Running science nerd alert.


by Thomas Solomon PhD and Matt Laye PhD
August 2020.

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.

Full paper access: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237027

What was the hypothesis or research question?
Because traditional measures of training load metrics assess average load during the entire session, they fail to accurately evaluate sessions that include intermittent efforts. This study aimed to compare traditional measures of training load with updated alternative measures during a series of high-intensity interval sessions run at 800m race velocity. The authors did not state a hypothesis.
What did they do to test the hypothesis or answer the research question?
Ten healthy, young (19.2±1.2 years old) recreational middle and long distance male runners volunteered to participate. Over a 3-week period, the volunteers completed 4 exercise tests with standardised warm-up/cool-downs, at the same time of day (10 am), and under similar ambient air temperature and humidity conditions. Normal training continued but strenuous sessions were avoided. The 4 tests were run at 800m race velocity (determined during a pre-testing 800m race visit): three of the test days included 400m intervals with work:recovery time ratios of 2:1, 1:1 and 1:2 and one test day included 200m intervals at a 1:1 work:recovery time ratio. Each session was run to failure (defined as when 800m race velocity could no longer be sustained). For each session, 6 measures of training load were calculated: TRIMP; session-RPE; Work-Endurance-Recovery (WER), and their updated alternatives, TRIMPcumulative; RPEalone; and New-WER. To compare the various estimates of training load, the authors used correlation coefficients to compare relationships between variables and either linear mixed models or Friedman tests to examine differences between metrics. Note that the total training load was calculated for each session but a training load 800 was also calculated at the point when 800m of total distance had been completed during the session (a highly arbitrary decision which they do not explain and neither can I).

Since you might not know what these training load metrics are, here is an overview:
— TRIMP estimates load as the duration of time spent at the average (mean) percentage of heart rate reserve;
— TRIMPcumulative estimates load as the sum of TRIMPS calculated for each work and rest interval;
— session-RPE is the overall RPE (the athlete’s self-rated feeling of effort out of 10 for the entire session) multiplied by the total minutes of the session;
— RPEalone is simply the overall RPE of the session;
— WER estimates load by factoring in the time spent doing work intervals relative to the endurance limit at such intensity (which, in this case, is the time to complete 800m) and the durations of the work and rest intervals, a model that requires logarithmic modeling to achieve; and
— New-WER is briefly described by the authors as “NeWER quantified sessions’ TL by using the ratio of the session’s accumulated exercise duration against the individual maximum duration in the considered exercise construction”, which is highly unclear but what I think is supposed to mean that New-WER quantifies training load as the time to exhaustion in the session as a percentage of the endurance limit at the session intensity (in this case, as a percentage of the time to complete 800m). Phew!

