ReviewInternet interventions2021
A review of mobile health interventions for public private mix in tuberculosis care.
Review in Internet interventions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled 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.
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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Key requirements for developing a self-care mobile application for tuberculosis: A mixed-method approach based on systematic review and needs assessment.Scientific reports · 2026Pooled it
- Pooled it
- An mHealth-based social support program to improve antenatal care engagement and facility-based births in Uganda: A type I hybrid effectiveness-implementation clinical trial.medRxiv : the preprint server for health sciences · 2026Article
- Implementation of digital tuberculosis information systems: perspectives from 10 high TB burden countries.BMC infectious diseases · 2026Article
- A Cost Analysis of Implementing Asynchronous Video Directly Observed Therapy for Monitoring Treatment in Patients with Tuberculosis Disease in Uganda.Research square · 2025Article
- Integration of a Patient-Centered mHealth Intervention (Support-Moms) Into Routine Antenatal Care to Improve Maternal Health Among Pregnant Women in Southwestern Uganda: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2025Article
- Enhancing the implementation and integration of mHealth interventions in resource-limited settings: a scoping review.Implementation science : IS · 2024Article
- Impact of a pilot mHealth intervention on treatment outcomes of TB patients seeking care in the private sector using Propensity Scores Matching-Evidence collated from New Delhi, India.PLOS digital health · 2024Article
- Enhancing the Implementation and Integration of mHealth Interventions in Resource-Limited Settings: A Scoping Review.Research square · 2024Article
- Feasibility and utility of mobile health interventions for depression and anxiety in rural populations: A scoping review.Internet interventions · 2024Article
- mHealth to improve implementation of TB contact investigation: a case study from Uganda.Implementation science communications · 2023Article
- Acceptability and feasibility of a mobile health application for enhancing public private mix for TB care among healthcare Workers in Southwestern Uganda.BMC digital health · 2023Article
- A Scoping Review on Accentuating the Pragmatism in the Implication of Mobile Health (mHealth) Technology for Tuberculosis Management in India.Journal of personalized medicine · 2022Article
- Barriers and Motivators of private hospitals' engagement in Tuberculosis care in Uganda.Global implementation research and applications · 2021Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe World Health Organization (WHO) recommends the use of mobile health (mHealth) technologies as emerging opportunities to closing the gaps in Tuberculosis (TB) care through enhancing Public Private Mix (PPM). However, little is known about mHealth interventions that have been used for enhancing PPM in TB care, those that worked and those that did not.
objectiveThis review summarizes the published evidence on the utilization and effectiveness of mHealth interventions for public private mix in TB care from literature.
methodsGoogle scholar, PUBMED, IEEE Xplore and ScienceDirect databases were searched for peer reviewed literature from 1st January 2003 to 31st December 2020 for studies about the mHealth interventions for public private mix in TB care. This was guided by the scoping review methodology proposed by Arksey and O' Malley. In order to assess the quality of the selected studies, mHealth evidence reporting and assessment (mERA) checklist was utilized. Studies that discussed the utilization of mHealth interventions for implementing PPM in TB care were included. Nine studies met the inclusion criteria and were analyzed for review.
resultsThe review found out the application of mHealth in Public Private Mix in TB care through the following ways; 1) TB screening, 2) TB case notification 3) TB treatment adherence 4) data collection and management 5) patient referral and follow up, and 6) education. This resulted into high user experience, significant time reduction in data aggregation, increased case notification and referrals and proactive tracking and provision of follow up care hence reduced treatment and completion gaps. One study yielded suboptimal utilization due to the technical and operational challenges encountered by the healthcare workers.
conclusionAlthough this scoping review highlights the role of mHealth technologies in enhancing PPM in TB care, its utilization is still limited in African settings. No Africa-based study was identified by this review. Future studies should focus on assessing the utilization of mHealth for PPM in Africa.
Indexed as
Identifiers
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.