Trial reportFrontiers in public health2024
Feasibility and acceptability of the smart pillbox and medication label with differentiated care to support person-centered tuberculosis care among ASCENT trial participants - A multicountry study.
Trial report in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Effective mHealth interventions to improve tuberculosis treatment adherence in Asia: a systematic review and meta-analysis.BMC public health · 2026Pooled it
- Effects of an Internet of Things-Based Medication Assistance System on Real-World ART Adherence and Treatment Response in People Living with HIV.Journal of clinical medicine · 2026Article
- Engagement With Digital Adherence Technologies as Measures of Intervention Fidelity Among Adults With Drug-Susceptible Tuberculosis and Health Care Providers: Descriptive Analysis Using Data From Cluster-Randomized Trials in Five Countries.JMIR public health and surveillance · 2025Article
- Process Evaluation of Pragmatic Cluster-Randomized Trials of Digital Adherence Technologies for Tuberculosis Treatment Support: A Mixed-Method Study in Five Countries.Tropical medicine and infectious disease · 2025Article
- Acceptability of Digital Adherence Technologies to support people with drug-susceptible TB in South Africa.PloS one · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 7 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Digital adherence technologies (DATs) can offer alternative approaches to support tuberculosis treatment medication adherence. Evidence on their feasibility and acceptability in high TB burden settings is limited. We conducted a cross-sectional survey among adults with drug-sensitive tuberculosis (DS-TB), participating in pragmatic cluster-randomized trials for the Adherence Support Coalition to End TB project in Ethiopia (PACTR202008776694999), the Philippines, South Africa and Tanzania (ISRCTN 17706019). Methods: From each country we selected 10 health facilities implementing the DAT intervention (smart pillbox or medication labels, with differentiated care support), ensuring inclusion of urban/rural and public/private facilities. Adults on DS-TB regimen using a DAT were randomly selected from each facility. Feasibility of the DATs was assessed using a standardized tool. Acceptability was measured using a 5-point Likert-scale, using the Capability, Opportunity, Motivation, Behavior (COM-B) model. Mean scores of Likert-scale responses within each COM-B category were estimated, adjusted for facility-level clustering. Data were summarized by country and DAT type. Results: Participants using either the pillbox ( Conclusion: From client-perspective, pillboxes and medication labels with differentiated care support were feasible to implement and acceptable in variety of settings. However, implementation challenges related to network, phone access, stigma, additional costs to people with TB to use DATs need to be addressed.
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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.