We did a 5-day Fast Food only experiment so you don't have to! So what does the data say about eating a steady diet of only fast food?
There’s plenty of research on the impact fast food has on your health. For example:
"Today, eating processed foods and fast foods may kill more people prematurely than cigarette smoking."The hidden dangers of fast and processed food, American Journal of Lifestyle Medicine, Joel Fuhrman, MD.
"Just a few days of junk food could change your metabolism. A small study of 12 healthy young men found eating junk food for just five days led to a reduced ability of their muscles to turn glucose into energy, even though they didn’t eat more calories as part of the study. Over the long term, this change could lead to insulin resistance and eventually type 2 diabetes."
This is your body on fast food, Washington Post
But we’ve never seen how fast food impacts the body in real-time, over 5 days. We decided to put it to the test with another N of 1 experiment (i.e. not a statistically significant sample size but interesting to do nonetheless).
Before we get into the data, some key metrics that Basis tracks and for you to follow along:
Fasting glucose: This is the glucose test you're likely most familiar with. It's the test you do in the morning, after you've fasted for 8-10 hours. The normal range for blood glucose is 70 to 100 mg/dl (but values may vary for different labs). Levels between 100 and 126 mg/dl are referred to as impaired fasting glucose or pre-diabetes. Diabetes is typically diagnosed when fasting blood glucose levels are 126 mg/dl or higher. Basis acquires its data from a glucose sensor that measures glucose in the interstitial fluid - not in serum (blood). Measurements can vary up to 10-15% between the results you would get in a lab.
Time in range: Minimizing the number of glucose spikes and more time spent in the optimal glucose range of 70-110 is observed in healthy (non-diabetic) adults.
Glucose variability: Glucose variability is the amount of variation that you experience with your glucose throughout the day. Maintaining steadier glucose variation over time leads to better health outcomes. In healthy, non-diabetic adults, glucose variability targets are <13%.
Heart rate variability (HRV): HRV measures the beat-to-beat variations of your heart rate throughout the day. HRV provides insights into how your heartbeats change over time and is now a crucial measure of overall well-being and physical fitness.
Disclaimer: We don’t recommend that anyone try eating fast food with every meal for a week. If you’re already doing it, we hope this post can have even the slightest of impact in helping you transition towards eating healthier.
Low: 85 / Average: 102 / High: 144
Fasting glucose: 88
Time in range: 85.5%
Glucose variability: 13.12
Low: 49 / Average: 99 / High: 174
Breakfast: Starbucks coffee w/ milk and sugar + butter croissant.
Lunch: Cheeseburger with fries and a beer.
Dinner: Pizza and a Coke.
Low: 76 / Average: 103 / High: 142
Fasting glucose: 88
Time in range: 71%
Glucose variability: 11.82
Low: 51 / Average: 73 / High: 113
Breakfast: Starbucks coffee w/ milk and sugar + a chocolate croissant.
Lunch: KFC nuggets w/ fries and a Coke.
Dinner: Chicken in sweet sauce with fries and a 7up.
This was the worst day of the week. I was very sluggish - no energy to think or work. I also developed a temperature of 98.6 (my normal temperature range is 95.5 - 96.8 throughout the day). Side note: What a normal temperature is has changed over the years. And, yes I got tested for Covid just in case.
Low: 82 / Average: 104 / High: 139
Fasting glucose: 92
Time in range: 73%
Glucose variability: 10.94
Low: 52 / Average: 66 / High: 120
Breakfast: Coffee and chocolate cake.
Lunch: Pasta and meat with a beer.
Dinner: Pizza and a Coke
I hit the holy trifecta on Day 4. Pizza Hut, McDonalds and KFC.
Low: 80 / Average: 105 / High: 157
Fasting glucose: 87
Time in range: 68%
Glucose variability: 14.78
Low: 52 / Average: 69 / High: 102
Lunch: Pizza and Coke.
Snacks: Big Mac meal.
Dinner: KFC popcorn chicken, fries, sweet and sour sauce, Zinger burger and a Coke.
Work commitment kept me from getting any lunch so I just had a bigger dinner.
Low: 78 / Average: 103 / High: 158
Fasting glucose: 93
Time in range: 76%
Glucose variability: 18.69
Breakfast: Cottage cheese with honey and coffee.
Lunch: No lunch
Snacks: No snacks
Dinner: Pasta bolognese, pizza + a tiramisu and a beer.
Below you can compare what a normal couple of days look like for me.
Day 6: Skipped - One day in between so the data isn’t skewed by Day 5’s dinner.
Low: 79 / Average: 97 / High: 121
Fasting glucose: 83
Time in range: 84%
Glucose variability: 12.82
Breakfast: Black coffee
Lunch: Lean pork, lentils and salad.
Snacks: Cottage cheese and nuts.
Dinner: Lean pork, lentils and salad.
Low: 73 / Average: 89 / High: 113
Fasting glucose: 81
Time in range: 94%
Glucose variability: 9.04
Breakfast: Black coffee
Lunch: Veal with salad
Snacks: Banana with nut butter.
Dinner: Veal with salad
According to the American Diabetes Association, normal values are under 100 mg/dL. Results between 100-125 mg/dL indicate pre-diabetes. This is a very small sample size, but this was trending upwards, fast (and consistent with the findings from the studies at the start of this post).
According to the American Diabetes Association, normal values should be under 140 mg/dL 2 hours after eating when running a conventional Glucose Tolerance Test (drink 75g of a sugar drink). Results between 140 - 199 mg/dL can indicate pre-diabetes. And while the standard measure for Glucose Tolerance Tests is a blood serum test, research suggests continuous glucose monitoring can be even more accurate in the diagnosis of pre-diabetes.
This is an N of 1 experiment, but the key observation is that my average glucose, fasting glucose and glucose variability all trended upwards in just five days. That's consistent with what research (cited at the start of this post) suggests happens when you increase consumption of fast food. Sure, we suspected this would happen. But now we have real-time data to confirm just how drastic that change can be.
If you’re interested in seeing real-time data from a 3-day fast, check it out HERE.
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