Prediction for Retrospection: Integrating Algorithmic Stress Prediction into Personal Informatics Systems for College Students’ Mental Health

by | Apr 29, 2022 | Research Publications

Author(s):

  • Taewan Kim
  • Haesoo Kim
  • Ha Yeon Lee
  • Hwarang Goh
  • Shakhboz Abdigapporov
  • Mingon Jeong
  • Hyunsung Cho
  • Kyungsik Han
  • Youngtae Noh
  • Sung-Ju Lee
  • Hwajung Hong

Abstract:

Reflecting on stress-related data is critical in addressing one’s mental health. Personal Informatics (PI) systems augmented by algorithms and sensors have become popular ways to help users collect and reflect on data about stress. While prediction algorithms in the PI systems are mainly for diagnostic purposes, few studies examine how the explainability of algorithmic prediction can support user-driven self-insight. To this end, we developed MindScope, an algorithm-assisted stress management system that determines user stress levels and explains how the stress level was computed based on the user’s everyday activities captured by a smartphone. In a 25-day field study conducted with 36 college students, the prediction and explanation supported self-reflection, a process to re-establish preconceptions about stress by identifying stress patterns and recalling past stress levels and patterns that led to coping planning. We discuss the implications of exploiting prediction algorithms that facilitate user-driven retrospection in PI systems.

Documentation:

https://doi.org/10.1145/3491102.3517701

References:

