Abstract
BackgroundMobile apps generate vast amounts of user data. In the mobile health (mHealth) domain, researchers are increasingly discovering the opportunities of log data to assess the usage of their mobile apps. To date, however, the analysis of these data are often limited to descriptive statistics. Using data mining techniques, log data can offer significantly deeper insights.ObjectiveThe purpose of this study was to assess how Markov Chain and sequence clustering analysis can be used to find meaningful usage patterns of mHealth apps.MethodsUsing the data of a 25-day field trial (n=22) of the Start2Cycle app, an app developed to encourage recreational cycling in adults, a transition matrix between the different pages of the app was composed. From this matrix, a Markov Chain was constructed, enabling intuitive user behavior analysis.ResultsThrough visual inspection of the transitions, 3 types of app use could be distinguished (route tracking, gamification, and bug reporting). Markov Chain–based sequence clustering was subsequently used to demonstrate how clusters of session types can otherwise be obtained.ConclusionsUsing Markov Chains to assess in-app navigation presents a sound method to evaluate use of mHealth interventions. The insights can be used to evaluate app use and improve user experience.
Highlights
BackgroundThe development of wearable technology, in health care, medical, and fitness contexts, provides people with devices that give detailed information about various aspects of their health, for example, by registering users’ daily physical activity in terms of step counts, calories, or by providing detailed information about exercise parameters [1].At the same time, mobile phone technology is reaching significant adoption and penetration rates in both developed and developing countries [2]
This study aims to be a methodological example of how this thinking was applied to in-app navigation such that the Markov Chains model the order of page views made by a user within an mobile health (mHealth) app
This study demonstrated the potential of data mining in development and evaluation of mHealth apps
Summary
Mobile phone technology is reaching significant adoption and penetration rates in both developed and developing countries [2]. This offers an excellent platform to reach a large audience with health-oriented mobile phone apps. Most mobile phones contain sensors that afford measurement of parameters similar to those measured by wearables, including accelerometer and global positioning system. Mobile health (mHealth) apps are increasingly available to people worldwide. Mobile apps generate vast amounts of user data. In the mobile health (mHealth) domain, researchers are increasingly discovering the opportunities of log data to assess the usage of their mobile apps. Log data can offer significantly deeper insights
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