Alli Fellger CREU Blog 2018-2019

Once I managed to get the frame clean and my code working, I checked my results against Dr. Sprint’s to ensure their accuracy. There were a few minor tweaks to make, but the vast majority of my output matched hers. We compared our results for two different subjects to ensure that each case was tested. Now, I’m ready to start calculating the daily statistics for my output. Firstly, I need to change my DataFrame’s indices to DateTime objects so that I can reference each epoch by its time, not an arbitrary index.     My plan for collecting the statistic data is to create a new DataFrame with columns corresponding to each calculated value and rows representing each day during the period the watch was worn. In order to ensure that one “day” will capture an entire day and an entire night, our clock will begin at 7:00 am and end at 6:59 am the following real-day. The day is then further classified into “lights-out” and “lights-on” periods, which I will also be calculating summary statistics

I’ve successfully implemented the sleep-checking script on my test data, but I’m having a bit of trouble transitioning to the real thing. Before I can even run my code, I need to clean up the data and ensure that I get the correct header row while dropping all blank rows and N/A columns. This has been trickier than I expected, as I’m having a bit of trouble discerning the proper indexing method (.loc[], .iloc[], or []) in each situation. I am confident, however, that once I am able to set up the frame properly, my algorithms should work effectively.     I’m going to spend the next week working on cleaning the DataFrame and picking up any loose ends that remain. My next objective will be to begin calculating summary statistics for the data – minutes of activity in a day, minutes of sleep in a day, etc..

 Now that I’m more confident using DataFrames, I’m beginning to practice using them in a small-scale version of the sort of analyses we’ll be using later in the project. My objective is to write code that will use a series of rules to check each minute of Actigraph data and determine whether or not the wearer was asleep or awake at that point in time.     Before I test my code on the actual data, I’ve created a test dataset and found the desired result by hand. This way, I can test each rule and ensure that it is working before I attempt to process the massive data set.

    I didn’t return to St. Luke’s until this weekend. My cohort, Alexa, was able to join me in shadowing Sarah, the remaining research assistant. Dr. Weeks had consented a new patient during the week, and another had just been discharged. After going through the procedures again with Alexa, I am fairly confident in my ability to complete the research tasks on my own. I will be doing so later this week when I discharge the new patient.     My work on the online course material has continued at a steady pace. I have entered the section involving NumPy, SciPy, and Pandas. In the upcoming week, I hope to gain a more in-depth understanding of these.

The third and final element in project preparation was one that I did not see coming initially. When Dr. Sprint initially discussed the potential research topic with me, she explained her background in the area of activity analysis and association with Dr. Doug Weeks, the Director of Clinical Research at St. Luke’s Rehabilitation Institute. Dr. Weeks was involved in several projects utilizing data from wearable Actigraph watches to study the recovery patterns of stroke and traumatic brain injury, patients. Dr. Weeks’ research assistants were two volunteer physical therapy students who were due to begin clinical rotations at the end of the summer. Thus, it made sense for Alexa and me to replace them. I met with one of the students, Ellie, on a Saturday morning. She explained how there were two patients in the study at current, but that they typically had anywhere from one to three.      Moving forward from this training, my goals will be to get comfortable with the day to day proce