Running science nerd alert.
by Thomas Solomon PhD
and Matt Laye PhD 
June 2021.
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.
Welcome to this month's installment of our "Nerd Alert". We hope you enjoy it.
Click the title of each article to "drop-down" the summary.
Full paper access: click here
What was the hypothesis or research question?
The “repeated bout effect” refers to the collection of changes in neural adaptations, muscle mechanical properties, structural remodeling of the extracellular matrix, and biochemical signaling that collectively attenuate delayed-onset muscle soreness and the loss of muscle strength following a specific (eccentric) exercise during subsequent bouts (check out a good 2017 review on this topic by Hyldahl et al.). This phenomenon has been well-studied in single joint movements but the impacts of the repeated bout effect on neuromuscular fatigue (central vs. peripheral), running economy, and biomechanics after downhill running are less clear. Therefore, this study investigated the effects of a repeated bout of downhill running on acute and delayed changes in neuromuscular function, energy cost of running (economy), and biomechanics during level running. The authors hypothesized that the repeated bout effect would attenuate neuromuscular fatigue and muscle soreness, preserving biomechanics and economy during level running.
What did they do to test the hypothesis or answer the research question?
— On two occasions, 3-weeks apart, 10 healthy recreational male runners performed a 30-min bout of downhill running at a -20% grade and 2.8 metres/second (~10 kph or ~6:00/km).
— Neuromuscular fatigue, level running biomechanics during slow and fast running, running economy, soressness, and plasma levels of creatine kinase were measured immediately before and after the downhill bouts, and at 24 h, 48 h, 72 h, 96 h, and 168 h thereafter.
— Neuromuscular function was measured by maximal voluntary contraction (MVC) of the knee extensors in the presence and absence of femoral nerve stimulation to decouple the central (nervous system) from peripheral (muscular) fatigue.
— Biomechanics (ground reaction force, ground contact time, flight time, and step frequency) were assessed at 2 different speeds: “slow” (2.5 m/s, 9 km/h) and “fast” (3.9 m/s, 14 km/h).
— Running economy was assessed during a 5-min level running bout at 2.8 m/s (~10 kph or ~6:00/km) and was calculated as the energy (Joules) required to run 1 meter normalized to kg body mass. Hence the running economy was reported as J/m/kg.
What did they find?
— The first downhill running bout increased muscle soreness and creatine kinase and impaired neuromuscular function and several aspects of biomechanics.
— The “repeated bout effect” was demonstrated since the increase in muscle soreness and creatine kinase were blunted after the second downhill run compared to the first.
— The “repeated bout effect” was also observed in maximum voluntary contraction (MVC) force, where losses in MVC and voluntary activation in the days following downhill running were blunted after the second downhill bout.
— The second downhill run also improved biomechanical aspects of center of mass and lower-limb compliance during level running.
— The energy cost of running (economy) was not significantly altered by downhill running but visual inspection indicates they may be underpowered to detect an effect.
What were the strengths?
— During the 6 months prior to data collection, the participants had not performed prolonged downhill running, meaning that the stimulus was novel for these runners.
— The longitudinal design permits confidence in conclusions (although we do not know what the subjects did during the 3-week break between testing visits.
What were the weaknesses?
— The authors don’t define “recreational runner” — no training/fitness data are provided.
— Only male subjects were studied.
—
— The reliability of measuring running economy at a fixed speed (6 min/km) for all subjects is unclear since the fitness of the runners is unknown. If 6 min/km is faster than a runner's aerobic threshold (or first ventilatory threshold, VT1) then economy cannot be assessed because beyond VT1 the relationship between speed and VO2 is no longer linear.
— Biomechanics were assessed at 2 different speeds: “slow” (2.5 m/s, 9 km/h) and “fast” (3.9 m/s, 14 km/h). But without any info about the runner’s fitness, “slow” and “fast” has no context — are these speeds below/above their ventilatory thresholds? Or, are they below/above their Easy/interval paces?
— Economy was reported as J/m/kg. While not incorrect, these are unusual and unrelatable units. Running economy is most often reported as mLO2/km/min to relate the amount of oxygen consumed as a function of distance travelled. Adding these units would add some context in the face of other work in the field.
— During the 6 months prior to data collection, the participants had not performed prolonged downhill running. Therefore, this would not be relevant to athletes who typically train on hilly/mountainous terrain.
Are the findings useful in application to training/coaching practice?
Yes.
These findings add to the evidence that downhill running can be a useful tool to help overcome the muscle soreness and loss of muscle strength caused by downhill running. Although, likely underpowered to detect the effect, prior work. Also finds that repeated downhill bouts can help maintain running economy, which is very important during long duration races. But we should note that a novel stimulus is more likely to have a benefit in runners unaccustomed to such stimuli — in runners who dwell and regularly train in the hills/mountains, there will likely be little benefit with these protocols.
What was the hypothesis or research question?The “repeated bout effect” refers to the collection of changes in neural adaptations, muscle mechanical properties, structural remodeling of the extracellular matrix, and biochemical signaling that collectively attenuate delayed-onset muscle soreness and the loss of muscle strength following a specific (eccentric) exercise during subsequent bouts (check out a good 2017 review on this topic by Hyldahl et al.). This phenomenon has been well-studied in single joint movements but the impacts of the repeated bout effect on neuromuscular fatigue (central vs. peripheral), running economy, and biomechanics after downhill running are less clear. Therefore, this study investigated the effects of a repeated bout of downhill running on acute and delayed changes in neuromuscular function, energy cost of running (economy), and biomechanics during level running. The authors hypothesized that the repeated bout effect would attenuate neuromuscular fatigue and muscle soreness, preserving biomechanics and economy during level running.
