Author(s):

  • Shazmin Majid
  • Richard Morriss
  • Grazziela Figueredo
  • Stuart Reeves

Abstract:

Bipolar Disorder (BD) is a complex, cyclical and chronic mental illness where self-tracking is central to self-management. Mobile technology is often leveraged to support this. Limited research has investigated the everyday practices of self-tracking for BD, and it is unclear how the normative ontology that is seen in existing self-tracking technology discourses (e.g. the Quantified Self movement) is applicable to the domain of mental health. Combining principles of Patient and Public Involvement (PPI)—a staple research design principle in mental healthcare—with design and HCI-oriented research approaches, we conducted interviews and workshops with people with lived experience of BD to explore reasons and methods for self-tracking, and challenges and opportunities for technology. Our results describe recommendations for the design of self-tracking mental health technology. We also reflect upon the complex role of researchers working at the intersection of emerging mental health technologies, the principles of PPI, and HCI research.

Documentation:

https://doi.org/10.1145/3532106.3533531

References:
  1. Jorge Alvarez-lozano, Mads Frost, Venet Osmani, Jakob Bardram, Lars Vedel Kessing, and Maria Faurholt-jepsen. 2014. Tell Me Your Apps and I Will Tell You Your Mood: Correlation of Apps Usage with Bipolar Disorder State. In in ACM Proceedings 7th International Conference on Pervasive Technologies Related to Assistive Environments, Rhodes Island. https://doi.org/10.1145/2674396.2674408
  2. American Psychiatric Association. 2013. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). American Psychiatric Pub.
  3. Jakob E Bardram, Mads Frost, Karoly Szanto, Maria Faurholt-Jepsen, Maj Vinberg, and Lars Vedel Kessing. Designing Mobile Health Technology for Bipolar Disorder: A Field Trial of the MONARCA System. Mental Health: 10.
  4. Till Beiwinkel, Sally Kindermann, Andreas Maier, Christopher Kerl, Jörn Moock, Guido Barbian, and Wulf Rössler. 2016. Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study. JMIR Mental Health 3, 1: e2. https://doi.org/10.2196/mental.4560
  5. Maarten den Braber. 2016. The Emergence of Quantified Self as a Data-driven Movement to Promote Health and Wellness. In Proceedings of the first Workshop on Lifelogging Tools and Applications (LTA ’16), 1. https://doi.org/10.1145/2983576.2983584
  6. Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2: 77–101. https://doi.org/10.1191/1478088706qp063oa
  7. Eun Kyoung Choe, Nicole B. Lee, Bongshin Lee, Wanda Pratt, and Julie A. Kientz. 2014. Understanding quantified-selfers’ practices in collecting and exploring personal data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14), 1143–1152. https://doi.org/10.1145/2556288.2557372
  8. Daniel A Epstein, Nicole B Lee, Jennifer H Kang, Elena Agapie, Jessica Schroeder, Laura R Pina, James Fogarty, Julie A Kientz, and Sean A Munson. 2017. Examining Menstrual Tracking to Inform the Design of Personal Informatics Tools. 13.
  9. Daniel A. Epstein, An Ping, James Fogarty, and Sean A. Munson. 2015. A lived informatics model of personal informatics. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’15), 731–742. https://doi.org/10.1145/2750858.2804250
  10. World Leaders in Research-Based User Experience. Affinity Diagramming: Collaboratively Sort UX Findings & Design Ideas. Nielsen Norman Group. Retrieved February 15, 2022 from https://www.nngroup.com/articles/affinity-diagram/
  11. Maria Faurholt-Jepsen, Maj Vinberg, Mads Frost, Ellen Margrethe Christensen, Jakob E Bardram, and Lars Vedel Kessing. 2015. Smartphone data as an electronic biomarker of illness activity in bipolar disorder. Bipolar Disorders 17, 7: 715–728. https://doi.org/10.1111/bdi.12332
  12. Enrique Garcia-Ceja, Michael Riegler, Petter Jakobsen, Jim Torresen, Tine Nordgreen, Ketil J. Oedegaard, and Ole Bernt Fasmer. 2018. Motor Activity Based Classification of Depression in Unipolar and Bipolar Patients. In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), 316–321. https://doi.org/10.1109/CBMS.2018.00062
