The paradigm shift in exercise prescription cannot / will not come from the propagators of the paradigm. Kuhn told us that "almost always" those who achieve "fundamental inventions of a new paradigm have been either very young or very new to the field whose paradigm they change."
Kuhn predicted that paradigm change will come from iconoclasts and "mavericks": "being little committed by prior practice to the traditional rules of normal science, [they] are particularly likely to [...] conceive another set that can replace them."
So, we are now beginning to see multiple (geographically detached and politically emancipated) organizations issue exercise prescription guidelines that depart from the ACSM-dictated paradigm in important ways.
One crucial difference is the elevation of behavior change, and the psychological mechanisms that support it, to a central consideration. Exercise prescriptions must be (a) effective, (b) safe, and (c) sustainable for self-determined people (ideally, over the entire life course).
Another point of departure is the abandonment of the "range-based model" of exercise intensity prescription (used since the 1970s) in favor of the more conceptually defensible "threshold-based model" (based on the gas-exchange and respiratory compensation thresholds).
In sum, kudos to the Secondary Prevention and Rehabilitation Section of the European Association of Preventive Cardiology for being important agents of change!

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More from @Ekkekakis

8 Nov 20
"Affect and DMT useless for HIIT." Inspired by this thoughtful and incisive assessment of Dual-Mode Theory (DMT) by Dr Little (who has clearly "moved beyond petty name calling"), let me again offer some thoughts that may help colleagues better understand what the issues are here.
Let's start from some basic points. First, the "debate" over HIIT is not really about "options" or about public health in general. As explained in a recent thread (see link), nearly all people avoid vigorous-intensity (let alone "high-intensity") activity.
Instead, one of the main engines propelling the HIIT phenomenon is that it presents an argument that seems believable to non-experts, including non-specialists who review grant applications: "look, we discovered a silver bullet for the #1 reason for inactivity: lack of time!"
Read 46 tweets
30 Oct 20
I teach a Critical Appraisal course to seniors in Exercise Science & Pre-Health. Our 1st project is to redo a published meta-analysis. Students always find errors of various degrees of severity. But yesterday, they hit the jackpot. A meta that is so made-up, it's funny. A thread.
The paper is published in "BioMed Research International," a @Hindawi journal indexed in PubMed, and has been cited 39 times in 3 years as having shown that exercise benefits patients with end-stage renal disease (e.g., see below). pubmed.ncbi.nlm.nih.gov/28316986/
Supposedly, this was a meta-analysis of RCTs comparing exercise interventions to control. Well, let's see... We'll focus on just one of the several outcomes, namely blood pressure since it's so important for end-stage renal disease.
Read 13 tweets
28 Oct 20
Inspired by the proposal of this large multinational collaboration, consisting of many outstanding colleagues from the field of exercise science, I wanted to share some thoughts that may be useful to readers who wish to place more emphasis on vigorous intensity in public health.
Let's begin with the argument that vigorous/high intensity is needed because it provides "time-efficient" physical activity options, & time efficiency is the answer to the primary barrier to PA participation, namely "lack of time." The argument is popular for the past 15-20 years
This argument is fallacious. Strangely, in adopting this argument, we have chosen to disregard decades of research in labor patterns and time use. Time-use surveys show large increases in discretionary time in Western countries (e.g., +4-5 hours per day in the US, 1965-2003).
Read 20 tweets
17 Oct 20
Inspired by the latest article by @GretchenReynold in @NYTHealth about how even initially reluctant adults discover how pleasant HIIT can be, I wanted to share some tips for colleagues who review related manuscripts. This is what to look for. A thread...
The first thing to check is the participants. Look for signs that the participants were not exercise-science students of the researchers. Usually, such students already know what the researchers want to find, have seen their videos online, and their posters on the walls.
If the participants are 20-somethings, then scrutinize the characteristics of the sample. When the researchers say "recreationally active," then there is a good chance the participants were athletic. Check the VO2max, if available.
Read 23 tweets
29 Sep 20
I am really surprised that, when I comment on the myriad of (mostly HIIT-related) studies involving tiny samples (e.g., 5-10 per group) and a large number of dependent variables and tests of probability, most people do not see what the problem is. doi.org/10.1038/nmeth.…
In short, the combination of these two factors essentially guarantees two things: (1) you will find something "significant," especially if you don't take any steps to address the inflation of alpha, & (2) whatever you find will likely be non-replicable -- in other words, a fluke.
I am afraid that the Kinesiology literature is becoming so inundated with the combination of small samples and long lists of dependent variables and probability tests (almost always at .05), that we have become desensitized to its devastating consequences.
Read 15 tweets
6 Nov 19
Since we haven't done a critical-appraisal quiz in a while and people seem so fascinated with the finding that "High-intensity exercise [is] best for improving memory," let's do one. The person who identifies the most problems wins.

standard.co.uk/lifestyle/heal…
So, we have two versions of the sample-size calculations, arriving at the conclusion that either N = 61 or N = 64 provides adequate power for a THREE-group trial (i.e., roughly n = 20-21 per group). The 1st is from the original thesis, the 2nd is from the peer-reviewed paper.
Here, the argument is that the target effect size, based on Colcombe and Kramer (see next tweet), is d = 0.41. And that for 80% power and α = 0.05, the required sample size is 61. Be specific about any problems you see. Image
Read 5 tweets

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