Clarifying interactions

XVII.1 January + February 2010
Page: 76
Digital Citation

FEATUREIs wellness informatics a field of human-centered health informatics?


Authors:
Rebecca Grinter, Katie Siek, Andrea Grimes

The past decade has seen an explosion of health-related, human-centered computing research and practice focused on wellness (e.g., good nutrition and exercise promotion) to help people avoid the need for medical care. And while health informatics may appear to be the obvious home for these activities, it is a discipline that has focused on the design and evaluation of systems to process health care data and, through that, aid in patient treatment. Given the ubiquity of wellness systems, we think it’s time to create a wellness informatics community of researchers and practitioners, and through this define opportunities and challenges in the design and evaluation of information and communications technologies (ICTs) that help people stay well.

Wellness informatics is a human-centered computing science focused on the design, deployment, and evaluation of human-facing technological solutions to promote and manage wellness acts such as the prevention of disease and the management of health [1]. It is human-centered because it requires that technologies are married with innovations in how the ICT communicates with the user, in ways that are psychologically, sociologically, culturally, and societally relevant—without which wellness will not be promoted and sustained. It is also a computing science because it requires hardware and software innovations to make devices that people can use anywhere and everywhere wellness occurs.

We offer some themes that we think characterize wellness informatics:

Data sources are numerous. In wellness, the individual manages their health information, and consequently relies on multiple data streams that come from numerous sources [2]. Wellness informatics applications may collect data from the patient themselves (e.g., exercise completed), or incorporate data from the medical and health communities about what constitutes healthy practice (e.g., the appropriate amount of exercise). Wellness informatics applications also frequently have to collect other behavioral measures of health outcomes (e.g., how long an individual stays engaged with a particular practice). Other sources of data inputs to the system stem from the socioeconomic and cultural nature of wellness (e.g., what traditions influence cooking practices). These sources have a greater degree of heterogeneity than some fields in health informatics, whose data sources come from health care and medical sources.

Example: During a design workshop with low-income caregivers, we met single moms Maria and Sophia, who each have three children and work long hours at multiple jobs. They told us about their typical hectic day. After 12 hours on her feet working at McDonald’s, Maria picks up dinner at Burger King and heads home. She cannot eat fast food anymore because of a health issue. Instead, she takes a nap while her children eat dinner and watch television. Her brother arrives to watch the kids and wakes her up in time to go to her night job. Maria’s family does not live near affordable grocery stores, where they could purchase quality food. They do, however, pass multiple fast-food restaurants coming and going to their homes.

In this scenario, we would ideally know Maria’s health goals, dietary restrictions, shopping list, context, and schedule. We envision a mashup system that merges these personal inputs with publicly available data streams, such as public transportation and maps; to recommend that in her 30-minute break, she could walk three blocks and purchase good produce from a store near her work.

Challenges: Understanding all the sources of data that might be necessary to incorporate into the system that not only support the user’s health goal, but also facilitate steps necessary to achieving that goal. Another challenge is that although human-centered computing research is well suited to address this challenge, we still must build on social science methodologies to identify the needs of the diverse target populations participating in wellness activities.

The end user (the person staying well) is the primary user of the information. In many health informatics fields, the end user is a source of data (e.g., input into clinician-used systems). By contrast, in wellness, the end user is not just a producer of data, but also the primary consumer of information. For example, consider how socioeconomic status and culture affect the design of technologies. A nutrition system designed for a particular individual must account for how easy it is to find inexpensive, healthy food locally (or the time/money costs of leaving the neighborhood to shop) [3]; and whether the food recommendations fit the food-consumption practices of that person [4]. Finally, those applications must present his information in ways that make sense, such as being actionable [4, 5].

Example: Extending our previous example, Maria explained that the only time she thinks about herself is when she puts on her make-up each morning. Thus, she designed a make-up clamshell case where the mirror could look at her and let her know what she needs (in this case, more water) [6]. Although she constantly thinks about the wellness of her family, she cannot always be there because of work constraints. Her solution to this problem was a portable game system that would provide children points on their game for each task they completed—in this case, drinking more milk. The system could also remind the child of other family and wellness-based tasks, such as eating as a family to engender communication.

