The running science “nerd alert”
from Thomas Solomon PhD
and Matt Laye PhD 
November 2022
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 instalment of our “Nerd Alert”. We hope you enjoy it.
Welcome to this month's instalment of our “Nerd Alert”. We hope you enjoy it.
Click the title of each article to reveal our summary.
Full paper access: click here
What is the hypothesis or research question?
Although it is intuitive to combine both objective and subjective measures when monitoring recovery and training load, there’s a lack of research on such an approach. Therefore, the authors aimed to compare the effect of individualised training with a predefined training program in endurance-trained recreational-level athletes. They hypothesised that individualised training would better improve running performance and decrease the likelihood of a low response to training.
What do the authors do to test the hypothesis or answer the research question?
— They recruited 40 recreational runners (20 male, 20 female) with an average V̇O2max of approx. 46 mL/min/kg and randomised them into two groups to undergo 15-weeks of training.
— The first 3 weeks were “preparatory”, the next 6 weeks focused on volume, and the final 6 weeks focused on intensity using a 2 weeks hard, 1 week easy periodization.
— Participants were not given any specific nutrition or fluid intake guidelines during the study but were instructed to maintain their usual nutritional habits.
— Heart rate during sessions and nocturnal heart rate and heart rate variability (HRV) were measured via wrist-based photoplethysmography (Polar Vantage V2). (NOTE: the Polar Vantage V2 measures nocturnal HRV by taking the average of wrist-derived HRV measured every 5-mins during the first 4-hours of detected sleep onset.)
— The preparatory weeks of training were identical between groups but the subsequent 6-week blocks of volume and intensity differed between groups:
Determination logic of the training load in the IND group. Training load was adjusted twice a week on evaluation days (Monday and Thursday). If the training load was maintained, no modifications were made compared with the current level. The training load was increased via adding volume (VOL) by 5% (e.g., 1.10 × baseline level to 1.15 × baseline level) or via increasing the number of HIT sessions (INT). The training load was decreased via reducing volume by 25% compared with the current level (VOL), or via reducing volume by 25% from the current level and excluding HIT sessions (INT). After the recovery block, the training continued from the level preceding the recovery block (two-thirds of the markers within normal range) or the next level (VOL). During INT, the progression always started from one HIT. After reaching a maximum number of HIT sessions within a block (two or three sessions), no additional sessions were performed. After the last evaluation day of INT, a maximum of one HIT session was performed to ensure sufficient recovery before final tests.
(NOTE: training frequency and timing of different types of sessions within a week were the same between the predefined and individualised training groups.)
— Performance testing was completed before training, after the volume block (week 9), and after the interval week (week 18). This included tests for running speed at the first and second lactate turnpoints (LT1 and LT2), V̇O2max, countermovement jump height, and an outdoor 10km race.
— To quantify individual runners’ responses to the training interventions, the authors calculated the percentage coefficient of variation (CV) of changes in performance outcomes during the standardised training period (which was identical for both groups). CV is calculated as the standard deviation (the spread of values) divided by the mean (the average value) and represents the typical error in a test caused by day-to-day variation in a person’s performance and/or environmental factors. The authors estimated that the smallest worthwhile change in a performance outcome would be equal to 0.5 × CV and set “thresholds” representing trivial (less than 0.5 × CV), moderate (0.5 to 2 × CV), or high (greater than 2 × CV) responses to training. For example, a runner who had a change in a performance variable greater than or equal to 2 × CV of that variable was categorised as a “high responder”.
What do they find?
— During the study, several subjects dropped out for various reasons (see paper for details). The final analysis was completed on 30 runners (15 male, 15 female).
— Overall, there were no significant differences in training load but there was a trend for the individualised training group to do slightly more high-intensity sessions.
— Training did not affect countermovement jump height (a marker of muscle strength) in either group.
— Blood markers of stress and muscle damage (testosterone, cortisol, creatine kinase) were also unchanged by training in either group.
— Training increased running speed at the second lactate turnpoint (LT2), running speed at V̇O2max, and V̇O2max in both groups. But, these changes in these variables were not different between groups.
— Race performance (10 km running time) improved in both training groups. More specifically, predefined training lowered 10 km time by −2.9±2.4% (P = 0.004; Cohen’s d effect size = 0.20, 95% confidence interval [CI] −0.35 to 0.75) while individualised training lowered 10 km time by −6.2±2.8% (P≤0.001; d = 0.46, 95% CI −0.07 to 0.97).
— Furthermore, the change in 10 km running time differed between the groups (P = 0.002; d = 1.23, 95% CI 0.42 to 2.02) with the individualised training group showing a greater improvement than the predefined training group.