What did they find?
— Athletes were able to spend more time above 95% HRmax and cover more distance when doing 200m intervals at 800m race velocity to failure at a 1:1 work to rest ratio, when compared to 400m 1:1, 400m 1:2, or 400m 2:1 sessions (see Table 1).
— This trend was generally reflected by the training load data for most metrics except for RPEalone and New-WER. (see Fig 2). This is interesting because if training load is defined as a “dose of effort” (as the authors call it) and exercise dose is defined as frequency x intensity x time, the actual dose of each session is very different according to Table 1 but not different at all when using the new index, the newWER.
— The various indices were well-correlated with each other (all r-values above 0.5, indicating at least a moderate relationship, while some r-values were above 0.9, indicating a very strong relationship. But, no P-values are reported. (see Table 2).
What were the strengths?
— Well-controlled testing days (same time of day, temp, humidity, training status, etc).
— Reporting confidence intervals for their correlation coefficients, which indicates variability in the accuracy of the “lines of best fit”.
— Reporting effect sizes to help understand the magnitude of differences in cardiovascular, metabolic, and performance variables between trials.
What were the weaknesses?
— A small n size (10), which is not justified prior to commencing the study using a power calculation.
— The lack of detail of or rationale for their new metric, the newWER.
— The findings are only relevant to sessions run at 800m race pace.
— The fact that there is no gold standard of training load since we do not really know what we are trying to measure when we say we are measuring “training load” (defined in this paper as “dose of effort”), means that we have no comparator. I.e. Yes, TRIMP was the first metric to be published in the 1970s, which is based on heart rate reserve, but that does not mean it is the gold standard comparator. Other metrics measure things like RPE and pace and grade-adjusted pace, but each of these is a different variable, e.g. increased speed determines the need for an increase in cardiac output and therefore an increase in heart rate while RPE would be determined by the work rate and the environmental and psychological factors influencing your levels of effort.
Are the findings useful in application to training/coaching practice?
No.
The findings show that you can spend more time above 95% HRmax and cover more distance when doing 200m intervals at 800m race velocity to failure at a 1:1 work to rest ratio when compared to 400m 1:1, 400m 1:2, or 400m 2:1 sessions. I.e., that the 200m 1:1 format provides more cardiovascular strain when run to failure, which sounds great! But this does not mean it is always the best session — “best” would depend on the context and one’s overall training goal.
Training load metrics are an oddity in that they cannot be validated because we cannot truly know what they are attempting to measure nor how they can then be used to accurately predict and not just “model” training adaptations (like the fitness:fatigue or acute:chronic load models). These are all “theoretical” models. For this reason, constantly trying to refine a model when we do not have the gold standard comparator might be redundant. Nonetheless, such metrics have become a mainstay in online platforms like Strava and Trainingpeaks, etc. And, coaches and athletes can make good use of training load tools for helping to learn about and understand individual athlete stress:rest relationships. But, in isolation, these metrics are not informative because the variables fed into the models (usually heart rate, pace, or RPE) are just one reductionist slice of the training adaptation pie (ignoring sleep, nutrition, mood, and injury/illness). As the famous statistician, George Box, once said, “all models are wrong; some are useful”. For training load, I would extend this to say, “some models might be useful if you know what you are trying to model”.
For a practical outcome, I would suggest that you learn to understand your fitness/fatigue or acute/chronic load responses — don’t overcomplicate your stress response to simple bipedal movements. If today’s session was hard, don’t train hard tomorrow (and perhaps also the day after). If you plan to train hard tomorrow, don’t train hard today. For more on this topic, please see here and here.

Full paper access: https://journals.physiology.org/doi/abs/10.1152/japplphysiol.00349.2020

