As I mentioned earlier, data analysis is a lifestyle. Nowadays, more and more people tend to care fitness. While, to maintain your fitness effectively, data analysis is an important tool.
I use an app called Withings to track my fitness everyday.
It records your weights and fat mass data, calculates your BMI (Body Mass Index), and even track your steps walking. It’s useful. But I’m not introducing the app today. The data analysis mindset used during the fitness tracking is the key.
With the help of digital devices, people generated data almost every second. How to utilize the data to improve our live becomes more critical. Let me show you the example about my using data to improve my fitness step by step:
Step 1: Set a goal
This step is very important, it not only shows your direction but also establishes the gap base on your current situation. With out a goal, any data to you is just a number without any meaning. Here comes another question: how to ensure the goal is reasonable? Unfortunately, no one has a clear answer at a very early stage like this. Smart people always adjust their goals throughout the tracking process.
Here is my goal of fitness:
- Weight: between 65~68kg
- Fat Mass: below 12%
Step 2: Record Data
Data analysis is all about dealing with data, so the completion and accuracy of the data is essential to the whole process. It’s better to establish a data collection process. And never complain about the extra effort you spend on it. It’s worth anyway. Nevertheless, there are more and more tools available to help data gathering, just like I using Withings to record my weights and fat mass data.
Step 3: Compare the data with goal
This is the very beginning and simple method of data analysis. Just see the gap you currently have and you will have a rough idea about which way to move. For example, today, my weight is 69kg and fat mass is 15%. Compared with my goal, I’m 1 kg heavier and having 3% more fat. It obviously tells me that I should try to lose weight and more specifically reduce the fat mass. While, you don’t have to compare the data with goal each time you got a new record of data. You can set a tracking period such as weekly. Buy a set of data, you can see the trend, since most of the data will floating within a reasonable range.
Step 4: Look back to the historical data
It makes more sense to look back to the historical data. It will show you a clear picture how good or bad you did in the past time. And base on the long period trend, you can generate the experience, evaluate your actions and may need to adjust your goal. Here’s the example of my weight data in a long period:
I have pointed out 4 part of the data. The first part is from May 2016 to Aug 2016. I call it unstable period, since the data seemed to be random. I recalled my memory that it was the period just after me and my partner’s MBA graduation. We travelled a lot at that time, and did nothing for fitness.
The second part is at the end of Sep 2016. There showed a great rising of my weight. I call it out of control period, because my weight is far more beyond my goal. It was the time just after my parents visiting us. I really missed their home cooking and I ate almost double the size of dinner during their visit.
The third part is from Oct 2016 to Jan 2017. My weight became stable and it was reduce into my target range. I realized my weight is out of control at the end of Sep, so I did a lot practice after my parents went back to China. And I call this part the achievement period. While~ the trends seemed to be below my target range. Then, it gave me a warning that, there might be a risk that my weight will be out of control in another side.
There is the forth part of the pointed data. It is an outlier since the data is not along with the trend in the same period. It can be excluded at an exceptional, which means we assume the same situation will not happen frequently. To exclude an outlier, attention should be paid that once the same situation happened again and again, one should look into it deeply to figure out the root cause.
To be more accurate, let’s look at my fat mass trend:
The Unstable, Out of control and Achievement parts are align with my weight trend. And there is no outlier in the fat mass trend.
So we can make the conclusion:
Traveling leads to unstable, diet is important to control the fitness and practice is effective. And since my weight tends to below the target and my fat mass is almost above the target all the time, I have to shift from the losing weight to body building.
That’s all for my data analysis for fitness sharing. It is just a simple way to do it. One can do it in a more professional way. For example, record the calorie taken in, the hours for practice, the miles walked and hours for sleep every day. Then do the regression for all the data set to see any correlation.
I should say, as long as you do something, life will become better. Please don’t be hesitate to share your data analysis life story with me:)