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 stateof-
the-art sequential neural networks. The data processing pipeline for the latter includes task-specific preprocessing
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.