Challenges: Once the hurdle of accurately identifying data and processing the data in context is cleared, we must address the issue of presenting this aggregate and contextualized data. It is not just enough to provide all the necessary information; it needs to be given in such a way that the end user can make sense of it on the screen size in use. An additional challenge is that the end user will presumably be using wellness tools throughout their lives in different capacities; thus, we must be able to present 80-plus years of an individual’s data in an understandable way that encourages reflection and an understanding of appropriate cause and effect.


Wellness informatics is a human-centered computing science focused on the design, deployment, and evaluation of human-facing technological solutions to promote and manage wellness acts such as the prevention of disease and the management of health.

 


The individual, group, and community are emphasized as the appropriate levels of data granularity. Wellness is personal: Consider systems that support individual reflection and learning [4, 7]. But many wellness informatics systems also support communication among friends, family, and coworkers [2]. Such collaborations provide either emotional (e.g., through praise) or informational (e.g., through education about healthy lifestyles) support. This contrasts with health informatics that often emphasizes population-based data (e.g., public health and hospital information systems).

Example: In a field study, we met Mary and David, who explained they want their children to be aware of their exercise habits, because they value transparency [8]. So they were excited by an application that could help them to visualize their fitness patterns as a way to promote family conversations about health. Simultaneously, they hesitated to share this information because of a conflicting value: protection. For example, if a technology made Mary’s habits visible to her children, what happens when she cannot exercise because of increased obligations at work? Her children might grow concerned or potentially misinterpret such information. David and Mary’s competing values have design implications.

Challenges: Wellness informatics highlights the importance of interpersonal, online, and local communities that support individuals; however, open research questions remain about how we design systems that navigate what is shared and how. So while human-centered computing has a history of exploring value interaction, this domain presents new challenges around wellness information.

There may be little or no interaction with the health care establishment. A focus on wellness also suggests a different relationship between the end user and the health care establishment. Wellness systems may contain data that does not come from medical sources but that still provides value to the end user. For example, a person decides that they need to make life adjustments as a result of discussions with family and then implements those changes. These changes may draw on medical information but not involve, for example, any direct consultations.

Example: EatWell is a system that we developed to promote the sharing of information among community members [4]. It is an audio-based technology, and we encouraged people to call into the system and leave memories (tips for healthy eating in their community) and listen and comment on other people’s suggestions. The tips people shared included healthy cooking methods, where to eat out in the neighborhood, and where to find fresh vegetables. When our participants evaluated it, many spoke enthusiastically about the tips, saying that they were meaningful to them because they were community focused. This encouraged people to share and apply the information.

Challenge: One drawback of diminished interaction between the health-care establishment and the end user is that the medical soundness of the information can be called into question. At the same time, people can and want to share information, so a design trade-off exists between encouraging wellness behaviors and risking that some will not be as medically sound as others. If our aim, as we discussed earlier, is to help people avoid needing the formal health-care system through wellness applications, then how can we let people redefine health to meet their own needs (e.g., walking up the stairs more easily) that have not been clinically proven to improve health? We must evaluate if redefining health truly helps the individual.

We offer wellness informatics as a starting point for defining a set of common characteristics that can frame the fundamental contributions that we have collectively made in this space. We also believe it may help in defining the challenges that still remain. For example, what is the full range of social and cultural values that influence wellness practices and consequently need to be accounted for in the design of health ICTs? How do we evaluate wellness informatics solutions, including addressing how frequently they have to be used in order to make an impact? We are not suggesting that people are not currently working on these questions; rather, we want to argue that wellness informatics could be used to draw together the many distinct projects that now compose the space. In conclusion, our goal in writing this article was to begin the all-important process of engaging in dialogue with our colleagues working on health-related, human-centered computing research. We offer this as a starting point for what we hope will be a series of conversations that collectively define wellness informatics. We look forward to hearing from you; to encourage conversation, we have created a Facebook group called “Wellness Informatics.”

