02473nas a2200181   4500000000100000008004100001653001200042653001600054653001100070653001800081653001800099100001300117700001100130245018100141300001100322490000600333520195200339       2016                            d10aMhealth10aCommunities10aAfrica10aInterventions10aMobile health1 aKruijf J1 aKrah E00aExploring the ambivalent evidence base of mobile health (mHealth): A systematic literature review on the use of mobile phones for the improvement of community health in Africa.  a1–200 v23 a<p>Background: Africa is labelled the world's fastest-growing ‘mobile region’. Considering such growth and the fragility of the continent's healthcare, mHealth has flourished. This review explores mHealth for community health in Africa in order to assess its still ambivalent evidence base.<br />
Methods: Using PubMed, Web of Science, OvidSP and Google Scholar. A systematic review was conducted of one decade (2005–2015) of peer-reviewed literature on mHealth in Africa. Data analysis focused on qualifications of success and failure. Impact evaluations of project assessments (n = 65) were complemented with general analyses/overviews of mHealth's effectiveness (n = 35).<br />
Results: Review of these texts reveals ambivalence in the appraisal of mHealth; essentially, the critical stance in general analyses/overviews is absent from project assessments. Especially weak evidence concerning sustainability and scalability is stressed in overviews. Project assessments are more optimistic. Their analysis suggests a causal connection between simplicity and success. Effective interventions are thus characterized by straightforward design and modest objectives. Greatest impediments of impact are general technology-related issues and intervention inappropriateness due to insufficient understanding of beneficiaries and specific context of use (circumstantial complications).<br />
Conclusion: Distinguishing between these two categories of complications helps to break the deadlock that marks the mHealth debate and add nuance to claims that mHealth's evidence<br />
base is weak. Constructive realism – rather than unfounded optimism or pessimism without nuance – should guide the design of interventions. Besides anticipative of technology-related complications, such realism must lead to either basic interventions or to smart mHealth shaped by deep understanding of the context of implementation<br />
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