Running time in the 10-km test before the VOL (T 1), between the VOL and INT (T 2), and after the INT (T 3) periods in the PD and IND training groups. **P < 0.01, ***P < 0.001 within groups compared with T 1 +++P < 0.001 within groups compared with T 2.
— For the training-induced change in treadmill velocity at V̇O2max, the individualised training group had more “high responders” (50% of runners vs. 29%) and fewer “low responders” (0% vs 21%) compared to the predefined training group.
— Similarly for the training-induced change in 10 km race performance, the individualised training group had more “high responders” (81% of runners vs. 23%) and fewer “low responders” (0% vs 8%) compared to the predefined training group.
— The authors concluded that “individualised training may increase the likelihood of positive endurance training adaptations”.
What are the strengths?
— The study includes male and female runners (although no formal sex difference analyses were conducted).
— Used power calculations to justify the sample size (although insufficient details are given to understand exactly how).
— Cohen’s d effect sizes (and corresponding 95% confidence intervals) were reported therefore providing an estimate of the magnitude of the within-group (post- vs. pre-training) changes and between-group (individualised vs. predefined training ) differences.
— The authors lowered participants’ training load before the testing week that preceded the training intervention to help facilitate sufficient recovery ahead of performance tests.
— The use of an outdoor 10 km race to determine performance has a high level of ecological validity (i.e. it represents real life rather well).
What are the weaknesses?
— The study includes a small sample of runners (n=40) and is only relevant to recreational level 10 km runners not sub-elite or elite athletes.
— Participant recruitment is not described. Therefore, we do not know whether sampling was random or, therefore, whether the sample is representative of the population.
— The inclusion and exclusion criteria for participant recruitment are not stated. Therefore, we have no idea how participants were recruited, what types of participants were planned to be recruited, or what criteria for exclusion were used either before or during the study.
— It is unclear what time of year the training took place or whether all participants completed the training during the same time of year. The authors do report that the mean training day temperature was 17.3±7.5°C but this is a massive range of air temperature and no humidity measures are provided.
— Since nutritional intake was not prescribed or monitored, we do not know whether participants were in energy balance during the study (i.e. was energy intake equal to energy expenditure?) or whether participants were in an appropriate state of energy availability (i.e. when accounting for exercise energy expenditure, was energy intake sufficient for maintaining normal bodily function?).
— It is a shame that the HR–RS index, which is derived from a session’s average running speed ( Savg) and HR (HRavg) and used to make training decisions, incorporates an arbitrary estimate of standing heart rate (HRstanding = resting HR + 26) when it could easily be measured at the individual level to provide more accuracy:
Are the findings useful in application to training/coaching practice?
Yes.
Coaches (and many athletes) already know that following a rigid plan is foolish and that it is always sensible to be flexible in your training, adjusting it on-the-fly based on how you feel (subjective feelings) and how things are going (objective measures of training load performance). This is because feelings give context to the physical stimuli we place athletes under. This study from Nuutila and colleagues is useful because it adds experimental evidence in support of the empirical evidence coaches already possess and will, therefore, help improve athletes’ understanding of how to train effectively.
What is our Rating of Perceived Scientific Enjoyment?
RP(s)E = 7 out of 10.
What is the hypothesis or research question? Although it is intuitive to combine both objective and subjective measures when monitoring recovery and training load, there’s a lack of research on such an approach. Therefore, the authors aimed to compare the effect of individualised training with a predefined training program in endurance-trained recreational-level athletes. They hypothesised that individualised training would better improve running performance and decrease the likelihood of a low response to training.
What do the authors do to test the hypothesis or answer the research question? — They recruited 40 recreational runners (20 male, 20 female) with an average V̇O2max of approx. 46 mL/min/kg and randomised them into two groups to undergo 15-weeks of training.
— The first 3 weeks were “preparatory”, the next 6 weeks focused on volume, and the final 6 weeks focused on intensity using a 2 weeks hard, 1 week easy periodization.
— Participants were not given any specific nutrition or fluid intake guidelines during the study but were instructed to maintain their usual nutritional habits.
— Heart rate during sessions and nocturnal heart rate and heart rate variability (HRV) were measured via wrist-based photoplethysmography (Polar Vantage V2). (NOTE: the Polar Vantage V2 measures nocturnal HRV by taking the average of wrist-derived HRV measured every 5-mins during the first 4-hours of detected sleep onset.)
— The preparatory weeks of training were identical between groups but the subsequent 6-week blocks of volume and intensity differed between groups:
→ The predefined training group did every workout as prescribed (see Table 2 in the paper for the training programme).
