App Usage Factor: A Simple Metric to Compare the Population Impact of Mobile Medical Apps - PubMed (original) (raw)

Comparative Study

. 2015 Aug 19;17(8):e200.

doi: 10.2196/jmir.4284.

Affiliations

Comparative Study

App Usage Factor: A Simple Metric to Compare the Population Impact of Mobile Medical Apps

Thomas Lorchan Lewis et al. J Med Internet Res. 2015.

Abstract

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.

Keywords: mHealth; medical app; medical informatics apps; metric; mobile app; mobile health; mobile phone; patient safety; population impact; risk assessment.

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Conflict of interest statement

Conflicts of Interest: TLL is a writer and editor for iMedicalApps.com, a website dedicated toward providing news on the integration of mobile technology into medical care and the reviewing of medical apps for mobile devices. He does not consult nor receive reimbursement from app developers or creators. JW has no competing interests to declare.

Figures

Figure 1

Figure 1

A contour plot illustrating the stability of the app usage factor as a function of Au and Du, including determination of metric limits.

Figure 2

Figure 2

A combined scatterplot (input data, left) and histogram (relative frequency of both Au and Du, right) showing the initial sample dataset of 20,000 mobile medical apps.

Figure 3

Figure 3

A histogram showing the frequency distribution of the app usage factor for the sample dataset of 20,000 simulated mobile medical apps, including mean and standard deviation for the data.

Figure 4

Figure 4

A graph showing app usage factor as a function of time for a single mobile app which is subject to a number of simulated app ecosystem events.

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