Quantified Diet: Measuring Food Fatigue

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tracking food fatigue personal analytics diet analytics nutrition analytics quantified dietIn order to cut down expenses on groceries and make it easier to measure my caloric intake, I have been cooking only one type of hot meal on Sunday and eating it for lunches and dinners all weekdays. Occasionally, I would “diversify” my diet by making a tuna salad for dinner, usually, on the gym days. For instance, last week I ate buckwheat with pepper steak eight times, and tuna salad two times. That way, I know how much calories I consume on any given day (I will share my approach to calculating the caloric intake in the future posts), and I don’t need to buy too many ingredients. I have been on this diet for 5 weeks, but started noticing lately that I am not enjoying my meals anymore, and have been experiencing more occurrences of the acid reflux. I decided to look at the data that I have collected so far to see if it tells me anything.

First, I looked at the acid reflux data for the past 5 weeks. I am asking myself about severity of the acid reflux every morning, afternoon, and evening on weekdays, and in the evening on weekends, with severity measured on a 10-point scale from 1 = No Symptoms at All to 10= Extreme Symptoms. All I had to do is to put together data for the past 5 weeks and calculate average severity ratings across the days of the week. As you can see, the data confirms my suspicions: the severity of acid reflux is more likely to spike on Thursday and Friday, most likely because my stomach gets “tired” of the same meal:

Acid_reflux_severity

Furthermore, this “fatigue” manifests itself not only physically, but also psychologically. In my effort to track “healthiness” of my food, in addition to recording the nutritional content of every meal, I have been rating its “fullness” and “gratification”. By “fullness” I mean how heavy the meal feels in my stomach after I eat it. In theory, by relating this number to the actual weight of the meal, I hope to derive the “optimal” weight for my lunches and dinners, so I do not overeat. By “gratification”, I mean the overall feeling of satisfaction with the meal, and its taste, in particular. I record this data in an effort to track what makes me happy. Both “fullness” and “gratification” are rated on a 10-point scale. In this particular study, I was interested to see how “meal gratification” changes throughout the week (unfortunately, I don’t track “gratification” for meals on Saturday and Sundays, but I am considering to do so in the nearest future):

Meal_gratification
Lo and behold, gratification levels go down towards the end of the week! Even my favorite protein shakes that I have for breakfast do not taste as good on Fridays as they do on Mondays, because I eventually get tired of them.

I must also note that the analysis above is based on data from last 4 weeks, and not 5. That’s because the idea for this little research study came to me right at the end of the fourth week. So when I started rating meals during the fifth week, I knew why I am doing that (to see if I am experiencing the meal fatigue), and my knowledge undoubtedly affected my gratification ratings. This is a perfect example of the “observer effect” bias that I discussed in one of my previous posts! Just compare my average gratification ratings before and after the “observer effect” kicked in:

Observer_effect_table

Note how ratings for breakfast, lunch and dinner on Thursday and Friday went down on average 0.25 – 0.4 points after I started rating meals more “consciously”. I rule out the other possible explanation – that meals I had during the fifth week were especially tasteless, because I buckwheat and pepper steak are one of my favorite dishes.

The major implications of this little study is my decision to start cooking two kinds of hot meals from now on, and to diversify ingredients as well. Perhaps, in a couple of months I will replicate this analysis to see if this little change will help me to enjoy the home cooked food again!

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