treatment of Tuberculosis in Kilimanjaro, Tanzania: Preimplementation Qualitative Needs Assessment.JMIR formative research · 2026Article
- Culturally Responsive Counseling and Tuberculosis Treatment Adherence: Development and Validation of an Integrated Model.Nursing reports (Pavia, Italy) · 2026Article
- Article
- Effectiveness and safety of bedaquiline, pretomanid, linezolid and moxifloxacin in drug-resistant tuberculosis patients in selected public hospitals in Ethiopia: a multicenter retrospective cohort study.BMC infectious diseases · 2026Article
- Article
- Discovery of novel InhA inhibitors through structural bioinformatics and machine learning-driven QSAR screening of natural products.Biology methods & protocols · 2026Article
- Combating Drug-Resistant Tuberculosis: Advancements in Diagnostics and Therapeutics.Pulmonary medicine · 2026Review
- Time to first and recurrent missed video directly observed therapy sessions and associated factors among people with Tuberculosis in Kampala, Uganda.Frontiers in digital health · 2026Article
- Evaluating a biochemical TB medication adherence test: Insights from image-based reporting through a patient companion app.PLOS global public health · 2026Article
- Tuberculosis: An Ongoing Global Threat.Advances in experimental medicine and biology · 2026Review
- Epidemiology and clinicodemographic features of microbiologically confirmed and presumptive extrapulmonary tuberculosis in Ethiopia: a multicenter study.IJID regions · 2025Article
- Advanced Nanosystems and Emerging Therapies: Innovations in Tuberculosis Treatment and Drug Resistance.Pharmaceutics · 2025Review
- Overcoming Treatment Challenges in HIV-Associated Mycobacterial Diseases: New Therapeutic Frontiers.International journal of molecular sciences · 2025Review
- Barriers to treatment adherence among patients with tuberculosis: a qualitative study of Pakistani nationals and Afghan refugees.BMJ open · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 3 institutions in 1 country.
Funding
Abstract
Tuberculosis (TB) is a significant public health concern. Globally, TB is among the top 10 and the leading cause of death due to a single infectious agent. Providing standard anti-TB therapy for at least 6 months is recommended as one of the crucial strategies to control the TB epidemic. However, the long duration of TB treatment raised the issue of non-adherence. Non-adherence to TB therapy could negatively affect clinical and public health outcomes. Thus, directly observed therapy (DOT) has been introduced as a standard strategy to improve anti-TB medication adherence. Nonetheless, the DOT approach has been criticized due to inconvenience, stigma, reduced economic productivity, and reduced quality of life, which ultimately could complicate adherence issues. Apart from that, its effectiveness in improving anti-TB adherence is debatable. Therefore, digital technology could be an essential tool to enhance the implementation of DOT. Incorporating the health belief model (HBM) into digital technology can further increase its effectiveness in changing behavior and improving medication adherence. This article aimed to review the latest evidence regarding TB medication non-adherence, its associated factors, DOT's efficacy and its alternatives, and the use of digital technology and HBM in improving medication adherence. This paper used the narrative review methodology to analyze related articles to address the study objectives. Conventional DOT has several disadvantages in TB management. Integrating HBM in digital technology development is potentially effective in improving medication adherence. Digital technology provides an opportunity to improve medication adherence to overcome various issues related to DOT implementation.
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What OpenQuestion holds
Registered trials
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.