research-article

Clinical Data in Context: Towards Sensemaking Tools for Interpreting Personal Health Data

Authors:

Ashley Garrity,

Mark W. NewmanAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 1

Article No.: 22, Pages 1 - 20

https://doi.org/10.1145/3314409

Published: 29 March 2019 Publication History

Abstract

Clinical data augmented with contextual data can help patients with chronic conditions make sense of their disease. However, existing tools do not support interpretation of multiple data streams. To better understand how individuals make sense of clinical and contextual data, we interviewed patients with Type 1 diabetes and their caregivers using context-enhanced visualizations of patients' data as probes to facilitate interpretation activities. We observed that our participants performed four analytical activities when interpreting their data -- finding context-based trends and explaining them, triangulating multiple factors, suggesting context-specific actions, and hypothesizing about alternate contextual factors affecting outcomes. We also observed two challenges encountered during analysis -- the inability to identify clear trends challenged action planning and counterintuitive insights compromised trust in data. Situating our findings within the existing sensemaking frameworks, we demonstrate that sensemaking can not only inform action but can guide the discovery of information needs for exploration. We further argue that sensemaking is a valuable approach for exploring contextual data. Informed by our findings and our reflection on existing sensemaking frameworks, we provide design guidelines for sensemaking tools to improve awareness of contextual factors affecting patients and to support patients' agency in making sense of health data.

References

[1]

Eric P.S. Baumer, Vera Khovanskaya, Mark Matthews, Lindsay Reynolds, Victoria Schwanda Sosik, and Geri Gay. 2014. Reviewing Reflection: On the Use of Reflection in Interactive System Design. In Proceedings of the 2014 Conference on Designing Interactive Systems (DIS '14), 93--102.

Digital Library

[2]

Kirsten Butcher and Tamara Sumner. 2011. Self-Directed Learning and the Sensemaking Paradox. Human-Computer Interaction 26, 1: 123--159.

[3]

Eun Kyoung Choe, Bongshin Lee, and others. 2015. Characterizing visualization insights from quantified selfers' personal data presentations. IEEE computer graphics and applications 35, 4: 28--37.

[4]

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 (PervasiveHealth'17). ACM, New York, NY, USA.

Digital Library

[5]

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, 1143--1152.

Digital Library

[6]

Heather J. Cole-Lewis, Arlene M. Smaldone, Patricia R. Davidson, Rita Kukafka, Jonathan N. Tobin, Andrea Cassells, Elizabeth D. Mynatt, George Hripcsak, and Lena Mamykina. 2016. Participatory approach to the development of a knowledge base for problem-solving in diabetes self-management. International Journal of Medical Informatics 85, 1: 96--103.

[7]

Dan Cosley, Elizabeth Churchill, Jodi Forlizzi, and Sean A. Munson. 2017. Introduction to This Special Issue on the Lived Experience of Personal Informatics. Human--Computer Interaction 32, 5-6: 197--207.

Digital Library

[8]

Nediyana Daskalova, Danaë Metaxa-Kakavouli, Adrienne Tran, Nicole Nugent, Julie Boergers, John McGeary, and Jeff Huang. 2016. SleepCoacher: A Personalized Automated Self-Experimentation System for Sleep Recommendations. 347--358.

Digital Library

[9]

Daniel Epstein, Felicia Cordeiro, Elizabeth Bales, James Fogarty, and Sean Munson. 2014. Taming data complexity in lifelogs: exploring visual cuts of personal informatics data. In Proceedings of the 2014 conference on Designing interactive systems, 667--676. Retrieved April 18, 2017 from http://dl.acm.org/citation.cfm?id=2598558

Digital Library

[10]

Sarah Faisal, Ann Blandford, and Henry WW Potts. 2013. Making sense of personal health information: challenges for information visualization. Health informatics journal 19, 3: 198--217.

[11]

Paul Glasziou, Les Irwig, and David Mant. 2005. Monitoring in chronic disease: a rational approach. British Medical Journal 330: 644--648.

[12]

Garrett Grolemund and Hadley Wickham. 2014. A Cognitive Interpretation of Data Analysis: A Cognitive Interpretation of Data Analysis. International Statistical Review 82, 2: 184--204.

[13]

D. A. Hema, S. O. Roper, J. W. Nehring, A. Call, B. L. Mandleco, and T. T. Dyches. 2009. Daily stressors and coping responses of children and adolescents with type 1 diabetes. Child: Care, Health and Development 35, 3: 330--339.

[14]

Felicia Hill-Briggs. 2003. Problem solving in diabetes self-management: a model of chronic illness self-management behavior. Annals of Behavioral Medicine 25, 3: 182--193.

