Biofeedback Videogames for Anxiety Regulation

Project Lead Category Project status
Joanneke Weerdmeester Anxiety Data Collection

This project aims to develop and assess the use of biofeedback videogames to help youth cope with stress and anxiety. In addition it aims to identify physiological markers and patterns of emotion regulation. The current studies within this project focus on exploring the potential of the virtual reality biofeedback game DEEP where players use deep diaphragmatic breathing to move through a beautiful underwater world.

Project team

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15-12-2017

Biofeedback is the process of measuring an individual’s physiological states and feeding back that information to them in order to help them gain awareness and control over their physiological activity for the purpose of health improvement [1]. Biofeedback has an extensive history in scientific literature and has already been shown to be efficacious as a treatment for a large variety of physical [2-4], as well as mental health issues including stress and anxiety [5-7]. However, it is still not widely implemented in standard treatment programs [19] and mechanisms through which biofeedback works remain largely unexplored. To address these gaps, the current project is aimed at assessing the potential of novel, interactive biofeedback interventions as well as identifying important factors that could contribute to its efficacy.

Current studies focus on assessing the potential of DEEP, a biofeedback-based virtual reality (VR) game. In this game, players move through an enchanting underwater world by using their own breath. The game is developed by Owen Harris and Niki Smit (Monobanda PLAY) to provide players with a sanctuary where they can de-stress. Within this project, the game is currently being further developed and validated as a possible intervention for children with anxiety. DEEP stimulates players (through biofeedback) to use deep, diaphragmatic breathing. It is played by using a VR headset combined with a customized belt that measures the expansion of the diaphragm. The values of this sensor influence the game and are reflected in feedback in various ways. First of all, the player is informed about their breathing by way of their movement. Breathing in deeply gives the players an upward (when close to the ground) or forward force, and these forces are strengthened by deeply breathing out. The player is also given various forms of visual feedback. For example, a circle in the center of the screen shrinks and expands in accordance to the expansion of the player's diaphragm. In addition, elements in the environment (such as plants) change color or change in size/movement, mirroring the player's breathing. By giving this (bio)feedback to the player, they can become more aware of their breathing and are stimulated to breath in a deep and calm manner. 

We believe that biofeedback games like DEEP have a lot of potential to be used as a tool for youth with anxiety. One core aspect of anxiety in youth is heightened physiological reactivity to stress and fear signals in the environment, influencing for instance heart rate and breathing. [8-9]. The core mechanism of DEEP, specifically the stimulation of deep, calm, diaphragmatic breathing, nicely reflects common techniques that are used to regulate stress [10-11] and that are used in current evidence-based anxiety treatments [8-9, 12-13]. In addition to the focus on breathing, games like DEEP offer, in comparison to conventional treatments, a fun and interactive way of learning, which can increase children’s motivation, thereby possibly increasing adherence[14]. The virtual reality environment and biofeedback also helps to fully embody newly learned skills[15]. A first pilot study has already shown promising results in children (N=86) between the ages of 8-12 [16]. However, more research is needed to assess the potential of DEEP (and biofeedback games in general). Within this project, DEEP will be further developed by implementing and rigorously testing evidence-based techniques in future prototypes such as (visual) feedback, modeling [17]  and exposure [18]. 


Publications

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Project team

Joanneke Weerdmeester title=
Joanneke Weerdmeester

Researcher, psychologist, PhD-candidate, gamer, language enthusiast and lover of all things nerdy.

Function

PhD-Candidate

Contact

E-mail Joanneke

Isabela Granic title=
Isabela Granic

Professor and Chair of the Developmental Psychopathology department in the Behavioural Science Institute; writer; voracious podcast consumer; mother of two upstanding little gamers

Function

Director of GEMH Lab

Contact

E-mail Isabela

Rutger Engels title=
Rutger Engels

Function

Chairman at Trimbos Instituut

Contact

E-mail Rutger

Marieke van Rooij title=
Marieke van Rooij

Assistant prof. and data geek at the GEMH lab, dynamical modelling, personalisation, wants to put the I back into AI, news junkie, cat lover.

