A few #fitness hypotheses I want to test with @MyFitnessPal data

I have been using the MyFitnessPal app for the past 14 or so months – using the app everyday to meticulously log every food intake and exercise. It is almost a habit now to do the data entry just before digging in or right after.

The app has been tremendous help in giving me awareness of how easy it is to overeat – there are calories rich food everywhere and the calorie density  (calories per volume) is so high for the food you least expect and most like – like those Specialty’s cookies and Panera sandwiches. Having to log every bite I eat gave me visibility into these foods and eventually led me to reduce and void them. Note that I did not say anything about good vs. bad calories, this app does not help you yet and I will write more on that later.

And the result you have been anxiously waiting for? I did lose somewhere between 15-20lbs since I started using the App. But I am not assigning it causation nor should you. My journey stated six months before I started using MyFitnessPal. The initial motivation, drive and discipline existed before the app. You could say that precondition was the one that led me to search for and use an app like MyFitnessPal. Besides using the app I made several changes to my daily routine, cuisine etc. Having clarified that I still believe this is a service I would gladly pay a monthly subscription price for.

I am not the only entering data of intake, millions of people are doing the same. Everyone entering, almost everyday,

  1. Mix of breakfast, lunch, snacks and dinner foods
  2. Exercises they do
  3. Their weekly weights

So MyFitnessPal is sitting on a treasure trove of data (dare I say #bigdata?) that I believe can be put to some good use in the name of fitness science. I do not mean go data dredging into these to tease out interesting correlations but start with specific informed hypotheses and test them with random sampling. All I need is 300-400 data profile samples to test the following hypotheses

Fitness Hypotheses to Test

  1. When people exceed their calorie limit they had set themselves for the day they exceed by significant margin (20-40%) and definitely not 1-10%
  2. There exists a limited specific set of food categories that constitute significant portion of daily budget
  3. For those who exercise regularly (as shows by their logs) on days they skip are also the days they eat extremely poorly
  4. Eating within the limits is a far better predictor of weight loss than exercise
  5. Among those who lost weight, larger portion of calorie intake consists of homemade food and less of packaged processed food.

That is it for now. Next I will write about a few product enhancements their product management team should consider that will lead them to monetization through a subscription service. I sincerely hope their business model involves adding value to the users and getting paid for it in the form of subscription than selling their data for marketers.