Health is important to us all. If you've seen my previous fitness post, you know how far I've come in my fitness journey. I wanted to dig deep into my past so I downloaded the data and did some analysis.
I was curious to see how many steps/day I was hitting and how many calories I was burning to help better understand my health.
Here we go!
The Plan
My inspiration behind this project was to see how my fitness journey was measured over the past 3 years. I personally don't use an Apple Watch, which I regret because the metrics would have been much more accurate. But luckily enough for me, my Apple iPhone does it own sort of tracking through Apple Health.
In hindsight, I am an extremely active person. You don't become a powerlifter without putting in 2+ hours/day in the gym 6 days/week. That being said, my stationary phone does not always record all my activity but it will track it outside of the gym and that information is still useful to me.
Data Collection
If I were to track calories burned manually I probably wouldn't be doing this project right now. We live in the age of smart devices that can estimate that for us. Even thought they track it for us, they don't always make it the simplest process to extract all that data. I had to use a 3rd party app called QS Access on the Apple App Store to export all the data aggregated in
Apple Health.
Here is what the data looked like once extracted:
As you can see, the data has many two date/time columns, active calories, distance, flights, and even steps columns.
Once I had this data exported and loaded as a CSV, I was ready to start my analysis.
Analyzing the Data
I wanted to use Python for this project to do some quick and automated analysis, and also to learn to use Python in a data analysis setting.
I used Google Colab as my IDE, which was amazing because it is a cloud based notebook.
Check out my code below:
First, I used Pandas to import the CSV for analysis.
Then, I used the term df.describe() to get some basic descriptive analytics for the data table.
This was so cool!
Insight: I learned I burn on average 26 calories/day. This is astonishing for someone of my activity level. If I didn't know better I would have to increase that drastically but since I know most if not all of my daily activity is recorded I can start to collect data more accurately.
Action: I want to invest in an Apple Watch which will open the doors for me in terms of seeing actually how active I am in a day to day basis. The technology has come pretty far where I can even gain access to see how well my sleep patterns are which would be an interesting data point to see.
Distance:
My max miles on distance was 10 miles. I was surprised to see this big a number because I am not a big runner or hiker. I checked the date and it shows March 19, 2018. I did what anyone would do and checked my phone's library for that date and sure enough I had some photos with me and my bestfriend at the top of Mission Peak in Livermore over 3 years ago.
This is amazing, the data brought me back to a memory I had forgotten and I was actually able to represent an event that had happened in my past in a meaningful way. I remember it took me and my friend about 5 hours to complete that hike and I vowed to beat that time.
Conclusion:
This data set was a really really fun exercise for me and I gained a lot of valuable insight, I can't help but feel a slight bit cheated without having the data collecting powers of an Apple Watch but the techniques I learned surely will be repeatable. So here's how i'm going to improve this project:
Get an Apple Watch!
Keep eating healthy
Keep working out
The rest is to just passively collect my own data!
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