Function

Assistant Professor

Contact

E-mail Marieke

Tom Hollenstein title=
Tom Hollenstein

Associate Professor in Developmental Psychology at Queen's University in Kingston, Ontario, Canada.

Function

Associate Professor - Collaborator

Contact

E-mail Tom

Sources

All sources
  1. Gilbert, C. & Moss, D (2003). Biofeedback and biological monitoring. Handbook of mind-body medicine in primary care: Behavioral and physiological tools, 109-122.
  2. Gevirtz, R. (2013). The promise of heart rate variability biofeedback: Evidence-based applications. Biofeedback, 41(3), 110-120.
  3. Kaushik, R. M. (2007). Biofeedback in Medicine. Retrieved 20 February, 2017 from http://apiindia.org/pdf/pg_med_2007/Chapter-3.pdf
  4. Wheat, A. L., & Larkin, K. T. (2010). Biofeedback of heart rate variability and related physiology: A critical review. Applied psychophysiology and biofeedback, 35(3), 229-242.
  5. Lantyer, A. D. S., Viana, M. D. B., & Padovani, R. D. C. (2013). Biofeedback in the treatment of stress and anxiety-related disorders: a critical review. Psico-USF, 18(1), 131-140.
  6. Schoenberg, P. L., & David, A. S. (2014). Biofeedback for psychiatric disorders: a systematic review. Applied psychophysiology and biofeedback, 39(2), 109-135.
  7. Yucha, C., & Montgomery, D. (2008). Evidence-based practice in biofeedback and neurofeedback. Wheat Ridge, CO: AAPB.
  8. Silverman, W. K., Pina, A. A., & Viswesvaran, C. (2008). Evidence-based psychosocial treatments for phobic and anxiety disorders in children and adolescents. Journal of Clinical Child & Adolescent Psychology, 37(1), 105-130.
  9. Karver, M. S., Handelsman, J. B., Fields, S., & Bickman, L. (2006). Meta-analysis of therapeutic relationship variables in youth and family therapy: The evidence for different relationship variables in the child and adolescent treatment outcome literature. Clinical psychology review, 26(1), 50-65.
  10. Fried, R. (1993). The psychology and physiology of breathing: In behavioral medicine, clinical psychology, and psychiatry. Springer Science & Business Media.
  11. Sovik, R. (1999). The science of breathing—the yogic view. Progress in brain research, 122, 491-505.
  12. Kendall, P. C. (1994). Treating anxiety disorders in children: Results of a randomized clinical trial. Journal of consulting and clinical psychology, 62(1), 100.
  13. Kendall, P. C., Flannery-Schroeder, E., Panichelli-Mindel, S. M., Southam-Gerow, M., Henin, A., & Warman, M. (1997). Therapy for youths with anxiety disorders: A second randomized clincal trial. Journal of consulting and clinical psychology, 65(3), 366.
  14. Karver, M. S., Handelsman, J. B., Fields, S., & Bickman, L. (2006). Meta-analysis of therapeutic relationship variables in youth and family therapy: The evidence for different relationship variables in the child and adolescent treatment outcome literature. Clinical psychology review, 26(1), 50-65.
  15. Glenberg, A. M. (2008). Embodiment for education. Handbook of cognitive science: An embodied approach, 355-372.
  16. van Rooij, M., Lobel, A., Harris, O., Smit, N., & Granic, I. 2016. DEEP: A Biofeedback Virtual Reality Game for Children At-risk for Anxiety. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 1989-1997). ACM.
  17. Bandura, A. (1977). Social learning theory. Oxford, England: Prentice-Hall.
  18. Seligman, L.D., Swedish, EF., & Flannery-Schroeder, E. (2014). Anxiety Asessment and Treatment in Typically Developing Children. In Davis III, Thompson E., White, Susan W., Ollendick, Thomas H. (Eds.), Handbook of Autism and Anxiety (pp 61-71). Switzerland: Springer International Publishing.
  19. Kessler, R. C., Soukup, J., Davis, R. B., Foster, D. F., Wilkey, S. A., Van Rompay, M. I., & Eisenberg, D. M. (2001). The use of complementary and alternative therapies to treat anxiety and depression in the United States. American Journal of Psychiatry, 158(2), 289-294.


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