Each month we compile a short-list of recently-published papers (full list here) in the world of running science and break them into bite-sized chunks so you can digest them as food for thought to help optimise your training. To help wash it all down, we even review our favourite beer of the month.
Welcome to this month's installment of our "Nerd Alert". We hope you enjoy it.
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Click the title of each article to "drop-down" the summary.
What was the hypothesis or research question?
Persistent daily low energy availability has negative consequences on many physiological functions and suppresses ovarian function in female athletes. High training stress can rescue the ability to recover and cause functional overreaching (best case), non-functional overreaching, or overtraining syndrome (worst case). But it is not known whether low energy availability during training overload increases the risk of overreaching. The authors hypothesised that athletes diagnosed as overreached have a lower energy availability with more pronounced endocrine-metabolic signs of energy conservation and more frequent menstrual disturbances than well-adapted runners. To test the hypothesis, the authors quantified the changes in ad libitum energy intake, exercise energy expenditure, and energy availability of normally menstruating female distance runners following 4-weeks of training overload and a subsequent recovery period.
What did they do to test the hypothesis or answer the research question?
— 16 normally menstruating female distance runners completed a baseline training period followed by a 4-week training overload and a 2-week recovery period.
— Baseline began on the first day of menses and lasted the entire menstrual cycle. During this time, subjects maintained their training habits.
— The 4 wk training overload began on day 1 of their next menses. Frequency and intensity of sessions was maintained while daily training volume was increased to 130% of baseline volume.
— A 2-week recovery period immediately followed. Frequency and intensity continued but training volume was reduced to 50% of baseline volume.
— Full training logs were kept and HR and RPE were recorded. Session RPE was calculated as RPE x duration in minutes.
— Eating disorder questionnaire and Recovery-Stress Questionnaire for Athletes were completed. Blood samples were drawn, and running performance was measured as time to exhaustion during an incremental workload test.
— To be diagnosed as overreached, athletes had to show increased ratings of perceived fatigue on the Recovery-Stress Questionnaire and a decrease in running performance that was larger than a 1.8% decrease from baseline (based on prior work from Le Meur et al. 2013). Overreached athletes were then further categorized as functionally or non-functionally overreached depending on the presence or absence of an increase in running performance after the 2-week recovery period.
— Exercise energy expenditure was measured using accelerometry (Actiheart) and energy intake was measured via analysis of diet records using the US FoodData Central database.
— Energy availability was calculated as energy intake minus exercise energy expenditure, divided by fat-free mass (FFM, estimated using DXA). Low energy availability was classified as below ∼30 kcal/kg FFM/day.
— Urinary luteinizing hormone (LH) was measured to determine whether each cycle was ovulatory and to assess the length of follicular and luteal phases. Follicular phase length was calculated as the number of days from the first day of the cycle to the day of the LH surge and luteal phase length as the number of days from the day following the LH surge, to the day preceding the first day of the next menses. A menstrual cycle was qualified as abnormal if it met any of the following criteria:
→ cycle lasted longer than 35 days, or
→ no LH surge detected, with no mid-cycle estradiol rise (anovulatory cycle), or
→ luteal phase lasting < 10 days (luteal phase defect).
Menstrual fluctuations in estradiol production were assessed by asking subjects to collect a 2 mL saliva sample via passive drool into an ultra-pure polypropylene tube each morning, upon waking and fasted, throughout the study.
What did they find?
— Of the 16 runners, 7 were diagnosed as overreached after 4-weeks training overload (decreased performance, −9.2 ± 2.0%,
P=0.01the probability that the result is as or more extreme than that observed under a null-hypothesis. In very basic terms, P = probability that the effect could be explained by random chance, and if P is small, the observed difference is big enough to disprove (reject) the null hypothesis.
; increased fatigue from 1.5 ± 0.2 to 2.6 ± 0.4 points,
P=0.02the probability that the result is as or more extreme than that observed under a null-hypothesis. In very basic terms, P = probability that the effect could be explained by random chance, and if P is small, the observed difference is big enough to disprove (reject) the null hypothesis.
). After 2-weeks recovery, these 7 athletes’ performance was still decreased compared with baseline (−5.7 ± 4.2%, P = 0.56) and they were further categorized as non-functionally overreached.
— The 9 remaining runners were classified “well adapted” with a nonsignificant increase in performance (+4.4 ± 1.8%, P = 0.10) after 4-weeks overload, no increase in fatigue (1.6 ± 0.2 to 2.0 ± 0.3 points, P = 0.34) and a significant increase in performance after 2-weeks recovery (+5.0 ± 1.4%,
P=0.02the probability that the result is as or more extreme than that observed under a null-hypothesis. In very basic terms, P = probability that the effect could be explained by random chance, and if P is small, the observed difference is big enough to disprove (reject) the null hypothesis.
), i.e. functional overreaching.
Percent change in running performance (RUNPERF) from baseline, in well adapted (WA) and non-functionally overreached (NFOR) runners. REC, recovery period; TO, training overload. *, Significantly different from baseline (P < 0.05). Dashed lines indicate the odds ratio performance threshold.
— Training load, running distance, running time, and training difficulty were all equally increased during the training overload in the “well adapted” functionally overreached and non functionally overreached athletes. .
— During the 4-week overload, well adapted runners increased their energy intake from baseline (+4.2 ± 1.1 kcal/kgFFM/day, P<0.05) — about 184 kcal/day on average — which matched their increase in daily exercise energy expenditure (+4.0 ± 0.7 kcal/kgFFM/day), while energy availability was maintained. The increase in energy intake came from an increase in carbohydrate intake (213 ± 10 to 251 ± 8 g/day, P = 0.002). Non-functionally overreached athletes did not increase their energy intake during training overload and their energy availability decreased.
— The change in energy availability from baseline was positively correlated with the change in running performance, such that increased energy availability during training overload = increased performance.
Correlation between the magnitude of change in energy availability and running performance (RUNperf) from baseline to post-training overload. EA, energy availability; FFM, fat free mass.
— Concentrations of plasma leptin, a marker of energy balance and energy storage, decreased after 4-weeks training overload in every non functionally overreached runner (−17 ± 2%, P = 0.001) but not in the “well adapted” functionally overreached runners. The decreased in leptin was positively correlated to the decrease in resting energy expenditure (
r=0.54the strength (effect size) of a relationship (correlation) between two variables, where less than 0.1 (or -0.1) indicates no relationship, (-)0.1 to (-)0.3 indicates a small relationship, (-)0.3 to (-)0.5 a moderate relationship, and greater than (-)0.5 is large.
, P = 0.03).
— All athletes reported regular ovulatory menstrual cycles at baseline and cycle length did not change after 4-weeks training overload. Well-adapted runners showed no significant changes in estradiol concentrations or in cycle phase lengths between baseline and overload. But, estradiol was reduced mid-cycle and in the first 5-days of the luteal phase in the non-functionally overreached runners along with an increase in their follicular phase length (P = 0.047, d = 1.1).
Two-day rolling mean ± SE resting concentrations of salivary estradiol ([estradiol]s) in well adapted (WA; A) and non-functionally overreached (NFOR; B) runners during baseline and training overload (TO) cycles. Day 0, day of peak midcycle [estradiol]s. Days 10 to 1, follicular phase; days 1–10, luteal phase. *, **, ***, significant difference between baseline and Post-TO (p < 0.05, 0.01 and 0.001, respectively). Gray symbols indicate significance before, but not after, the Bonferroni adjustment.
What were the strengths?
— It’s a physiological study in female athletes.
— Ecologically valid design — real athletes doing their own training — with outcomes generalisable to trained female runners.
— Power calculations to justify the sample size.
— Thorough menstrual investigation.
