Visual Toggle
In research we ask our participants to give us their data. Moreover, many studies have no data deletion date, so participants must trust researchers to guard their data in perpetuity. This is a big ask from participants--but what are we giving them in return? Researchers should give participants access to their own data. This practice follows the basic research principles of beneficence and respect for autonomy. In addition, if participants derive value from participating, they are more likely to stay in the study.

But don’t stop at giving participants access to their data. Help them make use of their data by giving them more than one way of viewing it. Let participants explore data in different ways to gain insights about their health. Allow participants to easily switch back and forth between different views of the data. And be upfront about the limits of the data. Make sure to tell participants that their research data cannot be used for medical self-management without the input of a medical provider.
Participants struggle to interpret data that is poorly presented.
Participants approach their data with many kinds of questions. Presenting data in the wrong format makes it more difficult for them to answer those questions. For instance, presenting stepcount data only over the last week, or as a daily number, makes it more difficult for participants to identify trends.
Worst Case Scenario: Participant Can't Make Use of Their Data
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Give participants an easy way to explore their data, so that they benefit from participating in the study.
Give participants access to their own data within the app. In addition, build ways for participants to explore their data. Think about the kinds of questions participants might want to answer. Help participants explore correlations between different data types and view data over different time frames. And keep the interface simple, so it is easy to use.
Best Practice: Toggles to Support Different Views of Data
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What to prepare?
Step 1
List all the data types being collected about the participant.
Make sure that all, or at least as many of them as possible, are accessible by participants.
Step 2
Identify what kinds of questions participants are likely to have.
Are your participants likely to be concerned about trends over time? What about what symptoms happen at the same time? Think through the different kinds of questions you anticipate your participants will have. Do some user research to find out what kinds of things participants want to know.
Step 3
Determine what kinds of visualizations make most sense.
Think about both what is feasible for you to build and what your participants will be able to understand. Remember that many adults struggle with reading graphs, so offer options that are not graphs (like timelines of numbers). In addition, since many people have low graph literacy, choose common graph formats that people are more likely to be familiar with. Think about what axes and scales will make most sense for your users. For example, users may be less familiar with things like logarithmic scales. However, remember that some data types should be represented as raw numbers, while other types may need to be normalized or transformed in some way in order to be more easily understood.
Step 4
Keep it simple and usable.
Build only what you can maintain for at least the length of the study. Although you should offer more than one option for exploring data, you don’t need to reinvent R or Excel. Instead, keep your eye on simplicity and ease of use. Small and fine-grained controls can be hard to use on a phone. Strike a balance between making visualizations informative and stripping out unnecessary detail. If needed, consider a web interface for more complex data exploration. And don’t forget to do user testing to make sure your interface is usable!
Case Studies
Better Visualizing of Fitness App Data Helps Discover Trends, Reach Goals
Michelle Ma
See It, Believe It. The Web Visualization Library
David Haddad
A Systematic Review of Patient-Facing Visualizations of Personal Health Data
MR Turchioe, A Myers, S Isaac, D Baik, LV Grossman, JS Ancker, RM Creber
Understanding Literacy and Numeracy
Centers for Disease Control and Prevention
Health History Timeline
Katie McCurdy
Mistakes, We've Drawn A Few
Sarah Leo
You’ve Been Reading Charts Wrong. Here’s How a Pro Does It.
Christopher Ingraham
Visualizing Health
University of Michigan
Data Visualizations
Institute for Health Metrics and Evaluation
Visual Toggle Patterns