Mobile Health Monitor

The University of Maastricht has rolled out a mobile application with the help of ThinkNexT enabling the easy capture of mental health data. The app and its backend systems handle 3000 simultaneous users, in 200 studies, across 20 research institutes world wide.


PsyMate – University of Maastricht

The PsyMate® is a mobile app, conceived by the University of Maastricht, facilitating the monitoring of daily life experience and behaviour. PsyMate® is a tool to apply momentary assessment technology to mental health practice. It can be programmed to generate beeps at unpredictable moments of the day and participants use a touch screen to fill out small questionnaires on current mood state, social context and activities in response to a beep signal.

Reporting application

Supporting the mobile app, a reporting tool was built by ThinkNexT. The reporting tool offers insights and analysis of patient data by medical personnel. The tool has real time access to the data and visualizes all data into easy-to-understand graphs.




The mobile app is developed for iPhone and Android using PhoneGap. App deployment is done through Apple’s App Store and Google Play Store.


The medical data is protected with industry level encryption standards.

Application technology

All ThinkNexT applications are Java based. They use the Tapestry web framework in combination with Hibernate for database connectivity. Hibernate allows us to build our applications on top of any type of database whether that is MySQL, MSSQL, Oracle or others.

Testing and deployment

Continuous integration techniques, based on Jenkins, are used to test the applications. Continuous deployment driven by Maven and custom technology is integrated in the application. That gives us the advantage to upgrade the application in less than a minute with just a push on a button.


Transactionality is implemented on the level of jobs: either a job succeeds and all data is changed consistently or it fails and the data is rolled back to the previous consistent state. That approach follows the unit of work pattern. Import and export of file based data is implemented using a two-phased commit protocol.


The application is being centrally monitored by our custom monitoring system Logness. Logness monitors application availability and stores the application logs. Automated pattern analysis is performed on error and warning logs. This allows us to preemptively solve problems.