→ The individualised training group had either the duration of their sessions or the number of high-intensity interval sessions adjusted each day, based on three parameters:
1. Heart rate variability (a marker of stress and autonomic nervous system balance)
2. Heart rate running speed (HR‐RS) index (heart rate relative to running speed during steady running), and
3. Subjective feelings of fatigue and soreness.
→ If all three measures were “normal” (i.e. within an individual runner’s normal range), the training load for the next 3 days was increased by 5%, either volume or intervals. If two of three measures were “normal”, training was unaltered. If two or more of the measures were “abnormal” (i.e. outside the runner’s normal range), training load was reduced by 25% (volume or intervals). The training decision algorithm is shown in the figure below.
→ The individualised training group had either the duration of their sessions or the number of high-intensity interval sessions adjusted each day, based on three parameters:
1. Heart rate variability (a marker of stress and autonomic nervous system balance)
2. Heart rate running speed (HR‐RS) index (heart rate relative to running speed during steady running), and
3. Subjective feelings of fatigue and soreness.
→ If all three measures were “normal” (i.e. within an individual runner’s normal range), the training load for the next 3 days was increased by 5%, either volume or intervals. If two of three measures were “normal”, training was unaltered. If two or more of the measures were “abnormal” (i.e. outside the runner’s normal range), training load was reduced by 25% (volume or intervals). The training decision algorithm is shown in the figure below.
— Performance testing was completed before training, after the volume block (week 9), and after the interval week (week 18). This included tests for running speed at the first and second lactate turnpoints (LT1 and LT2), V̇O2max, countermovement jump height, and an outdoor 10km race.
— To quantify individual runners’ responses to the training interventions, the authors calculated the percentage coefficient of variation (CV) of changes in performance outcomes during the standardised training period (which was identical for both groups). CV is calculated as the standard deviation (the spread of values) divided by the mean (the average value) and represents the typical error in a test caused by day-to-day variation in a person’s performance and/or environmental factors. The authors estimated that the smallest worthwhile change in a performance outcome would be equal to 0.5 × CV and set “thresholds” representing trivial (less than 0.5 × CV), moderate (0.5 to 2 × CV), or high (greater than 2 × CV) responses to training. For example, a runner who had a change in a performance variable greater than or equal to 2 × CV of that variable was categorised as a “high responder”.
What do they find? — During the study, several subjects dropped out for various reasons (see paper for details). The final analysis was completed on 30 runners (15 male, 15 female).
— Overall, there were no significant differences in training load but there was a trend for the individualised training group to do slightly more high-intensity sessions.
— Training did not affect countermovement jump height (a marker of muscle strength) in either group.
— Blood markers of stress and muscle damage (testosterone, cortisol, creatine kinase) were also unchanged by training in either group.
— Training increased running speed at the second lactate turnpoint (LT2), running speed at V̇O2max, and V̇O2max in both groups. But, these changes in these variables were not different between groups.
— Race performance (10 km running time) improved in both training groups. More specifically, predefined training lowered 10 km time by −2.9±2.4% (P = 0.004; Cohen’s d effect size = 0.20, 95% confidence interval [CI] −0.35 to 0.75) while individualised training lowered 10 km time by −6.2±2.8% (P≤0.001; d = 0.46, 95% CI −0.07 to 0.97).
— Furthermore, the change in 10 km running time differed between the groups (P = 0.002; d = 1.23, 95% CI 0.42 to 2.02) with the individualised training group showing a greater improvement than the predefined training group.
— Similarly for the training-induced change in 10 km race performance, the individualised training group had more “high responders” (81% of runners vs. 23%) and fewer “low responders” (0% vs 8%) compared to the predefined training group.
— The authors concluded that “individualised training may increase the likelihood of positive endurance training adaptations”.
What are the strengths? — The study includes male and female runners (although no formal sex difference analyses were conducted).
— Used power calculations to justify the sample size (although insufficient details are given to understand exactly how).
— Cohen’s d effect sizes (and corresponding 95% confidence intervals) were reported therefore providing an estimate of the magnitude of the within-group (post- vs. pre-training) changes and between-group (individualised vs. predefined training ) differences.
— The authors lowered participants’ training load before the testing week that preceded the training intervention to help facilitate sufficient recovery ahead of performance tests.
— The use of an outdoor 10 km race to determine performance has a high level of ecological validity (i.e. it represents real life rather well).
What are the weaknesses? — The study includes a small sample of runners (n=40) and is only relevant to recreational level 10 km runners not sub-elite or elite athletes.