— Use of
Cohen’s da type of effect size that quantities the average change score relative to the standard deviation (i.e. the range) of the change scores.
to quantify the
effect sizea quantitative measure of the magnitude of the experimental effect. Less than 0.2 is no effect, 0.2 to 0.5 is small, 0.5 to 0.8 is moderate, greater than 0.8 is large.
of the intervention.
What were the weaknesses?
— Recruitment of local runners means the sample is non-random.
— Lack of a non-overload control group (randomised controlled trial with a parallel group) or a non-overload 4-week control period (randomised controlled crossover trial).
— Lack of doubly-labelled water (gold standard) to measure daily energy expenditure.
— Only reporting
Cohen’s da type of effect size that quantities the average change score relative to the standard deviation (i.e. the range) of the change scores.
effect sizesa quantitative measure of the magnitude of the experimental effect. Less than 0.2 is no effect, 0.2 to 0.5 is small, 0.5 to 0.8 is moderate, greater than 0.8 is large.
for changes in endocrine function and not for any other variable (energy intake, expenditure, availability, etc).
Are the findings useful in application to training/coaching practice?
Yes.
This study is important because it highlights that, when undertaking a short-term block of training overload, some runners spontaneously increase their energy intake sufficiently to maintain energy availability. Not only does this maintain normal bodily functions but it also facilitates performance improvements. On the contrary, some athletes do not spontaneously increase their energy intake causing a state of low energy availability triggering endocrine indications of energy conservation — decreased leptin (i.e. low energy stores) and disrupted menstrual function.
Consequently, when planning a period of training overload, it is very important to monitor your (or your athletes) energy intake to help maintain your (reproductive) health and to ensure you achieve the intended goal of the training overload — increased performance. According to this study, the increment in energy intake needed to achieve this is subtle (~180 kcal per day). So, all it might take is an additional daily snack or during-session drink/energy bar.
The next step in this story is to understand why some female athletes do not spontaneously eat more when training more and to help understand the physiological, psychological, and emotional factors that will help remedy that lack of energy intake compensation.
What is my Rating of Perceived Scientific Enjoyment?
RP(s)E = 9 out of 10.
What was the hypothesis or research question?
Group training can result in an inappropriate training stimulus for some athletes due to different physiological responses to the same stimulus — a set absolute workload (watts or speed) can be high-intensity for one athlete but low-intensity for another. To examine this, the authors aimed to quantify the training intensity distribution of a collegiate cross-country team and the associated physiological adaptations. No hypothesis was stated.
What did they do to test the hypothesis or answer the research question?
— Sixteen runners (8 male, 8 female) from the University of Oklahoma Men’s and
Women’s Cross-Country Team (an NCAA Division 1 college in the USA) completed a graded exercise test before and after a 5-week observational period to determine peak oxygen consumption (V̇O2peak) and the speed, heart rate (HR), and oxygen consumption (V̇O2) associated with 2 and 4 mmol/L of blood lactate.
— Training intensity distribution was quantified by assessing “time in zone” spent in the 3 biological intensity domains (zones), where:
→ zone 1 = low intensity, HR values less than the first lactate turnpoint (LT1; less than 2 mM lactate),
→ zone 2 = moderate intensity, HR values between LT1 (2 mM lactate) and LT2 (4 mM lactate).
→ zone 3 = high intensity, HR values above the second lactate turnpoint (LT2).
— GPs and HR data were collected during training over 5-weeks, under the direction of the college team coach.
What did they find?
— Athletes recorded between 36 and 45 total training sessions. Training weekly training volume ranged between 80.6 and 85.1 miles and 46.1 and 49.1 miles for male and female subjects, respectively. An example of their weekly training is shown below:
— After 5-weeks, VO2max (
P<0.05the probability that the result is as or more extreme than that observed under a null-hypothesis. In very basic terms, P = probability that the effect could be explained by random chance, and if P is small, the observed difference is big enough to disprove (reject) the null hypothesis.
