research-article

Health Mashups: Presenting Statistical Patterns between Wellbeing Data and Context in Natural Language to Promote Behavior Change

Authors:

Konrad Tollmar,

Peter Stephenson,

Scott Robertson,

Richard Catrambone,

Jeff WilsonAuthors Info & Claims

ACM Transactions on Computer-Human Interaction (TOCHI), Volume 20, Issue 5

Article No.: 30, Pages 1 - 27

https://doi.org/10.1145/2503823

Published: 01 November 2013 Publication History

Abstract

People now have access to many sources of data about their health and wellbeing. Yet, most people cannot wade through all of this data to answer basic questions about their long-term wellbeing: Do I gain weight when I have busy days? Do I walk more when I work in the city? Do I sleep better on nights after I work out?

We built the Health Mashups system to identify connections that are significant over time between weight, sleep, step count, calendar data, location, weather, pain, food intake, and mood. These significant observations are displayed in a mobile application using natural language, for example, “You are happier on days when you sleep more.” We performed a pilot study, made improvements to the system, and then conducted a 90-day trial with 60 diverse participants, learning that interactions between wellbeing and context are highly individual and that our system supported an increased self-understanding that lead to focused behavior changes.

References

[1]

Adomavicius, G. and Tuzhilin, A. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 6, 734--749.

Digital Library

[2]

Albers, D. J. and Hripcsak, G. 2010. A statistical dynamics approach to the study of human heath data: Resolving population scale diurnal variation in laboratory data. Phys. Lett. A 374, 1159--1164.

[3]

Ancker, J. S. and Kaufman, D. 2007. Rethinking health numeracy: A multidisciplinary literature review. J. Am. Med. Inform. Assoc. 14, 713--721.

[4]

Anderson, I., Maitland, J., Sherwood, S., Barkhuus, L., Chalmers, M., Hall, M., Brown, B., and Muller, H. 2007. Shakra: Tracking and sharing daily activity levels with unaugmented mobile phones. Mob. Netw. Appl. 12, 2--3, 185--199.

Digital Library

[5]

BUPA. 2011. http://www.bupa.co.uk/individuals/health-information/directory/o/child-obesity.

[6]

Burke, L., Sereika, S., Music, E., Warziski, M., Styn, M., and Stone, A. 2008. Using instrumented paper diaries to document self-monitoring patterns in weight-loss. Contemp. Clin. Trials. 29, 2, 182--193.

[7]

CDC. 2010. U.S. Obesity trends. Centers for Disease Control and Prevention. http://www.cdc.gov/obesity/data/trends.html (Accessed 09/06/11).

[8]

Christakis, N. and Fowler, J. 2007. The spread of obesity in a large social network over 32 years. New Eng. J. Medic. 357, 4, 370--379.

[9]

Connelly, K., Faber, A., Rogers, Y., Siek, K., and Toscos, T. 2006. Mobile applications that empower people to monitor their personal health. Elektrotechnik und Informationstechnik 123, 4, 124--128

[10]

Consolvo, S., Everitt, K., Smith, I., and Landay, J. 2006. Design requirements for technologies that encourage physical activity. In Proceedings of CHI’06.

Digital Library

[11]

Consolvo, S., Klasnja, P., McDonald, D., Avrahami, D., Froehlich, J., Legrand, L., Libby, R., Mosher, K., and Landay, J. 2008a. Flowers or a robot army? Encouraging awareness & activity with personal, mobile displays. In Proceedings of UbiComp’’08.

Digital Library

[12]

Consolvo, S., McDonald, D., Toscos, T., Chen, M., Froehlich, J., Harrison, B., Klasnja, P., Lamarca, A., Legrand, L., Libby, R., Smith, I., and Landay, J. 2008b. Activity sensing in the wild: A field trial of ubifit garden. In Proceedings of CHI’08.

Digital Library

[13]

Consolvo, S., McDonald, D., and Landay, J. 2009. Theory-driven design strategies for technologies that support behavior change in everyday life. In Proceedings of CHI’09.

Digital Library

[14]

Emmons, R. A. and McCullough, M. E. 2003. Counting blessings versus burdens: Experimental studies of gratitude and subjective well-being in daily life. J. Person. Soc. Psych. 84, 377--389.

[15]

Fogg, B. J. 2002. Persuasive technology: Using computers to change what we think and do. Ubiquity.

Digital Library

[16]

Fogg, B. J. 2003. Persuasive Technology: Using Computers to Change What We Think and Do. Morgan Kaufmann Publishers, San Francisco, CA.

[17]

Fogg, B. J. and Allen, E. 2009. 10 uses of texting to improve health. In Proceedings of Persuasive’09.

Digital Library

[18]

Galesic, M. and Garcia-Retamero, R. 2011. Graph literacy: A crosscultural comparison. Medical Decision Making 31, 444--457.

[19]

Geigerenzer, G., Gaissmaier, W., Kurz-Milcke, E., Schwartz, L., and Woloshin, S. 2007. Helping doctors and patients make sense of health statistics. Psych. Sci. Public Int. 8, 2, 53--96

[20]

Howarth, E. and Hoffman, M. S. 1984. A multidimensional approach to the relationship between mood and weather: Brit. J. Psych. 75, 15--23.