What was the hypothesis or research question?
A high level of endurance running performance is dependent on having a high VO2max (and high velocity at VO2max or maximal aerobic speed, MAS), a great ability to sustain a high fractional utilisation of VO2max for long periods, and a high running economy (low oxygen consumption per kilometre travelled). Men typically have a higher VO2max than women (even when normalized for differences in lean mass) but trained women can typically sustain a higher fraction of VO2max than trained men. Little comparative data on running economy exist. The authors hypothesised that females would exhibit a greater running economy than males, when matched for relative VO2max.
What did they do to test the hypothesis or answer the research question?
Twenty-three healthy recreational runners (13 males and 12 females, aged ~22, ~40 km/week of running) volunteered to complete two experimental visits separated by 72 hours. Tests were performed between 7 and 9 am on each day. To ensure that they only selected participants with superior cardiovascular performance, they included only those with VO2max scores 120-180% greater than sex-specific predicted averages (an arbitrary choice that the authors do not clearly justify). A graded-exercise treadmill test to exhaustion (1 kph increase every minute starting at 8 kph) was used to measure VO2max and to assess maximal aerobic speed (MAS). On the second day, participants completed a continuous submaximal treadmill running test: 6-minutes at 8, 10 and 12 KPH to measure running economy. To ensure aerobic contributions to energy productions, respiratory exchange ratio (RER) values of less than 1 were confirmed in all economy tests. Note: RER is the VCO2 to VO2 ratio. During exercise testing, when VCO2 is greater than VO2, RER rises above 1 and indicates that excessive anaerobic energy sources are being used. When RER is less than 1, VO2 is greater than VCO2 and indicates that aerobic energy sources are predominant. To measure economy, this is important because economy (VO2/km) should be ~constant at submaximal running speeds that are below ~aerobic threshold.
What did they find?
— Women had lower VO2max and maximal aerobic speed than men but reached ventilatory thresholds 1 and 2 at a higher percentage of VO2max than men.
— At submaximal speeds, the rise in VO2 from 8 to 10 to 12 kph was similar between men and women. But, when the data were allometrically scaled, the rise in VO2 with increasing speed was less steep in women than men. Note: “allometrically scaled” means adjusting the VO2 to bodyweight relationship inline with prior findings that VO2 increases less than proportionally to bodyweight when the workload is increased. Instead of direct proportional increases (mL/kg/min), the iteration mL/kg-0.75/min is used, where 0.75 is the weighted mean coefficient derived from a sample of 134 male and female runners, biathletes, cross-country skiers, ski-orienteers, and trained non-athletes (see https://pubmed.ncbi.nlm.nih.gov/2017016/).
— There were no differences in the oxygen cost of running between men and women, except when this allometric bodyweight scaling was used, in which case women had a lower oxygen cost than men (i.e. a greater running economy than men).
— When maximal aerobic speed was matched between groups (a subgroup analysis of 3 men and 3 women), men remained to have a higher VO2max than women but the rise in VO2 with increasing speed was less steep in women compared to men, which resulted in a lower oxygen cost of running (better economy in women compared to men with similar maximal aerobic speed).
— One final thing that plays on my mind is the exceptionally high values of VO2max recorded in their participants, particularly the men (70.4 ± 8.4 mL/kg/min). This range indicates some of the highest values ever recorded in runners but this is a group of recreational athletes running about 40 km per week!? In relation to these monumental VO2max, the corresponding maximal aerobic speeds (i.e. velocity at VO2max) of 18.1 ± 1.8 kph, are pretty slow. Athletes with such phenomenal aerobic power should be running a heck of a lot faster and are likely training ineffectively because their economy is quite poor (220 mL/kg/km vs. values of ~180-200 typically measured in elite runners).
What were the strengths?
— The study includes women. Metabolic research is, sadly, scarce in female inclusion — young, healthy males tend to be the focus.
— All female participants were tested during the early follicular phase of the menstrual cycle (days 2-6) to reduce the confounding effects of reproductive hormones on running economy and ventilatory threshold.
— Power calculations were used prior to the study to select an appropriate sample size of 25 (although, strangely, they only include 23 participants).
— Data normality and homoscedasticity were confirmed to allow for use of parametric statistical tests: namely, two-way ANOVA was used to examine differences in means, with Bonferroni post hoc tests to adjust for multiple comparisons.
— Tests were conducted at the same time of day to prevent effects of diurnal variation.
— Definitions of VO2max and ventilatory thresholds (VT1 and VT2), etc, are very clearly described, so we know exactly what they did and how they derived the data.
— To further explore whether the oxygen cost of running exhibited a sexually dimorphic pattern between men and women with similar running performance, they selected 6 participants (3 males and 3 females) with the same maximal aerobic speed (16 kph). The trend in this subgroup analysis, albeit far too small to be statistically analysed, confirmed their main analysis.
— Data analysis was conducted by an independent researcher in their lab who was blinded to group allocation - blinding the outcome assessor to the group allocation is a tremendous strength and something many exercise science papers fail to do.
What were the weaknesses?
— All female participants were tested during the early follicular phase of the menstrual cycle (days 2-6) to control for possible effects of menstrual cycle on running economy and ventilatory threshold. While this is a strength it also limits applicability to the other 25 days of the month when women are not in the early follicular phase.
— No info is provided as to the feeding status of the participants when they were tested. Since participants were tested at 7 am to 9 am, one might assume that they had fasted overnight.
— Between-group effect sizes were not calculated so we cannot know the magnitude of the differences, only the probability (P) that they are not different.
Are the findings useful in application to training/coaching practice?
Yes and no.
It is an important educational tool for coaches and athletes to understand that VO2max is not the sole predictor of endurance performance. Fractional utilisation and running economy matter too! This study confirms prior knowledge that the same maximal aerobic speed (MAS, or velocity at VO2max) can be achieved with different combinations of VO2max and economy. However, the finding that women might have lower VO2max values but greater economy and fractional utilisation than men could be a misleading finding of a study that only finds that to be true when body weight is “adjusted” by being raised to the power of -0.75 (which is a weighted mean value derived from another study that had a standard deviation of 0.08). The findings also cannot inform specific training methodology or gender-nuanced training approached. That said, it is important to always recognise that if you have maxed out some aspect of your fitness (say VO2max, through lots of high-intensity, short-duration interval sessions) then you might consider adding some more variety into your training to improve another performance-boosting aspect of your fitness (say, your economy, with lots of easy volume, some strength/plyometric work, and short sprints with lots of rest).