  13. John Gideon, Emily Mower Provost, and Melvin McInnis. 2016. Mood state prediction from speech of varying acoustic quality for individuals with bipolar disorder. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2359–2363. https://doi.org/10.1109/ICASSP.2016.7472099
  14. John Goodwin, John Cummins, Laura Behan, and Sinead O’Brien. 2016. Development of a mental health smartphone app: perspectives of mental health service users: Journal of Mental Health: Vol 25, No 5. Retrieved April 26, 2019 from https://www.tandfonline.com/doi/full/10.3109/09638237.2015.1124392?casa_token=41ZFiXxQv-QAAAAA:fI5wZ6tdICAIayIJQYC3DmsS1oZzr6Im9NvpTTkZ9IDnZgURc79TgVd992hE4f-xQT14avA3dX7zhQ
  15. Agnes Gruenerbl, Venet Osmani, Gernot Bahle, Jose C. Carrasco, Stefan Oehler, Oscar Mayora, Christian Haring, and Paul Lukowicz. 2014. Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients. In Proceedings of the 5th Augmented Human International Conference on – AH ’14, 1–8. https://doi.org/10.1145/2582051.2582089
  16. Stefanie A. Hlastala, Ellen Frank, Jeanne Kowalski, Joel T. Sherrill, Xin M. Tu, Barbara Anderson, and David J. Kupfer. 2000. Stressful life events, bipolar disorder, and the “kindling model.” Journal of Abnormal Psychology 109, 4: 777–786. https://doi.org/10.1037/0021-843X.109.4.777
  17. Pat Hoddinott, Alex Pollock, Alicia O’Cathain, Isabel Boyer, Jane Taylor, Chris MacDonald, Sandy Oliver, and Jenny L. Donovan. 2018. How to incorporate patient and public perspectives into the design and conduct of research. F1000Research 7: 752. https://doi.org/10.12688/f1000research.15162.1
  18. Helen Jennings, Mike Slade, Peter Bates, Emma Munday, and Rebecca Toney. 2018. Best practice framework for Patient and Public Involvement (PPI) in collaborative data analysis of qualitative mental health research: methodology development and refinement. BMC Psychiatry 18, 1: 213. https://doi.org/10.1186/s12888-018-1794-8
  19. Zahi N. Karam, Emily Mower Provost, Satinder Singh, Jennifer Montgomery, Christopher Archer, Gloria Harrington, and Melvin G. Mcinnis. 2014. ECOLOGICALLY VALID LONG-TERM MOOD MONITORING OF INDIVIDUALS WITH BIPOLAR DISORDER USING SPEECH. Proceedings of the … IEEE International Conference on Acoustics, Speech, and Signal Processing / sponsored by the Institute of Electrical and Electronics Engineers Signal Processing Society. ICASSP (Conference) 2014: 4858–4862. https://doi.org/10.1109/ICASSP.2014.6854525
  20. Ian Li, Anind Dey, Jodi Forlizzi, Kristina Höök, and Yevgeniy Medynskiy. 2011. Personal informatics and HCI: design, theory, and social implications. In CHI ’11 Extended Abstracts on Human Factors in Computing Systems (CHI EA ’11), 2417–2420. https://doi.org/10.1145/1979742.1979573
  21. Fiona Lobban, Ivonne Solis-Trapala, Wendy Symes, Richard Morriss, and ERP Group, University of Liverpool. 2011. Early warning signs checklists for relapse in bipolar depression and mania: utility, reliability and validity. Journal of Affective Disorders 133, 3: 413–422. https://doi.org/10.1016/j.jad.2011.04.026
  22. Yuhan Luo. 2021. Designing Multimodal Self-Tracking Technologies to Promote Data Capture and Self-Reflection. In Companion Publication of the 2021 ACM Designing Interactive Systems Conference (DIS ’21 Companion), 11–15. https://doi.org/10.1145/3468002.3468232
  23. Shazmin Majid, Stuart Reeves, Grazziela Figueredo, Susan Brown, Alexandra Lang, Matthew Moore, and Richard Morriss. 2021. The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review. JMIR Mental Health 8, 12: e27991. https://doi.org/10.2196/27991
  24. Aryn Martin and Michael Lynch. 2009. Counting Things and People: The Practices and Politics of Counting. Social Problems 56, 2: 243–266. https://doi.org/10.1525/sp.2009.56.2.243
  25. Emily Martin. 2009. Bipolar Expeditions: Mania and Depression in American Culture. Princeton University Press.
  26. Mark Matthews, Elizabeth Murnane, and Jaime Snyder. 2017. Quantifying the Changeable Self: The Role of Self-Tracking in Coming to Terms With and Managing Bipolar Disorder. Human–Computer Interaction 32, 5–6: 413–446. https://doi.org/10.1080/07370024.2017.1294983
  27. Alban Maxhuni, Angélica Muñoz-Meléndez, Venet Osmani, Humberto Perez, Oscar Mayora, and Eduardo F. Morales. 2016. Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients. Pervasive and Mobile Computing 31: 50–66. https://doi.org/10.1016/j.pmcj.2016.01.008