  1. Phil Adams, Mashfiqui Rabbi, Tauhidur Rahman, Mark Matthews, Amy Voida, Geri Gay, Tanzeem Choudhury, and Stephen Voida. 2014. Towards Personal Stress Informatics: Comparing Minimally Invasive Techniques for Measuring Daily Stress in the Wild. In Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare (Oldenburg, Germany) (PervasiveHealth ’14). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels, BEL, 72–79. https://doi.org/10.4108/icst.pervasivehealth.2014.254959Google ScholarDigital Library
  2. William Albert and Thomas Tullis. 2013. Measuring the user experience: collecting, analyzing, and presenting usability metrics. Newnes.Google Scholar
  3. Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, 2019. Guidelines for human-AI interaction. In Proceedings of the 2019 chi conference on human factors in computing systems. 1–13.Google ScholarDigital Library
  4. Gerhard Andersson and Pim Cuijpers. 2009. Internet-based and other computerized psychological treatments for adult depression: a meta-analysis. Cognitive behaviour therapy 38, 4 (2009), 196–205.Google Scholar
  5. Jakob E. Bardram, Mads Frost, Károly Szántó, and Gabriela Marcu. 2012. The MONARCA Self-Assessment System: A Persuasive Personal Monitoring System for Bipolar Patients. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (Miami, Florida, USA) (IHI ’12). Association for Computing Machinery, New York, NY, USA, 21–30. https://doi.org/10.1145/2110363.2110370Google ScholarDigital Library
  6. Jakob E Bardram and Aleksandar Matic. 2020. A decade of ubiquitous computing research in mental health. IEEE Pervasive Computing 19, 1 (2020), 62–72.Google ScholarCross Ref
  7. Frank Bentley, Konrad Tollmar, Peter Stephenson, Laura Levy, Brian Jones, Scott Robertson, Ed Price, Richard Catrambone, and Jeff Wilson. 2013. Health Mashups: Presenting Statistical Patterns between Wellbeing Data and Context in Natural Language to Promote Behavior Change. ACM Trans. Comput.-Hum. Interact. 20, 5, Article 30 (nov 2013), 27 pages. https://doi.org/10.1145/2503823Google ScholarDigital Library
  8. Sofian Berrouiguet, David Ramírez, María Luisa Barrigón, Pablo Moreno-Muñoz, Rodrigo Carmona Camacho, Enrique Baca-García, and Antonio Artés-Rodríguez. 2018. Combining continuous smartphone native sensors data capture and unsupervised data mining techniques for behavioral changes detection: a case series of the evidence-based behavior (eB2) study. JMIR mHealth and uHealth 6, 12 (2018), e197.Google Scholar
  9. A Bhattacharya, P Vasant, N Barsoum, C Andreeski, T Kolemisevska, Abdurrahman Talha Dinibütün, and Georgi M Dimirovski. 2006. Decision making in TOC-product-mix selection via fuzzy cost function optimization. IFAC Proceedings Volumes 39, 23 (2006), 51–56.Google ScholarCross Ref
  10. JesúS Bobadilla, Fernando Ortega, Antonio Hernando, and Jesús Bernal. 2012. A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-based systems 26 (2012), 225–238.Google Scholar
  11. Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77–101.Google Scholar
  12. Sandra Bucci, Matthias Schwannauer, and Natalie Berry. 2019. The digital revolution and its impact on mental health care. Psychology and Psychotherapy: Theory, Research and Practice 92, 2(2019), 277–297.Google ScholarCross Ref
  13. Clara Caldeira, Yu Chen, Lesley Chan, Vivian Pham, Yunan Chen, and Kai Zheng. 2017. Mobile apps for mood tracking: an analysis of features and user reviews. In AMIA Annual Symposium Proceedings, Vol. 2017. American Medical Informatics Association, 495.Google Scholar
  14. Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794.Google ScholarDigital Library
  15. Eun Kyoung Choe, Bongshin Lee, Matthew Kay, Wanda Pratt, and Julie A. Kientz. 2015. SleepTight: Low-Burden, Self-Monitoring Technology for Capturing and Reflecting on Sleep Behaviors. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Osaka, Japan) (UbiComp ’15). Association for Computing Machinery, New York, NY, USA, 121–132. https://doi.org/10.1145/2750858.2804266Google ScholarDigital Library
  16. Eun Kyoung Choe, Bongshin Lee, Haining Zhu, Nathalie Henry Riche, and Dominikus Baur. 2017. Understanding Self-Reflection: How People Reflect on Personal Data through Visual Data Exploration. In Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare (Barcelona, Spain) (PervasiveHealth ’17). Association for Computing Machinery, New York, NY, USA, 173–182. https://doi.org/10.1145/3154862.3154881Google ScholarDigital Library
  17. Sheldon Cohen, Tom Kamarck, Robin Mermelstein, 1994. Perceived stress scale. Measuring stress: A guide for health and social scientists 10, 2(1994), 1–2.Google Scholar
  18. Victor P Cornet and Richard J Holden. 2018. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of biomedical informatics 77 (2018), 120–132.Google ScholarCross Ref
  19. Shipi Dhanorkar, Christine T. Wolf, Kun Qian, Anbang Xu, Lucian Popa, and Yunyao Li. 2021. Who Needs to Know What, When?: Broadening the Explainable AI (XAI) Design Space by Looking at Explanations Across the AI Lifecycle. Association for Computing Machinery, New York, NY, USA, 1591–1602. https://doi.org/10.1145/3461778.3462131Google Scholar
  20. Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, and Justin D. Weisz. 2021. Expanding Explainability: Towards Social Transparency in AI Systems. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3411764.3445188Google Scholar
  21. Malin Eiband, Hanna Schneider, Mark Bilandzic, Julian Fazekas-Con, Mareike Haug, and Heinrich Hussmann. 2018. Bringing Transparency Design into Practice. In 23rd International Conference on Intelligent User Interfaces (Tokyo, Japan) (IUI ’18). Association for Computing Machinery, New York, NY, USA, 211–223. https://doi.org/10.1145/3172944.3172961Google ScholarDigital Library
  22. Elizabeth V. Eikey and Madhu C. Reddy. 2017. ”It’s Definitely Been a Journey”: A Qualitative Study on How Women with Eating Disorders Use Weight Loss Apps. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 642–654. https://doi.org/10.1145/3025453.3025591Google Scholar
  23. Daniel A. Epstein, Clara Caldeira, Mayara Costa Figueiredo, Xi Lu, Lucas M. Silva, Lucretia Williams, Jong Ho Lee, Qingyang Li, Simran Ahuja, Qiuer Chen, Payam Dowlatyari, Craig Hilby, Sazeda Sultana, Elizabeth V. Eikey, and Yunan Chen. 2020. Mapping and Taking Stock of the Personal Informatics Literature. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 4, Article 126 (Dec. 2020), 38 pages. https://doi.org/10.1145/3432231Google ScholarDigital Library
  24. 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 (Osaka, Japan) (UbiComp ’15). Association for Computing Machinery, New York, NY, USA, 731–742. https://doi.org/10.1145/2750858.2804250Google ScholarDigital Library
  25. Elliot G. Mitchell, Elizabeth M. Heitkemper, Marissa Burgermaster, Matthew E. Levine, Yishen Miao, Maria L. Hwang, Pooja M. Desai, Andrea Cassells, Jonathan N. Tobin, Esteban G. Tabak, David J. Albers, Arlene M. Smaldone, and Lena Mamykina. 2021. From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 206, 17 pages. https://doi.org/10.1145/3411764.3445555Google ScholarDigital Library
  26. Enrique Garcia-Ceja, Venet Osmani, and Oscar Mayora. 2015. Automatic stress detection in working environments from smartphones’ accelerometer data: a first step. IEEE journal of biomedical and health informatics 20, 4(2015), 1053–1060.Google Scholar
  27. Ben Green and Salomé Viljoen. 2020. Algorithmic Realism: Expanding the Boundaries of Algorithmic Thought. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* ’20). Association for Computing Machinery, New York, NY, USA, 19–31. https://doi.org/10.1145/3351095.3372840Google ScholarDigital Library
  28. Victoria Hollis, Artie Konrad, Aaron Springer, Matthew Antoun, Christopher Antoun, Rob Martin, and Steve Whittaker. 2017. What does all this data mean for my future mood? Actionable analytics and targeted reflection for emotional well-being. Human–Computer Interaction 32, 5-6 (2017), 208–267.Google Scholar
  29. Victoria Hollis, Alon Pekurovsky, Eunika Wu, and Steve Whittaker. 2018. On Being Told How We Feel: How Algorithmic Sensor Feedback Influences Emotion Perception. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 3, Article 114 (Sept. 2018), 31 pages. https://doi.org/10.1145/3264924Google ScholarDigital Library
  30. Sture Holm. 1979. A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics(1979), 65–70.Google Scholar
  31. Bumsoo Kang, Chulhong Min, Wonjung Kim, Inseok Hwang, Chunjong Park, Seungchul Lee, Sung-Ju Lee, and Junehwa Song. 2017. Zaturi: We Put Together the 25th Hour for You. Create a Book for Your Baby. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (Portland, Oregon, USA) (CSCW ’17). Association for Computing Machinery, New York, NY, USA, 1850–1863. https://doi.org/10.1145/2998181.2998186Google Scholar