What did they do to test the hypothesis or answer the research question? — On two occasions, 3-weeks apart, 10 healthy recreational male runners performed a 30-min bout of downhill running at a -20% grade and 2.8 metres/second (~10 kph or ~6:00/km).
— Neuromuscular fatigue, level running biomechanics during slow and fast running, running economy, soressness, and plasma levels of creatine kinase were measured immediately before and after the downhill bouts, and at 24 h, 48 h, 72 h, 96 h, and 168 h thereafter.
— Neuromuscular function was measured by maximal voluntary contraction (MVC) of the knee extensors in the presence and absence of femoral nerve stimulation to decouple the central (nervous system) from peripheral (muscular) fatigue.
— Biomechanics (ground reaction force, ground contact time, flight time, and step frequency) were assessed at 2 different speeds: “slow” (2.5 m/s, 9 km/h) and “fast” (3.9 m/s, 14 km/h).
— Running economy was assessed during a 5-min level running bout at 2.8 m/s (~10 kph or ~6:00/km) and was calculated as the energy (Joules) required to run 1 meter normalized to kg body mass. Hence the running economy was reported as J/m/kg.
What did they find? — The first downhill running bout increased muscle soreness and creatine kinase and impaired neuromuscular function and several aspects of biomechanics.
— The “repeated bout effect” was demonstrated since the increase in muscle soreness and creatine kinase were blunted after the second downhill run compared to the first.
— The “repeated bout effect” was also observed in maximum voluntary contraction (MVC) force, where losses in MVC and voluntary activation in the days following downhill running were blunted after the second downhill bout.
— The second downhill run also improved biomechanical aspects of center of mass and lower-limb compliance during level running.
— The energy cost of running (economy) was not significantly altered by downhill running but visual inspection indicates they may be underpowered to detect an effect.
What were the strengths? — During the 6 months prior to data collection, the participants had not performed prolonged downhill running, meaning that the stimulus was novel for these runners.
— The longitudinal design permits confidence in conclusions (although we do not know what the subjects did during the 3-week break between testing visits.
What were the weaknesses? — The authors don’t define “recreational runner” — no training/fitness data are provided.
— Only male subjects were studied.
—
Effect sizesa quantitative measure of the magnitude of the experimental effect. Less than 0.5 is small, greater than 0.8 is large.
were not reported and the sample size of 10 was not justified with power calcs. — The reliability of measuring running economy at a fixed speed (6 min/km) for all subjects is unclear since the fitness of the runners is unknown. If 6 min/km is faster than a runner's aerobic threshold (or first ventilatory threshold, VT1) then economy cannot be assessed because beyond VT1 the relationship between speed and VO2 is no longer linear.
— Biomechanics were assessed at 2 different speeds: “slow” (2.5 m/s, 9 km/h) and “fast” (3.9 m/s, 14 km/h). But without any info about the runner’s fitness, “slow” and “fast” has no context — are these speeds below/above their ventilatory thresholds? Or, are they below/above their Easy/interval paces?
— Economy was reported as J/m/kg. While not incorrect, these are unusual and unrelatable units. Running economy is most often reported as mLO2/km/min to relate the amount of oxygen consumed as a function of distance travelled. Adding these units would add some context in the face of other work in the field.
— During the 6 months prior to data collection, the participants had not performed prolonged downhill running. Therefore, this would not be relevant to athletes who typically train on hilly/mountainous terrain.
Are the findings useful in application to training/coaching practice? Yes.
These findings add to the evidence that downhill running can be a useful tool to help overcome the muscle soreness and loss of muscle strength caused by downhill running. Although, likely underpowered to detect the effect, prior work. Also finds that repeated downhill bouts can help maintain running economy, which is very important during long duration races. But we should note that a novel stimulus is more likely to have a benefit in runners unaccustomed to such stimuli — in runners who dwell and regularly train in the hills/mountains, there will likely be little benefit with these protocols.
Full paper access: click here
What was the hypothesis or research question?
Due to the metabolic demands of ultra-trail races, athletes should be nutritionally prepared to delay fatigue. However, high carbohydrate intake may increase gastrointestinal (GI) problems. The aim of this systematic review was to examine carb intake in ultra trail athletes, its relationship with fatigue and GI problems.
What did they do to test the hypothesis or answer the research question?
— They conducted a systematic review according to PRIMSA (Preferred Reporting Items for Systematic Review and Meta-Analyses) guidelines. The search included Web of Science, Cochrane Library and Scopus databases up to 16 March 2021.
— Studies were included if: (I) studies that were carried out during a single-day ultra-trail race; (II) reporting on carb intake (quantity and/or time of intake); (III) inclusion of post-exercise recovery, internal load information and/or description of GI complications associated with carb intake during single-day ultra-trail events; (IV) inclusion of those performed on any number or type of athlete regardless of category, training status or gender; (V) languages restricted to English, German, French, Italian, Spanish and Portuguese.
— Studies were excluded if: (I) ultra-trail events that exceed 24 h or ultra-endurance races on the road, (II) those performed by bike or some other specialty that is not agreed; (III) absence of information about carb intake; (IV) the study was conducted on participants with a pathological condition; (IV) those that exclusively describe other nutrients during a marathon, ultra-trail or ultra-marathon.
— A qualitative synthesis of methodological quality and risk of bias was made.
What did they find?
— The initial search found 93 relevant articles but only 8 articles met all the inclusion criteria.
— Most runners did not meet carb intake recommendations for ultra trail events (which they claim is 90 g/h but the actual guidelines are actually “up to 90 g/h” in a range of 30 to 90 grams/hour according to food palatability, tolerance, and savoury vs. sweet preference).
