Each month we compile a short-list of recently-published papers (full list here) in the world of running science and break them into bite-sized chunks so you can digest them as food for thought to help optimise your training. To help wash it all down, we even review our favourite beer of the month.
Welcome to this month's installment of our "Nerd Alert". We hope you enjoy it.
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Click the title of each article to "drop-down" the summary.
What was the hypothesis or research question?
The importance of deliberate practice as a predictor of performance has not been studied in long-distance runners. Therefore, the authors aimed to determine the effect of deliberate practice (i.e. specific sessions) and easy runs on performance in the 1st 7-years of elite & world-class long-distance runners’ careers.
What did they do to test the hypothesis or answer the research question?
— N=85 male elite/world class 5000m to marathon runners aged 18 and 43 years old were included. Their best race times ranged from 2:03:23 (a former World Record holder) to 2:36:15 in the marathon, and from 58:54 to 1:08:48 in the half marathon. The current World Record holder for the 10 km road race (26:44) was also included in the sample.
— Athletes were sent a questionnaire asking them to recall information for each 2-year interval from the time they began systematic practice (i.e., at 1, 3, 5 and 7 years). They were asked to provided info that reflected a typical week of training 10 weeks before their season goal race (e.g., Olympic Games, World Championships, European Championships, national championships).
— Athletes reported their running PB times (which were converted into points using the IAAF scoring system) and their amounts of tempo runs and short and long interval sessions and 1 easy continuous run after 1, 3, 5 and 7 years of systematic training. Note: 70% of subjects did not report competition times for the first year of systematic training, so details reported for this stage were not included in the final analysis.
What did they find?
— Simple linear regression showed that the total volume of distance run was also strongly related to performance after 3, 5 and 7 years of systematic training (
r = 0.75 to 0.77the strength (effect size) of a relationship (correlation) between two variables, where less than 0.1 (or -0.1) indicates no relationship, (-)0.1 to (-)0.3 indicates a small relationship, (-)0.3 to (-)0.5 a moderate relationship, and greater than (-)0.5 is large.
). This indicates that running volume explains up to 59% (R-squared=0.592) of the variance in performance between athletes.
— Simple linear regression also showed that accumulated easy runs (r = 0.68 – 0.72), tempo runs (r =0.50 – 0.58) and short interval training volumes (r = 0.53 – 0.56) had stronger relationships
with running performance scores after either 3, 5 or 7 years of systematic training than long interval training (r = 0.22 – 0.31).
— However, using simple linear regression for the purpose of comparing strengths is not appropriate since these variables are not independent and since we have no idea what the
confidence intervalsthe range of values within which the true value would be found 95% of the time if the data was repeatedly collected in different samples of people.
around the r-values are.
— Multiple regression found that volume of easy runs and tempo runs were the activities that accounted for ~57% of the variance in performance at years 3, 5, and 7 (P<0.01 or P<0.001).
— The authors concluded that although “deliberate practice” activities, particularly tempo runs and short interval training, are important for improving performance, easy running was crucial for better performance.
What were the strengths?
— Inclusion of world class endurance athletes, including some of the best ever (world records holders).
— Use of multiple regression to weight the contributions of the various training metrics to performance.
What were the weaknesses?
— Asking people to retrospectively recall highly-specific and date-sensitive info over a 7-year period is ridiculous. Fortunately, the authors stated that “72% of the subjects indicated that they used a training log to help them to complete the questionnaire.”. But, we do not know how detailed or accurate these logs are.
— Use of simple linear regression to inform the multiple regression — the whole point of multiple regression is to minimise the bias (and therefore error) introduced by conducting multiple simple linear regressions on variables that are interrelated and dependent on one another.
— We are not told how the 4 models used for the multiple regression were generated.
— A lack of physiological descriptors for subjects (e.g. resting/max HR, VO2max, economy, lactate threshold, etc etc).
Are the findings useful in application to training/coaching practice?
Kinda.
A retrospective recall over several years is a poor way to answer such an important research question. That said, the findings do confirm what we have learned from other studies and what endurance coaches observe in their empirical observations — that a bigger volume of “easy” is associated with better performance. With the commonality of online training platforms, such analysis can be (and have been) conducted real time without the need for retrospective recall.
