BME Design

Casey and Dooley do a PHP

Posted on February 17, 2016

 

Some initial realizations:

In relation to counting calories, some challenges we encountered were:

• portion size is hard to control when cooking
• knowing exactly what’s in everything is challenging
• recording everything exactly
• trying to work towards the goal while collecting data is hard

In relation to training for a ten-miler:

• figuring out how to count cross-training as data was hard
• the fact that this was a project for Casey dramatically increased motivation to do it
• I would get bored before the end of it
• process becomes very repetitive

 

After greater contemplation, our results can be summarized as follows:

• Data visualization is hard.
  • How do you present it in a way that makes sense for what it is visually and quantitatively?
  • How do you make sure it will make sense to others? (I mean they made sense to us while we were working on them… but then we presented… and found the holes.)
  • Which part of the data is important/relevant and should be prominent in the visualization?
  • How do you present meaningful findings with limited data points? (We only did eleven days…)
• It seems that you get better at this the more you do it. This was pretty much our first shot, so it makes sense that we fell a little short. After seeing everyone else present, and all the various ways that you could present data, we now have a better understanding of what works and what doesn’t. One that worked well was how Kelly presented Tom’s stretching data all in reference to a central toe line. This increased understanding should give us a better starting point for the second round of the PHP.

 

• Data collection is hard.
  • How do you motivate a partner to generate data?
  • How do you decide what aspects of their life would make relevant data points?
  • Is there a way to measure what you determine to be a relative data point?
  • Can you check that these data points are an accurate representation of what you’re actually trying to accomplish?
  • How can you minimize the effects of the data collection on the behavior trying to be measured?
• Our data collection seemed largely based on trial and error. We would attempt one intervention and see if that worked. When it failed, we would try something else. In the end it’s that wee know how to get the other to do something, but more that we know a bunch of ways that won’t get them closer to their goal. (Sort of like Edison and the light bulb.) The act of needing to collect data for our partner motivated us to collect it. (We’ve both fallen off the wagon since the project ended.) Seeing this dramatic difference between when we were collecting and now that we’re not leads us to believe that the simple act of someone else holding us accountable greatly increases the likelihood of our compliance with the intervention.

 

• Life is hard.

 

On our honor, we wrote this together. E.A.Dooley & W.C.Harrison