Using physiological signals to measure the Quality-of-Experience of Health Care Professionals when interacting with a clinical guideline mobile app

Digital content adaptation and personalisation is a crucial component in increasing user engagement, and becoming of interest to designers/developers in areas related to clinical information delivery. In order to achieve this, new data-intensive methods are required that go beyond traditional user-centred design approaches. In this position paper, we discuss how although user-centred design has shown to be useful for generating generalised design guidelines (predominantly driven by qualitative data collection techniques), more quantitative methods and the use of measures such as Quality of Experience, could not only augment standard user research methods but also provide data to inform the adaption and personalisation of interfaces. In this paper we propose a solution-by-design to gather personal preferences through users’ physiological data (using pupillometry) and how it would be useful for applications such as mobile apps for clinical guidelines, where access to in-situ data collection is increasingly more challenging.

Content

Author and article information

Contributors

J. Mitchell

N. Kanwal

E. de Quincey

Conference

Publication date: July 2022

Publication date (Print): July 2022

Pages: 1-5

Affiliations

[0001]School of Computing and Mathematics

Keele University

Article

DOI: 10.14236/ewic/HCI2022.22

SO-VID: 9c8b1b25-4e30-41f9-ae77-13196bfecc8b

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 35th International BCS Human-Computer Interaction Conference

Conference acronym: HCI2022

Conference number: 35

Conference location: Keele, Staffordshire

Conference date: July 11th to 13th, 2022

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Towards a Human-Centred Digital Society

