Tuesday, November 20, 2012

Thumb Hypoplasia: First Test

I found out today that one of my friends has thumb hypoplasia (diagnosed with Type 1).  Since I had my tablet with a beta build of my thumb hypoplasia app, we decided to try it out and see how well it worked.

I am not a doctor or a medically trained professional.  I am a high school student who loves science and programming.  The following analysis of the data is my interpretation of the data. I have received permission from my friend ("the patient") to post this information here.

The patient told me that he has type 1 thumb hypoplasia in his right hand only.  To run this test, the patient held my Acer A500 (10.1" screen) in both hands in the landscape position.  The full application looks like this:

He swept each thumb, one at a time, across the screen in an arc that started with his thumb parallel to the edge of the screen.

My software recorded his thumb position roughly ten times a second.  The slow sample rate is due to the painting of the "data field" (the area with the grid).  Once I learn how to cache the background pixmap of a QGraphicsScene, then I can increase the performance of this app.  Once he was done with swiping his thumbs across the screen, I saved the data as a *.png and the test was over.

Once home, I started to analyze the data that I had just taken.  I printed it out to the proper size (with each grid box being one square centimeter) and began taking measurements.  

I printed out the data field image (life sized, I checked it with a pair of calipers) and used a pair of calipers and a compass to obtain my measurements.  You can see the calculations overlayed on the data above (Keep in mind that the bezel of the tablet moved the centers of the circles off of the data field.  By printing out the data field image on an 8.5" by 11" piece of paper, I had enough room to the sides to find the center of the circles using a compass.  The area calculations include the area off-screen).  The afflicted thumb (right hand) has 86% of the range of motion (ROM) of the non-afflicted thumb on the plane that we measured on.  

The patient told me during the test that he has a smaller abductor pollicis brevis, which would result in a weaker abduction of the thumb manifested in a smaller angle of abduction from the starting position (aligned with the side of the palm, parallel to the index finger).  The data gathered from this test shows that symptom.  In other words, my app works (so far)!  

My application gathers data at a relatively constant rate that varies by device (because of the screen painting time).  In the future, I hope to make each data point represent the same change in time no matter which device it is used on, but that's lower on my list of priorities at the moment.  What you can see here, though, is how hard the patient had to work to force his thumb to sweep across the screen.  The data points on both thumbs end (at the bottom of the arc) with a much closer grouping than they do at the middle of the arc.  Therefore, one can deduce that the patient was struggling harder at the end of the arc to try to push his thumb as far as it could go.  On the non-afflicted side, the close grouping at the end is only 8mm long.  On the afflicted side, however, it is 13mm long.  The tighter the grouping near the end, the harder the patient was trying to move his thumb.  The data shows us that the patient struggled harder to abduct his afflicted thumb than his non-afflicted thumb, which supports the conclusion of a smaller abductor pollicis brevis. 

The tighter grouping of the data points at the beginning of the arc (at the top) can be explained by a slow velocity as the thumb accelerates during its sweeping motion.  The generally tighter grouping of the afflicted side versus the non-afflicted side can probably be attributed to the fact that the patient did the first test with his afflicted side.  He wasn't familiar with the testing process, so he did not know how fast he could move his thumb.  More tests are needed to confirm these hypotheses.

All of my interpretations are based on a single test with only one trial, which means that the data could be inaccurate.  More testing is needed to see how effective this application will be, but these are definitely promising first results!   One of my goals for this application is to automate the data analysis process.  I also want to optimize how the data field is rendered so that each data point is taken at a known time interval.

Thanks for stopping by!

Chris Konstad

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