[15]

Ravi Karkar, Jasmine Zia, Jessica Schroeder, Daniel A. Epstein, Laura R. Pina, Jeffrey Scofield, James Fogarty, Julie A. Kientz, Sean A. Munson, and Roger Vilardaga. 2017. TummyTrials: A Feasibility Study of Using Self-Experimentation to Detect Individualized Food Triggers. 6850--6863.

Digital Library

[16]

Ravi Karkar, Jasmine Zia, Roger Vilardaga, Sonali R Mishra, James Fogarty, Sean A Munson, and Julie A Kientz. 2016. A framework for self-experimentation in personalized health. Journal of the American Medical Informatics Association 23, 3: 440--448.

[17]

Gary Klein, Brian Moon, and Robert R. Hoffman. 2006. Making sense of sensemaking 2: A macrocognitive model. IEEE intelligent systems 21, 5: 88--92.

Digital Library

[18]

Gary Klein, Jennifer K. Phillips, Erica L. Rall, and Deborah A. Peluso. 2007. A data-frame theory of sensemaking. In Expertise out of context: Proceedings of the Sixth International Conference on Naturalistic Decision Making. Lawrence Erlbaum Associates Publishers, Mahwah, NJ, US, 113--155.

[19]

Sukwon Lee, Sung-Hee Kim, Ya-Hsin Hung, Heidi Lam, Youn-Ah Kang, and Ji Soo Yi. 2016. How do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novice's Information Visualization Sensemaking. IEEE Transactions on Visualization and Computer Graphics 22, 1: 499--508.

Digital Library

[20]

Ian Li, Anind Dey, and Jodi Forlizzi. 2010. A stage-based model of personal informatics systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 557--566. Retrieved April 18, 2017 from http://dl.acm.org/citation.cfm?id=1753409

Digital Library

[21]

Ian Li, Anind K. Dey, and Jodi Forlizzi. 2011. Understanding my data, myself: supporting self-reflection with ubicomp technologies. In Proceedings of the 13th international conference on Ubiquitous computing, 405--414. Retrieved April 18, 2017 from http://dl.acm.org/citation.cfm?id=2030166

Digital Library

[22]

Haley MacLeod, Anthony Tang, and Sheelagh Carpendale. 2013. Personal informatics in chronic illness management. In Proceedings of Graphics Interface 2013, 149--156. Retrieved April 17, 2017 from http://dl.acm.org/citation.cfm?id=2532155

Digital Library

[23]

Lena Mamykina, Elizabeth M Heitkemper, Arlene M Smaldone, Rita Kukafka, Heather Cole-Lewis, Patricia G Davidson, Elizabeth D Mynatt, Jonathan N Tobin, Andrea Cassells, Carrie Goodman, and George Hripcsak. 2016. Structured scaffolding for reflection and problem solving in diabetes self-management: qualitative study of mobile diabetes detective. Journal of the American Medical Informatics Association 23, 1: 129--136.

[24]

Lena Mamykina, Elizabeth M. Heitkemper, Arlene M. Smaldone, Rita Kukafka, Heather J. Cole-Lewis, Patricia G. Davidson, Elizabeth D. Mynatt, Andrea Cassells, Jonathan N. Tobin, and George Hripcsak. 2017. Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data. Journal of Biomedical Informatics 76: 1--8.

Digital Library

[25]

Lena Mamykina, Elizabeth D. Mynatt, and David R. Kaufman. 2006. Investigating health management practices of individuals with diabetes. In Proceedings of the SIGCHI Conference on Human factors in Computing Systems, 927--936. Retrieved December 2, 2015 from http://dl.acm.org/citation.cfm?id=1124910

Digital Library

[26]

Lena Mamykina, Elizabeth Mynatt, Patricia Davidson, and Daniel Greenblatt. 2008. MAHI: investigation of social scaffolding for reflective thinking in diabetes management.

Digital Library

[27]

Lena Mamykina, Arlene M. Smaldone, and Suzanne R. Bakken. 2015. Adopting the sensemaking perspective for chronic disease self-management. Journal of Biomedical Informatics 56: 406--417.

Digital Library

[28]

Rene Mayrhofer. AN ARCHITECTURE FOR CONTEXT PREDICTION. 8.

Digital Library

[29]

Margaret E Morris, Qusai Kathawala, Todd K Leen, Ethan E Gorenstein, Farzin Guilak, Michael Labhard, and William Deleeuw. 2010. Mobile Therapy: Case Study Evaluations of a Cell Phone Application for Emotional Self-Awareness. Journal of Medical Internet Research 12, 2: e10.

[30]

Shantanu Nundy, Chen-Yuan E. Lu, Patrick Hogan, Anjuli Mishra, and Monica E. Peek. 2014. Using patient-generated health data from mobile technologies for diabetes self-management support: provider perspectives from an academic medical center. Journal of diabetes science and technology 8, 1: 74--82.

