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XXV.1 January + February 2018
Page: 62
Digital Citation

Rethinking assumptions in the design of health and wellness tracking tools


Authors:
Sean Munson

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Today’s interactions with technology leave digital traces that are a potential font of insights into our lives. Emails contain receipts of our social and business transactions. Phones record our locations, estimate our physical activity, and infer how we traveled between different places. Wearables collect increasingly reliable and detailed data about our physical activity, while researchers keep finding ingenious ways to repurpose the sensors in personal devices to collect an ever broader range of data about our health and the everyday factors that affect it.

back to top  Insights

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In 2010, Ian Li, Anind Dey, and Jodi Forlizzi published a model of personal informatics [1]. This five-stage model consists of preparing, collecting, integrating, reflecting, and acting on personal data, the digital traces and sensor data described above. It has served as a guide for researchers and designers creating tools that help people track and make sense of personal data to address health concerns and other goals.

Like any model and the products it inspires, the five-stage model and people’s interpretations of it contain many assumptions.

As personal informatics capabilities become embedded in more products and personal data touches more facets of life, these assumptions merit another look. How broadly do they hold up? Which use cases—and who—do they support, and which do they exclude?

In this article, I discuss some of the assumptions I have made in my own research and that I have encountered in working with my students, professionals, and other researchers. I draw extensively on my group’s work on food tracking—one of the most commonly tracked health behaviors, but also one of the most difficult to aggregate and use effectively [2]—to illustrate these assumptions, but they occur across health tracking.

back to top  Assumption 1: Action is the Goal

The personal informatics model ends in action, and the most prevalent application of personal informatics has been to support behavior change: losing weight, exercising more, or adopting a particular diet or reducing consumption of a particular type of food.

However, when researchers survey or interview people who use self-tracking tools, we find that action is not the only goal [3]. Yes, some people want actionable insights. Others are just curious—they want to know where they stand relative to others; they want to get a sense of their current routine; or they just want to know how often they experience symptoms. Other people track because they want to have a record of their behavior, or just because they love having the data.

Still others track because it enables other incentives. For example, some people use Fitbits or other physical-activity tracking tools to receive incentives as part of their workplace wellness programs. Unfortunately, these incentives can create conflicts in goals. For example, imagine you are a swimmer, but your workplace wellness program only lets you automatically log steps. You might resist and keep swimming, or you might be motivated by the financial incentives (gift cards, a discount on your deductible) and replace swimming with walking—even though you enjoy it less and it may not support your health goals as well as swimming. The wellness program might even see this as a success, since it does not know you gave up swimming to achieve all those steps you logged.

When applications support only one goal, they can force people to abandon a product when their goals or priorities change. Not only that, but because data is often stored in application- or platform-specific silos, people also often have to abandon all of their data when they change products or platforms. In our research on menstrual tracking apps, we have seen how an assumed goal in an application can conflict with a user’s intent [4]. Many application designers assume that people track either to prevent pregnancy or to become pregnant. Even if that covered every goal for someone using a menstrual tracking application—and it doesn’t—it’s a mistake to assume that people will always have this goal or that they will want to stop tracking if their goal changes.

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We’re starting to see some good progress here. Google Fit and Apple Health allow people to share and reuse personal data between applications, though many companies are reluctant to add the data their products collect to these personal-data libraries, even as they pull data from them. Withings, a connected-health platform from Nokia, also recently updated its weight apps to enable a pregnancy mode, allowing people to continue using the same tools and data while reflecting on changed goals or status.

back to top  Assumption 2: People Will Track Continuously and Indefinitely

In research and commercial products, I see two common patterns for use of personal informatics tools. In the first—closest to the pattern described by Li et al.—people use the tools long enough to gain actionable insights, make some changes, and maybe repeat if they aren’t satisfied with the outcomes. In the second (e.g., Fitbits), people make ongoing use of the tools to monitor their behavior and outcomes, continually fine-tuning their behavior.

In practice, people sporadically use tracking tools. For some tracking approaches, such as a food journal, most people find it too burdensome to do day in and day out. They track for a bit, and then the device breaks and they don’t replace it, or they just forget to charge it. Others find regularly attending to their data to be too burdensome. As a result, many people alternate between tracking and lapsing, often with longer periods of abandonment.

To better describe people’s actual tracking practices and help designers better envision how to support them, we created a model of personal informatics in everyday life, the Lived Informatics Model (Figure 1) [3].

ins03.gif Figure 1. The Lived Informatics Model describes the often concurrent processes of tracking and acting on data alongside collecting, integrating, and reflecting on that data. It also surfaces the common steps of lapsing and resuming tracking, and it further breaks out the critical steps of deciding to track and selecting tools.

