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.
Cho and Sohng (2014) was not an RCT and it did not include an assessment of blood pressure (patients who received hemodialysis on Mon, Wed, Fri were the exercise group; those on Tue, Thu, Sat were the control group). The BP numbers in the meta are evidently made-up.
Cupisti et al. (2004) was not an RCT or an exercise intervention. It was a comparison between keto and low-protein diet. The BP numbers in the meta appear to be completely made-up.
Greenwood et al. (2015) was an exercise RCT (yeah!) and they did assess blood pressure (double yeah!), though the meta-analysts, for some reason, entered the data from the middle of the intervention (6 months), not the end (12 months).
Henrique et al. (2010) was an uncontrolled intervention (single-sample). So, the meta-analysts used the baseline BP numbers as "control group" and divided the sample size (N = 14) by 2, saying that each "group" had 7 participants.
Li et al. (2012) was a Chinese study that was irrelevant to the topic (no exercise, not an RCT, no blood pressure). The numbers in the meta-analysis are entirely made-up.
McMahon et al. (1999) examined responses to an exercise bout before and after hemoglobin normalization. The meta-analysts used the numbers for peak work rate (!!!) in the younger group before the intervention as "control" and the old group after the intervention as "exercise."
Messonnier et al. (2012) compared responses to a bout of exercise between sickle-cell patients & non-patients. There was no blood pressure, so the meta-analysts entered the data for Na+ at the end of exercise. The non-patients were "exercise" and the patients were the "control."
Molsted et al. (2004) was an exercise RCT (yeah!) but they reported blood pressure as median and range, so the meta-analysts entered the medians as means and seem to have made up the standard deviations.
Svarstad et al. (2002) was not an exercise RCT. They compared responses of hypertensive patients before & during exercise, either untreated or treated with drugs (plus healthy controls). The meta-analysts used a number from patients before exercise as both "exercise" & "control."
I have not really seen anything like this. This is, at the same time, both hilarious in its ingenuity and absolutely tragic as an indication of the state of peer review in pay-to-play journals. The lesson is: never cite a meta-analysis blindly. Always make an effort to check!!!

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More from @Ekkekakis

28 Oct
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
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
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
20 Oct 19
Word of caution: Neologisms such as "affective attitudes," "affective judgments," and "anticipated affective responses" are certainly NOT "affective determinants of behavior." They are cognitions. As such, interventions to change them rely on information, not lived experiences. Image
If you catch yourself thinking that, to change someone's "affective determinants of behavior," all you need to do is TELL someone that she or he will feel better when they exercise, please stop. And read our chapter, in which we try to clear the confusion.
doi.org/10.1093/oso/97…
Please see Kuhn: he explains that, if a paradigm is threatened by anomalies, its proponents "devise numerous articulations and ad hoc modifications" to avoid denouncing the paradigm leading them to crisis. If "affective determinants" are information-based, the paradigm can stand. Image
Read 4 tweets
10 Oct 19
A thread based on slides I am presenting at @theNASEM on Oct 10. This is part of advising for the National Institute of Aging as it plans funding programs for the primary prevention of #dementia and #Alzheimer's Disease. Here we go... Image
@theNASEM Exercise has been shown to be perhaps the most effective neuroprotective intervention. This evidence is mainly based on animal research but some has been corroborated by human studies. At this point, no other intervention can claim the same conglomeration of benefits on the brain Image
According to a meta-analysis in 2018 exercise has a medium positive effect on cognition in individuals with mild cognitive impairment and Alzheimer's. However, most studies are small and some effects seem implausibly large. Image
Read 27 tweets

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