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Data mining in the development of mobile health apps : assessing in-app navigation through Markov chain analysis

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Abstract
Background: 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. 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. Objective: The 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. Methods: Using 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. Results: Through 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. Conclusions: Using 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.
Keywords
eHealth, mHealth, Markov Chain, log data, data analytics, GAMIFICATION

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MLA
Stragier, Jeroen et al. “Data Mining in the Development of Mobile Health Apps : Assessing In-app Navigation Through Markov Chain Analysis.” JOURNAL OF MEDICAL INTERNET RESEARCH 21.6 (2019): n. pag. Print.
APA
Stragier, J., Vandewiele, G., Coppens, P., Ongenae, F., Van den Broeck, W., De Turck, F., & De Marez, L. (2019). Data mining in the development of mobile health apps : assessing in-app navigation through Markov chain analysis. JOURNAL OF MEDICAL INTERNET RESEARCH, 21(6).
Chicago author-date
Stragier, Jeroen, Gilles Vandewiele, Paulien Coppens, Femke Ongenae, Wendy Van den Broeck, Filip De Turck, and Lieven De Marez. 2019. “Data Mining in the Development of Mobile Health Apps : Assessing In-app Navigation Through Markov Chain Analysis.” Journal of Medical Internet Research 21 (6).
Chicago author-date (all authors)
Stragier, Jeroen, Gilles Vandewiele, Paulien Coppens, Femke Ongenae, Wendy Van den Broeck, Filip De Turck, and Lieven De Marez. 2019. “Data Mining in the Development of Mobile Health Apps : Assessing In-app Navigation Through Markov Chain Analysis.” Journal of Medical Internet Research 21 (6).
Vancouver
1.
Stragier J, Vandewiele G, Coppens P, Ongenae F, Van den Broeck W, De Turck F, et al. Data mining in the development of mobile health apps : assessing in-app navigation through Markov chain analysis. JOURNAL OF MEDICAL INTERNET RESEARCH. 2019;21(6).
IEEE
[1]
J. Stragier et al., “Data mining in the development of mobile health apps : assessing in-app navigation through Markov chain analysis,” JOURNAL OF MEDICAL INTERNET RESEARCH, vol. 21, no. 6, 2019.
@article{8619094,
  abstract     = {Background: 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. 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.
Objective: The 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.
Methods: Using 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.
Results: Through 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.
Conclusions: Using 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.},
  articleno    = {e11934},
  author       = {Stragier, Jeroen and Vandewiele, Gilles and Coppens, Paulien and Ongenae, Femke and Van den Broeck, Wendy and De Turck, Filip and De Marez, Lieven},
  issn         = {1438-8871},
  journal      = {JOURNAL OF MEDICAL INTERNET RESEARCH},
  keywords     = {eHealth,mHealth,Markov Chain,log data,data analytics,GAMIFICATION},
  language     = {eng},
  number       = {6},
  pages        = {12},
  title        = {Data mining in the development of mobile health apps : assessing in-app navigation through Markov chain analysis},
  url          = {http://dx.doi.org/10.2196/11934},
  volume       = {21},
  year         = {2019},
}

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