Original Title	Supplementary Material for the Paper "The Promise and Challenges of Computer Mouse Trajectories in DMHIs - A Feasibility Study on Pre-Treatment Dropout Predictions"
Handle	20.500.14123/1743
Kinds of Data	Programs and Applications Context Materials / Supporting information
Resource Type	Dataset
Creator	Zantvoort, Kirsten  0000-0001-9876-054X (Institut für Wirtschaftsinformatik (IIS), Leuphana Universität Lüneburg  02w2y2t16) Matthiesen, Jennifer  0000-0003-0344-9682 (Institut für Wirtschaftsinformatik (IIS), Leuphana Universität Lüneburg  02w2y2t16) Bjurner, Pontus  0000-0002-4967-9128 (Karolinska Institutet  056d84691) Bendix, Marie  0000-0001-8901-166X (Karolinska Institutet  056d84691) Funk, Burkhardt  0000-0001-5855-2666 (Institut für Wirtschaftsinformatik (IIS), Leuphana Universität Lüneburg  02w2y2t16) Kaldo, Viktor  0000-0002-6443-5279 (Karolinska Institutet  056d84691)
Description of the Dataset	Empirical evidence suggests that how one moves their mouse holds information on 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 study investigates how to gather and process mouse trajectory data on questionnaires in Digital Mental Health Interventions (DMHI). As a feasibility study, the researchers collected mouse trajectory data from 183 patients filling out a pre-intervention depression questionnaire.
Methods	Description Programming / Script-based data collection
Keywords	Data Science; Computermaus; Nutzerverhalten; Prognose; Digitale Gesundheit; Mentale Gesundheit; Intervention; Motivation; Aufmerksamkeit; Algorithmus; Data Science; Computer Mouse; User Behavior; Prediction; Digital Health; Mental Health; Intervention; Motivation; Attention; Algorithm
Thematic Classification	Nutzerverhalten
Published by	Medien- und Informationszentrum, Leuphana Universität Lüneburg
Superordinate Data Collection	× Superordinate Data Collection Close Supplementary Material PhD Kirsten Zantvoort
Related Resources	× Relations of the dataset Close Relations of the dataset