How can the design of biofeedback data visualization
collaborate with digital interactions to assist patients/users
in executing a self-oriented recovery process?
1. How can available personal technologies collaborate effectively to track, report, and
mediate health data generated by a patient/user through daily activities help him/her form
correct habits during a recovery process?
2. How can the design of interactions with data visualizations, analysis and archives help to
provide clarification so that users can build effective self-awareness of their performance
and satisfy their motives during a self-oriented recovery process?
3. What data is useful during a tracked activity and what data contributes long-term to the
recovery path? How would the design of interaction in these cases help build meanings and
connections between short-term and long-term data visualization?
The following three scenarios show three different potential ways of how users connect to the self-oriented recovery system on different occasions. Depending on their specific needs and what they are doing in real time, each user may not go through the entire system and functions. Thus, the entrance (where the user starts his/her interaction with the system) may vary.
Marco is a second year graduate student. He’s now working at his studio on a project. A few minutes later, Marco’s wearable device started to vibrate. Marco then sees a pumping circle show up on his watch. Simultaneously, a notification sent from his iPhone shows a picture on the locked screen. The notification indicated that specific movement has been tracked from his patella. He suddenly realized that he had been crossing his legs for a long time while he was concentrating on his work. Because he is suffering from Chondromalacia patella, he shouldn’t have crossed his legs, but it is hard to control unconscious behavior.
He unlocked the screen, the system directly opened the application, and an animation indicated that pain is occurring around his left knee. By clicking the next step, he can report details about this tracked movement. Marco swiped the options that were available to describe what he was doing. He found the option of “crossing legs”, and chose the pain type of “numb pain” with pain level of “1”, which means “acceptable” because the pain didn’t even attract his attention. Marco is then presented with a pattern consisting of many small dots, to represent the numb pain feeling, and uses his finger to paint on the screen over a leg anatomy to mark where exactly he felt the pain happening. By saving the report, Marco then got access to the data center, which shows all the movements that had been tracked so far today listed in a vertical way on the left side of the screen.
The system automatically showed the long-term data visualization according to the movement that just been reported, which indicated the reported times of “crossing legs” for the past whole week. Marco tapped on monthly view, he realized though he kept forgetting not to cross his legs, the trend showed he was performing better. Marco also noticed that most of the pains that happened recently are acceptable pains through the “pain types” long-term data visualization right under the movement types data visualization. Switching from “Instant data” to “Pain types” visualization, the comparison of pain types and pain levels percentage data through the whole month Marco saw on the screen. He found “numb pain” held 20% of all types of pains the past one month, and 40% of the total pains are acceptable pains. Compared with the beginning several weeks of recovery, Marco felt the sense of accomplishment and motivated. Thus, he shared the recovery database with his doctor right away to update with his doctor.
After 4 months self-recovery process with the assistance from the system, Marco fully recovered from Chondromalacia patella. And all the recovery data has been saved as an archive in his profile, meanwhile, Marco decide to join Chondromalacia patella group to share his recovery data and experience with other patients.
Emily, a User Experience designer who has been worked at a local company for 4 years. Two weeks ago, Emily felt sharp pain at her wrist of the right hand. She went to see doctor, and was told she got carpal tunnel problem. The bright side is she doesn’t have to take a surgery at this moment, and she need to try to do some exercises regularly.
It’s a Monday noon after lunch; Emily takes out her phone and opens the recovery assistant system choosing the exercise option directly. She then takes a quick warm up report, which the screen shows a gesture of hand anatomy each time while she can try to do the same and report if she can do it “easily”, “painfully”, “hardly”, or “never” by choosing an appropriate word in the list. After several gestures report, Emily chooses one of the carpal tunnel exercises to start with. Firstly, a short animation shows basically how to execute the exercise. While Emily is doing the same as the animation showing, a notification send to both her wearable device and smartphone. Her wearable device on her exercise wrist start a one-second long vibration, and simultaneously her smartphone screen shows a picture that indicates the wrong gesture she might doing right now. After the exercise, Emily goes to the exercise performance data report center in the system, there she see her exercises frequency, how many wrong gestures tracked from each exercise, as well as her performance improvement over time according to the gesture she reported each time at the beginning of exercise. The whole process takes Emily only 7 minutes, and then she goes back to her work again.
Nolan has been suffering sharp pain around his left knee for one month, and then he decides to see his doctor. The doctor told him that he got Chondromalacia patella problem, but the bright side is he doesn’t have to take surgery to recover from it. But Nolan also been told that he had to keep himself avoid from certain types of leg postures, those postures sometimes may not cause sever pains, but they could accumulate bad result over time, which is exactly what happened to Nolan. Thus Nolan has to recovery from Chondromalacia patella all by himself through over three months process.
It is really hard for Nolan to keep in mind of all types of leg postures that he shouldn’t do all the time. Thus he starts a new recovery in the self-oriented recovery assistant system. He taps “Start a recovery”, then he see a human body anatomy, which allows him to choose specific body part to locate a certain problem. By choosing the left leg patella, Nolan starts a recovery plan. He set up the estimate recovery time with 3 months, and selects a few of postures listed in the category that will start to track his daily movement. In order to get real time reminder, Nolan goes to the “Notification” center in the system to set up specific notifications that will send to him whenever certain movement has been tracked from sensor. Though every movement will be tracked, Nolan doesn’t want himself will receive notification from every single time. Thus, he set up one notification that will only sent him notification during the daytimes of weekdays, and only the every 5th movement being tracked that will he receive a notification. Nolan sets the wearable device will keep vibrating, while his smartphone will simultaneously shows up an image as well as a short message. He set the message as ”you are doing something wrong with your knee now”, and also turns on the Siri, which will read the message out. In order to make data become more accurate and better help in reflection, Nolan also activates the data report process, which will allow him to add detail information whenever he receives a notification.