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Digital mental health
Evaluation of a temporal causal model for predicting the mood of clients in an online therapy
  1. Dennis Becker1,
  2. Vincent Bremer1,
  3. Burkhardt Funk1,
  4. Mark Hoogendoorn2,
  5. Artur Rocha3,
  6. Heleen Riper4
  1. 1 Institute of Information Systems, Leuphana University of Lüneburg, Luneburg, Germany
  2. 2 Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
  3. 3 Centre for Information Systems and Computer Graphics, INESC TEC, Porto, Portugal
  4. 4 Department of Clinical, Neuro- & Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
  1. Correspondence to Mr Dennis Becker, In­sti­tu­te of In­for­ma­ti­on Sys­tems, Leuphana University of Lüneburg, Luneburg 21335, Germany; dbecker{at}


Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity.

Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients’ future mood levels.

Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes.

Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis.

  • predictive modelling
  • temporal causal model
  • mood prediction
  • online treatment

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  • Contributors Implementation of the algorithms and analysis: DB. Writing on the publication: DB, VB, BF. Contributions to the psychological aspect: HR. Initial idea: BF. Data made available: HR. Support in the methodical aspects: MH, AR. Reviewing during the writing process and additional ideas and remarks: MH, HR, AR.

  • Funding The European Comparative Effectiveness Research on Internet-based Depression Treatment (E-COMPARED) is a project with funding from the European Union Seventh Framework Programme (grant agreement No: 603098).

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available upon reasonable request. The utilized data can be made available for researchers upon request and via the web-page: