PREDICT 2 Study Experience: Day 2 Reflection

This post is the fourth of a series on my participation in the PREDICT 2 study. Click the links below to read earlier parts of the series:

Well, I made my first major blunder as a PREDICT 2 study participant: I forgot to put my activity tracker back on my wrist after taking a shower! I realized what I had done after arriving to work. With my commute being 45 minutes, I knew it wouldn’t be feasible to return home to retrieve the tracker. I went to the “Help” feature on the study app and tapped “Chat with us.” I sent a message explaining that I had forgotten to put the tracker on that morning, and I asked if it would be okay to remain as inactive as possible before returning home at the end of the work day.

The study team member who answers in the morning, Haya, thanked me for letting her know and that she would make a note of it. She said to resume normal activity, log any exercise in the study app, and to put the motion tracker back on when I got home. Crisis averted! 

Day 2: Return of the Muffins

I had my day 2 breakfast at 8:03am, consisting of three standardized muffins, looking and tasting the same as the muffins from day 1. I also had a cup of coffee, which I logged in the study app. 

It was just breakfast muffins today, so I was free to eat whatever I wanted for lunch and dinner. Today for lunch I had a chicken breast (~10oz) roasted with thyme, salt, and pepper and a simple ratatouille (1 cup) made up of eggplant, zucchini, and tomatoes seasoned with thyme, salt, pepper, garlic, and lemon. I had the same dish in the same servings last night, so I used the study app’s meal-copying function to add the same item as last night’s dinner. 

It’s definitely more difficult to log made-at-home meals than I anticipated. Obviously it would be easier to scan a barcode or look up an exact meal off of a restaurant menu. But in a normal week, I cook most nights, and I want my habits during the study days to be as close to my normal as possible so my results represent my usual responses to food.

I read an interesting article in the blog on joinzoe.com about the muffins used in PREDICT. While as a participant I am blind to the details, there are three different muffins I could be consuming:

  • High-carb, low-fat
  • Low-fat, high-carb
  • Average fat and carb

All of the muffins are plain vanilla flavored and the look basically the same except for the blue muffins I’ll eat tomorrow for breakfast. No, they aren’t blue because of any blueberry flavoring. It’s food dye, and it serves the purpose of helping the study team identify “GI Transit Time” (GI = gastrointestinal as in the GI tract or “gut”). Catching on yet? After eating the blue muffins, I’ll be able to let the study team know through the app when I “see them again,” and this data tells them how fast certain food components move through my digestive system and out of my body.

The PREDICT “Muffin Master is nutrition scientist Dr. Sarah Berry from King’s College London. Together with test baker Kate, Dr. Berry created the magical muffin recipe for PREDICT so the study team would have palatable standardized food with known amounts of food components like fat and carbohydrate. 

For the most part, the muffins remove natural variability found in foods because of something called the “whole food matrix.” This is useful for the study, as their aim is to identify how people uniquely respond to high-fat meals, high-carb meals, and anything in between. But how does this data translate to the “real world” where people don’t eat standardized science muffins? I’ll be interested in learning more about the answer to this question as I read more about PREDICT and the science behind the study’s methodology. While I doubt the answer is simply “Well ScienceKara, the data translates seamlessly, and there’s nothing to worry about,” I also think my own answer right now would be: Well ScienceKara, we have to start somewhere.

Read the ZOE blog about the muffins.

Listen to the Gastropod podcast episode where the two hosts talk about their experience in PREDICT 1. Hosts interview:

  • PREDICT study scientist, Professor of Genetic Epidemiology at King’s College London, director of the Twins UK study, and ZOE co-founder Tim Spector, PhD
  • Timothy Caulfield, BSc, LLM, LLB, a Canadian professor of law at the University of Alberta, the Research Director of its Health Law Institute, and current Canada Research Chair in Health Law and Policy. Caulfield provides an interesting opinion on the efforts to personalize nutrition. He makes the point that with this type of technology ZOE is building with data from PREDICT, populations that arguably need a lifestyle change to improve health and prevent disease the most are not likely to get involved (i.e. low socioeconomic status).

Stay tuned for more posts about the science behind the study tomorrow.

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