App usage factor: A simple metric to compare the population impact of mobile medical apps

Background: one factor when assessing the quality of mobile apps is quantifying the impact of a given app on a population. there is currently no metric which can be used to compare the population impact of a mobile app across different health care disciplines. objective: the objective of this study is to create a novel metric to characterize the impact of a mobile app on a population. methods: we developed the simple novel metric,app usage factor (auf),defined as the logarithm of the product of the number of active users of a mobile app with the median number of daily uses of the app. the behavior of this metric was modeled using simulated modeling in python,a general-purpose programming language. three simulations were conducted to explore the temporal and numerical stability of our metric and a simulated app ecosystem model using a simulated dataset of 20,000 apps. results: simulations confirmed the metric was stable between predicted usage limits and remained stable at extremes of these limits. analysis of a simulated dataset of 20,000 apps calculated an average value for the app usage factor of 4.90 (sd 0.78). a temporal simulation showed that the metric remained stable over time and suitable limits for its use were identified. conclusions: a key component when assessing app risk and potential harm is understanding the potential population impact of each mobile app. our metric has many potential uses for a wide range of stakeholders in the app ecosystem,including users,regulators,developers,and health care professionals. furthermore,this metric forms part of the overall estimate of risk and potential for harm or benefit posed by a mobile medical app. we identify the merits and limitations of this metric,as well as potential avenues for future validation and research. ©thomas lorchan lewis,jeremy c wyatt.