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Qualitative Evaluation of mHealth Implementation for Infectious Disease Care in Low- and Middle-Income Countries:
Josephine Greenall-Ota1, H Manisha Yapa2,3, Greg J Fox4,5,6
1Faculty of Science, University of Sydney, Sydney, NSW, Australia.
Background:
Mobile health (mHealth) interventions have the potential to improve health outcomes in low- and middle-income countries (LMICs) by aiding health workers to strengthen service delivery, as well as by helping patients and communities manage and prevent diseases. It is crucial to understand how best to implement mHealth within already burdened health services to maximally improve health outcomes and sustain the intervention in LMICs.
Objective:
We aimed to identify key barriers to and facilitators of the implementation of mHealth interventions for infectious diseases in LMICs, drawing on a health systems analysis framework.
Methods:
We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist to select qualitative or mixed methods studies reporting on determinants of already implemented infectious disease mHealth interventions in LMICs. We searched MEDLINE, Embase, PubMed, CINAHL, the Social Sciences Citation Index, and Global Health. We extracted characteristics of the mHealth interventions and implementation experiences, then conducted an analysis of determinants using the Tailored Implementation for Chronic Diseases framework.
Results:
We identified 10,494 titles for screening, among which 20 studies met our eligibility criteria. Of these, 9 studies examined mHealth smartphone apps and 11 examined SMS text messaging interventions. The interventions addressed HIV (n=7), malaria (n=4), tuberculosis (n=4), pneumonia (n=2), dengue (n=1), human papillomavirus (n=1), COVID-19 (n=1), and respiratory illnesses or childhood infectious diseases (n=2), with 2 studies addressing multiple diseases. Within these studies, 10 interventions were intended for use by health workers and the remainder targeted patients, at-risk individuals, or community members. Access to reliable technological resources, familiarity with technology, and training and support were key determinants of implementation. Additional themes included users forgetting to use the mHealth interventions and mHealth intervention designs affecting ease of use.
Conclusions:
Acceptance of the intervention and the capacity of existing health care system infrastructure and resources are 2 key factors affecting the implementation of mHealth interventions. Understanding the interaction between mHealth interventions, their implementation, and health systems will improve their uptake in LMICs.
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