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In a recent study, described here, we collected actigraphy data. This is data that describes how many steps a person takes during the day and also when the person sleeps. Many people are used to looking at this data from fitness trackers like Fitbit. The data that is collected is saved in excel files, like this sample. This data is very important to the study because it indicates a person's circadian pattern of activity. There's a great deal of literature indicating that people with depression have disruption in their circadian rhythm.
In this study, we test that hypothesis by:
recruiting participants who suffer from depression
monitoring their activity with actigraphy (a small monitor they wear on their wrist like a Fitbit)
treating the participants with an antidepressant
The hypothesis that we are examining is that: When depression severity decreases, circadian rhythms normalize.
To test this, we need to examine the data carefully and extract the circadian rhythm, and determine how it changes, over time.
Data Analysis
Data Analysis
The raw data collected from the actigraphy monitor is shown to the left in red. Our goal is to fit a sinusoidal wave to the data to extract the circadian period. There are numerous ways to fit this data as outlined here. One example is shown in black. Our goal is to use the various techniques to fit the data and assess how they differ. We will then use the optimal technique to fit the data and examine how the circadian pattern changes over time.
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