Don Lawrence Mudzengi

Mobile health (mHealth) technologies are increasingly used to support community-based healthcare. However, their real-world impact often remains unclear. Understanding implementation factors is essential for advancing their use and achieving meaningful health outcomes. We evaluated an mHealth tool (AitaHealth) after customising its modules and workflows for household Tuberculosis (TB) contact tracing and other community-based data collection by community health workers (CHWs). We describe the acceptability, feasibility, and implementation fidelity of this approach. We conducted a mixed-methods evaluation in two South African districts: uMkhanyakude and Ekurhuleni. We collected qualitative data through focus group discussions (FGDs) and in-depth interviews (IDIs) with CHWs, team leaders, and key stakeholders. We used deductive thematic analysis grounded in the Technology Acceptance Model (TAM) to assess the acceptability and implementation feasibility of the mHealth tool. We used quantitative data from the AitaHealth metadata to assess implementation fidelity. CHWs appreciated AitaHealth’s efficiency, data security, and credibility. Across the two districts, 103 CHWs recorded data for 2,452 households and 10,649 household members. However, they reported challenges in ease of use, with unreliable devices, weak support, and safety concerns hindering data collection. These issues led to inconsistent engagement, with 48.5% of CHWs logging in fewer than 15 times during implementation. Despite these challenges, when used, AitaHealth ensured high-quality data collection and household coverage, with TB-related fields completed in over 94% of households, demonstrating its potential under better conditions. AitaHealth`s limitations stemmed from system constraints rather than user resistance. To achieve full impact, mHealth tools require reliable infrastructure and supportive environments for both the tools and their implementers.

Newer oxazolidinones will be required to advance regimens in pan-TB indications. The addition of host-directed agents may help promote the recovery of lung

Background: Literature on the development of mobile health (mHealth) tools for public health interventions is scarce. This scarcity creates a knowledge gap, and new tools may repeat the mistakes of past implementations. Objective: In this paper, we describe the development of Shesha, a WhatsApp-based chatbot designed to facilitate linkage to care for household contacts of people being treated for tuberculosis (TB). Shesha facilitates linkage by providing TB test results, TB preventive treatment (TPT) information, nudges, reminders, and personalized support. We developed Shesha to address the human resource capacity challenges posed by South Africa’s new universal TB testing and TPT policies. Method: We applied a design thinking framework with 7 phases: empathize, discover, define, prototype, build and launch, improve, and evaluate. The process started with gathering insights from TB contact tracing studies and consulting with global and local experts to address the challenges of universal TB testing and TPT. Based on these findings, we defined the core functionalities of Shesha and incorporated them in the Health Belief Model to encourage health-seeking behavior. In collaboration with the developers, we developed the WhatsApp-based chatbot. We selected WhatsApp for its wide accessibility and user-friendliness. Results: We successfully developed and launched the Shesha in September 2023, with implementation expected to continue until March 2025. Early user acceptance revealed that users generally valued the information provided on the tool regarding TB and TPT; however, they required ongoing engagement to link to care. Ongoing evaluations, guided by the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework, will assess the tool’s impact on reducing community health worker workloads and improving linkage to care. Conclusions: Documenting the development of mHealth technologies is crucial for guiding future projects and improving health interventions. In our study, frameworks like design thinking and the Health Belief Model aligned Shesha with user needs and programmatic goals. Comprehensive documentation may help assess the chatbot’s performance and guide future improvements, supporting scalability and efficiency in mHealth interventions across public health settings.

Background: Tuberculosis infection (TBI) testing, and treatment are fundamental to achieve TB elimination. TBI testing among close or household contacts (HHCs) has been limited, in part due to perceived complexity and high operational cost. We evaluated the performance of a new near-patient and field-based QIAreach QuantiFERON-TB (QIAreach) against QuantiFERON-TB-Gold-Plus (QFT-Plus) among HHCs of people with TB. Methods: A cross-sectional study was conducted from July 2021 to September 2022 in Lesotho, South Africa and Tanzania. Blood samples were collected from HHCs for paired QFT-Plus and QIAreach processing, testing and interpretation. To evaluate the performance of QIAreach against QFT-Plus as a reference, we determined the: i) prevalence of TBI, ii) total concordance using Cohen’s Kappa, iii) predictors of discordant results using logistic regression, and iv) relationship between time to results and interferon-gamma (IFN-γ) response levels using linear correlation. Results: Out of 964 enrolled HHCs, 464 had paired results, of whom 64.9% (302/465) were female with a cohort median age of 27 years (interquartile range (IQR): 13–45). Overall, 50.9% (236/464) tested positive on QFT-Plus, while 57.1% (265/464) were positive on QIAreach assay. Total concordance between QFT-Plus and QIAreach was 78.4% [353/450, 95% confidence interval (CI): 74.4–82.2, Cohen’s Kappa: 0.5627, p < 0.001]. Discordance between assays was 23.9% (111/464) and was associated with Lesotho site (adjusted odds ratio 2.70, 95%CI: 1.48–4.92, p = 0.001). HHCs with higher IFN-γ response (QFT-Plus) (≥0.35 IU.ml-^l) had a shorter time to results on QIAreach. In addition, a strong negative correlation between QIAreach time to results and IFN-γ response (QFT-Plus) levels (R = −0.64, 95% CI: −0.87 to −0.41, p < 0.001) was observed. Conclusion: QIAreach demonstrated a moderate concordance against QFT-Plus among HHCs in three high-burden countries. Further work is needed to understand and improve its usability in high TB and low resource settings.