,
d=0.84a quantitative measure of the magnitude of the experimental effect. Less than 0.2 is no effect, 0.2 to 0.5 is small, 0.5 to 0.8 is moderate, greater than 0.8 is large.
in males,
d=0.80a quantitative measure of the magnitude of the experimental effect. Less than 0.2 is no effect, 0.2 to 0.5 is small, 0.5 to 0.8 is moderate, greater than 0.8 is large.
in females), and running speeds at 2 mM (
P<0.05the probability that the result is as or more extreme than that observed under a null-hypothesis. In very basic terms, P = probability that the effect could be explained by random chance, and if P is small, the observed difference is big enough to disprove (reject) the null hypothesis.
,
d=1.03a quantitative measure of the magnitude of the experimental effect. Less than 0.2 is no effect, 0.2 to 0.5 is small, 0.5 to 0.8 is moderate, greater than 0.8 is large.
in males,
d=0.23a quantitative measure of the magnitude of the experimental effect. Less than 0.2 is no effect, 0.2 to 0.5 is small, 0.5 to 0.8 is moderate, greater than 0.8 is large.
in females) and 4 mM lactate increased (
P<0.05the probability that the result is as or more extreme than that observed under a null-hypothesis. In very basic terms, P = probability that the effect could be explained by random chance, and if P is small, the observed difference is big enough to disprove (reject) the null hypothesis.
,
d=1.12a quantitative measure of the magnitude of the experimental effect. Less than 0.2 is no effect, 0.2 to 0.5 is small, 0.5 to 0.8 is moderate, greater than 0.8 is large.
in males,
d=0.52a quantitative measure of the magnitude of the experimental effect. Less than 0.2 is no effect, 0.2 to 0.5 is small, 0.5 to 0.8 is moderate, greater than 0.8 is large.
in females).
— The within-group variability in training intensity distribution is visually massive — time in zone ranges from 94% to 73% in males and 94% to 66% in females — see the figure below. But, no within-group analyses were conducted to indicate whether this within-group variability is meaningful. The arbitrary “performance decline” annotation on female subjects 9 and 10 is not given any context but is probably related to their decrease in speed at LT1 and LT2 after 5-weeks.
What were the strengths?
— Athletes were blinded to the HR and GPS data so they could not bias the data.
— Ecologically valid setting (normal training in the field) with generalisable outcomes (to elite and sub elite runners).
— Presentation of individual athlete training distributions.
— To prevent overestimation of “time in zone 1”, warm-up and cool downs for each session were excluded. This is sensible but… see weaknesses.
— Use of
Cohen’s da type of effect size that quantities the average change score relative to the standard deviation (i.e. the range) of the change scores.
to quantify the
effect sizea quantitative measure of the magnitude of the experimental effect. Less than 0.2 is no effect, 0.2 to 0.5 is small, 0.5 to 0.8 is moderate, greater than 0.8 is large.
of differences in training intensity distributions. But… see weaknesses.
What were the weaknesses?
— Major weakness: there is no analysis of within-group differences or variability in training intensity distribution (Figure 2) — that was the whole point of the study.
— The effect sizes were not calculated to examine the within group differences in training intensity distribution.
— With thorough laboratory testing, it is surprising for the authors to use fixed blood lactate levels of 2 and 4 mM to demarcate the intensity domains. It would be preferable to individually determine the lactate turnpoints (or ventilatory thresholds, VT1 and VT2) for each athlete, which they could do with the data collected at baseline.
— To prevent overestimation of “time in zone 1”, warm-up and cool downs for each session were excluded. The issue comes when a warm-up or a cool-down includes a prolonged run, which is common in elite/sub-elite athletes. In this case, you underestimate the total daily training load.
— A seemingly random multiple regression analysis was conducted to determine the relationship between running performance and total training time and training intensity. The regression models included either speed or V̇O2 at 4 mM lactate as the dependent variable with total training time (hours), percent of total training time spent in zone 3, and gender set as the predictor variables. The aim of the study was to examine within-group variance in training intensity distribution; this limited and randomly chosen regression model does nothing to help address this aim.