— Participant recruitment is not described. Therefore, we do not know whether sampling was random or, therefore, whether the sample is representative of the population.
— The inclusion and exclusion criteria for participant recruitment are not stated. Therefore, we have no idea how participants were recruited, what types of participants were planned to be recruited, or what criteria for exclusion were used either before or during the study.
— It is unclear what time of year the training took place or whether all participants completed the training during the same time of year. The authors do report that the mean training day temperature was 17.3±7.5°C but this is a massive range of air temperature and no humidity measures are provided.
— Since nutritional intake was not prescribed or monitored, we do not know whether participants were in energy balance during the study (i.e. was energy intake equal to energy expenditure?) or whether participants were in an appropriate state of energy availability (i.e. when accounting for exercise energy expenditure, was energy intake sufficient for maintaining normal bodily function?).
— It is a shame that the HR–RS index, which is derived from a session’s average running speed ( Savg) and HR (HRavg) and used to make training decisions, incorporates an arbitrary estimate of standing heart rate (HRstanding = resting HR + 26) when it could easily be measured at the individual level to provide more accuracy:
HR‐RS index = Savg − (HRavg − HRstanding) ÷ k
where k = (HRmax − HRstanding) ÷ Speak
and where HRstanding is estimated by adding 26 bpm to the resting HR.
where k = (HRmax − HRstanding) ÷ Speak
and where HRstanding is estimated by adding 26 bpm to the resting HR.
Are the findings useful in application to training/coaching practice? Yes.
Coaches (and many athletes) already know that following a rigid plan is foolish and that it is always sensible to be flexible in your training, adjusting it on-the-fly based on how you feel (subjective feelings) and how things are going (objective measures of training load performance). This is because feelings give context to the physical stimuli we place athletes under. This study from Nuutila and colleagues is useful because it adds experimental evidence in support of the empirical evidence coaches already possess and will, therefore, help improve athletes’ understanding of how to train effectively.
What is our Rating of Perceived Scientific Enjoyment? RP(s)E = 7 out of 10.
What is the beer called?
Barrel-Aged Dessert In A Can — Tonka & Caramel Swirl Ice-Cream.
Which brewery made it?
Amundsen Brewery (Oslo, Norway).
What type of beer is it?
Imperial double pastry stout.
How strong is the beer?
11.5% ABV.
How would I describe this beer?
Smells like caramel, looks like caramel, sits like caramel, tastes like caramel, and aftertastes like caramel but in a creamy way without being overpoweringly sweet and leaving that “mmm, dessert” sentiment in your brain.
What is my Rating of Perceived Beer Enjoyment?
RP(be)E(r) = 8 out of 10. 
What is the beer called?
Parabolita.
Which brewery made it?
Firestone Walker (CA, USA).
What type of beer is it?
A combination of barrel aged imperial stout and milk stout.
How strong is the beer?
9.2% ABV.
How would I describe this beer?
Not wanting Thomas to one-up me, I immediately went out and purchased this beer after reading about his dessert experience. The Parabolita has a sweet, vanilla, molasses taste. It has a medium body with a light bourbon finish from the barrel aged portion of the beer. It kind of tastes like a salted carmel brownie made with bourbon…yum. It’s not overly boozy or sweet, but nicely balanced.
What is my Rating of Perceived Beer Enjoyment?
RP(be)E(r) = 8.5 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…
If you find value in our nerd alerts, please help keep them alive by sharing them on social media and buying us a beer at buymeacoffee.com/thomas.solomon. For more knowledge, join Thomas @thomaspjsolomon on Twitter, follow @veohtu on Facebook and Instagram, subscribe to Thomas’s free email updates at veothu.com/subscribe, and visit veohtu.com to check out Thomas’s other articles, nerd alerts, free training tools, and his Train Smart Framework. To learn while you train, you can even listen to Thomas’s articles by subscribing to the Veohtu podcast.
Until next month, stay nerdy and keep empowering yourself to be the best athlete you can be…
If you find value in our nerd alerts, please help keep them alive by sharing them on social media and buying us a beer at buymeacoffee.com/thomas.solomon. For more knowledge, join Thomas @thomaspjsolomon on Twitter, follow @veohtu on Facebook and Instagram, subscribe to Thomas’s free email updates at veothu.com/subscribe, and visit veohtu.com to check out Thomas’s other articles, nerd alerts, free training tools, and his Train Smart Framework. To learn while you train, you can even listen to Thomas’s articles by subscribing to the Veohtu podcast.
Until next month, stay nerdy and keep empowering yourself to be the best athlete you can be…
Every day 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 thehundreds of scientific papers craft beers they read drink 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
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