Data Mining in the Development of Mobile Health Apps: Assessing In-App Navigation Through Markov Chain Analysis.

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.

Speckle disturbance limit in laser-based cinema projection systems.

In a multi-disciplinary effort, we investigate the level of speckle that can be tolerated in a laser cinema projector based on a quality of experience experiment with movie clips shown to a test audience in a real-life movie theatre setting. We identify a speckle disturbance threshold by statistically analyzing the observers' responses for different values of the amount of speckle, which was monitored using a well-defined speckle measurement method. The analysis shows that the speckle perception of a human observer is not only dependent on the objectively measured amount of speckle, but it is also strongly influenced by the image content. The speckle disturbance limit for movies turns out to be substantially larger than that for still images, and hence is easier to attain.