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 during your training sessions. 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?
There is growing interest in using ketone supplements to enhance exercise performance but prior studies have conflicting outcomes — some showing benefit, some showing no effect, and others showing detriment. In this study, the authors did not pose a hypothesis or research question. The paper appears to simply be an exploratory analysis of beta-hydroxybutyrate (a ketone) and medium-chain triglycerides on physiological, perceptual variables and running performance.
What did they do to test the hypothesis or answer the research question?
The authors used a randomized, double-blind, crossover design study to examine the effects of two doses of a novel ketone supplement (KETO//OS 2.1 Orange Dream, containing beta-hydroxybutyrate plus a blend of medium chain triglycerides) vs. flavoured-matched placebo on 5 km TT performance in 13 male distance runners (24.8 ± 9.6 years, 72.5 ± 8.3 kg, VO2max 60.1 ± 5.4 ml/kg/min). The first two sessions consisted of a 5-km running time trial familiarization and a VO2max test. The supplement (1- or 2-doses) or placebo was provided 60-mins prior to 5 km time trial on three separate days. Before, during, and after the 5 km TTs, heart rate, RPE, and VO2/VCO2, and capillary blood glucose, lactate, and ketones were measured. Cognitive tests (measuring cognitive flexibility, processing speed, and executive function) were completed before and after the TTs.
What did they find?
— Pre-trial diet and training habits were not different between trials.
— The ketone supplement elevated blood ketone levels above baseline in both supplement trials.
— The ketones supplements raised blood glucose prior to the TTs and blood lactate was higher immediately following 5km TTs in the ketone trials compared to placebo.
— Ketone supplementation did not affect physiological or perceptual variables or performance outcomes.
— Low dose ketone and placebo showed a significantly faster reaction time from pre- to post-TT and response time accuracy was greater in low dose ketone and placebo compared to high-dose ketone in the pre-TT condition. BUT, no pre-supplement cognitive testing was performed.
What were the strengths?
— Randomised cross-over placebo-controlled design.
— Examining a dose-response of the supplement.
— Use of an ecologically valid performance test for runners: a 5 km TT.
— Reporting of effect sizes and predetermined estimate of the smallest worthwhile change.
What were the weaknesses?
— The protocol was not registered as a clinical trial and the primary outcome was therefore not published before the study commenced.
— The study is very similar to the authors’ previous paper examining a single dose of their novel supplement on the same variables (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126257/).
— The study only examines male subjects.
— The sample size is not justified using power calculations.
— The lack of hypothesis or research question makes the paper appear to be a “fishing expedition” to hopefully find something.
— Use of a treadmill reduces the ecological validity of the 5 km TT performance test.
— Day-of feeding is unclear. Food and drink was forbidden for 3 hours prior to the TTs but the actual diet during the day before testing is unclear and whether muscle/liver glycogen levels were similar between trials cannot be known.
— The combination of ketones and MCTs in the supplement complicates any resolution of the direct effects of either ketones or MCTs on performance.
— The lack of pre-supplementation cognitive tests complicates the interpretation of the between-group differences in reaction time.
Are the findings useful in application to training/coaching practice?
No.
While there is growing interest in the effects of ketone supplementation on exercise performance, this study does not provide any useful evidence to help unravel the convoluted outcomes of prior work.
What was the hypothesis or research question?
To determine the effect of Complex strength training (heavy strength and plyometric exercises) combined with running endurance training on neuromuscular adaptations and running performance.
What did they do to test the hypothesis or answer the research question?
The authors used a parallel group repeated-measures design. 38 recreational marathoners (age 31.4 ± 3.8 years, VO2max 57.6 ± 6.8 mL/kg/min) who had not performed any strength training for the previous six months were allocated to one of three-groups:
Complex training (heavy strength training [back squat, split squat, lunge, 3 x 5-reps 70–85% 1RM] plus plyometric exercises [3 x 6-reps of drop jump, single leg hop, double leg hurdle hop], N=13, 4 female 9 male),
Heavy strength training (back squat, split squat, lunge, 5 x 5-reps 70–85% 1RM; N=13, 4 female 9 male), and
Endurance strength training (back squat, split squat, lunge, 5 x 10- to 20-reps 30-40% 1RM; N=12, 4 female 8 male),
Subjects strength trained 2-times per week alongside their normal running training performed 4 to 5 days per week, ~48 km/wk consisting of long-slow distance running at 60–80%HRmax and interval training at 80%HRmax (one or two sets of 3–5 times of 600- to 1000 m with 1:3 work:rest ratio). Before and after the 6-week intervention, 1RM, squat jump and countermovement jumps, leg press concentric and eccentric strength, running economy, and velocity at VO2max were measured.
