Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Aug 16, 2018
Open Peer Review Period: Aug 16, 2018 - Oct 11, 2018
Date Accepted: Mar 14, 2019
(closed for review but you can still tweet)
Data mining in the development of mHealth apps: assessing in-app navigation through Markov Chain analysis
ABSTRACT
Background:
Background:
Mobile applications generate vast amounts of user data. In the mHealth domain, researchers are increasingly discovering the opportunities of these data to assess the engagement levels of their developed mobile applications. To date however, the analysis of these data is often limited to descriptive statistics. Using the right data mining techniques, application log data can offer significantly deeper insights.
Objective:
Objective:
The purpose of this study was to assess how more advanced data mining techniques offer an opportunity to dig deeper into the data and afford to discover application mHealth app usage patterns using Markov Chain and sequence clustering analysis.
Methods:
Methods:
A transition matrix between the nine pages of the app was composed from which a Markov Chain was constructed, enabling intuitive user behavior analysis.
Results:
Results:
Five session types of app use were distinguished through the analysis, two of which represented usage of the main intended functions as envisioned by the developers. The two main functions were further automatically reconstructed by means of sequence clustering.
Conclusions:
Conclusions:
Using Markov Chains to assess in-app navigation presents an innovative method to evaluate mHealth interventions. The insights can be used to improve the navigation in the app, the flow between behavior change techniques and placement of features in the app.
Citation
Per the author's request the PDF is not available.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.