Are the findings useful in application to training/coaching practice?
Yes.
Although this study does not use appropriate statistical analyses to examine the within-group differences in training intensity distribution, there is large visual heterogeneity among runners. This is very important to acknowledge because a coach giving a blanket training prescription to an entire group of runners will inevitably result in some runners running too hard to match the speeds of the better athletes. This is something to be mindful of when coaching but also something to be aware of if you train with others — running harder than you should every day is a fast route to nonfunctional overreaching. If you ever find yourself in a group, be mindful of what your training goals are — don’t just blindly run with the fastest person.
What is my Rating of Perceived Scientific Enjoyment?
RP(s)E = 6 out of 10.
What was the hypothesis or research question?
While there have been several studies previously looking at weather and its effect on running performance, they have been overly focused on the marathon distance, limited by the types of events selected for the such analysis, and limited by the type of weather data collected. The first aim of this study was first to identify different weather parameters (air temperature, dew point, wind speed, and cloud coverage) associated with a wide range of races (from 3km to 50km race walk). The second aim was to determine which weather parameters were associated with peak performance for each running distance. The third aim was to determine which events were most susceptible to changes in performance due to weather.
What did they do to test the hypothesis or answer the research question?
— The authors selected elite level races from the following events: Commonwealth Games, Diamond Leagues, World Athletics Continental Cup, World Athletics Gold Label Races, Olympic Games, World Athletics Race Walking Team Championships, and World Championships. In total data was collected from 1258 races held between 1936 and 2019 in 42 different countries, consisting of 7867 athletes.
— The weather data was collected at the time the lead individual was halfway done with the event. The data was collected at the closest weather station and is publicly available from the National Oceanic and Atmospheric Administration for all but 232 of the races. Weather data for the other 232 races was determined via weather websites (wunderground.com and www.weatherspark.com). Dew points were converted to relative humidity. Heat index and wet bulb globe temperature (WBGT) were also calculated based on existing data. Data for the 1258 races was verified against a subset of 140 races in which race websites reported weather conditions using Spearman correlation, Wilcoxon signed-rank test and root mean square error statistical approaches. WBGT was used to determine if the conditions were ≤10.0°C = cold/cool; 10.1-18.0°C = neutral; 18.1-23°C = moderate heat; 23.1- 28.0°C = high heat; >28.0°C = extreme heat, based on the World Athletics medical guidelines.
— In order to determine the performance detriment for each event the authors would look at the event finishing times of the winner versus the event record and then subsequently for the 25th, 50th, 75th percentile for a total of 7867 athletes. If the event winner was slower than the event record a performance decrement was determined as a percentage of the race time. Examples they use are:
“For instance, Hicham El Guerrouj won the Olympic 5,000m event in 2004 in a time of 13:14.39, while the standing Olympic record was 13:05.59, resulting in a 1.12% decrement in performance. Likewise, Eliud Kipchoge won the 2018 Berlin Marathon in 2:01:39, while the standing Berlin Marathon record was 2:02:57, resulting in a 1.05% improvement in performance.”
They also computed the fastest time in a specific event to the world record to account for events in which the location changes (such as the Olympics or Commonwealth games).
— To calculate the effect of the various weather parameters on the race itself they used a machine learning technique called a decision tree algorithm that looked at the importance of each of the parameters based on how much it changed performances in the races. The model was tuned by testing various hyperparameters (https://towardsdatascience.com), which is a fancy way of adjusting the parameters of the model to make sure you end up with the best model overall. They used 70% of the races to train the model and the remaining 30% to test the model and calculate the R-squared and root mean square error to indicate how good the model was. The end result of all of this was a feature importance score that is an indicator of the usefulness of each weather parameter at influencing performance in each event (3km to 50km race). In addition to machine learning they used regression models to determine how a change in 1℃ would influence performance. Their regression models needed to meet specific statistical requirements of a means least squares fit criterion of p<0.005.