[21]

Kahn, E. B., Ramsey, L. T., Brownson, R. C., Heath, G. W., Howze, E. H., Powell, K. E., Stone, E. J., Rajab, M. W., Corso, P., and the Task Force on Community Preventive Services. 2002. The effectiveness of interventions to increase physical activity. A systematic review. Amer. J. Prevent. Med. 22, 73--107.

[22]

Kasper, S., Wehr, T. A., Bartko, J. J., Gaist, P. A., and Rosenthal, N. E. 1989. Epidemiological findings of seasonal changes in mood and behavior. Arch. Gen. Psych. 46, 823--833.

[23]

Klasnja, P., Consolvo, S., and Pratt, W. 2011. How to evaluate technologies for health behavior change in HCI research. In Proceedings of CHI’11.

Digital Library

[24]

Li, I. 2011. Personal informatics and context: Using context to reveal factors that affect behavior. Ph.D. dissertation.

Digital Library

[25]

Li, I., Dey, A., and Forlizzi, J. 2011. Understanding my data myself: Supporting self-reflection with Ubicomp technologies. In Proceedings of UbiComp’11.

Digital Library

[26]

Lipkus, I. 2007. Numeric, verbal, and visual formats of conveying health risks: Suggested best practices and future recommendations. Med. Decis. Making 27, 5, 696--713.

[27]

Maitland, J., Sherwood, S., Barkhuus, L., Anderson, I., Hall, M., Brown, B., Chalmers, M., and Muller, H. 2006. Increasing the awareness of daily activity levels with pervasive computing. In Proceedings of Pervasive Health’06.

[28]

Mamykina, L., Mynatt, E., Davidson, P., and Greenblatt, D. 2008. MAHI: Investigation of social scaffolding for reflective thinking in diabetes management. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’08). ACM, New York, 477--486.

Digital Library

[29]

Medynskiy, Y. and Mynatt, E. 2010. Salud!: An open infrastructure for developing and deploying health self-management applications. In Proceedings of Pervasive Health’10, 1--8.

[30]

Patrick, K., Raab, F., Adams, M., Dillon, L., Zabinsky, M., Rock, C., Griswold, W., and Norman, G. A. 2009. Text message-based intervention for weight loss: Randomized controlled trial. J. Med. Internet Res. 11, 1.

[31]

Prochaska, J. and Velicer W. F. 1997. The transtheoretical model of health behavior change. Am. J. Health Promot. 12, 1, 38--48.

[32]

Rachlin, H. 2004. The Science of Self Control. Harvard University Press, Cambridge, MA.

[33]

Thaler, R. and Sunstein, C. 2008. Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press.

[34]

Tollmar, K., Bentley, F., and Viedma, C. 2012. Mobile Health Mashups: Making sense of multiple streams of wellbeing and contextual data for presentation on a mobile device. In Proceedings of Pervasive Health’12.

[35]

van Eck, M., Berkhof, H., Nicolson, N., and Sulon, J. 1996. The effects of perceived stress, traits, mood states, and stressful daily events on salivary cortisol. Psychosom. Medi. 58, 447--458.

[36]

WHO. WHO-Five Well-being Index (WHO-5). http://www.who-5.org/ 2011 (Last accessed 9/21).

[37]

Merriam-Webster. 2011. Well-being. Merriam-Webster.com (Last accessed 9/21).

Cited By

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https://dl.acm.org/doi/10.1145/3679318.3685345
Lin GLi BLi JZhao CTruong KMariakakis A(2024)Users' Perspectives on Multimodal Menstrual Tracking Using Consumer Health DevicesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785758:3(1-24)Online publication date: 9-Sep-2024
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Health Mashups: Presenting Statistical Patterns between Wellbeing Data and Context in Natural Language to Promote Behavior Change

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Published In

cover image ACM Transactions on Computer-Human Interaction

ACM Transactions on Computer-Human Interaction Volume 20, Issue 5

November 2013

129 pages

ISSN:1073-0516

EISSN:1557-7325

DOI:10.1145/2533682

Issue’s Table of Contents

Copyright © 2013 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: 01 November 2013

Accepted: 01 June 2013

Revised: 01 June 2013

Received: 01 December 2012

Published in TOCHI Volume 20, Issue 5

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Cited By

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https://dl.acm.org/doi/10.1145/3679318.3685345
Lin GLi BLi JZhao CTruong KMariakakis A(2024)Users' Perspectives on Multimodal Menstrual Tracking Using Consumer Health DevicesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785758:3(1-24)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678575
Jung GLee UKostakos VKay JHoang T(2024)Counterfactual Explanations in Personal Informatics for Personalized Mental Health ManagementCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678427(756-760)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678427
Saksono HParker A(2024)Socio-Cognitive Framework for Personal Informatics: A Preliminary Framework for Socially-Enabled Health TechnologiesACM Transactions on Computer-Human Interaction10.1145/367450431:3(1-41)Online publication date: 30-Jun-2024
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Khalid MKlerman EMcHill APhillips ASano A(2024)SleepNetProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435088:1(1-34)Online publication date: 6-Mar-2024
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Loerakker MNiess JBentvelzen MWoźniak P(2024)Designing Data Visualisations for Self-Compassion in Personal InformaticsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314487:4(1-22)Online publication date: 12-Jan-2024
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