— Two studies associated higher carb intake (120 g/h) with improved internal exercise load and observed that runners whose intake was 120 g/h had less muscle damage (as indicated by lower blood creatine kinase, lactate dehydrogenase, and aspartate aminotransferase) and improved recovery of high intensity running capacity 24 h after a trail marathon.
— 6 studies showed that athletes reported GI symptoms 65–82% of the time during races. GI symptoms were recurrent in ultratrail athletes depending on altitude, environmental conditions and running speed.
— Authors concluded that a high carb intake during single-day ultra trail races is important to delay fatigue and avoid GI complications. They also concluded that, to ensure high intake, it is necessary to implement intestinal training protocols (but this was not investigated).
What were the strengths?
— They used PRISMA guidelines, provided the Boolean search equation used in their paper search, and clearly defined their inclusion/exclusion study search criteria.
— Study searches and data extraction were carried out independently by two authors and any disagreements about were resolved through discussion with a third author.
— Methodological quality and risk of study bias were well presented in Table 1.
What were the weaknesses?
— No quantitative assessments (e.g.
— No estimate of publication bias was provided (e.g. a funnel plot to show whether or not only positive studies are published).
— The search excluded ultra runs that were not on trails, which is a pity since there is experimental data from road/treadmill/running track ultras.
Are the findings useful in application to training/coaching practice?
Not yet.
Given the paucity of the data, no knowledge of publication bias, and the lack of a quantitative analysis to show summary statistics of
What was the hypothesis or research question?Due to the metabolic demands of ultra-trail races, athletes should be nutritionally prepared to delay fatigue. However, high carbohydrate intake may increase gastrointestinal (GI) problems. The aim of this systematic review was to examine carb intake in ultra trail athletes, its relationship with fatigue and GI problems.
What did they do to test the hypothesis or answer the research question? — They conducted a systematic review according to PRIMSA (Preferred Reporting Items for Systematic Review and Meta-Analyses) guidelines. The search included Web of Science, Cochrane Library and Scopus databases up to 16 March 2021.
— Studies were included if: (I) studies that were carried out during a single-day ultra-trail race; (II) reporting on carb intake (quantity and/or time of intake); (III) inclusion of post-exercise recovery, internal load information and/or description of GI complications associated with carb intake during single-day ultra-trail events; (IV) inclusion of those performed on any number or type of athlete regardless of category, training status or gender; (V) languages restricted to English, German, French, Italian, Spanish and Portuguese.
— Studies were excluded if: (I) ultra-trail events that exceed 24 h or ultra-endurance races on the road, (II) those performed by bike or some other specialty that is not agreed; (III) absence of information about carb intake; (IV) the study was conducted on participants with a pathological condition; (IV) those that exclusively describe other nutrients during a marathon, ultra-trail or ultra-marathon.
— A qualitative synthesis of methodological quality and risk of bias was made.
What did they find? — The initial search found 93 relevant articles but only 8 articles met all the inclusion criteria.
— Most runners did not meet carb intake recommendations for ultra trail events (which they claim is 90 g/h but the actual guidelines are actually “up to 90 g/h” in a range of 30 to 90 grams/hour according to food palatability, tolerance, and savoury vs. sweet preference).
— Two studies associated higher carb intake (120 g/h) with improved internal exercise load and observed that runners whose intake was 120 g/h had less muscle damage (as indicated by lower blood creatine kinase, lactate dehydrogenase, and aspartate aminotransferase) and improved recovery of high intensity running capacity 24 h after a trail marathon.
— 6 studies showed that athletes reported GI symptoms 65–82% of the time during races. GI symptoms were recurrent in ultratrail athletes depending on altitude, environmental conditions and running speed.
— Authors concluded that a high carb intake during single-day ultra trail races is important to delay fatigue and avoid GI complications. They also concluded that, to ensure high intake, it is necessary to implement intestinal training protocols (but this was not investigated).
What were the strengths? — They used PRISMA guidelines, provided the Boolean search equation used in their paper search, and clearly defined their inclusion/exclusion study search criteria.
— Study searches and data extraction were carried out independently by two authors and any disagreements about were resolved through discussion with a third author.
— Methodological quality and risk of study bias were well presented in Table 1.
What were the weaknesses? — No quantitative assessments (e.g.
effect sizea quantitative measure of the magnitude of the experimental effect. Less than 0.5 is small, greater than 0.8 is large.
and 95% confidence interval summary statistics or heterogeneity statistics) were presented. — No estimate of publication bias was provided (e.g. a funnel plot to show whether or not only positive studies are published).
— The search excluded ultra runs that were not on trails, which is a pity since there is experimental data from road/treadmill/running track ultras.
Are the findings useful in application to training/coaching practice? Not yet.
Given the paucity of the data, no knowledge of publication bias, and the lack of a quantitative analysis to show summary statistics of
effect sizea quantitative measure of the magnitude of the experimental effect. Less than 0.5 is small, greater than 0.8 is large.
s and 95% confidence intervals, this systematic review is perhaps a little premature and is simply a narrative review of recent experimental evidence. Coaches and athletes know that carb intake is useful for endurance performance and coaches/athletes engaged in ultra races are aware that food tolerance is key — developing strategies to help maintain energy intake and carbohydrate availability should be part of every ultra athlete's training toolbox. Full paper access: click here
What was the hypothesis or research question?
Pre- and post-exercise changes in body mass corrected for fluid intake and urinary loss can be used to estimate exercise sweat rate. In well-controlled settings, day-to-day variation in exercise sweat rate is reported to be 5–7%. Within-subject variability of sweat rate has not been studied in detail. Therefore, the purpose of this study was to determine the day-to-day variability in sweat rate within endurance trained individuals during regular training in non-lab-controlled, in-the-field conditions.
