App Usage Factor: A Simple Metric to Compare the Population Impact of Mobile Medical Apps
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.
Cell Phones, Humans, Mobile Applications, Programming Languages, Risk Assessment, Telemedicine, Comparative Study, Journal Article, Research Support, Non-U.S. Gov't
e200
Lewis, Thomas Lorchan
eaa659dd-0d62-4bf8-84f7-3aae1759ad3a
Wyatt, Jeremy C
8361be5a-fca9-4acf-b3d2-7ce04126f468
19 August 2015
Lewis, Thomas Lorchan
eaa659dd-0d62-4bf8-84f7-3aae1759ad3a
Wyatt, Jeremy C
8361be5a-fca9-4acf-b3d2-7ce04126f468
Lewis, Thomas Lorchan and Wyatt, Jeremy C
(2015)
App Usage Factor: A Simple Metric to Compare the Population Impact of Mobile Medical Apps.
Journal of Medical Internet Research, 17 (8), .
(doi:10.2196/jmir.4284).
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.
This record has no associated files available for download.
More information
Published date: 19 August 2015
Keywords:
Cell Phones, Humans, Mobile Applications, Programming Languages, Risk Assessment, Telemedicine, Comparative Study, Journal Article, Research Support, Non-U.S. Gov't
Organisations:
Wessex Institute
Identifiers
Local EPrints ID: 408682
URI: http://eprints.soton.ac.uk/id/eprint/408682
ISSN: 1438-8871
PURE UUID: 53e97898-30c1-4ebf-b20f-e1cf1289c307
Catalogue record
Date deposited: 26 May 2017 04:02
Last modified: 16 Mar 2024 04:23
Export record
Altmetrics
Contributors
Author:
Thomas Lorchan Lewis
Author:
Jeremy C Wyatt
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics