Building a second brain:
We consume more digital information than ever before. Think of all of the journal articles, videos, podcast episodes. blog articles, etc. that we come across and know will be useful at some point in the future but just not right now. Or all of the notes we make, knowing we may want to refer back to them in the future. How can we store and retrieve all of that information efficiently? Tiago Forte’s new book ‘Building a Second Brain’ is a guide to doing exactly that. As somebody whose entire memory/productivity seemingly relies on the notes apps on my phone, it was great to find a structure and system for doing this a little more effectively. I strongly recommend the book
for anybody in the same position.
Load monitoring FAQs:
Jo Clubb seeks to answer 10 commonly asked questions
related to ‘training load’ monitoring in sport. Examples include “Why do we monitor athlete load?
”, “What are internal and external load?
”, “What technology/metrics should I use?
”, “Can it predict injury?
”, and “How do I gain athlete buy-in?
”. I really liked this relatively simple introductory overview, aimed at a wide audience.
Contributions to maximal motoneuron output:
The origin of inputs underlying maximal motoneuron output is unclear. Škarabot et al.
tested a hypothesis that this output will increase in response to a startling cue (which activates specific neurons that make connections to motoneurons via fast-conducting axons). It’s a really nice study comparing reaction time, motor unit discharge rate, and maximal rate of force development in response to different cues.
“Trending towards significance”: p
= 0.052, we’ve all been there. That’s ‘trending towards significance’, right? No! This short article
simulates the effect of adding more data to results that are already close to the traditional threshold of significance. In summary, “We have shown that a P value is by no means assured to become smaller even with the addition of quite a substantial proportion of extra data, a finding that undermines any claim of a trend towards statistical significance
.” I’m a big fan of papers like this that demonstrate what is happening ‘under the hood’ and help with understanding what our results actually mean.
An introduction to mixed effects models:
Similarly, I love it when people share their learning journey and help others in the process. Ths blog post
by Meghan Hall shares their learning of mixed effects models, including fixed and random effects. This is all done in a “beginner’s Introduction” guide through the use of sample ice hockey data.