Why the study?
Hypertension is the leading global risk factor for mortality, particularly in LMICs, making mobile phones relevant to mHealth deployment for diagnosis and management in Africa.
What is the scope of literature on the feasibility, acceptability, and effectiveness of mobile phone-based interventions for hypertension diagnosis and management in Africa?
What is the scope of literature on the feasibility, acceptability, and effectiveness of mobile phone-based interventions for hypertension diagnosis and management in Africa?
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SMS remains the dominant mHealth technology for hypertension management in Africa, highlighting a need for more advanced AI and machine learning-based solutions.
Target 3.4 of the third Sustainable Development Goal (SDG) of the United Nations (UN) General Assembly proposes to reduce premature mortality from non-communicable diseases (NCDs) by one-third. Epidemiological data presented by the World Health Organization (WHO) in 2016 show that out of a total of 57 million deaths worldwide, approximately 41 million deaths occurred due to NCDs, with 78% of such deaths occurring in low-and-middle-income countries (LMICs). The majority of investigations on NCDs agree that the leading risk factor for mortality worldwide is hypertension. Over 75% of the world's mobile phone subscriptions reside in LMICs, hence making the mobile phone particularly relevant to mHealth deployment in Africa. This study is aimed at determining the scope of the literature available on hypertension diagnosis and management in Africa, with particular emphasis on determining the feasibility, acceptability and effectiveness of interventions based on the use of mobile phones. The bulk of the evidence considered overwhelmingly shows that SMS technology is yet the most used medium for executing interventions in Africa. Consequently, the need to define novel and superior ways of providing effective and low-cost monitoring, diagnosis, and management of hypertension-related NCDs delivered through artificial intelligence and machine learning techniques is clear.
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Oronti et al. (2022) studied this question.
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