What did they find?
— There were no differences in sleep time, muscle soreness, or motivation prior to testing, either before or after the interventions.
— Body composition was unaffected between groups.
— 1RM increased in all 3 groups (effect sizes = 1.12, 1.46, and 0.49 for Complex, Heavy, and Endurance groups).
— Leg press concentric strength increased in all 3 groups (effect sizes = 0.39, 0.74, 0.26 for Complex, Heavy, and Endurance groups).
— Squat jump, countermovement jump, and Leg press eccentric strength increased in the Complex (effect size 0.38, 0.62, 0.48) and Heavy groups (effect size 0.63, 0.23, 0.23).
— Running economy at 12 kph and 14 kph improved in Complex (effect size = 0.62 and 0.78) and Heavy groups (effect size = 0.91 and 0.72). Running economy was unaffected in the Endurance strength group.
— Velocity at VO2max increased by ~5% in the Complex (effect size = 0.92) and Heavy (effect size = 0.57) groups but was unchanged in the Endurance strength group.
— VO2max was not affected in any of the 3 interventions.
What were the strengths?
— Power calculations were used to justify the sample size.
— Screening procedures leading to final sample size are clearly described.
— Subjects were familiarized to the testing procedures prior to testing.
— Both men and women were studied.
— Very clear intervention details (sets, reps, intensity, etc).
— Appropriate statistics were used and effect sizes were reported.
— Figure 1 plots the individual subject changes in variables for each intervention, which provides visual clarity of any inter-individual variability.
What were the weaknesses?
— The study protocol was not registered or pre-published.
— No hypothesis was stated.
— During training diet records were not collected or analysed so energy intake or protein intake is unknown.
— Subjects were not randomised but allocated to one of the three intervention groups. Details of allocation are not provided.
— The lack of an ecologically-valid running performance test, like a time trial, limits the applicability of the findings.
— Figure 1 appears to indicate that many subject’s data are missing — it is supposed to be 12 to 13 subjects in each intervention group but many of the panels in Figure 1 show as few as 4 subject’s data — this is a major weakness and is not addressed.
Are the findings useful in application to training/coaching practice?
Yes.
The findings add to the growing evidence that strength training consisting of “heavy” lifting and/or plyometric work when added to existing running training can increase parameters associated with endurance running performance (in this case, running economy and velocity at VO2max). Every runner looking to maximise their potential should incorporate strength training into their approach but should ignore the old sentiments of lifting light and many; rather they should lift heavy and few. That said, this does not mean that if you have never lifted that you go to the weight room and immediately lift as heavy a weight as you can find. Like every new stimulus, it must be respected. Start low, go slow and seek advice when you do not know.
What was the hypothesis or research question?
Examining “big data” can help reveal patterns, trends, and associations and has potential utility for gaining knowledge about human behaviour. In this study, the authors aimed to use big data from wearable activity trackers to demonstrate the feasibility of extracting performance markers from runners’ race results. They also aimed to use these performance markers to predict accurate race times and evaluate the effectiveness of training habits. No hypothesis was stated nor was there a reason question — the study was an exploratory analysis intended to generate future questions.
What did they do to test the hypothesis or answer the research question?
Using Polar Flow (an online training platform that uploads from GPS devices), they extracted ~2.5-million activities from ~19,000 runners during the 180-days leading up to a marathon race. Subject inclusion required that they had completed a run over the marathon distance (42,195 m) in the period between 1 Jul 2015 and 31 Dec 2018; completed at least two different race distances, at least 30-runs, and ran at least 1x/week during the 180-days; and used the same GPS watch (Polar V800) for activity recording. Subject exclusion was based on race velocities not increasing with decreasing race distance (5km, 10km, half-marathon, and marathon) and race times being faster than current world records.