What did they do to test the hypothesis or answer the research question?
— 13 endurance-trained males (n = 3) and females (n = 10) were included. They were recreational runners and triathletes or collegiate x-country runners. All subjects were currently running at least 120 min/week for the previous 3-months.
— Participants completed at least a 30-min session (running or biking at a self-selected pace and intensity) at least 1x/wk between 5:30 and 9:30 am in a case-series design.
— Duration, distance, and pace were measured using GPS.
— Environmental conditions were recorded using a Wet-Bulb Globe Thermometer at the beginning and and every 15 min during exercise.
— Sweat rates were calculated from the change in body mass during exercise, with correction for fluid intake.
— Data collection took place outdoors over 7 months in all conditions except when raining.
— Data were divided into three temperature ranges: Low (less than 10°C), Moderate (between 10–20°C) and High (above 20°C).
— Within-subject variability in sweat rate (the primary outcome of the study) was assessed as the difference between highest and lowest sweat rates recorded under each of the 3 temperature conditions. (Regrettably, without using the subject ID as a covariate, this does not assess within-subject variability; it simply tells us how large, on average, was each subject’s highest-minus-lowest sweat rates).
What did they find?
— Sweat rates were generally higher in warm temperature conditions. Regrettably no regression analyses were completed to objectively examine the relationship between sweat rate and ambient temperature.
— They reported significant differences in sweat rate variability (the difference between highest and lowest sweat rates) within all three temperature sub-groups (low, moderate and high):
What were the strengths?
— Studied male and female subjects.
— High ecological validity — athletes studied under their normal habits of daily living.
What were the weaknesses?
— Small sample (10, 3 male / 7 females).
— The relative intensity of exercise during sessions is unknown because no baseline or during study (7-months) measures of VO2max were taken.
— It is not clear whether there was an a priori goal number of sessions to collect data. And, it is unclear how many sessions each subject recorded over the 7-month study period.
— It is also unclear why the specific “low” (less than 10°C), “moderate”, and “high” (greater than 20°C) threshold values were chosen.
— Only 4 subjects completed sessions in all 3 temperature ranges, “low”, “moderate”, and “high”.
— Although subjects peed before exercise, sweat rates were not corrected for urinary loss during exercise (i.e. they did not measure the volume of pee produced after exercise). This might not be large during a 30-min bout but if you’re gonna do something, do it properly).
— No regression analyses were completed to objectively examine the relationship between sweat rate and ambient temperature. And, no statistics of within-subject variability were calculated (very surprising since that was the primary outcome of the study).
Are the findings useful in application to training/coaching practice?
No.
This was a noble effort to conduct an ecologically valid study on an important topic. But the study is vastly underpowered with only 10 subjects and the methodological description lacks sufficient info to know exactly what was done (e.g. how many sessions did subjects do?). The study also uses an inappropriate analysis to determine whether there is within-subject variability in sweat rates. Consequently, we cannot pull much from the data.
Hydration is indeed important. In the intro, the authors allude to the important notion that measuring a sweat rate cannot be used as a rule of thumb. Rather than applying a single assessment of exercise sweat rate to all situations, athletes (and their coaches) need to understand the factors that influence their sweat rate and they need to be aware of their day to day variation. All that said, while calculating a specific sweat rate sounds sexy, the variability in the estimate means that you would need to measure your sweat rate regularly and under multiple conditions. That would be tedious. Plus, drinking fluid to a target is rarely safe, especially if it causes body weight gain (which has been detected in many races, including Western States) or hyponatremia (low plasma sodium levels, which can be fatal). In most situations, drinking fluid to quench thirst is sufficient for maintaining euhydration or for maintaining performance during exercise. Under extreme situations, more nuanced approaches to hydration may be needed but that is beyond my goal for today. Stay tuned for a deep dive article on this topic coming soon.
What was the hypothesis or research question?Pre- and post-exercise changes in body mass corrected for fluid intake and urinary loss can be used to estimate exercise sweat rate. In well-controlled settings, day-to-day variation in exercise sweat rate is reported to be 5–7%. Within-subject variability of sweat rate has not been studied in detail. Therefore, the purpose of this study was to determine the day-to-day variability in sweat rate within endurance trained individuals during regular training in non-lab-controlled, in-the-field conditions.
What did they do to test the hypothesis or answer the research question? — 13 endurance-trained males (n = 3) and females (n = 10) were included. They were recreational runners and triathletes or collegiate x-country runners. All subjects were currently running at least 120 min/week for the previous 3-months.
— Participants completed at least a 30-min session (running or biking at a self-selected pace and intensity) at least 1x/wk between 5:30 and 9:30 am in a case-series design.
— Duration, distance, and pace were measured using GPS.
— Environmental conditions were recorded using a Wet-Bulb Globe Thermometer at the beginning and and every 15 min during exercise.
— Sweat rates were calculated from the change in body mass during exercise, with correction for fluid intake.
— Data collection took place outdoors over 7 months in all conditions except when raining.
— Data were divided into three temperature ranges: Low (less than 10°C), Moderate (between 10–20°C) and High (above 20°C).
— Within-subject variability in sweat rate (the primary outcome of the study) was assessed as the difference between highest and lowest sweat rates recorded under each of the 3 temperature conditions. (Regrettably, without using the subject ID as a covariate, this does not assess within-subject variability; it simply tells us how large, on average, was each subject’s highest-minus-lowest sweat rates).
What did they find? — Sweat rates were generally higher in warm temperature conditions. Regrettably no regression analyses were completed to objectively examine the relationship between sweat rate and ambient temperature.