[31]

Tom Owen, Jennifer Pearson, Harold Thimbleby, and George Buchanan. 2015. ConCap: Designing to Empower Individual Reflection on Chronic Conditions using Mobile Apps. In roceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, 105--114.

Digital Library

[32]

Dana Pavel, Dirk Trossen, Matthias Holweg, and Vic Callaghan. 2013. Lifestyle stories: Correlating user information through a story-inspired paradigm. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2013 7th International Conference on, 412--415. Retrieved April 18, 2017 from http://ieeexplore.ieee.org/abstract/document/6563980/

Digital Library

[33]

Peter Pirolli and Stuart Card. 2005. The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In Proceedings of international conference on intelligence analysis, 2-4. Retrieved April 18, 2017 from https://www.e-education.psu.edu/geog885/sites/www.e-education.psu.edu.geog885/files/geog885q/file/Lesson_02/Sense_Making_206_Camera_Ready_Paper.pdf

[34]

Peter Pirolli and Daniel Russell. 2011. Introduction to this Special Issue on Sensemaking. Human-Computer Interaction 26, 1: 1--8.

[35]

Shriti Raj, Mark W. Newman, Joyce M Lee, and Mark S. Ackerman. 2017. Understanding Individual and Collaborative Problem-Solving with Patient-Generated Data: Challenges and Opportunities. PACM on Human-Computer Interaction 1, 2.

Digital Library

[36]

Daniel M. Russell, Mark J. Stefik, Peter Pirolli, and Stuart K. Card. 1993. The cost structure of sensemaking. In Proceedings of the INTERACT'93 and CHI'93 conference on Human factors in computing systems, 269--276. Retrieved April 27, 2017 from http://dl.acm.org/citation.cfm?id=169209

Digital Library

[37]

J Saldaña. 2013. The coding manual for qualitative researchers. Sage, Los Angeles, 260--273.

[38]

Stefan Särnblad, U. Ekelund, and J. \AAman. 2005. Physical activity and energy intake in adolescent girls with Type 1 diabetes. Diabetic Medicine 22, 7: 893--899.

[39]

Jessica Schroeder, Jane Hoffswell, Chia-Fang Chung, James Fogarty, Sean Munson, and Jasmine Zia. 2017. Supporting Patient-Provider Collaboration to Identify Individual Triggers using Food and Symptom Journals. In Proceedings of the Conference on Computer-Supported Cooperative Work, 1726--1739.

Digital Library

[40]

Michael Shapiro, Douglas Johnston, Jonathan Wald, and Donald Mon. 2012. Patient-generated health data. RTI International, April.

[41]

Brian K. Smith, Jeana Frost, Meltem Albayrak, and Rajneesh Sudhakar. 2007. Integrating glucometers and digital photography as experience capture tools to enhance patient understanding and communication of diabetes self-management practices. Personal and Ubiquitous Computing 11, 4: 273--286.

Digital Library

[42]

Cristiano Storni. 2014. Design challenges for ubiquitous and personal computing in chronic disease care and patient empowerment: a case study rethinking diabetes self-monitoring. Personal and Ubiquitous Computing 18, 5: 1277--1290.

Digital Library

[43]

Konrad Tollmar, Frank Bentley, and Cristobal Viedma. 2012. Mobile Health Mashups: Making sense of multiple streams of wellbeing and contextual data for presentation on a mobile device. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012 6th International Conference on, 65--72. Retrieved December 13, 2015 from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6240364

[44]

Haining Zhu, Joanna Colgan, Madhu Reddy, and Eun Kyoung Choe. 2016. Sharing Patient-Generated Data in Clinical Practices: An Interview Study. Proceedings of the American Medical Informatics Association. Retrieved April 17, 2017 from https://faculty.ist.psu.edu/choe/download/AMIA-2016-Zhu-PGD.pdf

[45]

Affinity Diagram. https://en.wikipedia.org/wiki/Affinity_diagram.

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Index Terms

Clinical Data in Context: Towards Sensemaking Tools for Interpreting Personal Health Data
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools
  2. Visualization
    1. Empirical studies in visualization

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 1

March 2019

786 pages

EISSN:2474-9567

DOI:10.1145/3323054

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Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 29 March 2019

Accepted: 01 January 2019

Revised: 01 November 2018

Received: 01 May 2018

Published in IMWUT Volume 3, Issue 1

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Yan XBaroudi LBei RBoudalia LCain SBarton KShorter KNewman M(2024)Proposing a Context-informed Layer-based Framework: Incorporating Context into Designing mHealth Technology for Fatigue ManagementProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661615(571-583)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661615
Lee HJung YShin YPark HChoi WLee U(2024)FamilyScope: Visualizing Affective Aspects of Family Social Interactions using Passive Sensor DataProceedings of the ACM on Human-Computer Interaction10.1145/36373348:CSCW1(1-27)Online publication date: 26-Apr-2024
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