Training during the 180-day “season” was quantified as volume (total running distance; dtrain), intensity (ptrain = average running velocity [vtrain] divided by the subject’s characteristic velocity [vm]) at their maximal aerobic power, and training load (TRIMP, which is essentially time x intensity).
The authors then analysed the training data using their previously-developed mathematical model of the exponential time vs. maximal power output relationship for running, which was modelled on world record performances in men and women and then validated against the personal best performances of the top 9 male and female British marathoners available online in 2015 at Power of 10, with prediction errors of less than 1%. The model has two parameters: endurance (“endurance index”, aka 90% of vm that can be sustained for a long time, aka fractional utilisation) and the velocity requiring maximal aerobic power output (“aerobic power index”, aka critical speed).
What did they find?
— For all racing seasons (180-days) with three and more races (N = 12,309), the mean error between model prediction and actual race time was 2.0% but it ranged from <-20% to >+20% (see Fig4b).
— The accuracy in prediction was better in faster runners — the prediction error in runners with a marathon time below 2:40 was less than 2.5%.
— The maximal fractional utilization of MAP that could be sustained for 1-hour in 24,858 subject’s race seasons peaked at about 82% of maximal aerobic power and ranged from ~68% to 92% — this highlights how much error there can be in guessing one’s lactate threshold or critical speed as a set percentage of maximum.
— Greater training volume during a season (dtrain) was positively correlated with a greater velocity at maximal aerobic power (vm), which might be explained by fitter runners with a larger MAP and hence higher vm simply log more kilometres during their season.
— Greater overall relative training intensity over the season (ptrain) was correlated with lower velocity at maximal aerobic power (vm). Indicating that faster runners typically trained at lower relative intensities.
— The range of training velocities increased as velocity at maximal aerobic power (vm) increased, which indicates that faster runners train at a wider range of intensities between minimal and maximal speeds.
— Marathon best times were positively-associated with an increasing number of TRIMPs accumulated over a 180-day season but only up to ~25,000 TRIMPs, beyond which marathon performance deteriorated (perhaps indicative of the point of “diminishing returns” or overreaching).
What were the strengths?
— Mahoosive dataset with “real world” observations and ecological-valid performance outcomes.
— Sensible parsing of data to exclude nonsensical data points.
— Extracting data only from one Polar device (Polar V800) to minimise technical error.
— High clarity and transparency in the model description, including access to code.
What were the weaknesses?
— The associations between model parameters and training data are correlative observations and do not imply causality nor can they indicate the true direction of correlation. I.e. the model did not monitor performance in each runner before and after the 180-day “season”, nor is it possible to know, for example, whether increased TRIMPs are a cause of increased endurance performance or whether increased endurance performance allows a runner to accumulate more TRIMPs.
— Because TRIMP is based on heart rate, the estimation of an 25000 TRIMP limit for maximal returns may be confounded by accurate heart rate data and accurate athlete assessment of resting and maximum heart rates (in order to calculate an accurate heart rate reserve from which which to derive TRIMP for each session).
— The model does not account for the type terrain or grade of incline/decline. Therefore, running velocity and, by extension, training intensity, may be underestimated in subjects who train on technical/mountainous terrain.
— Since only activity distance (metres) and time (seconds) were detected, the estimates of velocity (metres per second) may be underestimated due to rest-intervals or stopping with the device timer continuing to run (I for one, am someone who has the “auto-stop” function turned off because when running in the mountains is it common for the watch to believe you have stopped moving).
Are the findings useful in application to training/coaching practice?
No.
The data show that faster runners accumulated more volume and more TRIMPs during a season, and trained with more polarity between minimal and maximal speeds. These are known observations of elite athletes. This study simply advances that knowledge to a very large sample of observations. The obvious next step is to use the model to monitor within-subject changes in performance before vs. after a training “season”.
The power vs. time model is cool and it is indeed intuitive and has been well-validated but A.V. Hill first proposed the hyperbolic inverse relationship between maximal power output and exercise duration in 1925. Coaches are well-versed in this relationship without having to understand the physiology. The question you might ask, therefore, is “does this paper add any knowledge for our training/coaching toolbox?”. I am not sure that it does and I feel that this paper highlights the common disconnect between science and (empirical) evidence-based practice.