— They reported significant differences in sweat rate variability (the difference between highest and lowest sweat rates) within all three temperature sub-groups (low, moderate and high):
→ Low temp, av. difference of 0.15 L/h between highest and lowest (p≤0.01).
→ Moderate temp, av. difference of 0.14 L/h (p≤0.05).
→ High temp, av. difference of 0.16 L/h (p≤0.01).
Note: between-temperature differences were not examined.
→ Moderate temp, av. difference of 0.14 L/h (p≤0.05).
→ High temp, av. difference of 0.16 L/h (p≤0.01).
Note: between-temperature differences were not examined.
What were the strengths? — Studied male and female subjects.
— High ecological validity — athletes studied under their normal habits of daily living.
What were the weaknesses? — Small sample (10, 3 male / 7 females).
— The relative intensity of exercise during sessions is unknown because no baseline or during study (7-months) measures of VO2max were taken.
— It is not clear whether there was an a priori goal number of sessions to collect data. And, it is unclear how many sessions each subject recorded over the 7-month study period.
— It is also unclear why the specific “low” (less than 10°C), “moderate”, and “high” (greater than 20°C) threshold values were chosen.
— Only 4 subjects completed sessions in all 3 temperature ranges, “low”, “moderate”, and “high”.
— Although subjects peed before exercise, sweat rates were not corrected for urinary loss during exercise (i.e. they did not measure the volume of pee produced after exercise). This might not be large during a 30-min bout but if you’re gonna do something, do it properly).
— No regression analyses were completed to objectively examine the relationship between sweat rate and ambient temperature. And, no statistics of within-subject variability were calculated (very surprising since that was the primary outcome of the study).
Are the findings useful in application to training/coaching practice? No.
This was a noble effort to conduct an ecologically valid study on an important topic. But the study is vastly underpowered with only 10 subjects and the methodological description lacks sufficient info to know exactly what was done (e.g. how many sessions did subjects do?). The study also uses an inappropriate analysis to determine whether there is within-subject variability in sweat rates. Consequently, we cannot pull much from the data.
Hydration is indeed important. In the intro, the authors allude to the important notion that measuring a sweat rate cannot be used as a rule of thumb. Rather than applying a single assessment of exercise sweat rate to all situations, athletes (and their coaches) need to understand the factors that influence their sweat rate and they need to be aware of their day to day variation. All that said, while calculating a specific sweat rate sounds sexy, the variability in the estimate means that you would need to measure your sweat rate regularly and under multiple conditions. That would be tedious. Plus, drinking fluid to a target is rarely safe, especially if it causes body weight gain (which has been detected in many races, including Western States) or hyponatremia (low plasma sodium levels, which can be fatal). In most situations, drinking fluid to quench thirst is sufficient for maintaining euhydration or for maintaining performance during exercise. Under extreme situations, more nuanced approaches to hydration may be needed but that is beyond my goal for today. Stay tuned for a deep dive article on this topic coming soon.
What was the beerLiquid joy. The thing you drink when you don’t train. Tastes like amazing.
called?
Cwtch.
Which brewery made it?
Tiny Rebel Brewing Co, Wales.
What type of beerLiquid joy. The thing you drink when you don’t train. Tastes like amazing.
is it?
Red Ale.
How strong is the beerLiquid joy. The thing you drink when you don’t train. Tastes like amazing.
(ABV)?
4.6 % ABV.
How would I describe this beerLiquid joy. The thing you drink when you don’t train. Tastes like amazing.
?
Slightly sweet, almost toffee like, on the nose. Gloriously ginger to the eye. Mildly fruity, tangy, and bitter on the tongue. Smooth and fresh on the way down… And, an aftertaste like “a hug full of love”, i.e. like a cwtch.
What is my Rating of Perceived beerLiquid joy. The thing you drink when you don’t train. Tastes like amazing.
Enjoyment?
RP(be)E(r) = 8 out of 10. 
Full paper access: click here
What was the hypothesis or research question?
The title speaks to the question very well. The research question was to see whether training volume influenced the microbiome composition in highly trained middle distance runners. This is of interest because we know that things like physical activity and nutrition broadly influence the composition of the microbiome and that change in composition may alter performance. We know that increasing training volume generally improves performance so a link between this training approach and the microbiome was assessed. The researchers hypothesized that this increase in training volume would alter the microbiome, but that a taper would return the microbiome to pre increased training volume.
What did they do to test the hypothesis or answer the research question?
— They recruited 16 highly trained middle distance runners (8 males, 6 females) who were consistently training and competing in middle distance events with sub-elite PRs in 1500m distance. The study was 7 weeks long with 3 weeks of normal training, 3 weeks of increasing volume of training (10, 20, and 30% increases respectively) and one week taper that was 55% of the third increased week. The types of training made up a similar proportion of each week of training and were prescribed by a coach and monitored via GPS watches and software.
— For the most part the diet was free living and not controlled (the exception being dinner and breakfast during lab visits), although 3 day dietary records were collected before each laboratory visit.
— Before the start of each training block the subjects visited the lab for incremental running test, body composition testing, and fecal sample collection for microbiome analysis. The microbiome analysis was performed by a commercial laboratory and the alpha-diversity (diversity of species in a single sample) was calculated with several approaches (Shannon’s INdex, Chao1 Index, and Inverse Simpson Index)
What did they find?
— Training zone intensity did not change throughout the study, with no changes in diet, no changes in body mass, lean mass or body fat. But training volume increased as designed and performance increased after the reduced volume (taper) week.
— They identified a total of 366 species across all samples, but only 92 species were present in 60% of the samples, which was their cut off for analysis.
— With regards to their specific hypothesis/question - there was no statistical difference in diversity of the microbiome between any of the time points using any of the diversity measurements and the
What were the strengths?