Full paper access: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7387993/

What was the hypothesis or research question?
The authors aimed to compare fat oxidation rates at an intensity (by which they mean, running speed) that elicits maximal fat oxidation (Fatmax) during a bout of eccentric (downhill) running vs. concentric (flat) running, in healthy untrained men. The authors also hypothesize that “the intensity currently proposed to induce maximal fat oxidation causes low to mild muscle damage, such that it can increase exercise-induced fat oxidation with no impairment of postexercise free-living physical activity”, which, I find rather confusing because it does not align with the aim of the study.
What did they do to test the hypothesis or answer the research question?
Eleven lean, young, healthy and recreationally-active men volunteered to complete 4 trials in the morning following an overnight fast. The first two trials determined the running speed that elicited their maximal fat oxidation rates during flat and downhill running, using incremental workload tests: beginning at 4 kph the speed was increased by 1 kph every 3-minutes, at either 0% or -12% grade, until the respiratory exchange ratio (RER) reached 1.0. The second two tests were 40-minutes long and included constant workloads at the previously-determined “Fatmax” speeds at either 0% or -12% grade for a total of 40-minutes (4 ✕ 8-mins on, 2-mins walk). Substrate oxidation was measured using indirect calorimetry. During the day before, the day of, and the day following each test, participants wore ActiGraph triaxial accelerometers to estimate their sedentary time and physical activity levels.
What did they find?
— During the incremental tests, oxygen consumption rate (VO2) was expectedly higher during flat running than downhill running at all speeds from 4 to 8 kph (see Table 1).
— During the incremental tests, respiratory exchange values (RER = VCO2 ÷ VO2) values were similar between flat and downhill running at 4, 5, and 6 kph but were lower (i.e. greater fat oxidation) during downhill running at higher speeds of 7 and 8 kph (see Table 1).
— During the constant load tests, which I assume were run at each participant’s speed at Fatmax (this is not made clear), VO2 was not different between flat and downhill running (see Table 2).
— Time spent in sedentary activity was higher and physical activity time was lower during the day after downhill running compared to flat running (see Table 3) but these outcomes are “P-hacked” by the use of independent t-tests — the appropriate 2-way ANOVA test shows no effect.
What were the strengths?
— Participants maintained normal dietary intake between tests and replicated their average food intake on the day before the exercise tests. Participants also abstained from strenuous exercise and excessive consumption of caffeine for 24-hours before the tests.
— Exercise testing methodology was, for the most part, clearly described and there is a clear overview of how the accelerometry data were analysed and assessed for quality control (because accelerometry usually has missing or insufficient data).
What were the weaknesses?
— The rationale for studying the effects of each exercise test on the subsequent free-living sedentary time and physical activity levels is not clearly described.
— Contrary to the authors proposed idea, there is no single intensity currently proposed that induces maximal fat oxidation — Fatmax is unique to each person and is highly dependent on training status and nutritional status and habitual diet.
— It is never explicitly mentioned exactly how Fat max is determined or how the speed at fat max is used for the constant load test. Assumably, during the constant load test, each participant ran at his own Fatmax speed determined during the incremental test, rather than at the groups’ mean speed at Fatmax (this is not made clear).
— The mean and standard deviations suggest meaningful numerical differences in fat oxidation rates between flat and downhill running but these do not reach statistical significance or show a group*time interaction. In this case, it is a pity that effect sizes are not reported; particularly because studying only 11 participants appears to be vastly insufficient to detect potential group differences.
— The statistical sections states that the authors performed two-way ANOVA but then used “independent sample t-tests were conducted to evaluate significant differences between FR and DHR at each comparable time point of all tests”. This is highly inappropriate since the time points are not independent and such a test will find statistical significance with a massively inflated type 1 error rate (i.e. a high risk of a false positive).
— It is unclear whether the physical activity data (Table 3) made before, during and following the flat and downhill running are derived from the incremental test days, the constant load test days, or both.
— The authors conclude that “this study showed that acute aerobic eccentric exercise at an intensity eliciting maximal fat oxidation enhanced exercise-induced fat oxidation without worsening postexercise free-living physical activity, indicating it could be a useful training modality in weight management programs”. This is a big leap into the unknown since fat oxidation rates during a single exercise bout cannot be extrapolated to the cause of weight loss in the long term. Weight loss is about being in negative energy balance for a prolonged period of time. In this study, resting energy expenditure was not measured (the majority of our daily energy utilisation) and the flat and downhill runs induced approx 0.25 vs 0.40 g/min of fat oxidation, equal to ~10 and ~16 grams of total fat oxidation during a 40-minute bout... i.e., downhill running for 40-mins burns 6 grams more fat than flat running at the same speed. If you did that every day for a week, that would equal 42 grams (= 0.042 kg or 0.09 lbs) of weight loss if the calories burned during exercise are not replaced with food and if you didn’t get more economical with training — a splash in the ocean.
Are the findings useful in application to training/coaching practice?
No.
It is always important for coaches and athletes to know that the energy cost of running downhill is lower than the same speed on flat terrain and therefore that VO2 and heart rate responses will be lower. It is also important for coaches and athletes to be aware that downhill (eccentric) running causes more muscle damage than flat running and will, therefore, require more recovery. But this is intuitive for anyone who has run on flat and downhill surfaces and this study does not provide novel insight. Neither do these new findings provide info that usefully informs coaching or training habits for performance athletes, especially since the target group were untrained individuals with low fitness levels.

