Alli Fellger CREU Blog 2018-2019

The bar charts I created showing the relationships between sleep minutes prior to and following a day and its sum of activity counts are much easier to understand than my previous scatter plots, they are still not entirely coherent. In addition to the charts, I calculated the correlation coefficients between lights on activity and sleep minutes during lights out of each of the nights surrounding it. These numbers were quite interesting because in some cases, there was almost no relationship. In others, there was a relatively strong positive correlation, and in others still, there was a relatively strong negative one. This week, Dr. Skornyakov replied to our questions about sedentary activity classification and how we might be able to connect sleep bouts. In addition to answering our questions, Dr. Skornyakov notified us that we may want to look at differences between the first 26 subjects and the rest, as there was a change in the actigraphs used between subjects 26 and 27. We

Now that we have found a solid number of metrics by which we may classify sleep, we want to begin looking for ways to more effectively classify daytime activity. After reviewing more literature on the matter, it appears that a common way of describing daytime activity is to measure the amount of time that the subject is sedentary. The word 'sedentary' has a strict definition based on metabolic energy expenditure and body position, which is beyond what we will be able to easily determine from our activity counts. However, we want to attempt to find a threshold at which we can determine if a patient is moving or inactive. Since Dr. Skornyakov has been our resource for writing rules about when a patient is asleep or awake, we are going to request her assistance on how to determine what this threshold may be. I found earlier this year that our subjects' longest sleep bouts were very low - below 100 minutes of sleep per night, for many patients. This is not entirely surpris

To summarize the results of my change analysis exploration, I decided to graph the changes in three positive and three negative attributes for each patient and for each comparison (baseline, the previous day, average of all previous days). The resulting charts were quite busy, but there were a few observations that may be helpful going forward: most patients had positive outcomes across my chosen attributes when comparing against baseline average of all previous days may be a helpful comparison to make since it can give a measure of past data without being affected by day-to-day variability, which can be quite dramatic Based on feedback from Dr. Sprint and Alexa, I am going to re-format the line graphs into bar charts and remove the previous day comparison altogether. I am not discouraged completely by the lack of a solid conclusion based on this exploration, because the actuality of the situation is that I chose the features without a real, solid reason as to why they would

I have been looking at the way a subject's features change over the duration of their participation in the study. To do so, I am exploring several different routes of measuring change. The first is to compare daily metrics to the average of those of the first three 'baseline' days. These are the days that the subject wore the actigraph watch but did not receive light treatment. The second is to compare one day's metrics to the previous day's, and the final is to compare a day to the average metrics of all the days preceding it. We are uncertain as to which of these may be most helpful going forward. In order to avoid redundancy, I am going to only look at a few selected features for each subject: sleep, wake and transitions for lights on and lights out, longest bout, sleep onset latency and daytime activity ratio. I also want to represent each of the changes in several different ways: the raw difference between the day's value and the original value,