The promise and challenges of computer mouse trajectories in DMHIs – A feasibility study on pre-treatment dropout predictions

With the impetus of Digital Mental Health Interventions (DMHIs), complex data can be leveraged to improve and personalize mental health care. However, most approaches rely on a very limited number of often costly features. Computer mouse trajectories can be unobtrusively and cost-efficiently gathered and seamlessly integrated into current baseline processes. Empirical evidence suggests that mouse movements hold information on user motivation and attention, both valuable aspects otherwise difficult to measure at scale. Further, mouse trajectories can already be collected on pre-treatment questionnaires, making them a promising candidate for early predictions informing treatment allocation. Therefore, this paper discusses how to collect and process mouse trajectory data on questionnaires in DMHIs. Covering different complexity levels, we combine hand-crafted features with non-sequential machine learning models, as well as spatiotemporal raw mouse data with state-of-the-art sequential neural networks. The data processing pipeline for the latter includes task-specific pre-processing to convert the variable length trajectories into a single prediction per user. As a feasibility study, we collected mouse trajectory data from 183 patients filling out a pre-intervention depression questionnaire. While the hand-crafted features slightly improve baseline predictions, the spatiotemporal models underperform. However, considering our small data set size, we propose more research to investigate the potential value of this novel and promising data type and provide the necessary steps and open-source code to do so.