What is our Rating of Perceived Scientific Enjoyment?
RP(s)E = 5 out of 10.
What was the hypothesis or research question?
Running has a high risk of injury but little data in trail running exists. Therefore, the authors’ aimed to determine risk factors that predict gradual-onset running-related injuries in trail runners. Their secondary aim was to report the epidemiology and clinical characteristics of self-reported gradual-onset running-related injuries. This was an exploratory hypothesis-generating study.
What did they do to test the hypothesis or answer the research question?
— Observational study of baseline (pre-race) characteristics collected in runners at 4 annual trail run events.
— The subjects were 2824 trail running race entrants who entered mass participation 10 and 21 km trail races at the Two Oceans event in South Africa. The 2824 runners had the same age and sex distribution as all 3547 entrants in the race suggesting that the cohort was a random sample and therefore a good representation of all runners.
— Prerace medical screening used a questionnaire based on the European Association for Cardiovascular Prevention and Rehabilitation recommendations and included: history of cardiovascular disease (CVD), symptoms of CVD, risk factors for CVD, other chronic diseases, general prescription medication use, medication use during racing, injury, and a history of collapse during racing.
— Injury was defined per the 2020 International Olympic Committee consensus statement: “An injury that is/was severe enough to interfere with running or require treatment, eg, use medication or require you to seek medical advice from a health professional.”.
What did they find?
— 338 runners reported a total of 349 gradual-onset running-related injuries in the past 12-months, of whom 11 reported a second injury.
— The most common injury affected the knee (35%: n=123), followed by the shin/lower leg/calf (16%: n=55) and the thigh (11%: n=38) .
— The most common specific injury was iliotibial band syndrome (ITBS) (22%: n=78), followed by Achilles tendon injury (10%: n=35), hamstring injury (9%: n=30), calf muscle injury (7%: n=23), and foot/heel pain (5%: n=19).
— The overall
prevalencethe proportion of a particular population found to be affected by a medical condition.
of gradual-onset running-related injuries was 12% (
95% confidence intervalthe range of values within which the true value would be found 95% of the time if the data was repeatedly collected in different samples of people.
= 11–14%) and was not significantly different between males and females or across age groups. There was a higher prevalence of injury among trail runners participating in the longer-distance race (
prevalence ratiothe prevalence in one group compared to another where 1 equals no difference between groups.
= 1.8,
P<0.0001the probability that the result is as or more extreme than that observed under a null-hypothesis. In very basic terms, P = probability that the effect could be explained by random chance, and if P is small, the observed difference is big enough to disprove (reject) the null hypothesis.
) Note: the prevalence ratio is the prevalence in one group compared to another group; a ratio of 1 means the prevalence is the same but a ratio of 0.8 indicates a 20% lower prevalence and a ratio of 1.2 indicates a 20% greater prevalence.
— Greater average weekly training/running distance (miles) in the last 12 months was associated with an increased injury prevalence (
prevalence ratiothe prevalence in one group compared to another where 1 equals no difference between groups.
= 1.0 per 5-mile increase; P=0.0061).
— Pre-existing chronic disease was also associated with a higher prevalence of gradual-onset running-related injuries (
prevalence ratiothe prevalence in one group compared to another where 1 equals no difference between groups.
= 1.7;
P=0.0004the probability that the result is as or more extreme than that observed under a null-hypothesis. In very basic terms, P = probability that the effect could be explained by random chance, and if P is small, the observed difference is big enough to disprove (reject) the null hypothesis.
) in a “dose-dependent” fashion (see Figure below; an increasing number of chronic conditions more greatly increases prevalence of injury).
— Multiple regression, which included, showed that longer race distance (prevalence ratio = 1.9,
P<0.0001the probability that the result is as or more extreme than that observed under a null-hypothesis. In very basic terms, P = probability that the effect could be explained by random chance, and if P is small, the observed difference is big enough to disprove (reject) the null hypothesis.
), a higher chronic disease composite score (prevalence ratio = 1.6, P=0.0012), and a history of any allergies (prevalence ratio = 1.6, P=0.0056) best predicted a history of gradual-onset running-related injuries.
What were the strengths?