Background: Although tuberculosis preventive therapy guidelines support testing for tuberculosis infection in household contacts (HHCs), this adds operational complexity and cost and has often been abandoned. To understand utility of testing, we determined prevalence and risk factors for tuberculosis infection and tuberculosis preventive therapy eligibility among HHCs. Methods: In a cross-sectional study conducted from July 2021 to September 2022 in Lesotho, South Africa, and Tanzania, we enrolled people with microbiologically confirmed pulmonary tuberculosis and their HHCs. HHCs were screened and tested for tuberculosis and tuberculosis infection using Xpert Ultra and QuantiFERON-TB-Gold-Plus, respectively. Generalized linear modelling was used to determine factors associated with tuberculosis infection, using robust standard errors. Tuberculosis preventive therapy eligibility was determined using World Health Organization criteria. Results: We enrolled 342 people with pulmonary tuberculosis and 964 HHCs: 61.9% (597/964) were female with a median age of 18 years (interquartile range, 8–39 years). Overall, tuberculosis prevalence was 3.4% (25/739; 95% confidence interval [CI], 2.2%–4.9%), while tuberculosis infection prevalence was 48.7% (348/714; 95% CI, 45.0%–52.5%). Having tuberculosis infection increased with age per year (adjusted odds ratio [aOR], 1.02; 95% CI, 1.01–1.03), being from Lesotho (aOR, 1.82; 95% CI, 1.04–3.20), previous tuberculosis history (aOR, 2.25; 95% CI, 1.05–4.79), and being HIV negative (aOR, 2.30; 95% CI, 1.31–4.04). Overall, 62.2% (518/833; 95% CI, 58.8%–65.5%) were eligible for tuberculosis preventive therapy. Conclusions: Almost half of tuberculosis-exposed HHCs aged ≥5 years had tuberculosis infection. Approximately two-thirds of HHCs were eligible for tuberculosis preventive therapy, implying that providing tuberculosis preventive therapy without prior testing for tuberculosis infection may be warranted in this population. Further work on cost-effectiveness is warranted when new tests become available. Clinical Trials Registration: ISRCTN10003903.

Background Newer oxazolidinones will be required to advance regimens in pan-TB indications. The addition of host-directed agents may help promote the recovery of lung function during TB treatment and prevent post-TB lung disease. Methods The panTB-HM trial assesses the capacity of regimens containing the oxazolidinone sutezolid and the anti-oxidant N-acetylcysteine to meet the target criteria proposed by WHO for pan-TB indications in a phase 2C trial. Discussion This trial is ground-breaking in its objectives and design for a pan-TB indication and its evaluation of lung function recovery. Trial registration The protocol was first registered on clinicaltrials.gov as NCT05686356 on 13 Jan 2023.

BACKGROUND Documenting the development of mobile health (mHealth) tools is crucial for enabling future projects to build on existing knowledge. Effective documentation supports the refinement of design, implementation, and evaluation processes, facilitating the scale-up and adaptation of health interventions across diverse contexts. OBJECTIVE In this paper, we describe how we developed the eCase Manager, a WhatsApp-based chatbot designed to facilitate linkage-to-care for household contacts of TB patients by providing nudges, reminders, and personalised support. We developed the eCase Manager to address the human resource capacity challenges posed by South Africa's new universal TB testing and tuberculosis preventive treatment (TPT) policies. METHODS We applied a design thinking framework with seven phases—empathise, discover, define, prototype, build and launch, improve, and evaluate—to develop the eCase Manager. The process started with gathering insights from TB contact tracing studies and consulting with global and local experts to address the challenges of universal TB testing and TPT. From these findings, we defined the core functionalities of the eCase Manager and incorporated the health belief model (HBM) to encourage health-seeking behaviour. In collaboration with Reach Digital Health, we developed the WhatsApp-based chatbot, selecting WhatsApp for its wide accessibility and user-friendliness. RESULTS We successfully developed and launched the eCase Manager in September 2023, with implementation expected to continue until March 2025. Ongoing evaluations, guided by the RE-AIM framework, assess the tool’s impact on reducing community health worker workloads and improving linkage-to-care efficiency. Additionally, we plan to translate the chatbot into local languages to enhance accessibility and scale it up for broader use in programmatic settings. CONCLUSIONS Documenting the development of mHealth technologies is crucial for guiding future projects and improving health interventions. In our study, frameworks like design thinking and the health belief model aligned the eCase Manager with user needs and programmatic goals. This approach addressed challenges in TB linkage-to-cares and allowed for iterative improvements through stakeholder feedback. Sharing this process offers insights for future mHealth developers, helping them tackle common issues like limited human resources and patient engagement. Comprehensive documentation may help assess the chatbot's performance and guide future improvements, supporting scalability and efficiency in mHealth interventions across public health settings.