— There was a nice balance between free living (allowing the subjects to eat their normal diet) and also controlling for parameters specifically related to their hypothesis of training volume.
— They reported
What were the weaknesses?
— It would be impossible to control all of the diet for 7 weeks, but diet is VERY important and was only measured for 3 days prior to the laboratory visits. So differences in diet may have masked the effects from exercise volume.
— 16S sequencing as a method does not characterize the meta-genomic changes and is more suitable for large changes.
Are the findings useful in application to training/coaching practice?
This study shows that microbiome is robust and that changes in the microbiome will take a more significant intervention than a change in training volume in well trained individuals. While others studies do suggest an effect of exercise it may be that well trained individuals are more resistant to changes. Conversely if someone wants to change their microbiome then they are going to need a more invasive change (antibiotics, probiotics, or significant dietary changes) to alter their microbiome.
What was the hypothesis or research question?The title speaks to the question very well. The research question was to see whether training volume influenced the microbiome composition in highly trained middle distance runners. This is of interest because we know that things like physical activity and nutrition broadly influence the composition of the microbiome and that change in composition may alter performance. We know that increasing training volume generally improves performance so a link between this training approach and the microbiome was assessed. The researchers hypothesized that this increase in training volume would alter the microbiome, but that a taper would return the microbiome to pre increased training volume.
What did they do to test the hypothesis or answer the research question? — They recruited 16 highly trained middle distance runners (8 males, 6 females) who were consistently training and competing in middle distance events with sub-elite PRs in 1500m distance. The study was 7 weeks long with 3 weeks of normal training, 3 weeks of increasing volume of training (10, 20, and 30% increases respectively) and one week taper that was 55% of the third increased week. The types of training made up a similar proportion of each week of training and were prescribed by a coach and monitored via GPS watches and software.
— For the most part the diet was free living and not controlled (the exception being dinner and breakfast during lab visits), although 3 day dietary records were collected before each laboratory visit.
— Before the start of each training block the subjects visited the lab for incremental running test, body composition testing, and fecal sample collection for microbiome analysis. The microbiome analysis was performed by a commercial laboratory and the alpha-diversity (diversity of species in a single sample) was calculated with several approaches (Shannon’s INdex, Chao1 Index, and Inverse Simpson Index)
What did they find? — Training zone intensity did not change throughout the study, with no changes in diet, no changes in body mass, lean mass or body fat. But training volume increased as designed and performance increased after the reduced volume (taper) week.
— They identified a total of 366 species across all samples, but only 92 species were present in 60% of the samples, which was their cut off for analysis.
— With regards to their specific hypothesis/question - there was no statistical difference in diversity of the microbiome between any of the time points using any of the diversity measurements and the
effect sizesa quantitative measure of the magnitude of the experimental effect. Less than 0.5 is small, greater than 0.8 is large.
ranged from almost zero to minimal depending on the diversity measurement used. There were also no associations with any training parameters between the microbiome diversity. There were some specific species that differed after the higher training volume, but these species made up less than 0.1% of all the microbiota.
What were the strengths? — There was a nice balance between free living (allowing the subjects to eat their normal diet) and also controlling for parameters specifically related to their hypothesis of training volume.
— They reported
effect sizesa quantitative measure of the magnitude of the experimental effect. Less than 0.5 is small, greater than 0.8 is large.
which allowed them to conclude that even a larger sample size was unlikely to result in a significant effect.
What were the weaknesses? — It would be impossible to control all of the diet for 7 weeks, but diet is VERY important and was only measured for 3 days prior to the laboratory visits. So differences in diet may have masked the effects from exercise volume.
— 16S sequencing as a method does not characterize the meta-genomic changes and is more suitable for large changes.
Are the findings useful in application to training/coaching practice? This study shows that microbiome is robust and that changes in the microbiome will take a more significant intervention than a change in training volume in well trained individuals. While others studies do suggest an effect of exercise it may be that well trained individuals are more resistant to changes. Conversely if someone wants to change their microbiome then they are going to need a more invasive change (antibiotics, probiotics, or significant dietary changes) to alter their microbiome.
Full paper access: click here
What was the hypothesis or research question?
One of the major physiological determinants of distance running performance is running economy or how little oxygen you need for a given running speed. The authors wanted to explore the relationship between various neuromuscular parameters of the muscle and running economy given the known benefit of strength training on running economy. Specifically the authors wanted to establish the strength of the relationship between running economy and various neuromuscular tests.
What did they do to test the hypothesis or answer the research question?
— The study employed a cross sectional design with 28 collegiate runners with a minimum of 4 years of experience completing a running economy test at three different speeds; 12, 14, and 16km/hour. The study was conducted during the off season and required 3 testing days with a minimum of 24 hours of rest in between the sessions.
— In the first session running performed a one repetition max squat after a running warm up and some submaximal lifts they completed 3-4 maximal lifts. 15 minutes after the maximal squat they performed a countermeasure jump and drop jump test from a height of 40 cm. They used a force plate to record the measurements and had 3 trials of each test.
— In the next visit they measured concentric and eccentric muscular strength with a dynamometer. Leg stiffness was calculated with an overground run in which the subjects hit a force plate while various anatomical markers were tracked with high speed cameras to allow calculations of various forces. This was done at 12, 14, and 16 km/h with a 5% variation from the mean allowed.
— The last day subjects performed with running economy at those three speeds on the treadmill for 4 minutes a bout followed by a subsequent VO2max test.
— Pearson’s correlation were used for all of the analysis with the following demarcations: small (0.1–0.3), moderate (0.3–0.5), large (0.5–0.7), very large (0.7–0.9), and extremely large (0.9–1.0).
