Using location-based services to improve mental health interventions

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe rapid developments in the functionalities of smartphones and technological innovations play a vital role in providing location-based services in healthcare. A mental health sensor-based software platform has been developed by the Geospatial Technologies research group (Geotec), consisting of an application generation framework that offers basic geospatial building blocks (location tracking, trajectory recording, geo-fencing), communication building blocks (notifications) and a basic visualization of collected data for therapists. The framework has been successfully tested for building an application to treat agoraphobia, addiction, and depression, using location-based notifications. However, defining the places of interest for a patient is addressed to a limited extent only. Thus, therapists have difficulties of identifying and defining multiple places of interest, and the generated apps were therefore mostly limited to single places of interest, which were manually defined. Hence, they are difficult to use in larger areas. This thesis aims to use a location-based service to support therapists in defining places of interest, based on location and place categories. The work is carried out as an extension of the SYMPTOMS platform, and it allows therapists to define multiple places of interest automatically and for larger areas. The added value of the approach (in terms of automation, ease of use, and universally usable of therapies) by the location-based services in improving mental health interventions is evaluated. As a result, the application was found to be usable with SUS score of 91.875 and useful for therapists to define multiple places of interest at the same time which simplifies the configuration process and makes therapies universally usable. Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 1, 2, 2 (input data, pre-processing, methods, computational environment, results)