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Challenges in Assessing Mobile Health App Quality

A Systematic Review of Prevalent and Innovative Methods
Published:September 19, 2016DOI:https://doi.org/10.1016/j.amepre.2016.07.009

      Context

      Mobile apps are poised to become a major source of health guidance; however, concerns about their quality and safety remain. Governments, clinicians, and health researchers are struggling to determine how to adequately evaluate the content and function of mobile health apps to guide consumers toward apps that will effectively and safely support their health. Researchers are both adapting existing research methods, such as those for systematic reviews, and exploring novel methods toward this aim. The purpose of this systematic review was to describe prevalent and emerging methods for searching, data extraction, and analysis in the context of mobile health apps and commercial app stores.

      Evidence acquisition

      A systematic search was conducted on April 6, 2016, according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, for published studies that evaluated health-related apps targeted at consumers in a commercial app store, and a descriptive content analysis of their methods was performed.

      Evidence synthesis

      Although the majority of studies self-identified as “reviews” or “systematic reviews,” their claims to have performed an exhaustive, replicable, and systematic search and data extraction are unsupported, given the nature of commercial app stores’ search engines and personalized app content. Currently, studies of health-related app content are largely descriptive, and most use surrogate and one-dimensional outcome measures for app content quality.

      Conclusions

      This field is rapidly developing, and innovative methods for systematic searches, multidimensional quality assessment, and contextualizing content assessment are emerging. Areas for future methodologic development are highlighted, including expanding definitions of app “content” and assessing risk of bias.
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