  28. Amir Muaremi, Franz Gravenhorst, Agnes Grunerbl, Bert Arnrich, and Gerhard Troester. 2014. Assessing Bipolar Episodes Using Speech Cues Derived from Phone Calls. Pervasive Computing Paradigms for Mental Health. https://doi.org/10.1007/978-3-319-11564-1
  29. Elizabeth L Murnane, Dan Cosley, Pamara Chang, Shion Guha, Ellen Frank, Geri Gay, and Mark Matthews. 2016. Self-monitoring practices, attitudes, and needs of individuals with bipolar disorder: implications for the design of technologies to manage mental health. Journal of the American Medical Informatics Association 23, 3: 477–484. https://doi.org/10.1093/jamia/ocv165
  30. Christopher J. L. Murray, and Alan D. Lopez. 1996. The Global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020; Summary. Harvard School of Public Health. Retrieved May 4, 2019 from https://apps.who.int/iris/handle/10665/41864
  31. Jennifer Nicholas, Andrea S. Fogarty, Katherine Boydell, and Helen Christensen. 2017. The Reviews Are in: A Qualitative Content Analysis of Consumer Perspectives on Apps for Bipolar Disorder. Journal of Medical Internet Research 19, 4: e105. https://doi.org/10.2196/jmir.7273
  32. Jasmin Niess and Paweł W. Woźniak. 2018. Supporting Meaningful Personal Fitness: the Tracker Goal Evolution Model. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18), 1–12. https://doi.org/10.1145/3173574.3173745
  33. Venet Osmani, Agnes Gruenerbl, Gernot Bahle, Christian Haring, Paul Lukowicz, and Oscar Mayora. 2015. Smartphones in Mental Health: Detecting Depressive and Manic Episodes. IEEE Pervasive Computing 14, 3: 10–13. https://doi.org/10.1109/MPRV.2015.54
  34. Ashley Polhemus, Jan Novak, Shazmin Majid, Sara Simblett, Stuart Bruce, Patrick Burke, Marissa Fallon Dockendorf, Gergely Temesi, and Til Wykes. 2020. Data visualization in chronic neurological and mental health condition self-management: a systematic review of user perspectives (Preprint). https://doi.org/10.2196/preprints.25249
  35. A. Puiatti, S. Mudda, S. Giordano, and O. Mayora. 2011. Smartphone-centred wearable sensors network for monitoring patients with bipolar disorder. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3644–3647. https://doi.org/10.1109/IEMBS.2011.6090613
  36. Amon Rapp and Federica Cena. 2016. Personal informatics for everyday life: How users without prior self-tracking experience engage with personal data. International Journal of Human-Computer Studies 94: 1–17. https://doi.org/10.1016/j.ijhcs.2016.05.006
  37. Amon Rapp and Maurizio Tirassa. 2017. Know Thyself: A Theory of the Self for Personal Informatics. Human–Computer Interaction 32, 5–6: 335–380. https://doi.org/10.1080/07370024.2017.1285704
  38. John Rooksby, Mattias Rost, Alistair Morrison, and Matthew Chalmers. 2014. Personal tracking as lived informatics. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14), 1163–1172. https://doi.org/10.1145/2556288.2557039
  39. Katta Spiel, Fares Kayali, Sabine Harrer, Louise Horvath, Miguel Sicart, Michael Penkler, and Jessica Hammer. 2018. Fitter, Happier, More Productive?The Normative Ontology of Fitness Trackers. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (CHI EA ’18). Association for Computing Machinery, New York, NY, USA, Paper alt08, 1–10. https://doi.org/10.1145/3170427.3188401
  40. N. Vanello, A. Guidi, C. Gentili, S. Werner, G. Bertschy, G. Valenza, A. Lanatá, and E. P. Scilingo. 2012. Speech analysis for mood state characterization in bipolar patients. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2104–2107. https://doi.org/10.1109/EMBC.2012.6346375
  41. Snot, Sweat, Pain, Mud, and Snow | Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. Retrieved April 18, 2022 from https://dl.acm.org/doi/10.1145/2702123.2702482
  42. How to Run a Crazy Eights exercise | I Am Not My Pixels. Retrieved February 18, 2022 from https://www.iamnotmypixels.com/how-to-use-crazy-8s-to-generate-design-ideas/
  43. Understanding my data, myself | Proceedings of the 13th international conference on Ubiquitous computing. Retrieved April 18, 2022 from https://dl.acm.org/doi/10.1145/2030112.2030166