Crowdsourcing Users’ Comments for Clinical and Operational Features Analysis of Diabetes Mobile Apps

Date
2021-01-05
Authors
Ossai, Chinedu
Wickramasinghe, Nilmini
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Abstract
Today there exist a plethora of mobile apps focused on diabetes self-management. To understand the rate of inclusion and influences of these numerous diabetes mobile apps (DMAS), we crowdsourced and analyzed negative users’ comments and the design features of numerous apps, underpinned by fit viability as the theoretical analysis lens. Thus, by concentrating our efforts on apps written in English collected from google play and apple app store, we identified and classified DMAS as a health monitoring app (HMAS) and information repository apps (IRAS), and statistically determined the effects of different diabetes self-management indicators on their functionalities. Our results affirm that these solutions have limited functionalities to facilitate self-management of diabetes due to poor design which hinders intelligent decision support, as well as limits inclusion and performance of wellness support features. In addition, many of these apps are also operationally inefficient.
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ICT-enabled Self-management of Chronic Diseases and Conditions, crowdsourcing, data analytics, decision support, diabetes mobile apps, self-management
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