What did they find?
— The weather associated with all of the races is summarized by this figure. My takeaway is that marathons are very different from other events with far more cold weather days.
When the authors compared their collected data with the reported data they found a high degree of correlation (r between 0.82 and 0.92) with race locations reporting a 0.7℃ - 1.5℃ higher temperature on average. There was no effect size (d < 0.2) between their data and the reported data also indicating a high level of agreement.
— When looking at the effects of each of the different weather parameters on all race events based on the machine learning approach they too the authors found that air temperature was the most important (score of 40%) followed by relative humidity (26%), solar radiation (18%) and wind speed (16%) (left most column in figure below). Interestingly, there was slight variation in the importance of each of the weather parameters depending on the race distance. As shown in this figure:
We can also visualize this data via a decision tree.
— Using regression analysis for the authors third aim they were able to decrease that optimal performance was between 7.5℃ and 15℃ WBGT depending on the distance (marathon and 5k respectively). For every 1℃ increase in temperature there was a 0.4% decrease in performance while every 1℃ decrease below 7.5℃ decreased performance by 0.3%. Each race distance showed slightly different ideal WBGT temperatures.
— The authors also created tables that showed how a range of weather parameters would be expected to impact performance. An example for the marathon is shown here:
What were the strengths?
— They collected data from a large number of races across a number of years that take place in a wide range of weather conditions as well as races that change locations every cycle (Olympics, Commonwealth games). This should reduce the impact of any outliers in their analysis.
— They used well accepted machine learning approaches that included training the dataset and then testing its effectiveness as well as more typical regression approaches.
— They tested the validity of their weather data which was taken from weather stations ~9 km away from the race itself.
What were the weaknesses?
— Race courses can change from year to year and some courses are held at altitude.
— Some of the races were less represented than others and may have overly influenced the models developed.
— Races differ in their competition depth and thus the races with less overall depth may inaccurately portray the effects of weather since the finishers would be expected to be even further back relative to the race specific record.
— No separation of males versus females. Mostly done in elite athletes who are likely to favor colder conditions given the amount of heat that they produce.
Are the findings useful in application to training/coaching practice?
Yes.
This paper is relevant for coaches, athletes, and race directors. Given that a large number of the races were held in hot conditions this indicates that race organizers need to pay special attention to the medical consequences of such environments which can dramatically increase heat related medical issues.
Coaches and athletes can use the heat maps to determine the anticipated average consequences of various weather parameters. This information can help athletes and coaches employee proper pacing given the race or training conditions.
What is my Rating of Perceived Scientific Enjoyment?
RP(s)E = 9 out of 10.
To go deep on this topic Thomas recently released a 3-part deep-dive on “Training & racing in the heat”. Read on here:
What was the hypothesis or research question?
The researchers wanted to better understand how runners deal with the running injuries using an exploratory qualitative study design.
What did they do to test the hypothesis or answer the research question?
— This study did not use any quantitative experimental approaches, instead using interviews and qualitative approaches to create a framework around runners concepts of injuries. To do so they had each subject (n =12, 6 male) interviewed multiple times.
— Interviews were transcribed, codes were selected and the authors all met to agree upon a list of sub themes and themes that they identified.
— The authors developed a conceptual model based on those themes and subthemes they found.
What did they find?
— They selected the theme of motivation as to why runners run from the interviews. Motivation contained the sub-themes related to why they run, including health benefits, social contacts, performance, and distraction. Some exemplary quotes are:
Runner 9: ‘I am more challenging myself and being, you know, in a permanent challenge with my own performance.’
Runner 9: ‘I am working mostly mentally, being in the IT industry. So, running is also helpful to clean up my head after work.’
Runner 2: ‘I really am a social runner. It motivates me to train with a fixed group of people at a fixed time, and I like that more than running on a grey Thursday evening by myself.’