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2022.22

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

Poonam Madan. Digital marketing: a review. V Paradigm shifts in management practices in the era of industry, 4: 64–71, 2021.
Jennifer Romano Bergstrom, Sabrina Duda, David Hawkins, and Mike McGill. Physiological response measurements. In Eye tracking in user experience design, pages 81–108. Elsevier, 2014.
James Mitchell, Ed de Quincey, Charles Pantin, and Naveed Mustfa. The development of a point of care clinical guidelines mobile application following a user-centred design approach. In International Conference on Human-Computer Interaction, pages 294–313. Springer, 2020.
James Mitchell, EJ De Quincey, Charles Pantin, Naveed Mustfa, et al. 15 usability recommendations for delivering clinical guidelines on mobile devices. In 34th British HCI Conference, pages 82–93. BCS Learning & Development, 2021.
Brianna Richardson, Marsha Campbell-Yeo, and Michael Smit. Mobile application user experience checklist: A tool to assess attention to core ux principles. International Journal of Human–Computer Interaction, 37(13):1283–1290, 2021.
Erin Gilliam Haije, 2017. URL https://mopinion.com/user-experienceux-tools-tech-companies/. Last accessed: March 31, 2022.
Jakob Nielsen, 2001. URL https://www.nngroup.com/articles/first-rule-of-usability-dont-listen-to-users/. Last Accessed: March 31, 2022.
Ulrich Reiter, Kjell Brunnstr¨om, Katrien De Moor, Mohamed-Chaker Larabi, Manuela Pereira, Antonio Pinheiro, Junyong You, and Andrej Zgank. Factors Influencing Quality of Experience, pages 55–72. Springer International Publishing, Cham, 2014. doi: 10.1007/978-3-319-02681-7_4.
Khalil ur Rehman Laghari, Noel Crespi, B. Molina, and C.E. Palau. Qoe aware service delivery in distributed environment. In 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications, pages 837–842, 2011. doi: 10.1109/WAINA.2011.58.
B Joseph Pine and James H Gilmore. The experience economy. Harvard Business Press, 2011.
Andrius Dzedzickis, Art¯uras Kaklauskas, and Vytautas Bucinskas. Human emotion recognition: Review of sensors and methods. Sensors, 20(3):592, 2020.
Sidra Rafique, Nadia Kanwal, Mohammad Samar Ansari, Mamoona Asghar, and Zuhair Akhtar. Deep learning based emotion classification with temporal pupillometry sequences. In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), pages 1–6, 2021a. doi: 10.1109/ICECET52533.2021.9698663.
Peng Ren, Armando Barreto, Ying Gao, and Malek Adjouadi. Affective assessment of computer users based on processing the pupil diameter signal. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 2594–2597. IEEE, 2011.
Sidra Rafique, Nadia Kanwal, Irfan Karamat, Mamoona N. Asghar, and Martin Fleury. Towards estimation of emotions from eye pupillometry with low-cost devices. IEEE Access, 9:5354–5370, 2021b. doi: 10.1109/ACCESS.2020.3048311.
Eckhard H Hess. Attitude and pupil size. Scientific american, 212(4):46–55, 1965.
Daniel Hahnemann and Jackson Beatty. Pupillary responses in a pitch-discrimination task. Perception & Psychophysics, 2(3):101–105, 1967.
Wolfgang Ellermeier and Wolfgang Westphal. Gender differences in pain ratings and pupil reactions to painful pressure stimuli. Pain, 61(3):435–439, 1995.
Thomas Z Ramsøy, Morten Friis-Olivarius, Catrine Jacobsen, Simon B Jensen, and Martin Skov. Effects of perceptual uncertainty on arousal and preference across different visual domains. Journal of Neuroscience, Psychology, and Economics, 5(4):212, 2012.
Kati Nowack, Taciano L Milfont, and Elke van der Meer. Future versus present: Time perspective and pupillary response in a relatedness judgment task investigating temporal event knowledge. International Journal of Psychophysiology, 87(2):173–182, 2013.
Agnieszka Wykowska, Christine Anderl, Anna Schub¨ o, and Bernhard Hommel. Motivation modulates visual attention: evidence from pupillometry. Frontiers in psychology, 4:59, 2013.
Sylvain Sirois and Julie Brisson. Pupillometry. Wiley Interdisciplinary Reviews: Cognitive Science, 5(6):679–692, 2014.
Adriana A Zekveld and Sophia E Kramer. Cognitive processing load across a wide range of listening conditions: Insights from pupillometry. Psychophysiology, 51(3): 277–284, 2014.
Jonathan Strohl, Joseph Luchman, James Khun, Edward Pierce, and Kyle Andrews. Exploring the relationship between eye movements and pupillary response from formative user experience research. In International Conference on Universal Access in Human-Computer Interaction, pages 472–480. Springer, 2016.
Kassner Moritz and Will Patera, 2014. URL https://pupil-labs.com/products/invisible/. Last Accessed: March 31, 2022.

Comments

Comment on this article

[1] Poonam Madan. Digital marketing: a review. V Paradigm shifts in management practices in the era of industry, 4: 64–71, 2021.

[2] Jennifer Romano Bergstrom, Sabrina Duda, David Hawkins, and Mike McGill. Physiological response measurements. In Eye tracking in user experience design, pages 81–108. Elsevier, 2014.

[3] James Mitchell, Ed de Quincey, Charles Pantin, and Naveed Mustfa. The development of a point of care clinical guidelines mobile application following a user-centred design approach. In International Conference on Human-Computer Interaction, pages 294–313. Springer, 2020.

[4] James Mitchell, EJ De Quincey, Charles Pantin, Naveed Mustfa, et al. 15 usability recommendations for delivering clinical guidelines on mobile devices. In 34th British HCI Conference, pages 82–93. BCS Learning & Development, 2021.

[5] Brianna Richardson, Marsha Campbell-Yeo, and Michael Smit. Mobile application user experience checklist: A tool to assess attention to core ux principles. International Journal of Human–Computer Interaction, 37(13):1283–1290, 2021.

[6] Erin Gilliam Haije, 2017. URL https://mopinion.com/user-experienceux-tools-tech-companies/. Last accessed: March 31, 2022.

