Abstract
BackgroundMobile health (mHealth) interventions are becoming more common in low-income countries. Existing research often overlooks implementation challenges associated with the design and technology requirements of mHealth interventions.ObjectiveWe aimed to characterize the challenges that we encountered in the implementation of a complex mHealth intervention in Uganda.MethodsWe customized a commercial mobile survey app to facilitate a two-arm household-randomized, controlled trial of home-based tuberculosis (TB) contact investigation. We incorporated digital fingerprinting for patient identification in both study arms and automated SMS messages in the intervention arm only. A local research team systematically documented challenges to implementation in biweekly site visit reports, project management reports, and minutes from biweekly conference calls. We then classified these challenges using the Consolidated Framework for Implementation Research (CFIR).ResultsWe identified challenges in three principal CFIR domains: (1) intervention characteristics, (2) inner setting, and (3) characteristics of implementers. The adaptability of the app to the local setting was limited by software and hardware requirements. The complexity and logistics of implementing the intervention further hindered its adaptability. Study staff reported that community health workers (CHWs) were enthusiastic regarding the use of technology to enhance TB contact investigation during training and the initial phase of implementation. After experiencing technological failures, their trust in the technology declined along with their use of it. Finally, complex data structures impeded the development and execution of a data management plan that would allow for articulation of goals and provide timely feedback to study staff, CHWs, and participants.ConclusionsmHealth technologies have the potential to make delivery of public health interventions more direct and efficient, but we found that a lack of adaptability, excessive complexity, loss of trust among end users, and a lack of effective feedback systems can undermine implementation, especially in low-resource settings where digital services have not yet proliferated. Implementers should anticipate and strive to avoid these barriers by investing in and adapting to local human and material resources, prioritizing feedback from end users, and optimizing data management and quality assurance procedures.Trial RegistrationPan-African Clinical Trials Registration PACTR201509000877140; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=877
Highlights
Mobile health and other electronic health interventions are becoming more common in low-income countries as advances in technology have enabled researchers and practitioners to engineer seemingly simple solutions to complex public health problems [1,2]
Conclusions: Mobile health (mHealth) technologies have the potential to make delivery of public health interventions more direct and efficient, but we found that a lack of adaptability, excessive complexity, loss of trust among end users, and a lack of effective feedback systems can undermine implementation, especially in low-resource settings where digital services have not yet proliferated
MHealth; implementation; tuberculosis; consolidated framework for implementation science; Uganda; framework; intervention; app Mobile health and other electronic health interventions are becoming more common in low-income countries as advances in technology have enabled researchers and practitioners to engineer seemingly simple solutions to complex public health problems [1,2]
Summary
Mobile health (mHealth) and other electronic health (eHealth) interventions are becoming more common in low-income countries as advances in technology have enabled researchers and practitioners to engineer seemingly simple solutions to complex public health problems [1,2]. Implementation science is an emerging field that is highly relevant for answering such questions in mHealth by using interdisciplinary approaches, including quantitative and qualitative process evaluation. The goals of such investigations may be exploratory (ie, to plan for future implementation) or explanatory (ie, to identify causes of past implementation failures). Implementation science methods may be useful for evaluating complex interventions, which are characterized by the presence of multiple interacting components that are a common feature of mHealth deployments. These methods are well-suited to showing how changes in the implementation context may influence delivery [16]. Existing research often overlooks implementation challenges associated with the design and technology requirements of mHealth interventions
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