TY  - JOUR
AU  - Stragier, Jeroen
AU  - Vandewiele, Gilles
AU  - Coppens, Paulien
AU  - Ongenae, Femke
AU  - Van den Broeck, Wendy
AU  - De Turck, Filip
AU  - De Marez, Lieven
PY  - 2019
DA  - 2019/06/07
TI  - Data Mining in the Development of Mobile Health Apps: Assessing In-App Navigation Through Markov Chain Analysis
JO  - J Med Internet Res
SP  - e11934
VL  - 21
IS  - 6
KW  - eHealth
KW  - mHealth
KW  - Markov Chain
KW  - log data
KW  - data analytics
AB  - 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2019/6/e11934/
UR  - https://doi.org/10.2196/11934
UR  - http://www.ncbi.nlm.nih.gov/pubmed/31237838
DO  - 10.2196/11934
ID  - info:doi/10.2196/11934
ER  -