[7] Jakob Nielsen, 2001. URL https://www.nngroup.com/articles/first-rule-of-usability-dont-listen-to-users/. Last Accessed: March 31, 2022.

[8] Ulrich Reiter, Kjell Brunnstr¨om, Katrien De Moor, Mohamed-Chaker Larabi, Manuela Pereira, Antonio Pinheiro, Junyong You, and Andrej Zgank. Factors Influencing Quality of Experience, pages 55–72. Springer International Publishing, Cham, 2014. doi: 10.1007/978-3-319-02681-7_4.

[9] Khalil ur Rehman Laghari, Noel Crespi, B. Molina, and C.E. Palau. Qoe aware service delivery in distributed environment. In 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications, pages 837–842, 2011. doi: 10.1109/WAINA.2011.58.

[10] B Joseph Pine and James H Gilmore. The experience economy. Harvard Business Press, 2011.

[11] Andrius Dzedzickis, Art¯uras Kaklauskas, and Vytautas Bucinskas. Human emotion recognition: Review of sensors and methods. Sensors, 20(3):592, 2020.

[12] Sidra Rafique, Nadia Kanwal, Mohammad Samar Ansari, Mamoona Asghar, and Zuhair Akhtar. Deep learning based emotion classification with temporal pupillometry sequences. In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), pages 1–6, 2021a. doi: 10.1109/ICECET52533.2021.9698663.

[13] Peng Ren, Armando Barreto, Ying Gao, and Malek Adjouadi. Affective assessment of computer users based on processing the pupil diameter signal. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 2594–2597. IEEE, 2011.

[14] Sidra Rafique, Nadia Kanwal, Irfan Karamat, Mamoona N. Asghar, and Martin Fleury. Towards estimation of emotions from eye pupillometry with low-cost devices. IEEE Access, 9:5354–5370, 2021b. doi: 10.1109/ACCESS.2020.3048311.

[15] Eckhard H Hess. Attitude and pupil size. Scientific american, 212(4):46–55, 1965.

[16] Daniel Hahnemann and Jackson Beatty. Pupillary responses in a pitch-discrimination task. Perception & Psychophysics, 2(3):101–105, 1967.

[17] Wolfgang Ellermeier and Wolfgang Westphal. Gender differences in pain ratings and pupil reactions to painful pressure stimuli. Pain, 61(3):435–439, 1995.

[18] Thomas Z Ramsøy, Morten Friis-Olivarius, Catrine Jacobsen, Simon B Jensen, and Martin Skov. Effects of perceptual uncertainty on arousal and preference across different visual domains. Journal of Neuroscience, Psychology, and Economics, 5(4):212, 2012.

[19] Kati Nowack, Taciano L Milfont, and Elke van der Meer. Future versus present: Time perspective and pupillary response in a relatedness judgment task investigating temporal event knowledge. International Journal of Psychophysiology, 87(2):173–182, 2013.

[20] Agnieszka Wykowska, Christine Anderl, Anna Schub¨ o, and Bernhard Hommel. Motivation modulates visual attention: evidence from pupillometry. Frontiers in psychology, 4:59, 2013.

[21] Sylvain Sirois and Julie Brisson. Pupillometry. Wiley Interdisciplinary Reviews: Cognitive Science, 5(6):679–692, 2014.

[22] Adriana A Zekveld and Sophia E Kramer. Cognitive processing load across a wide range of listening conditions: Insights from pupillometry. Psychophysiology, 51(3): 277–284, 2014.

[23] Jonathan Strohl, Joseph Luchman, James Khun, Edward Pierce, and Kyle Andrews. Exploring the relationship between eye movements and pupillary response from formative user experience research. In International Conference on Universal Access in Human-Computer Interaction, pages 472–480. Springer, 2016.

[24] Kassner Moritz and Will Patera, 2014. URL https://pupil-labs.com/products/invisible/. Last Accessed: March 31, 2022.

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