In this new "Study Finds" episode, Dr. Matt Law looks behind the headlines once again to figure out if late eaters are doomed in life. Will eating after 5pm kill you? Listen to find out!

Episode Resources:

Concerning study finds 'serious consequences for health' for people who eat after 5pm - Daily Mail

Eating After 5 PM Could Be Damaging Your Metabolic and Heart Health - SciTechDaily

Late eating is associated with poor glucose tolerance, independent of body weight, fat mass, energy intake and diet composition in prediabetes or early onset type 2 diabetes - Nutrition & Diabetes

TREAT to Improve Cardiometabolic Health (NY-TREAT) - ClinicalTrials.gov

Díaz-Rizzolo, D.A., Santos Baez, L.S., Popp, C.J., Borhan, R., Sordi-Guth, A., Manoogian, E.N.C., Panda, S., Cheng, B., & Laferrère, B. Late eating is associated with poor glucose tolerance, independent of body weight, fat mass, energy intake and diet composition in prediabetes or early onset type 2 diabetes. Nutr. Diabetes 14, 90 (2024). https://doi.org/10.1038/s41387-024-00347-6

In this new "Study Finds" Episode, Dr. Matt Law digs into the truth behind the headlines claiming that maple syrup can prevent heart disease. Is it true? You'll have to listen to find out.

Episode Resources:

Morning Ag Clips - Why Substitute Sugar With Maple Syrup?

Study Abstract

Morissette, A., Agrinier, A.-L., Gignac, T., Ramadan, L., Diop, K., Marois, J., Varin, T. V., Pilon, G., Simard, S., Larose, É., Gagnon, C., Arsenault, B. J., Després, J.-P., Carreau, A.-M., Vohl, M.-C., & Marette, A. (2024). Substituting Refined Sugars With Maple Syrup Decreases Key Cardiometabolic Risk Factors in Individuals With Mild Metabolic Alterations: A Randomized, Double-Blind, Controlled Crossover Trial. The Journal of Nutrition, 154(10), 2963–2975. https://doi.org/10.1016/j.tjnut.2024.08.014

On today's episode, I'm joined by Dr. Todd Loushine, Ph.D., P.E., CSP, CIH to talk about research, the difference between master's degrees and doctoral programs, correlation vs. causation, and how to read and interpret the good, the bad, and the ugly in research.

Dr. Loushine is an associate professor at the University of Wisconsin-Whitewater, specializing in everything from basic OSHA compliance to advanced data analysis techniques and research methods in EHS. Last year, Professor Loushine put his over 30 years of experience “to the test” by working part-time as a safety manager for Research Products Corporation in Madison Wisconsin. Todd’s career began with a B.S. degree in Chemical Engineering from the University of Minnesota and a fortuitus career initiation as a compliance officer with Minnesota-OSHA. He completed his M.S. and Ph.D. in Industrial Engineering from the University of Wisconsin-Madison, with special emphasis on psychology and sociology. Professor Loushine has dedicated his life to educating and assisting others on how to systematically evaluate work, and manage organizations to improve safety, productivity, and job satisfaction. Todd’s approach to safety is systems-based and data-driven, which defines safety as an attribute of work and utilizes a quality management approach. He strives to learn from workers (and students) to understand it from their perspective to be a better instructor while optimizing the design and function of the work processes and relationships.

Glossary of Terms:

Variable

A variable is an observable characteristic. In research, a variable needs to be measured in some way. There are all sorts of different types of variables in research, but today we are just focused on two types of variables. The first is independent. The independent variable is the variable that changes, and in research we try to measure whether changing the independent variable influences the dependent variable. For example, if we want to find out the relationship between watering an apple tree and the number of apples it produces, the amount of water is the independent variable, and the number of apples produced is the dependent variable.

Treatment

Treatment, in simple terms, is another way to refer to the independent variable and the changes made to the independent variable in the research. In the same example I just gave, the treatment is changing the amount of water given to the apple tree.

Correlation

This is a measurement of the relationship between two variables, and in research it is a statistical calculation. Keeping with the same example, if I observe that more water given to the tree results in more apples, I have observed a correlation. In fact, this would be a positive correlation, because more water means more apples. A negative correlation would be if more water meant less apples.

Causation

Causation is different from correlation in that we are able to prove, statistically, that the independent variable, and nothing else, has a direct effect on the dependent variable. If we go back to the apple tree, causation would mean that we have observed the watering of enough apple trees to determine almost exactly how much more water I needed to get a certain amount of apples. However, it’s not causation until we have also determined that nothing else is affecting apple growth, so we would also have to measure and either rule out or control the potential effects of soil health, sunlight, the age of the tree, the amount of wildlife and insects that interact with the tree, the proximity of the tree to other trees and what types of trees those are… You get the idea, right? By the way, all those other variables like soil and sunlight would be confounding variables that affect the validity and reliability of my study.

Validity and Reliability

Validity is the extent to which a study accurately measures something. Reliability means we are able to get the same result over and over again. These are extremely important parts of research, and yes, there are several statistical tests that let us calculate validity and reliability.

Type 1/Type 2 error

Type 1 error is a false-positive, meaning that our study reflected that more water means more apples, but in reality this is not true. Type 2 error is a false-negative, which would mean that our study showed that more water did not give us more apples, but in reality more water does give us more apples.