That said, folks join the running world every day and often have no clue what they are doing. It is always our responsibility to pass on knowledge and help prevent newbies from reinventing the wheel. So, if you were thinking of ditching your GPS watch, perhaps keep it on you if only to provide more data for such models and thereby perhaps enable big data to one day make more informed training decisions for people who are new to the training game and do not have access to a coach.
What was the hypothesis or research question?
Nine out of 10 individuals breath polluted air each day according to the WHO. Physical activity increases ventilation and therefore increases the exposure to air pollutants. Warnings about when it is safe to exercise do not often consider the type, duration, or intensity of exercise, which can vary the exposure to air pollutants dramatically. This study sought to determine whether there was also an effect of obesity on air pollution exposure when conducting various types of exercise.
What did they do to test the hypothesis or answer the research question?
The study was a cross sectional design in which 135 individuals were recruited to perform a VO2max test in which two separate thresholds were calculated. The threshold values were then used to predict ventilation levels during two hypothetical 5km runs, one at a moderate intensity and one at a vigorous intensity. This was done due to ethical concerns about actually exposing subjects to air pollution. The researchers then used historical data on air pollution in Sao Paulo Brazil to model what the exposure to air pollution would be. The researchers also extrapolated the exposure to a level of exercise that is recommended by the American College of Sports Medicine (5 days per a week of 30 minutes of moderate intensity exercise)
What did they find?
— Obese individuals had a higher ventilation at the first, but not second threshold determined, increasing their exposure to air pollutants.
— The exposure to small particulate matter PM2.5 of 115 ug/m3 for 30 minutes would reach a toxic level or running for 1 hour and 25 minutes at the level of pollution in Sao Paulo.
What were the strengths?
— Used real air pollution data
— Collected actual ventilation data from a wide range of participants.
What were the weaknesses?
— Was unclear how they determined which air pollution measurements to use for their modeling.
— Only did modeling and did not model based on other locations, which could have easily been done.
— Did not translate the air pollution data into AQI (air quality index) which is commonly used by the public.
— All hypothetical modeling and non of it was tied to actual changes in acute or chronic health outcomes.
— Do not consider differences in the depth of ventilation versus changes in the rate of respiration, which may expose different parts of the lungs to different levels of pollutants.
— No hypothesis.
Are the findings useful in application to training/coaching practice?
No. This data is a little bit of “no duh”. Obese individuals will have higher levels of ventilation and therefore be exposed to more pollution. From my understanding the AQI would need to be in the Poor (200-300 level) in order for damage to occur due to ventilation. Additional modeling based on various AQIs would have been useful to use as a guideline for when exercise would start to have more of a negative than positive effect on health. Others have estimated that 75 minutes of exercise in cities with high levels of PM2.5 (defined at what exactly, who knows??) would no longer be beneficial. While the data is out there the modeling does not really exist to provide solid recommendations on when people should refrain from exercise (and how much and for how long).
What was the hypothesis or research question?
During endurance exercise there is a reduction in blood flow to the splanchnic region of the intestinal system because blood is shunted to the skeletal muscle and other working tissues. One consequence of this shunting is believed to be gastrointestinal stress and damage of the intestinal lining. The authors hypothesize that increasing blood flow to the splanchnic region will reduce gastrointestinal damage and preserve the integrity of the intestinal epithelium, an idea supported by several animal model studies and human dietary interventions. The authors hypothesized that compression socks which increase venous return may increase splanchnic blood flow and therefore reduce intestinal damage during exercise.
What did they do to test the hypothesis or answer the research question?
The authors used a natural experiment in the form of the Gold Coast Marathon in Australia. Subjects were randomized to either receive compression socks (n=23) or not (n = 23). The compression socks were 2XU which provide foot-to-knee compression of about 25 mm Hg. Markers of intestinal damage were measured before and after the marathon. Specifically they measured intestinal fatty acid-binding protein (I-FABP) which is considered a specific and sensitivity marker of gastrointestinal damage. Subjects did not differ by training volume or race finish time.
What did they find?
— The authors found that both groups increased the circulating levels of I-FABP after the marathon, however the compression sock group only increased the circulating levels 38% versus 107% in the control group. The levels between the groups were both statistically significant and showed a moderate effect size change (d = 0.6).