Full paper access: https://pubmed.ncbi.nlm.nih.gov/32769665/

What was the hypothesis or research question?
In recent years the American College of Sports Medicine and American Medical Association have recognized that exercise is medicine. As medicine, exercise is prescribed as a “dose”. The authors were interested in determining at what “dose” does exercise stop being beneficial from a health perspective and become potentially “toxic”. They start with the idea that the therapeutic dose of exercise is very high, but not infinite, and an upper limit beyond which may cause harm does exist.
What did they do to test the hypothesis or answer the research question?
The authors did not do any original experiments themselves but rather collected several decades of research which examined the effects of ultra-marathons on cardiorespiratory system function.
What did they find?
— In general survey data shows that ultramarathons are healthier than the general population with the exception of asthma and allergies prevalence, but this may be due to selection bias. Lung function following ultras is reduced for unknown reasons (potentially related to inflammation), but not too a level that is considered “clinically deficient” for those with normal lung function entering the race. There is a potential to develop aspects of pulmonary edema and the development of more chronic exercise induced asthma. The muscles needed for ventilation are fatigued at the end of the an ultramarathon, but the long term clinical consequences of this are not understood. Most metrics of lung function return to normal within 24-48 hours.
— Circulating markers of cardiac tissue are present after both training and racing ultramarathons, but are not necessarily associated with damage of the tissue. Mild impairments to left ventricle and right ventricle heart function exist following ultra events, but do not typically result in clinically abnormal levels. One potential exception is right ventricle function of the heart which shows a reduced stroke volume for at least one week post race and in which remodeling and fibrosis of the heart tissue is frequently observed.
— Overall the authors propose that there are clear transient decreases in lung and heart function immediately following an ultra, but that long term changes are far less clear and far less clinically meaningful in all likelihood for most populations (but not all). They point out that physical INactivity is far more dangerous than excessive activity and that there remain many additional sociological benefits to ultras making ultras on the whole a beneficial endeavor.
What were the strengths?
— True experts in the field have put together this review.
What were the weaknesses?
— They failed to discuss the long term potential to develop heart arrhythmias for which there is more supporting evidence.
Are the findings useful in application to training/coaching practice?
Yes, exercise remains better than inactivity and while the risk is not zero there is minimal risk known at this time to competing in ultramarathons.

