Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

If you’ve ever tried to lose fat or gain muscle, you may have noticed… it’s not easy. But it’s precisely its complexity that makes the science of exercise and nutrition fascinating.

This is the longest LBS episode so far, and you’ll understand why pretty quickly: we covered a very wide range of topics, starting with the concept of metabolic adaptation and how our physiology and brain react to caloric deficits or caloric surpluses.

We also talked about the connection between metabolic adaptation and exercise energy compensation, shedding light on the interactions between the two, and how they make weight management more complex.

Statistics are of utmost importance in these endeavors, so of course we touched on how Bayesian stats can help mitigate the challenges of low sample sizes and over-focus on average treatment effect.

My guest for this marathon episode, is no other than Eric Trexler. Currently at the Department of Evolutionary Anthropology of Duke University, Eric conducts research on metabolism and cardiometabolic health. He has a PhD in Human Movement Science from UNC Chapel Hill, and has published dozens of peer-reviewed research papers related to exercise, nutrition, and metabolism.

In addition, Eric is a former professional bodybuilder and has been coaching clients with goals related to health, fitness, and athletics since 2009.

In other words, get comfy for a broad and nerdy conversation about the mysteries related to energy expenditure regulation, weight management, and evolutionary mechanisms underpinning current health challenges.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar and Matt Rosinski.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

Eric’s webpage: www.trexlerfitness.com
Monthly Applications in Strength Sport (MASS) research review: https://massresearchreview.com/
Eric on Twitter: https://twitter.com/EricTrexler
Eric on Instagram: https://www.instagram.com/trexlerfitness/
Eric on YouTube: https://www.youtube.com/@erictrexler
Eric on Linkedin: https://www.linkedin.com/in/eric-trexler-19b8a9154/
Eric’s research: https://www.researchgate.net/profile/Eric-Trexler
The Metabolic Adaptation Manual – Problems, Solutions, and Life After Weight Loss: https://www.strongerbyscience.com/metabolic-adaptation/
MASS on Instagram: https://www.instagram.com/massresearchreview/
Burn – New Research Blows the Lid Off How We Really Burn Calories, Lose Weight, and Stay Healthy: https://www.amazon.com/Burn-Research-Really-Calories-Healthy/dp/0525541527
Causal quartets – Different ways to attain the same average treatment effect: http://www.stat.columbia.edu/~gelman/research/unpublished/causal_quartets.pdf
How to Change – The Science of Getting from Where You Are to Where You Want to Be: https://www.amazon.com/How-Change-Science-Getting-Where/dp/059308375X/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr=
The Sweet Spot – The Pleasures of Suffering and the Search for Meaning: https://www.amazon.com/Sweet-Spot-Pleasures-Suffering-Meaning/dp/0062910566
The Stoic Challenge – A Philosopher's Guide to Becoming Tougher, Calmer, and More Resilient: https://www.amazon.com/Stoic-Challenge-Philosophers-Becoming-Resilient/dp/0393652491
LBS #61 Why we still use non-Bayesian methods, with EJ Wagenmakers: https://learnbayesstats.com/episode/61-why-we-still-use-non-bayesian-methods-ej-wagenmakers/
LBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/

Abstract

by Christoph Bamberg

In episode 89, we cover a so-far underrepresented topic on this podcast: Nutrition science, sports science, their relation and of course, the role of Bayesian statistics in that field.

Eric Trexler is the one introducing us to this topic. With his PhD in Human Movement Science from UNC Chapel Hill, previous career as professional bodybuilder and extensive experience as a health and fitness coach, he is perfectly suited for the job.

We cover a lot of ground in this episode, focusing on the science of weight-loss and the challenges to losing weight after a certain point due to an adapted energy expenditure.

We look at energy expenditure and changes in metabolism from several angles, including the evolutionary background for these adaptations and how they affect us in modern times.

We also discuss how individually people react to calorie restriction or surplus, different approaches to motivate oneself to loose weight and the overall complexity of this topic.

In the later half of the episode, we focus more on the scientific practices in sports science and how they can be improved.

One way forward is, of course, to use more Bayesian statistics, especially because of the oftentimes small sample sizes in Eric’s field.

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Parable of the Talents: Ch 14