References

1. By human facing we mean technologies that people are using i.e., adopting, accepting, and appropriating it into their lives; people are empowered through their usage; and it’s readily accessible technology.

2. Pratt, W., Unruh, K., Civan, A., and Skeels, M. “Personal Health Information Management.” Communications of the ACM 49, 1 (2006): 51–55.

3. Maitland, J., Siek, K.A., and Chalmers, M. “Persuasion Not Required: Improving our Understanding of the Sociotechnical Context of Dietary Behavioural Change.” In Proc. 3rd International Conference on Pervasive Computing Technologies for Health Care 2009, IEEE.

4. Grimes, A., Bednar, M., Bolter, J.D., and Grinter, R.E., “EatWell: Sharing Nutrition-Related Memories in Low-Income Communities.” In Proc. ACM Conference on Computer Supported Cooperative Work, ACM Press (2008): 87–96.

5. Maloney-Krichmar, D. and Preece, J. “A Multilevel Analysis of Sociability, Usability, and Community Dynamics in an Online Health Community.” ACM Transactions on Computer-Human Interaction, 12, 2 (2005): 201–232.

6. Siek, K.A., LaMarche, J.S., and Maitland, J. Bridging the Information Gap: Collaborative Technology Design with Low-Income At-Risk Families to Engender Healthy Behaviors. OzCHI 2009, 89–96.

7. Consolvo, S., Everitt, K., Smith, I., and Landay, J.A. “Design Requirements for Technologies that Encourage Physical Activity.” In Proc. ACM Conference on Human Factors in Computing Systems (CHI 2006). ACM Press (2006): 457–466.

8. Grimes, A., Tan, D. and Morris, D. “Toward Technologies that Support Family Reflections on Health.” In Proc. ACM 2009 International Conference on Supporting Group Work (GROUP ‘09), ACM Press (2009): 311–320.

Authors

Rebecca E. Grinter is an associate professor in the School of Interactive Computing at Georgia Tech. Her research focuses on problems at the intersection of computing and humanity. Specifically, she applies empirical methods to understand how information and communications technologies (ICTs) are both human-built and human-used machines. Her research has shown how human-centered problems affect the production of complex technologies, and how patterns of appropriation shape system use. Grinter received her Ph.D. in information and computer science from the University of California, Irvine. Prior to joining the Georgia Institute of Technology, she worked in the computer science division of Bell Laboratories and in Xerox PARC’s Computer Science Laboratory.

Katie A. Siek is an assistant professor in computer science at the University of Colorado at Boulder, where she leads the Wellness Innovation and Interaction Lab. Her primary research interests are in human-computer interaction, health informatics, and ubiquitous computing. More specifically, she is interested in how sociocentric technology interventions affect personal health and well-being. Her research is supported by the National Institutes of Health, the Robert Wood Johnson Foundation, and the National Science Foundation, including a five-year NSF CAREER award. Prior to her appointment at Colorado, Siek completed her Ph.D. and M.S. at Indiana University-Bloomington in computer science and her B.S. in computer science at Eckerd College.

Andrea E. Grimes is a human-centered computing Ph.D. candidate at Georgia Tech. Her research lies within the fields of human-computer interaction and computer-supported cooperative work (CSCW). In particular, she studies how technology can be designed to fit within specific socio-cultural contexts. Much of her work has focused on designing technology to address diet-related health disparities in the African-American population. This research has involved designing and evaluating a nutrition-oriented mobile game and a community-based information-sharing application. Her other research includes examining the future of human-food interaction research in HCI and designing technologies to support family health. Grimes received a B.S. in computer science from Northeastern University. She is a Microsoft Research Fellow and was an NSF Graduate Research Fellow, a Google Anita Borg Scholar, and she received a Yahoo! Key Technical Challenge Grant to support her dissertation research.

Footnotes

DOI: http://doi.acm.org/10.1145/1649475.1649494

Figures

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