1/ #ListeningThread — actually didn’t know this, but @BioLayne has a podcast. I actually found it in reverse as I’ve been hunting down all things Hypertrophy - and they have a recent podcast with @YngvaiMalmsteve on the topic so I’ll give it a listen… open.spotify.com/episode/2tgWUE…
2/ @BioLayne “… if you torture the data enough, you can get it to show what you would like it to show.”
This is actually a variation I was one of my favorite quotes of all time.👇
This is actually a variation I was one of my favorite quotes of all time.👇
3/ it’s also very prescient in its timing. I was actually just talking with @NutritionMadeS3 yesterday, and why I tend to be more interested in studies that work off open or shared data sets given the level of transparency in the statistical instruments being used…
4/ or put another way, I don’t tend to see a lot of statistical abstraction with these open datasets compared to those that are closed/proprietary.
A PhD student once joked on this topic, (extending the quote) “it’s a lot harder to torture the subject in view of everyone”.
A PhD student once joked on this topic, (extending the quote) “it’s a lot harder to torture the subject in view of everyone”.
5/ I’ll concede I started skipping through a bunch in the beginning and middle. A big portion was devoted to discussion on the carb/insulin model, which is a topic I’m not that invested in. They touch on hypertrophy near end, but mostly for phys performance.
6/ I do like @BioLayne’s comment, “we’re finally asking the right questions…” (with regard to hypertrophy) and that yes, it’s really just been advanced most in the last 5 to 7 years.
It’s also super relevant to my work given its metabolic influences on lipid profiles.
It’s also super relevant to my work given its metabolic influences on lipid profiles.
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