— The themes for onset and care of physical complaints included; Overloading, Complaints, Self-Regulation, and Influencing factors. Overloading sub-themes included no proper build up, lack of rest, and general fatigue. Complaints were related to small pains and the ability to continue, whereas self-regulation was related to their ability to adjust load. The influencing factors on how they viewed and dealt with injury were competitive drive/performance goals, race planning/training schedule, everyday life and social control. Some exemplary quotes are:
Runner 2: ‘If you suddenly scale up your intervals, training sessions, or distances because you want to train from a half marathon to a full marathon. Those are the risky points.’
Runner 5: ‘If I really seriously couldn't run anymore, that would really, yes, be the point for me to say, well, yes, that is really an injury. But look, if I have a pain here or there, I will not immediately stop and see that as an injury.’
Runner 6: ‘As long as it is not too much, just a pain, then it is not so bad. Then I think that it will go away, and often it will.’
— The themes for dealing with an injury were: injury, influencing factors and self-regulation. Sub-themes included loss of autonomy, unable to run, training level, runners experience, absolute rest, and medical care. Some of the exemplary quotes are:
Runner 1: ‘I had muscle pain in my buttock the week before a race, and that did not go away. So then I went to the physiotherapist, and he said, well, buddy, I think this is not your buttock because after 1.5 weeks muscle pain should be gone. So I’m under treatment now.’
Runner 12: ‘An injury for me is when I have pain, complaints or some dysfunction that hinders the training that I want to do and when I cannot do my training in an adapted form.’
Runner 5: ‘I think it’s very personal, of course. I think a recreational runner might be more likely to say yes, listen, this isn't worth it to me. While someone who really trains hard for something will then think yes, this is not fitting my planning.’
— Most of the themes centered around the idea of self-regulation and a runner's ability to deal with any injuries on their own. The approaches that they took included their own experience, online resources, peer opinion, and expert advice. Some of the exemplary quotes for this section included:
Runner 10: ‘We do have now at our running group a number of people who know very well what they're doing as trainers. We have a physiotherapist as a trainer. We have a professor in exercise science or something. But anyway, those are the people who really do drill us in a proper way to learn habits that make us less likely to get injured.
Runner 1: ‘I first consulted Doctor Google and to find out about supplements you need for
muscle building or strengthening of joints and bones and so on. So yes, I am actively looking for that.’
Runner 5: ‘I think it’s a little bit of both, a little bit of just experience, a little bit of living up to your own feelings and also a little bit of, yeah, some knowledge that you just know. I just know that if you overwork your body, you overload it.’
— Some important aspects of the model developed were themes that were not detected. For instance runners did not consider the other stress in their lives as something that might contribute to overall load. They also demarcate differences between a complaint and an injury even though they recognize that a complaint can lead to an injury. There was also a lack of discussion about approaches runners specifically took to prevent injuries and the approaches mentioned (doing core and buying shoes frequently) are not evidence based. The overall model that the authors developed is depicted in this figure:
The injury pathway as experienced by recreational runners.
What were the strengths?
— They followed proper qualitative research design with progressing through codes, subthemes, and themes having multiple authors take an active role.
— The coder was not experienced with running injuries while the other authors and interviews were. This prevents bias from the authors more familiar with the topic.
What were the weaknesses?
— The runners were club level and already part of a social group which may make them different from ‘solo’ runners. They also were all Dutch.
— Qualitative research in general can not provide any evidence, but rather can inform specific hypotheses that may need evaluating using quantitative approaches in the future.
— Given the small sample size and wide range of ages and demographics it is hard to say whether individuals represent a broader population of a similar demographic.
Are the findings useful in application to training/coaching practice?
Yes.