— There was a moderate inverse correlation between race time and levels of I-FABP in the control group, but not in the compression sock group.
What were the strengths?
— The experimental condition was a real world situation.
— The study was randomized and reported effect sizes.
What were the weaknesses?
— No control for nutritional intake during the marathon or prior to the marathon, which is known to alter gastrointestinal stress and damage.
— No power calculation was done prior to the study and it seems that this was a secondary aim of a larger study examining the effect of compression garments on performance and recovery.
— There are many different types of compression garments with varying compression, so it is hard to say that this is the ideal pressure and there is no rationale (other than they received the garments as a gift) for why these particular socks were selected.
— No measurement or survey of how the runners felt about any gastrointestinal symptoms that they might have experienced. Hard to say whether a change in I-FABP leads to a change in performance or recovery.
Are the findings useful in application to training/coaching practice?
Yes. Some data suggests that compression garments might be helpful for recovery and exercise performance. This study suggests an alternative benefit of compression socks, specifically related to lower intestinal damage associated with better absorption of post exercise nutrition. This might be particularly important in multi-day stage races in which recovery in between stages is paramount. But the study had several weaknesses.
What was the hypothesis or research question?
Diurnal variations in physiology are due to circadian rhythms and can influence many different physiological systems, such as metabolic, circulatory, immune and neuroendocrine responses. Differences in physiological responses to day time or night time exercise via neuroendocrine alterations are postulated to alter immune function and gastrointestinal integrity in a way that may negatively impact exercise performance through gastrointestinal discomfort and stress. Gastrointestinal function is known to have circadian cycles of altered metabolism and emptying rate. The authors aimed to compare the effects of night time versus day time exercise with regards to gastrointestinal integrity and discomfort, feeding tolerance, and systemic inflammatory profile.
What did they do to test the hypothesis or answer the research question?
— Subjects were 16 male and female recreationally trained runners (mean age = 43) who performed two 3 hour runs at 60% of VO2max starting at either 0900h or 2100h in a counterbalanced design.
— During the run subjects consumed a mixed carbohydrate drink (1.0 g/kg BW/h for males, 0.8g/kg BW/h for females) for the first 2 hours.
— Gastrointestinal stress was measured with a modified visual analogue scale of 1-10 (1-4 mild, 5-9 severe, 10 stopping because of gastrointestinal symptoms).
— At two hours subjects underwent an orocecal transit time test, which measures the speed of gastrointestinal transit.
— Oxygen consumption, heart rate, perceived effort, thermal comfort were all collected during exercise as well.
— Blood samples were taken pre and post exercise for various markers of intestinal integrity and inflammation status.
— The day prior to the exercise subjects were fed a low FODMAP diet to limit gastrointestinal stress.
— Female subjects were aligned so that their cycle was in the mid-follicular phase for each trial.
— Power calculations were completed to determine sample size.
What did they find?
— Gastric emptying was slowed and incidence of gastrointestinal symptoms were worse at night compared to day.
— Two participants could not finish the night exercise sessions.
— Cortisol was the hormone with the largest difference between trials, with a much larger increase following the night exercise compared to the day exercise.
— Only modest changes in systemic inflammation occurred with no difference between trials.
— No difference in markers of intestinal integrity (I-FABP concentrations) were found.
What were the strengths?
— Lots of good controls. Diet, menstrual cycle.
— Robust design with appropriate sample size calculations and analysis of data.
— Had multiple markers of many of the measures that they were trying to examine.
What were the weaknesses?
— Nothing major.
Are the findings useful in application to training/coaching practice?
Yes. For those that are training for long or overnight events they should be aware that symptoms experienced in the day may be worse at night when it comes to gastrointestinal issues. This may lead athletes to back off of eating and drinking at night or to train at night to mimic the potential symptoms they may experience. It’s good to know what you might expect and good to have your athletes practice in ways that will mimic race conditions (a few hard night efforts might be a good thing).
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. Until next month, keep active, stay nerdy, and train smart.
Disclaimer: Any interpretations and recommendations we make are, and always will be, based on our own views and opinions shaped by the evidence available to us. Before making any changes to your training 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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What was the hypothesis or research question?
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