Skip to main content

The Effectiveness of Social Robots in Stress Management Interventions for University Students

  • Conference paper
  • First Online:
Social Robotics (ICSR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14453 ))

Included in the following conference series:

  • 322 Accesses

Abstract

Stress affects many students, leaving them vulnerable to burnout. Social robots can provide personalized and non-judgmental support for individuals to engage in behavioral and cognitive therapy. This study investigated the effectiveness of a robot-assisted stress management intervention in reducing stress among university students. In a between-subjects design, students practiced a deep breathing exercise, either guided by a Pepper robot or using a laptop. To evaluate the effect of each technology, Galvanic Skin Response (GSR), Perceived Stress Questionnaire (PSQ) and the Unified Theory of Acceptance and Use of Technology (UTAUT) survey were collected. The results from PSQ and GSR showed no difference between the two technologies in reducing stress subjectively and physiologically. However, UTAUT reports indicated that participants in the Robot group were more inclined to use the robot in future practices, and that a more positive impression of the robot contributed to a stronger reduction of their self-reported stress levels.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dhabhar, F.S.: Effects of stress on immune function: the good, the bad, and the beautiful. Immunol. Res. 58(2–3), 193–210 (2014). https://doi.org/10.1007/s12026-014-8517-0

    Article  Google Scholar 

  2. Pascoe, M., Hetrick, E., Parker, A.: The impact of stress on students in secondary school and higher education. Int. J. Adolesc. Youth 25(1), 104–112 (2020)

    Article  Google Scholar 

  3. Boucher, E., et al.: Artificially intelligent chatbots in digital mental health interventions: a review. Expert Rev. Med. Devices 18(sup1), 37–49 (2021)

    Article  Google Scholar 

  4. De Nieva, J.O., Joaquin, J.A., Tan, C.B., Marc Te, R.K., Ong, E.: Investigating students’ use of a mental health chatbot to alleviate academic stress. In: 6th international ACM incooperation HCI and UX conference, pp. 1–10. Association for Computing Machinery, Jakarta & Bandung Indonesia (2020)

    Google Scholar 

  5. Kluge, M.G., et al.: Development of a modular stress management platform (performance edge VR) and a pilot efficacy trial of a biofeedback enhanced training module for controlled breathing. PLoS ONE 16(2), e0245068 (2021)

    Article  Google Scholar 

  6. Lau, N., O’Daffer, A., Colt, S., Joyce, P., Palermo, T. M., McCauley, E., Rosenberg, A., et al.: Android and iphone mobile apps for psychosocial wellness and stress management: systematic search in app stores and literature review. JMIR Mhealth Uhealth 8(5), e17798 (2020)

    Google Scholar 

  7. Scoglio, A.A., Reilly, E.D., Gorman, J.A., Drebing, C.E.: Use of social robots in mental health and well-being research: systematic review. J. Med. Internet Res. 21(7), e13322 (2019)

    Article  Google Scholar 

  8. Fasola, J., Matarić, M.J.: A socially assistive robot exercise coach for the elderly. J. Hum.-Robot Inter. 2(2), 3–32 (2013)

    Google Scholar 

  9. Robinson, N.L., Connolly, J., Suddery, G., Turner, M., Kavanagh, D.J.: A humanoid social robot to provide personalized feedback for health promotion in diet, physical activity, alcohol and cigarette use: a health clinic trial. In: 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), pp. 720–726. IEEE (2021)

    Google Scholar 

  10. Axelsson, M., Spitale, M., Gunes, H.: Robots as mental well-being coaches: Design and ethical recommendations. arXiv preprint arXiv:2208.14874 (2022)

  11. Jeong, S., et al.: A robotic positive psychology coach to improve college students’ wellbeing. In: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 187–194. IEEE, Naples, Italy (2020)

    Google Scholar 

  12. Spitale, M., Axelsson, M., Gunes, H.: Robotic mental well-being coaches for the workplace: An in-the-wild study on form. In: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, pp. 301–310. Association for Computing Machinery, Stockholm, Sweden (2023)

    Google Scholar 

  13. Rasouli, S., Gupta, G., Nilsen, E., Dautenhahn, K.: Potential applications of social robots in robot-assisted interventions for social anxiety. Int. J. Soc. Robot. 14(5), 1–32 (2022)