When applications support only one goal, they can force people to abandon a product when their goals change.


The Lived Informatics Model points to a range of opportunities, including:

  • Tools to better support people who are resuming from lapsed tracking
  • Designs that help people get some benefits in a limited amount of time, or from a lower-burden form of tracking, even if those benefits are less than what they might get from ongoing, more intensive tracking
  • Designs that better support people who have lapsed, whether or not they want to return to tracking.

back to top  Assumption 3: People Primarily Need Support for Self-Regulation, Maybe with a Little Social Pressure

Most personal informatics tools follow a pattern that roughly includes: 1) collect data about behaviors, 2) present it back to people on a graph or maybe a map, 3) some reflection happens, leading to 4) action. This works fine for people who need a bit more visibility into their behavior throughout the day, such as someone who just needs to see they haven’t walked as much as they think they have in a busy day.

Seeing a problematic behavior, however, is not the same as finding an opportunity to change it. When someone doesn’t see a way to walk more in their busy day, feedback that they won’t meet their step goal is not motivating—it just causes them to feel guilty, stressed, or frustrated. Other people have questions that go beyond what in-the-moment feedback can answer. Better awareness of one’s symptom level is not enough for someone struggling to understand complex relationships between what they eat, stress, physical activity, and the symptoms they face.


People need better personal informatics tools both to track the right data and to make sense of that data.


People need better personal informatics tools both to track the right data and to make sense of that data. This includes tools that help people answer their personal health questions by scaffolding a scientifically valid process of forming hypotheses and conducting self-experiments to test them [5].

I’m also not ready to write off graphs. While recent work shows mixed literacy for the types of graphs shown in personal informatics applications, my group has found that people can use graphs to analyze their data if those visualizations are adequately scaffolded and grounded in the individual’s data [5,6].

back to top  Assumption 4: More Data is Always Better

When designing new tools, it’s easy to assume that increased sensor data and lower-burden journaling tools should help provide users with more value. While that’s often true, adding more data and more resolution can be counterproductive at times. To reduce the burdens associated with tracking and making sense of data, I encourage designers to also consider what is the “minimum viable data” to help someone achieve their goal for tracking.

Consider food. Many food-journaling applications are oriented around calorie-tracking. This may certainly work for some people, but the orientation around calories is burdensome and can even nudge people away from healthy eating [2]. Beyond these challenges, a calorie focus also presents food journalers with an experience quite distant from the first things most people want to think of when sitting down for a meal. When you imagine a meal, you probably think of the tastes, the smells, the texture, and maybe the company—not spreadsheets.

To consider an alternative approach, colleagues and I built Food4Thought [7]. Rather than requiring users to keep a journal of everything they eat, Food4Thought prompts users to complete daily challenges, which they can do by simply taking a photo of a food that meets the challenge. We experimented with both nutritionally prescriptive challenges (e.g., “eat something high in fiber”) and non-nutritionally prescriptive challenges (e.g., “eat something that reminds you of your teenage years”). In a field experiment, both forms of challenges increased people’s reported food mindfulness, and the non-nutritionally prescriptive challenges created a less judgmental experience for participants.

We still need to learn whether this actually leads to healthier eating in the short or long term, but it does suggest a way forward around how designing an experience around minimal data can further a goal. Our research on self-experimentation to test the relationships between foods consumed and symptoms [5] similarly reduces the work to modifying and tracking only one meal a day and the resulting symptoms, rather than tracking symptoms and foods all day long.

back to top  Assumption 5: Self-Tracking is Self-Tracking

In everyday life, the decisions, behaviors, and outcomes related to the data people track are interconnected and often collaborative. Family members influence each other’s food choices. A bad night’s sleep can affect the whole family the next day. Managing chronic conditions requires marshaling the resources of family, friends, and other caregivers. Patients struggling to make sense of data turn to their health providers; others turn to retirement planners, dietitians, and personal trainers to answer questions.

Self-tracking rarely is truly individual, yet when discussing health trackers and other tools, the HCI and health communities often use the terms personal informatics, self-tracking, or self-management. Our use of these terms, and of models focusing on the individual, lead to products that are similarly focused on the individual.

Yes, many fitness tools allow data sharing, but typically in shallow ways that best support competition or other comparisons [8]. Just as tools fall short of providing many people with actionable insights, they also fail to help families better understand how family members’ decisions affect each other or provide opportunities to adjust their collective behavior.

