What is Moodbuster?

The Moodbuster platform is a research platform: the treatments take place in the context of research projects (see also: Projects). The platform consists of a web portal for patients and practitioners and a mobile application with which all kinds of things can be measured such as mood or daily activities (also called Ecological Momentary Assessment). Moodbuster can be used for both prevention and treatment, from self-help tool to guided online treatment or in combination with face-to-face therapy (blended treatment).

At present, Moodbuster consists of a cognitive behavioural therapy treatment for depression. There are 6 online modules, aimed at explaining depression, learning to think more positively, planning enjoyable activities structurally, solving problems associated with gloom and more exercise. The modules are structured. Texts and videos guide the user through the module, using exercises  whike homework assignments are  given.

Moodbuster is available in various languages ​​including Dutch (and Flemish), English, French, German, Portuguese and Polish.

MoodBuster 2.0

Moodbuster 2.0 consists of a content management system as well as the treatment platform. This system offers researchers and practitioners the opportunity to expand Moodbuster for depression and to make it suitable for other psychological problems such as anxiety disorders. 

Who can benefit from using  Moodbuster?

Moodbuster as a self-help tool

The Moodbuster as a self-help variant making it possible for users to work independently, without the intervention of a care provider, while reducing their complaints and seeking solutions. The user determines where and when he / she does that. This always takes place in the context of research.

Moodbuster in guided therapy

Moodbuster now consists of 6 online modules. They can be used for online treatment or in combination with face-to-face therapy. We call this combination of conversations with a practitioner and online treatment sessions a ‘blended’ treatment. This blended treatment has been investigated and found to be clinically effective (see research).

With a blended treatment, conversations with the practitioner and online sessions alternate. The conversations with the practitioner take place at the treatment location and the user conducts the online sessions independently on  the computer at home. The practitioner then provides online feedback.

Projects

Mood Buster has been developed, evaluated and used in ongoing and completed projects:

 

.

Moodbuster 1.0 has been developed within the European ICT4DEPRESSION (7th Framework). This project was completed in 2012. For more information about the development process, the partners and the results, see: www.ict4depression.eu

.

E-COMPARED: the ‘European Comparative Effectiveness Research on Internet-based Depression Treatment’ project evaluated the clinical effectiveness and cost-effectiveness of blended treatment of major depression in adults compared to regular treatment within regular care in 9 European countries, 5 of which have worked with Moodbuster. This project was successfully completed in 2017. (www.e-compared.eu)

.

eMen: This is an EU / INTERREG project that aims to improve e-mental health innovation and implementation through collaboration between private and public parties in the Netherlands, England, Germany, Belgium and France. Within this project, Moodbuster is being further developed and evaluated for use in primary care (unaccompanied) and within a hospital setting (supervised). This project runs until 2020. For more information: www.nweurope.eu/….emen

Protocol papers RCT’s in which Moodbuster has been applied

  1. Kemmeren, L. L., van Schaik, D. J., , Kleiboer, A. M., Bosmans, J. E., & Smit, J. H. (2016). Effectiveness of blended depression treatment for adults in specialised mental healthcare: Study protocol for a randomised controlled trial. BMC Psychiatry, 16(1), 113.doi:10.1186/s12888-016-0818-5
  2. 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., & . (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), 387. doi:10.1186/s13063-016-1511-1

Result papers in which Moodbuster has been featured

  1. Breedvelt, J. J., Zamperoni, V., Kessler, D., , Kleiboer, A. M., Elliott, I., Abel K., Gilbody S., &Bockting, C. L. (2019). GPs’ attitudes towards digital technologies for depression: an online survey in primary care. British Journal of General Practice, 69(680), e164-e170. doi:10.3399/bjgp18X700721
  2. Bremer, V., Funk, B., &  (2019). Heterogeneity Matters: Predicting Self-Esteem in Online Interventions Based on Ecological Momentary Assessment Data. Depression research and treatment, 2019. doi:10.1155/2019/3481624
  3. 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. doi:10.1016/j.invent.2018.03.002
  4. Mikus, A., Hoogendoorn, M., Rocha, A., Gama, J., Ruwaard, J., & . (2018). Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data. Internet Interventions, 12, 105-110. doi:10.1016/j.invent.2017.10.001
  5. Rocha, A., Camacho, R., Ruwaard, J., & . (2018). Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data. Internet Interventions, 12, 176-180. doi:10.1016/j.invent.2018.03.003
  6. Van de Ven, P., O’Brien, H., Henriques, R., Klein, M., Msetfi, R., Nelson, J., … & , on behalf of the E-COMPARED Consortium. (2017). ULTEMAT: A mobile framework for smart ecological momentary assessments and interventions. Internet Interventions, 9, 74-81. doi:10.1016/j.invent.2017.07.001
  7. Becker, D., Bremer, V., Funk, B., Asselbergs, J., Riper, H., & Ruwaard, J. (2016). How to predict mood? Delving into features of smartphone-based data. Proceedings of the Twenty-second Americas Conference on Information Systems (AMCIS 2016), San Diego, USA. https://aisel.aisnet.org/amcis2016/Health/Presentations/20/
  8. Van Breda, W., Pastor, J., Hoogendoorn, M., Ruwaard, J., Asselbergs, J., & . (2016). Exploring and 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. https://link.springer.com/chapter/10.1007/978-3-319-39687-3_4
  9. Abro, A. H., Klein, M. C., & Tabatabaei, S. A. (2015). An agent-based model for the role of social support in mood regulation. In Highlights of Practical Applications of Agents Multi-Agent Systems, and Sustainability. Springer International Publishing. https://link.springer.com/chapter/10.1007%2F978-3-319-19033-4_2
  10. Kop, R., Hoogendoorn, M., & Klein, M. C. (2014). A personalized support agent for depressed patients: Forecasting patient behavior using a mood and coping model. In 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)(Vol. 3, pp. 302-309). IEEE. https://research.vu.nl/…patients.pdf
  11. Warmerdam, L., Riper, Klein, M., van den Ven P., Rocha, A., Ricardo Henriques, M., Tousset, E., Silva, H., Andersson, G., & Cuijpers, P. (2012). Innovative ICT solutions to improve treatment outcomes for depression: The ICT4Depression project. Studies in Health Technology and Informatics,181, 339-43. doi:10.3233/978-1-61499-121-2-339

About Us

Contact

For more information or questions about Moodbuster you can contact Khadicha Amarti,

tel +31 (0) 20 59 83348
email info@moodbuster.science

Moodbuster Project Team

FAQ

Frequently Asked Questions

E-mental health is the use of ICT to support mental health care. It can be used for prevention, treatment and aftercare. There are many different forms of e-mental health, such as online self-help, guided self-help or blended treatments.

The modules that are currently available are aimed at treating depression. These are based on cognitive behavioural therapy (CBT). A form of therapy in which the user investigates how thoughts, feelings and behaviour are related. CBT is an effective treatment for depression, even when it is offered via the internet.

In the short term, modules for other psychological complaints will also be made available on the platform, such as modules for anxiety complaints.

With a blended treatment, conversations with the practitioner and online sessions alternate. The conversations with the practitioner take place at the treatment location and the user conducts online sessions independently at home on the computer. The practitioner then provides online feedback.

Moodbuster is now available in Dutch, English, German and French.