— Huge N size.
— Ecologically-relevant — using competitive & trained trail runners.
— The sample was a good model for the population because the authors analysed subject demographic data between the study participants (2824 runners) and all race entrants (3547 entrants) to confirm that they had a random sample. This is important to extrapolate the findings to all runners.
— Men (57%) and women (43% of the study sample) were included.
What were the weaknesses?
— The study did not collect info on chronic disease medications, some of which can independently increase injury risk (e.g. drug-induced tendinopathy is associated with the use of fluoroquinolones, statins, corticosteroids, aromatase inhibitors, and isotretinoin, and a higher risk for tendon ruptures and osteoporosis has been reported with the use of corticosteroids).
— Due to the observational, cross-sectional design, cause-effect relationships cannot be confirmed. That said, large scale epidemiological data sets such as this do provide important leads for further exploration.
— Training history was not well defined, i.e. we do not know whether injuries were caused by poor training methods.
— Injuries were self-reported and clinical diagnoses were not confirmed.
Are the findings useful in application to training/coaching practice?
No.
This is a good study — the findings are interesting from an epidemiological perspective and reveal areas for further investigation — but the study does not inform training or coaching practice. It is well known that running carries a risk of injury and we know that appropriate management of training load (and its progression) and the inclusion of strength training can help mitigate injury risk, two factors that should be included in your training. As with all injuries, don’t ignore them or self-treat; see a doctor and physiotherapist immediately.
What is my Rating of Perceived Scientific Enjoyment?
RP(s)E = 6 out of 10.
What was the hypothesis or research question?
Gastrointestinal issues are prominent in endurance events and are responsible for poor performance and even failure to finish in many events. The pathophysiology of how gastrointestinal issues develop involve many different factors which interact with each other. However, two of the prevalent factors are the intensity of exercise and the type of exercise, which have only been characterized in a few studies. This study aimed to further characterize the intensity of exercise and type of exercise by conducting two separate studies. They hypothesized that as exercise intensity increased from 40% to 60% to 80% of VO2max there would be an increase in gastrointestinal stress. They also hypothesized that due to increased movement running would lead to more gastrointestinal issues than cycling would.
What did they do to test the hypothesis or answer the research question?
-- In both studies they recruit healthy active (> 4hours of training per a week) subjects. N = 11 for the INTENSITY study and n = 10 for the TYPE study.
— For each study they measured the following. Intestinal fatty acid binding protein (I-FABP) which is a sensitive biomarker of intestinal damage, self-reported gastrointestinal symptoms, self-reported thermal stress, body weight changes, rating of perceived exertion, core temperature, aerobic intensity.
— The design of the INTENSITY study used cycling for 60 minutes at 40%, 60%, and 80% of VO2max.
— The design of the TYPE study used 45 minutes at 70% of cycling VO2max for either running or cycling.
What did they find?
— In the INTENSITY study they found that with increasing intensity I-FABP also increased pre to post exercise in the 80% of VO2max (
Cohen’s da type of effect size that quantities the average change score relative to the standard deviation (i.e. the range) of the change scores.
effect size = 1.16) trial, but not the 40% (
Cohen’s da type of effect size that quantities the average change score relative to the standard deviation (i.e. the range) of the change scores.
= 0.22) or 60% trials (d = 0.43). I-FABP was also significantly higher in the 80% VO2max group compared to either the 40% or 60% groups. There was higher core temp, more body weight lost and more thermal stress in the 80% of VO2max group as well.
— In the INTENSITY study GI symptoms were all in the mild range (0-3 on a scale from 0-10), but there were higher on average in the 80% trial relative to the 40% and 60% trials which did not differ. Overall 9 different GI issues were reported in the 80% versus 4 in the 40% and 60% trials.
— In the TYPE study they found, COUNTER to their hypothesis, that there was higher INCREASE in I-FABP in the cycling trial versus the running trial. However, overall 45% of the subjects reported minor GI symptoms during running versus 27% in the cycling trial, while 55% did not report any symptoms.
— In the TYPE study cycling results in a higher heart rate, more plasma loss, higher thermal stress sensation, and higher RPE.
What were the strengths?
— Controlled for water intake.