    Article  Google Scholar 

  14. Curtis, F., Cranmer, S.: “Laptops are better”: medical students’ perceptions of laptops versus tablets and smartphones to support their learning. In: Proceedings of the 9th International Conference on Networked Learning 2014, pp. 67–75. (2014)

    Google Scholar 

  15. Bravo Perucho, A., Alimardani, M.: Social robots in secondary education: Can robots assist young adult learners with math learning? In: Companion of the 2023 ACM/IEEE international conference on human-robot interaction, pp. 355–359. Association for Computing Machinery, Stockholm, Sweden (2023)

    Google Scholar 

  16. van Ewijk, G., Smakman, M., Konijn, E. A.: Teachers’ perspectives on social robots in education: an exploratory case study. In: Proceedings of the Interaction Design and Children Conference, pp. 273–280. Association for Computing Machinery, London, United Kingdom (2020)

    Google Scholar 

  17. Whelan, S., Kouroupetroglou, C., Santorelli, A., Raciti, M., Barrett, E., Casey, D.: Investigating the effect of social robot embodiment. In: Harnessing the Power of Technology to Improve Lives, pp. 523–526. IOS Press (2017)

    Google Scholar 

  18. Dedovic, K., Renwick, R., Mahani, N., Engert, V., Lupien, S., Pruessner, J.: The montreal imaging stress task: using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain. J. Psychiatry Neurosci. 30(5), 319–325 (2005)

    Google Scholar 

  19. De Couck, M., Caers, R., Musch, L., Fliegauf, J., Giangreco, A., Gidron, Y.: How breathing can help you make better decisions: two studies on the effects of breathing patterns on heart rate variability and decision-making in business cases. Int. J. Psychophysiol. 139, 1–9 (2019)

    Article  Google Scholar 

  20. Franke, T., Attig, C., Wessel, D.: A personal resource for technology interaction: development and validation of the affinity for technology interaction (ATI) scale. Int. J. Hum.-Comput. Interact. 35(6), 456–467 (2019)

    Article  Google Scholar 

  21. Levenstein, S., et al.: Development of the perceived stress questionnaire: a new tool for psychosomatic research. J. Psychosom. Res. 37(1), 19–32 (1993)

    Article  Google Scholar 

  22. Venkatesh, V., Thong, J.Y., Xu, X.: Unified theory of acceptance and use of technology: a synthesis and the road ahead. J. Assoc. Inf. Syst. 17(5), 328–376 (2016)

    Google Scholar 

  23. Aqajari, S.A.H., Naeini, E.K., Mehrabadi, M.A., Labbaf, S., Dutt, N., Rahmani, A.M.: Pyeda: an open-source python toolkit for pre-processing and feature extraction of electrodermal activity. Procedia Comput. Sci. 184, 99–106 (2021)

    Article  Google Scholar 

  24. Chen, Y.-C., Yeh, S.-L., Lin, W., Yueh, H.-P., Fu, L.-C.: The effects of social presence and familiarity on children–robot interactions. Sensors 23(9), 4231 (2023)

    Article  Google Scholar 

  25. Alimardani, M., Kemmeren, L., Okumura, K., Hiraki, K.: Robot-assisted mindfulness practice: Analysis of neurophysiological responses and affective state change. In: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 683–689. IEEE (2020)

    Google Scholar 

  26. Alimardani, M., Hiraki, K.: Passive brain-computer interfaces for enhanced human-robot interaction. Front. Robot. AI 7(125) (2020)

    Google Scholar 

  27. Staffa, M., D’Errico, L., Sansalone, S. Alimardani, M.: Classifying human emotions in HRI: applying global optimization model to EEG brain signals. Front. Neurorobotics 17(1191127) (2023)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andra Rice .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rice, A., Klęczek, K., Alimardani, M. (2024). The Effectiveness of Social Robots in Stress Management Interventions for University Students. In: Ali, A.A., et al. Social Robotics. ICSR 2023. Lecture Notes in Computer Science(), vol 14453 . Springer, Singapore. https://doi.org/10.1007/978-981-99-8715-3_16

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8715-3_16

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8714-6

  • Online ISBN: 978-981-99-8715-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics