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This article is part of a series:
→ Part 1 — Training load using your brain.
→ Part 2 — TRIMP, HRSS, and rTSS.
→ Part 1 — Training load using your brain.
→ Part 2 — TRIMP, HRSS, and rTSS.
How to measure your training load? Part 2 of 2:
Training load metrics and training stress scores — what are TRIMP, HRSS, and rTSS?
Thomas Solomon PhD.
25th Apr 2020.
Most of the commercially-available training platforms like Strava, Elevate, Training Peaks, and Final Surge use fitness vs. fatigue or acute- vs. chronic-load relationships to assess your “performance” or “form”. All such platforms calculate various “stress scores” that originate from something called the “training impulse”, aka TRIMP. Following on from part 1, stay with me to learn what that is, how it is useful, and how it is not.
Reading time ~15-mins (3000-words)
Or listen to the Podcast version.
Or listen to the Podcast version.
In 1968, Forrest Gump met Benjamin Buford "Bubba" Blue and sculpted a great friendship that formed a fishing empire leading to the production of pineapple trimp, lemon trimp, coconut trimp, pepper trimp, trimp soup, trimp stew, and trimp salad. After witnessing Forrest’s phenomenal running career that followed, in 1975, Dr Eric Banister developed the training impulse model to quantify overall training load. Banister’s TRIMP assumes that training load is equal to exercise duration multiplied by exercise intensity, which is estimated using heart rate reserve (HRR) and a “weighting” factor (y) that adjusts the intensity to make long-duration low-intensity activities produce a similar training load score to that induced by S.H.I.T. (short, high-intensity training) activities.
Where %HRR = (mean HR during session − HRrest ) ÷ (HRmax − HRrest ), and y = the nonlinear coefficient that models the relationship between the rise in blood lactate during exercise and the fractional elevation in HR during exercise above resting HR. In the original work, this relationship was different between men and women, so y is influenced by sex (i.e. y = 0.64e1.92✕%HRR in men and 0.64e1.67✕%HRR in women).
Banister proposed that every exercise bout produces both a “fitness” and a “fatigue” impulse, i.e. a positive and a negative training effect. By progressively increasing TRIMP over several weeks, the accumulation of fitness and fatigue can be modelled to predict “performance” by subtracting current fatigue from the accumulated fitness. He also posed that in response to a given load, fatigue initially outweighs fitness such that the subsequent performance capacity decreases, but that fatigue dissipates faster over time than fitness, suggesting that fitness eventually outweighs fatigue and performance capacity can be predicted to rise. This also theoretically predicts the appropriate “taper” period required to maximise performance (i.e. to find the day when fatigue is minimal and fitness is maximal). Without destroying your brains with too much with the mathematical modelling behind the fitness-fatigue impulse metric, you can read an in-depth overview by Drs David Clarke and Philip Skiba in the journal, Advances in Physiology Education.
Although Banister’s original paper only modelled TRIMP to predict the performance in a single athlete, TRIMP has since been applied to larger populations of swimmers, triathletes, cyclists, weightlifters, hammer throwers, and athletes in team sports, like football, demonstrating its utility for illustrating overload, overreaching, and reversibility or detraining. Fortunately for us, the TRIMP model has also been applied to running where it has been shown use in predicting running performance. Furthermore, the “fitness” and “fatigue” impulses have also shown promise in small studies showing correlation with physiological variables that are related to fitness and fatigue, like iron status, muscle damage markers, and mood. Therefore, in summary, the training impulse model is useful for demonstrating the basic principles of training theory.
The HRSS and rTSS indices are proprietary formulae based on TRIMP theory that take into account the amount of time you have spent above the heart rate (HRSS) or pace (rTSS) that demarcate your anaerobic/lactate threshold. The indices are then used in a proprietary algorithm to estimate accumulated fitness (a weighted running average of today’s stress relative to your past ~30-42 days), current fatigue (a weighted running average of today’s stress relative to your past ~7-10 days), and predicted performance (fitness minus fatigue). The indices are intuitively theorised but, unlike the TRIMP-derived fitness-fatigue model, have not been validated in peer-reviewed journals.
Where, time is measured in seconds; NGP is normalised grade pace, a weighted running average of your grade-adjusted pace, measured in metres/second; IF is an intensity factor equal to NGP ÷ FTP (like Banister’s weighting factor, y); and FTP is your functional threshold pace, i.e. your average pace at lactate threshold, in metres/second.
Note that the running-based rTSS and its associated FTP has been described on Dr Philip Skiba’s website Physfarm and is akin to Dr Andy Coggan’s training stress score (TSS), a cycling-based functional threshold power model described in his book Training and Racing with a Power Meter. Also note that the grade-adjusted pace (NGP) calculation, which attempts to account for the loss of speed on ascents and gain on descents, acts as a surrogate for normalised power in Coggan’s TSS and its algorithm is derived from treadmill tests and has not been validated on complex mountainous terrain.
These limitations, although important, are somewhat nitpicky. However, the most important limitation of any metric that uses just time and intensity to estimate overall training load, is that such metrics will oversimplify the relationship between training and performance and are just a single piece of a complex jigsaw puzzle, ignoring the many components that complete the picture. This issue is exemplified when trying to predict performance in highly-trained elite athletes where genetic potential has been fulfilled and minor advances in more nuanced aspects of fitness, such as skill, become key. Such limitations do in fact take us further away, rather than closer to, the ever-so-important individualisation requirement of training.
Nearly every endurance athlete uses intervals as a staple component of their training, yet TRIMP, HRSS, and rTSS are limited since they assess average session HR (or pace) and therefore fail to capture the stress induced by intermittent types of sessions. In 2007, Desgorces et al. proposed a work-endurance-recovery method to improve TRIMP’s utility by incorporating RPE, assessments of delayed onset muscle soreness, and lactate during interval sessions. This was an advance but lactate is not always measurable in the field. A further refinement was made in 2009, when Hayes and Quinn proposed a TRIMP that included bioenergetic modelling of the running speed to time-to-exhaustion relationship (aka “critical speed”). This improvement also included a “concentration” factor to quantify the intensity, duration, and intermittent nature of interval sessions. However, the approach requires at least 3 time-trials at regular intervals during training to update each athlete’s bioenergetic model. To help overcome the similar limitations, Manzi and colleagues derived an individualized TRIMP that measured the heart rate-velocity relationship in each subject to individualize the intensity weighting factor (y) that was previously only influenced by being male or female. This was a useful advance and its validity in tracking changes in fitness tests and running performance tests was more accurate than traditional TRIMPs. Indeed, iTRIMP requires the frequent assessment of the HR-velocity relationship to adjust y as and when changes in fitness occur, but this is not an additional burden since regular fitness tests are prudent and Banister’s TRIMP, and the HRSS and rTSS metrics, all require updates to either resting HR, HRmax, or HR/pace at lactate threshold (i.e. FTP).
So, where we currently stand, there are several metrics available for tracking training load, most of which are based on a function of just two variables, session duration (time) and intensity (heart rate or pace). Some metrics, like HRSS and rTSS, can be automated for free using online platforms but they require that you have a well-calibrated and accurate GPS watch coupled with accurate heart rate monitoring, which are luxuries not everyone can afford or wants to use. But above all, the popular and freely-available metrics do not assess the other important determinants of performance, like strength and skill, nor do they incorporate other components of training load like sleep, nutrition, mood, or feelings, which I recently discussed. Just to put that into perspective, here is an example of where such metrics fail…
Imagine doing a speed-specific session — 6 × 150m sprint off 5-min rest — that places you under a heavy load. Since this type of session won’t push your heart rate or speed above the lactate threshold for very long (probably less than 2-minutes), metrics like TRIMP, HRSS, or TSS won't detect this session as significant stress. Even though the biomechanical load and subsequent stress caused by such a session are large, TRIMP, HRSS, or TSS will not see it that way. That is a major problem.
So, while the freely-available, automated metrics have value for helping you understand basic training theory, they are often out-performed by our own brains. Who would have thought that your own limbic system would know how you feel better than that lump of plastic carefully tied around our wrist?
Unfortunately, the mountain running event incurred a SARS-CoV-2-induced cancellation so the true performance outcome will never be known. But the actual reason for the data collection was to model the data and compare the trends in the fitness-fatigue relationships between TRIMP, HRSS, rTSS, and session-RPE. The outcome? ... Well, all of the metrics predicted the same outcome: a peak in “performance” at the opportune time. Importantly, the prediction derived from session-RPE, i.e. the prediction derived from the athlete simply rating their perceived exertion of each session out of 10, provided no less information than the complex algorithms based on heart rate reserve, lactate thresholds, and GPS-derived grade-adjusted pace.
Note that the units of measurement of training load on the y-axes are arbitrary — a value of “zero” does not mean zero fitness or fatigue, it simply signifies the starting point of the 13-week training cycle. Also notice the initial decline in “performance” (yellow) at the outset of the training block, which recovers and then surpasses initial “performance”.
Image Copyright © Thomas Solomon. All rights reserved.
With my coached athletes, I monitor exercise dose (frequency, intensity, time) and exercise type (terrain, run, strength, etc), and use a “recovery assessment tool” that quickly assesses clients’ body weight, menses, nutrition, mood, injury, illness, and sleep, in less than 2-minutes. Athlete “buy-in” can be difficult since some folks disregard the need for load monitoring as an integral part of their training. Some athletes choose only to engage once there is a problem (i.e. when it is too late), while others have played “RPE chess”, where they manipulate their self-report to inappropriately influence my planning - not enjoyable and never successful. But for the many who have religiously engaged in my training load tool, I have a much closer relationship with them, my understanding of their training responses is thorough, and I am better able to tweak planned loads in safe and healthy ways to elicit better outcomes. When a signal stands out, I do my best to avoid “not seeing the forest for the trees” and help the clarity emerge by asking the athlete one simple question, “how are you doing?”.
Refining your training load down to one running-related metric, which the freely-available online platforms encourage you to do, is a rather reductionist approach in comparison to the comprehensive list of factors that can influence and monitor your training “stress”. So, before committing to relying on jazzy and cool-sounding automated metrics to tell you when to train and how long to recover, give some thought to the load imposed by the other aspects of your training, like strength and skill, as well as the factors that influence the load, like sleep, nutrition, etc, etc.
Remember, using metrics to indicate how hard you just trained has some utility, particularly for helping you learn about training theory, but the measurement of a single variable in isolation may be misleading and will only give you information about one factor at play. Yes, elevated morning heart rate may indicate illness, infection, or lack of recovery from prior exercise, but it may also reflect your natural diurnal rhythm, a large meal, an acute lack of sleep, or acute stress (baby didn’t sleep, a tight work deadline, husband/wife being a twat). On the other hand, simply asking yourself how you feel, forces your brain to instantaneously process everything that affects your mood, including factors like your prior exercise dose, sleep quality, nutrition quality, hormonal status, etc etc. Anyone who ignores self-reported assessments of how they feel will never maximise their genetic potential or fully-achieve their performance goals. Despite years of research, there is no single metric that can accurately quantify your fitness and fatigue responses to training or predict your performance outcomes. Single metrics and algorithms have to use many assumptions. Minimise the error of the assumptions by using your cerebral wonder box to ask yourself some simple questions, bringing neurological “art” into the science of training.
Thanks for joining me on this two-part journey on training load. I hope to have succeeded in helping you learn about the aspects of your training that impose stress and those that can be monitored to understand such stress. I also hope to have helped you understand the value and limitations of metrics and, in doing so, helped you consider how you might track your training load comprehensively but also with simplicity to get the most out of your performance. I will return soon with more content. Until that time, keep training smart.
Banister proposed that every exercise bout produces both a “fitness” and a “fatigue” impulse, i.e. a positive and a negative training effect. By progressively increasing TRIMP over several weeks, the accumulation of fitness and fatigue can be modelled to predict “performance” by subtracting current fatigue from the accumulated fitness. He also posed that in response to a given load, fatigue initially outweighs fitness such that the subsequent performance capacity decreases, but that fatigue dissipates faster over time than fitness, suggesting that fitness eventually outweighs fatigue and performance capacity can be predicted to rise. This also theoretically predicts the appropriate “taper” period required to maximise performance (i.e. to find the day when fatigue is minimal and fitness is maximal). Without destroying your brains with too much with the mathematical modelling behind the fitness-fatigue impulse metric, you can read an in-depth overview by Drs David Clarke and Philip Skiba in the journal, Advances in Physiology Education.
Although Banister’s original paper only modelled TRIMP to predict the performance in a single athlete, TRIMP has since been applied to larger populations of swimmers, triathletes, cyclists, weightlifters, hammer throwers, and athletes in team sports, like football, demonstrating its utility for illustrating overload, overreaching, and reversibility or detraining. Fortunately for us, the TRIMP model has also been applied to running where it has been shown use in predicting running performance. Furthermore, the “fitness” and “fatigue” impulses have also shown promise in small studies showing correlation with physiological variables that are related to fitness and fatigue, like iron status, muscle damage markers, and mood. Therefore, in summary, the training impulse model is useful for demonstrating the basic principles of training theory.
The growth of TRIMP.
TRIMP sounds great, right? Several commercial training platforms certainly think so. Scientists have used a systems modelling approach to quantify the dose-response nature of training for over 40-years and several companies have now exploited TRIMP theory to develop their own training load algorithms. Platforms like Strava (the paid subscription version only), Final Surge, Training Peaks, and Elevate calculate a training load score for each session in the form of a heart rate stress score (HRSS) or a running total stress score (rTSS). These are then used to estimate accumulated “Fitness” (aka chronic training load), current “Fatigue” (aka acute training load), and “Performance” (aka, form or training stress balance; fitness minus fatigue), which can help you adjust your training and tapering periods in order to optimise your performance.The HRSS and rTSS indices are proprietary formulae based on TRIMP theory that take into account the amount of time you have spent above the heart rate (HRSS) or pace (rTSS) that demarcate your anaerobic/lactate threshold. The indices are then used in a proprietary algorithm to estimate accumulated fitness (a weighted running average of today’s stress relative to your past ~30-42 days), current fatigue (a weighted running average of today’s stress relative to your past ~7-10 days), and predicted performance (fitness minus fatigue). The indices are intuitively theorised but, unlike the TRIMP-derived fitness-fatigue model, have not been validated in peer-reviewed journals.
Note that the running-based rTSS and its associated FTP has been described on Dr Philip Skiba’s website Physfarm and is akin to Dr Andy Coggan’s training stress score (TSS), a cycling-based functional threshold power model described in his book Training and Racing with a Power Meter. Also note that the grade-adjusted pace (NGP) calculation, which attempts to account for the loss of speed on ascents and gain on descents, acts as a surrogate for normalised power in Coggan’s TSS and its algorithm is derived from treadmill tests and has not been validated on complex mountainous terrain.
The limitations of TRIMP.
It is important to know that much of the data collected for Banister’s TRIMP was done so under laboratory conditions. But, there are other limitations. Firstly, the average heart rate (or power or pace) during a session may not reflect the fluctuations that occur during intermittent exercise sessions. The principal limitation of heart rate is its daily “drift” influenced by things like hydration, rest, illness, and its underestimation of training stress caused when working at high workloads that exceed your maximal aerobic capacity (VO2max). Also, the “weighting” factor, y, in Banister’s TRIMP was derived from a tiny sample and assumes that being either “male” or “female” is the only relevant difference between people. The “intensity factor” introduced in rTSS to prevent long easy sessions from creating unnecessary high “load” is also an assumption. The attempts of HRSS and rTSS to normalise your training loads to the time spent at heart rates or paces above lactate threshold sound ace but they also require that you know what your lactate threshold (or functional threshold pace) is and how to measure them accurately. Furthermore, the grade-adjusted pace used to calculate the NGP component of rTSS is based on an algorithm that factors in the loss of energy on inclines and the (less-than-efficient) return of energy on declines, based on treadmill-derived data collected in very small sample sizes (see here, here, and here for the energetic models). The grade-adjusted pace calculation does not consider differences in terrain (road vs. mud, or trails vs. rocky ascents) or technical skill and it very likely underestimates the musculoskeletal/neuromuscular stress of downhill running. Plus, nothing in the TRIMP model accounts for the load exerted by strength/power-based sessions like sprinting, lifting, climbing, plyometrics, etc, which place you under significant stress.These limitations, although important, are somewhat nitpicky. However, the most important limitation of any metric that uses just time and intensity to estimate overall training load, is that such metrics will oversimplify the relationship between training and performance and are just a single piece of a complex jigsaw puzzle, ignoring the many components that complete the picture. This issue is exemplified when trying to predict performance in highly-trained elite athletes where genetic potential has been fulfilled and minor advances in more nuanced aspects of fitness, such as skill, become key. Such limitations do in fact take us further away, rather than closer to, the ever-so-important individualisation requirement of training.
The improvements to TRIMP.
In her 1993 book, published by Polar, Sally Edwards’ proposed a TRIMP that uses the vastly-popularised but totally arbitrary five-zone 10%-increment heart rate model based on percentages of HRmax. In 2003, Lucia et al. proposed a TRIMP using the less-arbitrary demarcations (ventilatory thresholds) that separate the three metabolic domains, our “true” biological intensity zones. Lucia assigned each domain a coefficient that is multiplied by the time-in-zone to produce a TRIMP score that incorporates the amount of time spent in the “heavy” vs. “easy” domains. This has potential but requires complex laboratory testing to determine the heart rates at which these demarcations occur. The commercial HRSS and rTSS indices are akin to this approach in that they assess a session’s load in relation to the time spent above the heart rate or pace that demarcates the heavy domain.Nearly every endurance athlete uses intervals as a staple component of their training, yet TRIMP, HRSS, and rTSS are limited since they assess average session HR (or pace) and therefore fail to capture the stress induced by intermittent types of sessions. In 2007, Desgorces et al. proposed a work-endurance-recovery method to improve TRIMP’s utility by incorporating RPE, assessments of delayed onset muscle soreness, and lactate during interval sessions. This was an advance but lactate is not always measurable in the field. A further refinement was made in 2009, when Hayes and Quinn proposed a TRIMP that included bioenergetic modelling of the running speed to time-to-exhaustion relationship (aka “critical speed”). This improvement also included a “concentration” factor to quantify the intensity, duration, and intermittent nature of interval sessions. However, the approach requires at least 3 time-trials at regular intervals during training to update each athlete’s bioenergetic model. To help overcome the similar limitations, Manzi and colleagues derived an individualized TRIMP that measured the heart rate-velocity relationship in each subject to individualize the intensity weighting factor (y) that was previously only influenced by being male or female. This was a useful advance and its validity in tracking changes in fitness tests and running performance tests was more accurate than traditional TRIMPs. Indeed, iTRIMP requires the frequent assessment of the HR-velocity relationship to adjust y as and when changes in fitness occur, but this is not an additional burden since regular fitness tests are prudent and Banister’s TRIMP, and the HRSS and rTSS metrics, all require updates to either resting HR, HRmax, or HR/pace at lactate threshold (i.e. FTP).
So, where we currently stand, there are several metrics available for tracking training load, most of which are based on a function of just two variables, session duration (time) and intensity (heart rate or pace). Some metrics, like HRSS and rTSS, can be automated for free using online platforms but they require that you have a well-calibrated and accurate GPS watch coupled with accurate heart rate monitoring, which are luxuries not everyone can afford or wants to use. But above all, the popular and freely-available metrics do not assess the other important determinants of performance, like strength and skill, nor do they incorporate other components of training load like sleep, nutrition, mood, or feelings, which I recently discussed. Just to put that into perspective, here is an example of where such metrics fail…
Imagine doing a speed-specific session — 6 × 150m sprint off 5-min rest — that places you under a heavy load. Since this type of session won’t push your heart rate or speed above the lactate threshold for very long (probably less than 2-minutes), metrics like TRIMP, HRSS, or TSS won't detect this session as significant stress. Even though the biomechanical load and subsequent stress caused by such a session are large, TRIMP, HRSS, or TSS will not see it that way. That is a major problem.
Subjective self-report out-performs objective measures.
In 2018, a group of scientists in the UK published a meta-analysis of team-sport studies including 295 athletes and over 10,000 training sessions. They found that the session-RPE training load score was superior for tracking performance when compared to the traditional heart rate-derived TRIMPs. While a team sports analysis may not seem relevant to runners, training for running performance includes strength-specific and skill-specific approaches. This is further exemplified in OCR where there are heavy carries, spear throws, and grip intensive obstacles that require training for. Fortunately, the utility of the session-RPE method has been proven in runners, where session-RPE training load-derived “performance” was strongly-correlated with actual race finish times following 15-weeks of training. But to help seal the deal, a systematic review of fifty-six original studies found that subjective self-reported measures, including validated questionnaires like the Profile of Mood States (POMS), the Recovery Stress Questionnaire for Athletes (RESTQ-S), and the Daily Analyses of Life Demands of Athletes (DALDA), reflected chronic (accumulated fitness) and acute (current fatigue) training loads with superior sensitivity and consistency when compared to objective measures, which included heart rate, lactate, VO2max, performance tests, and blood test markers of endocrinology, haematology, immunology, inflammation, and muscle damage.So, while the freely-available, automated metrics have value for helping you understand basic training theory, they are often out-performed by our own brains. Who would have thought that your own limbic system would know how you feel better than that lump of plastic carefully tied around our wrist?
How do the metrics compare?
As I have said before, the freely-available metrics are convenient since they provide education in training theory surrounding the relationship between fitness and fatigue. Between Jan and April 2020, I collected training load data in a 39-year old sub-elite male athlete with a recent 5 km road time of ~15:55 and aiming to peak for a mountain running event in mid-April. During the 13-week training block, fitness increased, as evidenced by a faster steady-state pace at 2 mM and 4 mM lactate (measured outside on the same terrain under the same conditions using a Lactate Pro 2) and an improvement in 1-hour FTP from 3:40/km to 3:28/km. Weekly exercise dose was dependent on ongoing training and recovery feedback but was, in general, gradually increased over the training block with regular “recovery” weeks of a lower load. As race day approached, the dose was tapered to reduce fatigue in the presence of the fitness that had accumulated, to elevate performance.Unfortunately, the mountain running event incurred a SARS-CoV-2-induced cancellation so the true performance outcome will never be known. But the actual reason for the data collection was to model the data and compare the trends in the fitness-fatigue relationships between TRIMP, HRSS, rTSS, and session-RPE. The outcome? ... Well, all of the metrics predicted the same outcome: a peak in “performance” at the opportune time. Importantly, the prediction derived from session-RPE, i.e. the prediction derived from the athlete simply rating their perceived exertion of each session out of 10, provided no less information than the complex algorithms based on heart rate reserve, lactate thresholds, and GPS-derived grade-adjusted pace.
Image Copyright © Thomas Solomon. All rights reserved.
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Since the session-RPE metric is so simple, this data makes it appear to be some kind of superstar. Yes, the session-RPE is a cheap, simple, and rapid way to estimate the stress imposed on you by the duration and intensity of your sessions, and it does not require any GPS/HR/power-measuring tech, but in isolation it is just one small part of the jigsaw puzzle.
What can you add to your training toolbox?
As a practitioner, I collect the fewest possible data points that allow me to make the most well-informed and accurate decisions. Experimental studies, surveys of coaching practice at high-performance units, anecdotes from self-coached world-class athletes, and my own years of empirical data collection as a coach and an athlete, all provide clear evidence that simple approaches with minimal use of complex or overwhelming metrics are effective for monitoring training load and achieving performance goals.With my coached athletes, I monitor exercise dose (frequency, intensity, time) and exercise type (terrain, run, strength, etc), and use a “recovery assessment tool” that quickly assesses clients’ body weight, menses, nutrition, mood, injury, illness, and sleep, in less than 2-minutes. Athlete “buy-in” can be difficult since some folks disregard the need for load monitoring as an integral part of their training. Some athletes choose only to engage once there is a problem (i.e. when it is too late), while others have played “RPE chess”, where they manipulate their self-report to inappropriately influence my planning - not enjoyable and never successful. But for the many who have religiously engaged in my training load tool, I have a much closer relationship with them, my understanding of their training responses is thorough, and I am better able to tweak planned loads in safe and healthy ways to elicit better outcomes. When a signal stands out, I do my best to avoid “not seeing the forest for the trees” and help the clarity emerge by asking the athlete one simple question, “how are you doing?”.
Refining your training load down to one running-related metric, which the freely-available online platforms encourage you to do, is a rather reductionist approach in comparison to the comprehensive list of factors that can influence and monitor your training “stress”. So, before committing to relying on jazzy and cool-sounding automated metrics to tell you when to train and how long to recover, give some thought to the load imposed by the other aspects of your training, like strength and skill, as well as the factors that influence the load, like sleep, nutrition, etc, etc.
Remember, using metrics to indicate how hard you just trained has some utility, particularly for helping you learn about training theory, but the measurement of a single variable in isolation may be misleading and will only give you information about one factor at play. Yes, elevated morning heart rate may indicate illness, infection, or lack of recovery from prior exercise, but it may also reflect your natural diurnal rhythm, a large meal, an acute lack of sleep, or acute stress (baby didn’t sleep, a tight work deadline, husband/wife being a twat). On the other hand, simply asking yourself how you feel, forces your brain to instantaneously process everything that affects your mood, including factors like your prior exercise dose, sleep quality, nutrition quality, hormonal status, etc etc. Anyone who ignores self-reported assessments of how they feel will never maximise their genetic potential or fully-achieve their performance goals. Despite years of research, there is no single metric that can accurately quantify your fitness and fatigue responses to training or predict your performance outcomes. Single metrics and algorithms have to use many assumptions. Minimise the error of the assumptions by using your cerebral wonder box to ask yourself some simple questions, bringing neurological “art” into the science of training.
Thanks for joining me on this two-part journey on training load. I hope to have succeeded in helping you learn about the aspects of your training that impose stress and those that can be monitored to understand such stress. I also hope to have helped you understand the value and limitations of metrics and, in doing so, helped you consider how you might track your training load comprehensively but also with simplicity to get the most out of your performance. I will return soon with more content. Until that time, keep training smart.
Disclaimer: I occasionally mention brands and products but it is important to know that I am not affiliated with, sponsored by, an ambassador for, or receiving advertisement royalties from any brands. I have conducted biomedical research for which I have received research money from publicly-funded national research councils and medical charities, and also from private companies, including Novo Nordisk Foundation, AstraZeneca, Amylin, A.P. Møller Foundation, and Augustinus Foundation. I’ve also consulted for Boost Treadmills and Gu Energy on their research and innovation grant applications and I’ve provided research and science writing services for Examine — some of my articles contain links to information provided by Examine but I do not receive any royalties or bonuses from those links. These companies had no control over the research design, data analysis, or publication outcomes of my work. Any recommendations I make are, and always will be, based on my own views and opinions shaped by the evidence available. My recommendations have never and will never be influenced by affiliations, sponsorships, advertisement royalties, etc. The information I provide is not medical advice. Before making any changes to your habits of daily living based on any information I 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 author:
I am Thomas Solomon and I'm passionate about relaying accurate and clear scientific information to the masses to help folks meet their fitness and performance goals. I hold a BSc in Biochemistry and a PhD in Exercise Science and am an ACSM-certified Exercise Physiologist and Personal Trainer, a VDOT-certified Distance running coach, and a Registered Nutritionist. Since 2002, I have conducted biomedical research in exercise and nutrition and have taught and led university courses in exercise physiology, nutrition, biochemistry, and molecular medicine. My work is published in over 80 peer-reviewed medical journal publications and I have delivered more than 50 conference presentations & invited talks at universities and medical societies. I have coached and provided training plans for truck-loads of athletes, have competed at a high level in running, cycling, and obstacle course racing, and continue to run, ride, ski, hike, lift, and climb as much as my ageing body will allow. To stay on top of scientific developments, I consult for scientists, participate in journal clubs, peer-review papers for medical journals, and I invest every Friday in reading what new delights have spawned onto PubMed. In my spare time, I hunt for phenomenal mountain views to capture through the lens, boulder problems to solve, and for new craft beers to drink with the goal of sending my gustatory system into a hullabaloo.
Copyright © Thomas Solomon. All rights reserved.
I am Thomas Solomon and I'm passionate about relaying accurate and clear scientific information to the masses to help folks meet their fitness and performance goals. I hold a BSc in Biochemistry and a PhD in Exercise Science and am an ACSM-certified Exercise Physiologist and Personal Trainer, a VDOT-certified Distance running coach, and a Registered Nutritionist. Since 2002, I have conducted biomedical research in exercise and nutrition and have taught and led university courses in exercise physiology, nutrition, biochemistry, and molecular medicine. My work is published in over 80 peer-reviewed medical journal publications and I have delivered more than 50 conference presentations & invited talks at universities and medical societies. I have coached and provided training plans for truck-loads of athletes, have competed at a high level in running, cycling, and obstacle course racing, and continue to run, ride, ski, hike, lift, and climb as much as my ageing body will allow. To stay on top of scientific developments, I consult for scientists, participate in journal clubs, peer-review papers for medical journals, and I invest every Friday in reading what new delights have spawned onto PubMed. In my spare time, I hunt for phenomenal mountain views to capture through the lens, boulder problems to solve, and for new craft beers to drink with the goal of sending my gustatory system into a hullabaloo.
Copyright © Thomas Solomon. All rights reserved.