Publications

Abro, A. H., Klein, M. C. A., & Tabatabaei, S. A. (2015). An agent-based model for the role of social support in mood regulation. In Bajo, J., Hallenborg, K., Pawlewski, P., Botti, V., Sánchez-Pi, N., Méndez, N. D. D., … & Julian, V. (Eds.), Highlights of Practical Applications of Agents Multi-Agent Systems, and Sustainability (pp. 15-27). Cham, Switzerland: Springer International Publishing.

Abro, A. H., Klein, M. C., Manzoor, A. R., Tabatabaei, S. A., & Treur, J. (2014, November). A computational model of the relation between regulation of negative emotions and mood. In Loo, C. K., Yap, K. S., Wong, K. W., Teoh, A., & Huang, K. (Eds.), International Conference on Neural Information Processing (pp. 59-68). Cham, Switzerland: Springer International Publishing.

Becker, D., Bremer, V., Funk, B., Asselbergs, J., Riper, H., & Ruwaard, J. (2016, August). How to predict mood? Delving into features of smartphone-based data. In Proceedings of the Twenty-second Americas Conference on Information Systems (AMCIS). San Diego, USA: AIS electronic library, 2016.

Becker, D., van Breda, W., Funk, B., Hoogendoorn, M., Ruwaard, J., & Riper, H. (2018). Predictive modeling in e-mental health: A common language framework. Internet Interventions, 12, 57-67.

van Breda, W., Hoogendoorn, M., Eiben, A. E., & Berking, M., (2015, June). An evaluation framework for the comparison of fine-grained predictive models in health care. In Holmes, J. H., Bellazzi, R., Sacchi, L., & Peek, N. (Eds.), Conference on Artificial Intelligence in Medicine in Europe (AIME) (pp. 148-152). Cham, Switzerland: Springer International Publishing.

van Breda, W., Hoogendoorn, M., Eiben, A., Andersson, G., Riper, H., Ruwaard, J., & Vernmark, K. (2016). A feature representation learning method for temporal datasets. Processdings of the 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Athens, Greece. LINK

van Breda, W., Pastor, J., Hoogendoorn, M., Ruwaard, J., Asselbergs, J., & Riper, H. (2016, June). Exploringand comparing machine learning approaches for predicting mood over time. In Chen, Y., Tanaka, S., Howlett, R. J., & Lakhmi, C. J. (Eds.), International Conference on Innovation in Medicine and Healthcare (pp. 37-47). Cham, Switzerland: Springer International Publishing.

van Breda, W., Bremer, V., Becker, D., Hoogendoorn, M., Funk, B., Ruwaard, J., & Riper, H. (2017). Predicting therapy success for treatment as usual and blended treatment in the domain of depression. Internet Interventions, 12, 100-104.

Bremer, V., Mukherjee, A., Becker, D., Kolovos, S., Funk, B., Breda, W.V., Hoogendoorn, M., & Riper, H. (2018). Predicting therapy success and costs using baseline characteristics – An Approach for personalized treatment recommendations.

Bremer, V. & Funk, B. (2017). Heterogeneity matters: Predicting self-esteem in online interventions.  In  Thirty eighth International Conference on Information Systems, Seoul. San Diego, USA: AIS electronic library, 2016.

Ebert, D., Donkin, L., Andersson, G., Andrews, G., Berger, T., Carlbring, P., . . . Johansson, R. (2016). Does Internet-based guided-self-help for depression cause harm? An individual participant data meta-analysis on deterioration rates and its moderators in randomized controlled trials. Psychological Medicine, 46(13), 2679-2693. doi: 10.1017/S0033291716001562

Karyotaki, E., Kemmeren, L., Riper, H., Twisk, J., Hoogendoorn, A., Kleiboer, A., … & Littlewood, E. (2018). Is self-guided internet-based cognitive behavioural therapy (iCBT) harmful? An individual participant data meta-analysis. Psychological Medicine, 1-11.

Karyotaki, E., Riper, H., & Cuijpers, P. (2017). Internet-based self-help interventions for depression in routine care—Reply. JAMA Psychiatry, 74(8), 853.

Karyotaki, E., Kleiboer, A., Smit, F., Turner, D. T., Pastor, A. M., Andersson, G., … & Cuijpers, P. (2015). Predictors of treatment dropout in self-guided web-based interventions for depression: an ‘individual patient data’ meta-analysis. Psychological Medicine, 45(13), 2717-2726.