What did they find?
— Large correlations between the eccentric leg dynamometer measures, drop jump, leg stiffness while running and running economy were found. Moderate or small correlations were found between 1 rep max squat and countermeasure jumps with running economy. No significant correlation was found between concentric muscle strength (via leg dynamometer measure) and running economy.
What were the strengths?
— Multiple measures that represented eccentric strength.
— High agreement between trials (interclass correlation) for an individual and thus the tests were fairly reliable.
— Measure of leg stiffness using high speed cameras and force plate is more accurate than most studies which measure leg stiffness and estimate how the center of mass changes.
What were the weaknesses?
— All correlations, so we don’t have any cause and effect relationships.
— Not a longitudinal design and therefore we don’t know if manipulating those strength variables will lead to performance increases.
— No direct performance measurements, only submaximal running economy.
Are the findings useful in application to training/coaching practice?
Yes.
We know that non-running training is important for running economy. This work suggests that ancillary workouts that focus on improving eccentric strength, leg stiffness, and the stretch-shortening cycle will improve running economy. Thus, only plyometrics or only strength work will not fully adapt the neuromuscular system to maximize improvements in running economy. Integrating all of those types of training is necessary and should be programmed into a training program.
What was the hypothesis or research question?One of the major physiological determinants of distance running performance is running economy or how little oxygen you need for a given running speed. The authors wanted to explore the relationship between various neuromuscular parameters of the muscle and running economy given the known benefit of strength training on running economy. Specifically the authors wanted to establish the strength of the relationship between running economy and various neuromuscular tests.
What did they do to test the hypothesis or answer the research question? — The study employed a cross sectional design with 28 collegiate runners with a minimum of 4 years of experience completing a running economy test at three different speeds; 12, 14, and 16km/hour. The study was conducted during the off season and required 3 testing days with a minimum of 24 hours of rest in between the sessions.
— In the first session running performed a one repetition max squat after a running warm up and some submaximal lifts they completed 3-4 maximal lifts. 15 minutes after the maximal squat they performed a countermeasure jump and drop jump test from a height of 40 cm. They used a force plate to record the measurements and had 3 trials of each test.
— In the next visit they measured concentric and eccentric muscular strength with a dynamometer. Leg stiffness was calculated with an overground run in which the subjects hit a force plate while various anatomical markers were tracked with high speed cameras to allow calculations of various forces. This was done at 12, 14, and 16 km/h with a 5% variation from the mean allowed.
— The last day subjects performed with running economy at those three speeds on the treadmill for 4 minutes a bout followed by a subsequent VO2max test.
— Pearson’s correlation were used for all of the analysis with the following demarcations: small (0.1–0.3), moderate (0.3–0.5), large (0.5–0.7), very large (0.7–0.9), and extremely large (0.9–1.0).
What did they find? — Large correlations between the eccentric leg dynamometer measures, drop jump, leg stiffness while running and running economy were found. Moderate or small correlations were found between 1 rep max squat and countermeasure jumps with running economy. No significant correlation was found between concentric muscle strength (via leg dynamometer measure) and running economy.
What were the strengths? — Multiple measures that represented eccentric strength.
— High agreement between trials (interclass correlation) for an individual and thus the tests were fairly reliable.
— Measure of leg stiffness using high speed cameras and force plate is more accurate than most studies which measure leg stiffness and estimate how the center of mass changes.
What were the weaknesses? — All correlations, so we don’t have any cause and effect relationships.
— Not a longitudinal design and therefore we don’t know if manipulating those strength variables will lead to performance increases.
— No direct performance measurements, only submaximal running economy.
Are the findings useful in application to training/coaching practice? Yes.
We know that non-running training is important for running economy. This work suggests that ancillary workouts that focus on improving eccentric strength, leg stiffness, and the stretch-shortening cycle will improve running economy. Thus, only plyometrics or only strength work will not fully adapt the neuromuscular system to maximize improvements in running economy. Integrating all of those types of training is necessary and should be programmed into a training program.
Full paper access: click here
What was the hypothesis or research question?
Heat acclimation improves exercise performance in the heat and may improve performance in temperate climates as well. Many different protocols for heat acclimation exist, but most require the use of a sauna to create a hot environment and some require additional exercise in hot conditions (think bundling up in a puffy while running in the summer). The authors wanted to use two methods that are more accessible and practical; exercising in hot conditions and taking a hot bath following exercise. Thus, the objective was to compare these two acclimation protocols after 3 and 6 days in recreationally active individuals. .
What did they do to test the hypothesis or answer the research question?
— They recruited 27 recreationally runners who were randomized into three groups; a post exercise hot water immersion group (HWI), an exercise in heat acclimation (EHA) group, and an exercise in thermoneutral conditions control group (TNE).
— HWI was a 40 minute treadmill run at 65% of VO2max in temperate conditions followed by a 40C bath for up to 40 minutes. EHA was a less than 60 minute run at 65% of VO2max in 33C, 40% relative humidity condition. In both groups the time of the heat exposure was prematurely stopped if the subjects could no longer tolerate the bath or hot exercise or core temperature rose above 39.5C.
— Experimental trials were completed prior to starting acclimation, after 3 days, and then again after 6 days. In each of these trials they received a standardised breakfast, completed surveys of mood, had blood collected for blood volume/hemoglobin/hematocrit, skin temperature measures during exercise, 40 minutes running at 65% of VO2max in hot conditions, sweat rate, and then a run to exhaustion at 65% of VO2max in hot conditions.
What did they find?
— A significant and large reduction in core temperature with HWI compared to EHA and the control group at the end of exercise following 6 days of the intervention. A significant, but moderate effect at rest in a similar fashion. Similarly, the core temperature that elicited sweating was lower for those in the heated water immersion group (HWI) than exercise in the heat group.
