Investigating health management practices of individuals with diabetes
Abstract
Chronic diseases, endemic in the rapidly aging population, are stretching the capacity of healthcare resources. Increasingly, individuals need to adopt proactive health attitudes and contribute to the management of their own health. We investigate existing diabetes self-management practices and ways in which reflection on prior actions impacts future lifestyle choices. The findings suggest that individuals generate and evaluate hypotheses regarding health implications of their actions. Thus, health-monitoring applications can assist individuals in making educated choices by facilitating discovery of correlations between their past actions and health states. Deployment of an early prototype of a health-monitoring application demonstrated the need for careful presentation techniques to promote more robust understanding and to avoid reinforcement of biases.
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The application's ease and simplicity played an important role not only in enhancing motivation to use the application but also in the learning process because keeping track of performed actions in self-management activities is notoriously difficult.
“Such characteristics played an important role not only in enhancing motivation to use the application but also in the learning process because “keeping track of performed actions in self-management activities is notoriously difficult” [48].”
From Long-Term Engagement With a Mobile Self-Management System for People With Type 2 Diabetes (2013) · cites this paper as [48]
Previous studies claim that a supporting tool for people with diabetes should facilitate the learning process based on personal experiences on top of necessary knowledge provided by diabetes education.
“It is also claimed in previous studies that a supporting tool for people with diabetes should facilitate this learning process [48,58].”
From Long-Term Engagement With a Mobile Self-Management System for People With Type 2 Diabetes (2013) · cites this paper as [48]
Accuracy of data obtained by a sensor is critical in terms of giving proper credit to users.
“Accuracy of data obtained by a sensor is critical in terms of giving proper credit to users [48,68].”
From Long-Term Engagement With a Mobile Self-Management System for People With Type 2 Diabetes (2013) · cites this paper as [48]
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Ghahari, R. R., Holden, R. J., Flanagan, M., Wagner, S., Martin, E., Ahmed, R., Daley, C. N., Tambe, R., Chen, E. S., Allmandinger, T., et al. (2018) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2017.11.002 · Cited by 17 · see citing works
Design Considerations for Social Fitness Applications
Chen, Y., Chen, Y., Randriambelonoro, M., Geissbühler, A., & Pu, P. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998350 · Cited by 16 · see citing works
Yu, Y., & McDonald, D. W. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610097 · Cited by 16 · see citing works
van den Brandt, A., L’Yi, S., Nguyen, H. N., Vilanova, A., & Gehlenborg, N. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456298 · Cited by 14 · see citing works
Hentschel, J., Sherugar, S. M., Zhou, R., Kameswaran, V., Chandwani, R. K., & Kumar, N. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025656 · Cited by 15 · see citing works
Human Activity Recognition for Ambient Assisted Living
Sangavi, S., & Hashim, B. A. M. (2019) · International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN) · doi:10.1109/vitecon.2019.8899701 · Cited by 15 · see citing works
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Randriambelonoro, M., Chen, Y., Geissbühler, A., & Pu, P. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2800835.2800951 · Cited by 15 · see citing works
Towards negotiation as a framework for health promoting technology
Maitland, J. (2011) · ACM SIGHIT Record · doi:10.1145/1971706.1971710 · Cited by 13 · see citing works
Understanding parent-pediatrician interactions for the design of health technologies
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A Systematic Review for Mobile Monitoring Solutions in M-Health
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Designing Wearable Interfaces for Knee Rehabilitation
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Understanding Temporality of Reflection in Personal Informatics through Baby Tracking
Louie, J., Mukund, T., Vu, C., Epstein, D. A., & Papoutsaki, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713197 · Cited by 12 · see citing works
Designing for Older People: A Case Study in a Retirement Home
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Understanding Design Tradeoffs for Health Technologies
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Design and Care for Discordant Chronic Comorbidities
Ongwere, T., Cantor, G. S., Clawson, J., Shih, P. C., & Connelly, K. (2020) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3421937.3422013 · Cited by 10 · see citing works
- Authors
- Mamykina, L., Mynatt, E. D., & Kaufman, D. R.
- Affiliations
- Siemens (Germany) (DE)
- Georgia Institute of Technology (US)
- Columbia University (US)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2006-04-22
- Pages
- 927-936
- OpenAlex
- W2162406531
- Cited by
- 175
- References
- 24
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 8 · 2023: 8 · 2022: 5 · 2021: 7 · 2020: 7 · 2019: 7 · 2018: 9 · 2017: 11 · 2016: 10 · 2015: 13 · 2014: 8 · 2013: 17 · 2012: 10
- FWCI
- 56.87
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- diabetes self-management, health management practices, health monitoring applications, chronic disease, self-monitoring, hypothesis generation, lifestyle choices, health data visualization, cognitive biases, population aging
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