Karyotaki E., Riper H., Twisk J., Hoogendoorn, A., Kleiboer, A., Mira, A., Mackinnon, A., Meyer, B., Botella, C., Littlewood, E., Andersson, G., Christensen, H., Klein, J.P., Schröder, J., Bretón-López, J., Scheider, J., Griffiths, K., Farrer, L., Huibers, M.J.H., Phillips, R., Gilbody, S., Moritz, S., Berger, T., Pop, V., Spek, V., & Cuijpers, P. (2017, in press). Efficacy of Self-guided Internet-Based Cognitive Behavioral Therapy in the Treatment of Depressive Symptoms: A Meta-analysis of Individual Participant Data . JAMA Psychiatry. doi:10.1001/jamapsychiatry.2017.0044

Kemmeren  L., van Schaik  D., Riper  H., Kleiboer  A., Bosmans  J., & Smit  J., (2016). Effectiveness of blended depression treatment for adults in specialised mental healthcare: study protocol for a randomised controlled trial. BMC Psychiatry, 16, 113. doi:10.1186/s12888-016-0818-5   

Kleiboer, A., Smit, J., Bosmans, J., Ruwaard, J., Andersson, G., Topooco, N., Berger, T., Krieger, T., Botella, C., Baños, R., Chevreul, K., Araya, R., Cerga-Pashoja, A., Cieślak, R., Rogala, A., Vis, C., Draisma, S., van Schaik, A., Kemmeren, L., Ebert, D., Berking, M., Funk, B., Cuijpers, P., Riper, H. (2016). European COMPARative Effectiveness research on blended Depression treatment versus treatment-as-usual (E-COMPARED): study protocol for a randomized controlled, non-inferiority trial in eight European countries. Trials, 17, 1-10. doi:10.1186/s13063-016-1511-1

Kolovos, S., Bosmans, J. E., Riper, H., Chevreul, K., Coupé, V. M. H., van Tulder, M. W. (2017). Model-based economic evaluation of treatments for depression: A systematic literature review. Pharmacoecon Open, 1(3), 149-165. doi: 10.1007/s41669-017-0014-7

Kolovos, S., Bosmans, J. E., van Dongen, J. M., van Esveld, B., Magai, D., van Straten, A., van der Feltz-Cornelis, C., van Steenbergen-Weijenburg, K. M., Huijbregts, K. M., van Marwijk, H., Riper, H., & van Tulder, M. W. (2017). Utility scores for different health states related to depression: individual participant data analysis, Quality of Life Research, 1-10. doi:10.1007/s11136-017-1536-2

Kolovos, S., van Dongen, J. M., Riper, H., Buntrock, C., Cuijpers, P., Ebert, D. D., … & Bosmans, J. E. (2018). Cost effectiveness of guided internet‐based interventions for depression in comparison with control conditions: An individual–participant data meta‐analysis. Depression and Anxiety, 35(3), 209-219.

Kop, R., Hoogendoorn, M., and Klein, M.C.A. (2014) A Personalized Support Agent for Depressed Patients: Forecasting Patient Behavior Using a Mood and Coping Model. In: Slezak, D., Dunin-Keplicz, B., Lewis, M., and Terano, T. (eds.), Proceedings of the 2014 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2014), IEEE Computer Society Press, 2014, pp. 302-309. LINK

Mathiasen, K., Andersen, T. E., Riper, H., Kleiboer, A. A., & Roessler, K. K. (2016). Blended CBT versus face-to-face CBT: a randomised non-inferiority trial. BMC Psychiatry, 16(1), 432. doi:10.1186/s12888-016-1140-y

Mikus, M., Hoogendoom, M., Rocha, A., Gama, J., Ruwaard, J., & Riper, H. (2018). Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data. Internet interventions, 12, 105-110.

O’Connell, M. M., Kolovos, S., Bosmans, J. E., Forbes, J. F., & E-COMPARED Consortium. (2017). Discrete event simulation modelling of long term cost-effectiveness of internet-based blended cognitive behavioural therapy for major depressive disorder: Extrapolation of the E-Compared randomised controlled trial. Value in Health, 20(9), A403.

Rocha, A., Camacho, R., Ruwaard, J., & Riper, H. (2018). Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data. Internet Interventions, 12, 176-180. doi.org/10.1016/j.invent.2018.03.003

Titzler, I., Saruhanjan, K., Berking, M., Riper, H., & Ebert, D. D. (2018). Barriers and facilitators for the implementation of blended psychotherapy for depression: A qualitative pilot study of therapists’ perspective. Internet Interventions. http://doi.org/10.1016/j.invent.2018.01.002

Topooco, N., Riper, H., Araya, R., Berking, M., Brunn, M., Chevreul, K., Cieslak, R., Ebert, D., Etchmendy, E., Herrero, R., Kleiboer, A., Krieger, T., García-Palacios, A., Cerga-Pashoja, A., Smoktunowicz, E., Urech, A., Vis, C., Andersson, G., on behalf of the E-COMPARED consortium (in press, 2017). Attitudes towards digital treatment for depression: A European stakeholder survey. Internet Interventions. doi:10.1016/j.invent.2017.01.001

Urech, A., Krieger, T., Möseneder, L., Biaggi, A., Vincent, A., Poppe, C., Meyer, B., Riper, H., & Berger, T. (2018, accepted). A patient post-hoc perspective on advantages and disadvantages of blended cognitive-behavior therapy for depression: A qualitative content analysis. Psychotherapy Research. DOI:10.1080/10503307.2018.1430910

Van de Ven, P., O’Brien, H., Henriques, R., Klein, M., Msetfi, R., Nelson, J., … & E-COMPARED Consortium. (2017). ULTEMAT: A mobile framework for smart ecological momentary assessments and interventions. Internet Interventions, 9, 74-81.