Runners rely on a high degree of self-efficacy and self-regulation. As coaches we need to recognize and take advantage of this when talking about injuries and how to prevent injuries. Designing a way that the athletes can have control over the process is important and recognizing that they will likely lean on their previous experience as a way to deal with complaints and injuries. Another important aspect is to get athletes to recognize that complaints lead to injuries and to see these complaints as more than just something that happens with training but as a sign to intervene and start evidence-based injury prevention approaches. The evidence based is very important because most runners use approaches (stretching, new shoes) which are not supported by the evidence. As coaches we need to be the “peers” that offer evidence based approaches which runners can then autonomously apply to their training schedules allowing runners to practice their self-efficacy.
What is my Rating of Perceived Scientific Enjoyment?
RP(s)E = 6 out of 10.
What was the beer called?
Blurry Vision.
Which brewery made it?
BRLO (Berlin, Germany).
What type of beer is it?
New England IPA.
How strong is the beer?
6.5% ABV.
How would I describe this beer?
Give it a sniff: hints of tropical fruits. Sit it on the tongue: slightly creamy. Suck it down: full-bodied, slightly bitter, a wee bit fruity. Wait a sec: mildly bitter aftertaste. Blurry Vision? Only if you drink 10. Which you might because it's lovely.
What is my Rating of Perceived
BeerLiquid joy. The thing you drink when you don’t train.
Enjoyment?
RP(be)E(r) = 9 out of 10.
What was the
beerLiquid joy. The thing you drink when you don’t train.
called?
Lush IPA.
Which brewery made it?
Fremont Brewing (Seattle, USA).
What type of beer is it?
India Pale Ale.
How strong is the beer?
7% ABV.
How would I describe this beer?
Claaaaaassssssic West Coast IPA. West coast used to be the best coast for IPA’s until all these New England hazy IPAs took over the shelf space. Me, I prefer my old standard bitter IPA. This IPA has that classic bitter hop aroma and mouthfeel with a touch of sweetness. However, it’s got that sticky mouthfeel that leaves you smacking your lips. While there are some hints of citrus and tropical fruit that is all balanced out by the piney of this well done West Coast IPA. For the price you will not find much better.
What is my Rating of Perceived
BeerLiquid joy. The thing you drink when you don’t train.
Enjoyment?
RP(be)E(r) = 8 out of 10.
That is all for this month's nerd alert. We hope to have succeeded in helping you learn a little more about the developments in the world of running science. If not, we hope you enjoyed a nice beer…
Until next month, stay nerdy and keep training smart.
Everyday is a school day.
Empower yourself to train smart.
Think critically. Be informed. Stay educated.
Disclaimer: We occasionally mention brands and products but it is important to know that we are not sponsored by or receiving advertisement royalties from anyone. We have conducted biomedical research for which we have received research money from publicly-funded national research councils and medical charities, and also from private companies. We have also advised private companies on their product developments. These companies had no control over the research design, data analysis, or publication outcomes of our work. Any recommendations we make are, and always will be, based on our own views and opinions shaped by the evidence available. The information we provide is not medical advice. Before making any changes to your habits of daily living based on any information we provide, always ensure it is safe for you to do so and consult your doctor if you are unsure.
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About the authors:
Matt and Thomas are both passionate about making science accessible and helping folks meet their fitness and performance goals. They both have PhDs in exercise science, are widely published, have had their own athletic careers, and are both performance coaches alongside their day jobs. Originally from different sides of the Atlantic, their paths first crossed in Copenhagen in 2010 as research scientists at the Centre for Inflammation and Metabolism at Rigshospitalet (Copenhagen University Hospital). After discussing lots of science, spending many a mile pounding the trails, and frequent micro brew pub drinking sessions, they became firm friends. Thomas even got a "buy one get one free" deal out of the friendship, marrying one of Matt's best friends from home after a chance encounter during a training weekend for the CCC in Schwartzwald. Although they are once again separated by the Atlantic, Matt and Thomas meet up about once a year and have weekly video chats about science, running, and beer. This "nerd alert" was created as an outlet for some of the hundreds of scientific papers they read each month.
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and Matt Laye PhD 
What was the hypothesis or research question?
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