For a variety of behaviors and goals, personal informatics data offers families new opportunities to understand family members’ behavior and experiences. To do so, tools must support effective—and privacy- and impression-preserving—sharing of data as well as coordinating action based on insights gleaned [9].

People also need better tools for sharing their data with peers and experts [10]. Patients may bring weeks or months of self-tracked data to a clinical visit and ask their doctor to help them make sense of it, reviewing the data only on their mobile phones. Other times, doctors and their patients agree to use paper journals, which can be readily customized to their goals but result in data that is harder to aggregate and understand than digital data.

back to top  Where Should We Go?

As more personal informatics tools find their way into the wild, we see a rich set of goals, uses, and non-uses. To understand and inspire for this breadth of goals and uses, we need more flexible and more inclusive models for personal informatics and self-tracking, including models that account for changing goals and lapses in use. We should also continue to explore designs that reduce the burden of tracking and analyzing data alongside approaches that help people get more value from the data they do track. Finally, we need models and designs that account for the often-collaborative processes of tracking, understanding, and acting on data between families, caregivers, peers, and experts.

back to top  Acknowledgments

This article covers research with many great colleagues, including doctoral students Christina Chung, Daniel Epstein, Elena Agapie, Arpita Bhattacharya, Jessica Schroeder, and Ravi Karkar, post-doctoral scholar Laura Pina, and faculty Julie Kientz, James Fogarty, Gary Hsieh, Jasmine Zia, Allison Cole, and Roger Vilardaga. This research was funded in part by the National Science Foundation, the Agency for Healthcare Research & Quality, Microsoft, Intel, and the University of Washington, though the views expressed are my own.

back to top  References

1. Li, I., Dey, A., and Forlizzi, J. A stage-based model of personal informatics systems. Proc. CHI’10. ACM, New York, 2010, 557–566.

2. Cordeiro, F., Epstein, D.A., Thomaz, E., Bales, E., Jagannathan, A.K., Abowd, G.D., and Fogarty, J. Barriers and negative nudges: Exploring challenges in food journaling. Proc. CHI’15. AMC, New York, 2015, 1159–1162.

3. Epstein, D.A., Ping, A., Fogarty, J., and Munson, S.A. A lived informatics model of personal informatics. Proc. PUC’15. ACM, New York, 2015, 731–742.

4. Epstein, D.A., Lee, N.B., Kang, J.H., Agapie, E., Schroeder, J., Pina, L.R., ... and Munson, S. Examining menstrual tracking to inform the design of personal informatics tools. Proc. CHI’17. ACM, New York, 2017, 6876–6888.

5. Karkar, R., Schroeder, J., Epstein, D.A., Pina, L.R., Scofield, J., Fogarty, J., Kientz, J.A., Munson, S.A., Vilardaga, R., and Zia, J. TummyTrials: A feasibility study of using self-experimentation to detect individualized food triggers. Proc. CHI’17. ACM, New York, 2017, 6850–6863.

6. Schroeder, J., Hoffswell, J., Chung, C.F., Fogarty, J., Munson, S., and Zia, J. Supporting patient-provider collaboration to identify individual triggers using food and symptom journals. Proc. CSCW’17. ACM, New York, 2017, 1726.

7. Epstein, D.A., Cordeiro, F., Fogarty, J., Hsieh, G., and Munson, S.A. Crumbs: Lightweight daily food challenges to promote engagement and mindfulness. Proc. CHI’16. ACM, New York, 2016. 5632–5644.

8. Epstein, D.A., Jacobson, B.H., Bales, E., McDonald, D.W., and Munson, S.A. From nobody cares to way to go!: A design framework for social sharing in personal informatics. Proc. CSCW’15. ACM, New York, 2015,1622–1636.

9. Pina, L.R., Sien, S.W., Ward, T., Yip, J.C., Munson, S.A., Fogarty, J., and Kientz, J.A. From personal informatics to family informatics: Understanding family practices around health monitoring. Proc. CSCW’17. ACM, New York, 2017, 2300–2315.

10. Chung, C.F., Dew, K., Cole, A., Zia, J., Fogarty, J., Kientz, J.A., and Munson, S. A. Boundary negotiating artifacts in personal informatics: Patient-provider collaboration with patient-generated data. Proc. CSCW’16. ACM, New York, 2016, 770–786.

back to top  Author

Sean Munson studies, designs, and evaluates techniques for helping people collect and make sense of data about themselves and the world around them, with a focus on health. He is an assistant professor in human-centered design and engineering, and a member of the DUB Group at the University of Washington. smunson@uw.edu

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