Background: Mobile health (mHealth) technologies are increasingly used in contact tracing and case finding, enhancing and replacing traditional methods for managing infectious diseases such as Ebola, tuberculosis, COVID-19, and HIV. However, the variations in their development approaches, implementation scopes, and effectiveness introduce uncertainty regarding their potential to improve public health outcomes. Objective: We conducted this systematic review to explore how mHealth technologies are developed, implemented, and evaluated. We aimed to deepen our understanding of mHealth’s role in contact tracing, enhancing both the implementation and overall health outcomes. Methods: We searched and reviewed studies conducted in Africa focusing on tuberculosis, Ebola, HIV, and COVID-19 and published between 1990 and 2023 using the PubMed, Scopus, Web of Science, and Google Scholar databases. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review, synthesize, and report the findings from articles that met our criteria. Results: We identified 11,943 articles, but only 19 (0.16%) met our criteria, revealing a large gap in technologies specifically aimed at case finding and contact tracing of infectious diseases. These technologies addressed a broad spectrum of diseases, with a predominant focus on Ebola and tuberculosis. The type of technologies used ranged from mobile data collection platforms and smartphone apps to advanced geographic information systems (GISs) and bidirectional communication systems. Technologies deployed in programmatic settings, often developed using design thinking frameworks, were backed by significant funding and often deployed at a large scale but frequently lacked rigorous evaluations. In contrast, technologies used in research settings, although providing more detailed evaluation of both technical performance and health outcomes, were constrained by scale and insufficient funding. These challenges not only prevented these technologies from being tested on a wider scale but also hindered their ability to provide actionable and generalizable insights that could inform public health policies effectively. Conclusions: Overall, this review underscored a need for organized development approaches and comprehensive evaluations. A significant gap exists between the expansive deployment of mHealth technologies in programmatic settings, which are typically well funded and rigorously developed, and the more robust evaluations necessary to ascertain their effectiveness. Future research should consider integrating the robust evaluations often found in research settings with the scale and developmental rigor of programmatic implementations. By embedding advanced research methodologies within programmatic frameworks at the design thinking stage, mHealth technologies can potentially become technically viable and effectively meet specific contact tracing health outcomes to inform policy effectively.

BACKGROUND Mobile health (mHealth) technologies are increasingly used in contact tracing and case finding, enhancing and replacing traditional methods for managing infectious diseases such as Ebola, tuberculosis, COVID-19, and HIV. However, the variations in their development approaches, implementation scopes, and effectiveness introduce uncertainty regarding their potential to improve public health outcomes. OBJECTIVE We conducted this systematic review to explore how mHealth technologies are developed, implemented, and evaluated. We aimed to deepen our understanding of mHealth’s role in contact tracing, enhancing both the implementation and overall health outcomes. METHODS We searched and reviewed studies conducted in Africa focusing on tuberculosis, Ebola, HIV, and COVID-19 and published between 1990 and 2023 using the PubMed, Scopus, Web of Science, and Google Scholar databases. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review, synthesize, and report the findings from articles that met our criteria. RESULTS We identified 11,943 articles, but only 19 (0.16%) met our criteria, revealing a large gap in technologies specifically aimed at case finding and contact tracing of infectious diseases. These technologies addressed a broad spectrum of diseases, with a predominant focus on Ebola and tuberculosis. The type of technologies used ranged from mobile data collection platforms and smartphone apps to advanced geographic information systems (GISs) and bidirectional communication systems. Technologies deployed in programmatic settings, often developed using design thinking frameworks, were backed by significant funding and often deployed at a large scale but frequently lacked rigorous evaluations. In contrast, technologies used in research settings, although providing more detailed evaluation of both technical performance and health outcomes, were constrained by scale and insufficient funding. These challenges not only prevented these technologies from being tested on a wider scale but also hindered their ability to provide actionable and generalizable insights that could inform public health policies effectively. CONCLUSIONS Overall, this review underscored a need for organized development approaches and comprehensive evaluations. A significant gap exists between the expansive deployment of mHealth technologies in programmatic settings, which are typically well funded and rigorously developed, and the more robust evaluations necessary to ascertain their effectiveness. Future research should consider integrating the robust evaluations often found in research settings with the scale and developmental rigor of programmatic implementations. By embedding advanced research methodologies within programmatic frameworks at the design thinking stage, mHealth technologies can potentially become technically viable and effectively meet specific contact tracing health outcomes to inform policy effectively.