Fitbit Data's Exploratory Analysis

You Can see the code i used here on github.

Using Fitbit API to perform exploratory analysis on its data.

I have tried to analyse my FitBit data to see how my sleep affects my activity level and to check wether or not i have built up sleep deficit.

Note: In order to get my Fitbit data, I first had to set up a Fitbit API. Then, I ran a separate script.

Question 1: Does the amount of sleep I get the night before be used to predict or correlate my activity level for the next day ?

Notice that the distribution appears to be bimodal. However, I happen to know that this is because I take naps, and those tend to be 2 hours or less. Also, my Fitbit had some software bug initially, so it would sometimes run out of batteries in the middle of the night randomly.

For the purposes of this project, I will omit naps and/or artificially shortened sleep (< 3 hours).

Looks pretty normal.

Here, I define activity level as the number of steps taken. My activity distribution looks normal.

My sleep and activity data over the week:

Question 1 Results: My sleep does not predict my activity level the next day.

So, we can see from the regression table that how many hours I slept the previous night (‘hours_prev’) does not significantly predict the amount of steps I take the next day (p = .63). This might be for a variety of reasons.

First, I don’t have a lot of data yet! I have only used my Fitbit for a couple months. Second, I am a graduate student, and my sleep is incredibly variable. Third, I love to be active! Even if I am tired, a run usually makes me feel better. Long story short is, I need more data.

That flat line shows that there is no linear relationship.

Question 2: Do I need more sleep the night after I don’t get a lot of sleep? In other words, am I building up a sleep deficit?

In the model below, I predict the difference in amount of sleep for a given day from the previous nights’ hours of sleep. To do so, I created a variable to capture the difference in amount of sleep I got from the night before. I then used an ordinary least squares regression model.

Question 3 Results: Yes, I have built up a sleep deficit.

The regression table shows that how many hours I slept the previous night (‘hours_prev’) negatively predicts the difference in amount of sleep I get the next day. Put more simply, this means that if I get a good night’s sleep, the next night, I don’t need as much sleep. Conversely, if I don’t get a lot of sleep, the next night, I make up for it by sleeping longer.

To make this finding easier to digest, let us visualize the relationship.

Here is the negative linear relationship as seen in our regression table.

Summary and Next Steps

to recap, I imported data using Fitbit’s API, worked with .json data, visualized my data, ran linear models, and performed a t-test. I found that (1) my activity level is independent from how much sleep I get, and (2) I built up a sleep deficit over time.

What I would like to do next is collect more data! I would also like to create some interactive visuals and run some more sophisticated models on my data. Finally, I would like to more thoroughly explore my heart rate data. The closer I get to defending my critique, the higher it seems to get. Should be fun to test if that is statistically significant.