— Included females (and controlled for menstrual cycle)
— Included both subjective and objective measures of GI distress.
What were the weaknesses?
— Exercise intensity was actually different in the TYPE study because of the relative differences in running and cycling VO2max. They should have done a VO2max for running and cycling and did 70% of that, so that the cycling did not feel harder compared to the running.
— Low n size.
— Lack of any major GI symptoms reported makes it questionable whether there were any performance consequences and how this would translate to longer/harder events with more severe GI problems..
Are the findings useful in application to training/coaching practice?
Kinda.
If someone is consistently having GI issues in training then you need to consider alternative approaches. One approach is to lower the exercise intensity, another would be to try an alternative type of training if possible. Either way the response here seems very individual and also is highly dependent upon other factors such as stress and diet which makes any single recommendation unlikely to be the “magic bullet”. Still this paper gives you a little ammo in troubleshooting a common problem for endurance athletes.
What is my Rating of Perceived Scientific Enjoyment?
RP(s)E = 7 out of 10.
What was the
beerLiquid joy. The thing you drink when you don’t train.
called?
Shake it baby.
Which brewery made it?
Next Level Brewing (Vienna, Austria).
What type of beer is it?
Milkshake IPA.
How strong is the beer?
5.1% ABV.
How would I describe this beer?
Slightly fruity, slightly creamy, slightly juicy, slightly hoppy. Four things that, on their own, would be slightly over-powering but, in their current quantities, are just right and create a very tasty, very drinkable beverage.
What is my Rating of Perceived Beer Enjoyment?
RP(be)E(r) = 7.5 out of 10.
What was the
beerLiquid joy. The thing you drink when you don’t train.
called?
Joker and the Thief.
Which brewery made it?
Kasper Brew Co (Hårlev, Denmark).
What type of beer is it?
Imperial Stout and Barely Wine Blend
How strong is the beer?
12 % ABV.
How would I describe this beer?
Enjoyed this from the tap at a new microbrew beer bar in Copenhagen. This combines the wine notes from a barley wine with the robust malt from a stout all wrapped up in a boozy 12% ABV. It’s full on malty with some raisin and fig notes and a hint of coffee that finishes a little more bitter than sweet. I actually love this combination of styles.
What is my Rating of Perceived Beer Enjoyment?
RP(be)E(r) =9 out of 10.
That is all for this month's nerd alert. We hope to have succeeded in helping you learn a little more about the developments in the world of running science. If not, we hope you enjoyed a nice beer…
Until next month, stay nerdy and keep training smart.
Everyday is a school day.
Empower yourself to train smart.
Think critically. Be informed. Stay educated.
Disclaimer: We occasionally mention brands and products but it is important to know that we are not sponsored by or receiving advertisement royalties from anyone. We have conducted biomedical research for which we have received research money from publicly-funded national research councils and medical charities, and also from private companies. We have also advised private companies on their product developments. These companies had no control over the research design, data analysis, or publication outcomes of our work. Any recommendations we make are, and always will be, based on our own views and opinions shaped by the evidence available. The information we provide is not medical advice. Before making any changes to your habits of daily living based on any information we provide, always ensure it is safe for you to do so and consult your doctor if you are unsure.
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About the authors:
Matt and Thomas are both passionate about making science accessible and helping folks meet their fitness and performance goals. They both have PhDs in exercise science, are widely published, have had their own athletic careers, and are both performance coaches alongside their day jobs. Originally from different sides of the Atlantic, their paths first crossed in Copenhagen in 2010 as research scientists at the Centre for Inflammation and Metabolism at Rigshospitalet (Copenhagen University Hospital). After discussing lots of science, spending many a mile pounding the trails, and frequent micro brew pub drinking sessions, they became firm friends. Thomas even got a "buy one get one free" deal out of the friendship, marrying one of Matt's best friends from home after a chance encounter during a training weekend for the CCC in Schwartzwald. Although they are once again separated by the Atlantic, Matt and Thomas meet up about once a year and have weekly video chats about science, running, and beer. This "nerd alert" was created as an outlet for some of the hundreds of scientific papers they read each month.
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and Matt Laye PhD 
What was the hypothesis or research question?
Buy me a beer.