— There were no differences in heart rate, thermal sensations, skin temperature, plasma volume, blood volume or hematocrit between any group.
— Changes in time trial to exhaustion were associated with improvements in end of exercise core temperature, end of exercise physiological strain, and whole body sweat rate. The correlations were moderate, ranging from r value of 0.49 to 0.54.
What were the strengths?
— They included a thermoneutral control group.
— Measurements of core temperature for each of the training sessions and heat exposures.
— Each of the groups was matched for total thermal exposure.
What were the weaknesses?
— Relatively low cardiorespiratory fitness of the subjects that they used.
— Dropout of several subjects during the time trial to exhaustion (including one that was excluded for obvious lack of effort, ha!).
— Relatively small sample size and no sauna group.
Are the findings useful in application to training/coaching practice?
It appears that using a heat acclimation that also increases skin temperature and is warmer than 35oC is necessary for increased adaptations and a lower core temperature overall. While many heat acclimation protocols include exercise in heat as the primary mode for adaptation this may actually increase training load stress and impair the athlete’s taper to a greater degree than exercise in moderate temperature with the addition of a heat stress. Some caution is warranted as the only marker of performance was not different between the different intervention groups. However, the large effects on core temperature and imperfect nature of a time to exhaustion exercise do suggest that hot water immersion (if sufficiently hot) is a good approach for preparing to compete (or train) in hot weather.
What was the hypothesis or research question?Heat acclimation improves exercise performance in the heat and may improve performance in temperate climates as well. Many different protocols for heat acclimation exist, but most require the use of a sauna to create a hot environment and some require additional exercise in hot conditions (think bundling up in a puffy while running in the summer). The authors wanted to use two methods that are more accessible and practical; exercising in hot conditions and taking a hot bath following exercise. Thus, the objective was to compare these two acclimation protocols after 3 and 6 days in recreationally active individuals. .
What did they do to test the hypothesis or answer the research question? — They recruited 27 recreationally runners who were randomized into three groups; a post exercise hot water immersion group (HWI), an exercise in heat acclimation (EHA) group, and an exercise in thermoneutral conditions control group (TNE).
— HWI was a 40 minute treadmill run at 65% of VO2max in temperate conditions followed by a 40C bath for up to 40 minutes. EHA was a less than 60 minute run at 65% of VO2max in 33C, 40% relative humidity condition. In both groups the time of the heat exposure was prematurely stopped if the subjects could no longer tolerate the bath or hot exercise or core temperature rose above 39.5C.
— Experimental trials were completed prior to starting acclimation, after 3 days, and then again after 6 days. In each of these trials they received a standardised breakfast, completed surveys of mood, had blood collected for blood volume/hemoglobin/hematocrit, skin temperature measures during exercise, 40 minutes running at 65% of VO2max in hot conditions, sweat rate, and then a run to exhaustion at 65% of VO2max in hot conditions.
What did they find? — A significant and large reduction in core temperature with HWI compared to EHA and the control group at the end of exercise following 6 days of the intervention. A significant, but moderate effect at rest in a similar fashion. Similarly, the core temperature that elicited sweating was lower for those in the heated water immersion group (HWI) than exercise in the heat group.
— There were no differences in heart rate, thermal sensations, skin temperature, plasma volume, blood volume or hematocrit between any group.
— Changes in time trial to exhaustion were associated with improvements in end of exercise core temperature, end of exercise physiological strain, and whole body sweat rate. The correlations were moderate, ranging from r value of 0.49 to 0.54.
What were the strengths? — They included a thermoneutral control group.
— Measurements of core temperature for each of the training sessions and heat exposures.
— Each of the groups was matched for total thermal exposure.
What were the weaknesses? — Relatively low cardiorespiratory fitness of the subjects that they used.
— Dropout of several subjects during the time trial to exhaustion (including one that was excluded for obvious lack of effort, ha!).
— Relatively small sample size and no sauna group.
Are the findings useful in application to training/coaching practice? It appears that using a heat acclimation that also increases skin temperature and is warmer than 35oC is necessary for increased adaptations and a lower core temperature overall. While many heat acclimation protocols include exercise in heat as the primary mode for adaptation this may actually increase training load stress and impair the athlete’s taper to a greater degree than exercise in moderate temperature with the addition of a heat stress. Some caution is warranted as the only marker of performance was not different between the different intervention groups. However, the large effects on core temperature and imperfect nature of a time to exhaustion exercise do suggest that hot water immersion (if sufficiently hot) is a good approach for preparing to compete (or train) in hot weather.
What was the beerLiquid joy. The thing you drink when you don’t train. Tastes like amazing.
called?
Wheels Gose ‘Round.
Which brewery made it?
Left Hand Brewing, Longmont, Colorado, USA.
What type of beerLiquid joy. The thing you drink when you don’t train. Tastes like amazing.
is it?
Sour, fruited gose.
How strong is the beerLiquid joy. The thing you drink when you don’t train. Tastes like amazing.
(ABV)?
4.4 % ABV.
How would I describe this beerLiquid joy. The thing you drink when you don’t train. Tastes like amazing.
?
An aroma like sparkling wine (!?), light and crisp on the tongue and smooth and bubbly on the way down with a slightly citrusy aftertaste. Not much of a full flavour but certainly a nice “sip in the sun on a hot day” kind of beer.
What is my Rating of Perceived beerLiquid joy. The thing you drink when you don’t train. Tastes like amazing.
Enjoyment?
RP(be)E(r) = 7 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.
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.
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.
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.
To read more about the authors, click the buttons:
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