Full paper access: https://pubmed.ncbi.nlm.nih.gov/32796255/

What was the hypothesis or research question?
The research question was whether following a 16 hour fast, 8 hours of feeding intermittent eating strategy while training would improve body composition, running performance, and metabolic health in middle and long distance runners. The authors hypothesized that there would be no effect on any of the outcomes (primary outcome was body composition, the rest were secondary outcomes).
What did they do to test the hypothesis or answer the research question?
A group of 28 runners who had been training competitively for at least 2 years were randomized either to continue their current dietary habits or restrict their calories to only 8 hours during the day typically from 1200 to 2000 hours for a total of 16 weeks. There were no restrictions on the types of food they consumed. Subjects underwent a number of tests PRE and POST intervention. All testing was done at the same time of day with a standardized meal 2-3 hours prior to the testing. Lactate threshold, carbohydrate and fat metabolism during exercise, and VO2max were completed on the treadmill. Body composition was done via bioimpedance. Blood markers of metabolic status were also taken.
What did they find?
— The intermittent fasting group did in fact follow the protocol and there was no difference in training load between groups.
— The intermittent fasting group had a lower body weight, but no change in body composition following the 16 weeks.
— No differences in any aspect of the running performance or blood markers of metabolic health occured.
What were the strengths?
— The study seemed well controlled given the research question, properly recruited given their primary outcome, and well reported.
— They only changed the time period in which eating was done, which means that their conclusions are specific to dietary timing without the explicit confounders of dietary quality.
What were the weaknesses?
— Their ending sample size was smaller than the a priori sample size necessary to detect a difference in their primary outcome.
— One of the findings was about caloric intake being lower in the intermittent group. This relied on dietary diaries which are notoriously poor representations of actual diet.
— Body composition was measured using bioimpedance, which is not one of the better methods for measuring fat percentage.
— The subjects were well trained, healthy, young individuals that likely do not need to improve various metabolic markers of health.
Are the findings useful in application to training/coaching practice?
Not really, other than saying a 16:8 intermittent fasting protocol may be useful for those wishing to lose some weight without impacting performance significantly.

Full paper access: https://pubmed.ncbi.nlm.nih.gov/32079919/

What was the hypothesis or research question?
To compare neuromuscular fatigue and recovery in a single stage ultramarathon versus a multi-stage ultramarathon. The authors hypothesized that a single stage event would produce more central fatigue but less peripheral fatigue relative to a multi-stage event, that neuromuscular recovery would take longer after a single stage race, and that recovery from peripheral fatigue would be faster following the single-stage event.
What did they do to test the hypothesis or answer the research question?
They recruited 32 experienced ultra runners to run either 170 km in one attempt or 170 km in 4 consecutive stages of 40, 47, 42, and 42 km to cover the same 170 km route. To test for neuromuscular function of the knee extensors and plantar flexors subjects performed 3 maximal voluntary contractions with the last 2 contractions also included a neural stimulation meant to elicit maximal contraction centrally. Any decline in overall contraction could be due to either central or peripheral factors. To determine which type of fatigue was occurring researchers see if there was a reduction in force when the neural stimulation occurs. If so then the reduction in force is attributed to peripheral reasons. Subjects completed the neuromuscular testing prior to the event and ASAP after the event which ended up being about 24 minutes post finish. Additional testing was done 2, 5, and 10 days after the race. The stage-race group was also tested after each stage.
What did they find?
— Single stage racers took 34% longer to complete all of the running. The decrease in voluntary force production was similar to other studies at about 23% for knee extensor muscles in the single stage ultramarathon group, while it was only 7% for the multistage ultramarathoners. The authors report that the contribution of central fatigue to the drop in voluntary activation was greater in the multistage ultrarunners than the single stage ultrarunners whereas peripheral fatigue was similar for each type of race. After each stage the multi-stage ultra runners showed a decline in neuromuscular function in general and peripheral fatigue specifically, but were likely recovering in the 15 hours between stages and thus had a lower peripheral fatigue than maybe you would expect given the faster speed that they were competing at.
What were the strengths?
— The authors are experts in these types of neuromuscular measurements and despite a limited methods section it’s likely that the methodology was solid.
— The race distances and terrain were identical and overall the racers were of similar quality, each finishing about the same percentage behind the winners on average.
What were the weaknesses?
— The authors do not control diet and other studies show a significant effect of diet.
— The total time spent running was different, not necessarily a weakness they could do something about, but one to note regardless.
— The multi-stage ultrarunners completed the testing more times than the other group and if any learning effect happens they were likely to benefit from it.
Are the findings useful in application to training/coaching practice?
Yes, we need to recognize that time of training and intensity training are likely to cause fatigue in different ways. Peripheral fatigue from increased intensity of training requires more time to recover from, while central fatigue due to longer training runs requires less overall time to recover from (~24-48 hours). When applying these different types of training stress considering the recovery time course is important.


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.

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Dr Thomas Solomon and Dr Matt Laye. Running science nerd alert.
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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