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Training or overtraining? Stay on the right side
How can you best plan your training and reduce the chances of overtraining or illness? James Marshall looks at a simple measuring tool that allows you to measure your overall training load across all your sessions…
There are many different ways of measuring the work you do in training. If you are doing a single discipline sport like swimming, running or cycling then you could use a simple metric like distance covered. However, your 10km easy run on a Sunday would then look a lot tougher than your six sets of 60-metre hill sprints at full speed on a Monday, so even that is problematic. What about GPS, heart rates, lactate thresholds or power outputs? With all the recent developments in affordable training technology, it’s all too easy to get lost in the data smog and end up straying down the wrong path.
A good example of this is ‘fitness tracker’ apps. A recent study from the University of Pittsburgh looked at fitness trackers and weight loss1. This very well-designed study monitored 471 subjects over two years. All the subjects were given calorie-restricted diets and encouraged to exercise. But half the subjects were also given fitness tracking devices to show daily calorie burn.
The results showed that the subjects who used the trackers actually lost less weight compared to those with no trackers! Overall the participants without fitness trackers lost 13 pounds, while the tech-enhanced group lost less than 8 pounds (see figure 1). The researchers thought that by providing more data on work done (calories burned), the subjects would be encouraged to do more – but the opposite was observed!
Figure 1: Fitness app and weight loss
Subjects who used a fitness tracker lost an average of 7.7lbs over two years. Subjects not using a tracker lost 13.0lbs.
Why measure training load?
The underlying premise is to plan and measure progress to achieve a goal. A combination of training and adequate recovery allows the body to adapt and become better at what it needs to do2 3. Too much work and too little recovery leads to staleness, illness and potentially burnout or injury. Too little work and too much recovery mean your performance either stays the same or regresses.
However, measuring workload accurately only allows you to gain a better understanding of what works for you if your performance is measured too. Just measuring work, without any idea of performance has little relevance. This point may seem glaringly obvious, but I often see people measuring things that appear to have little relevance to performance. They measure things because they can be measured, rather than thinking “Will this help me go faster?” Let’s face it, no one ever won an Olympic medal for having the best heart rate, lactate threshold or power output!
Whilst the term ‘marginal gains’ has become a popular mantra, this only applies in reality to a very few (elite) athletes. Instead, most gains can be made by focussing on one or two variables that really impact your performance, measuring them and then manipulating them so that your body adapts and your performance improves. It’s all too easy to get distracted by measuring minor influences, for example because your friend uses this fancy gizmo!
As creatures of habit, many athletes get stuck with a very similar training load each week, or sometimes each day. Having variety of training, alternating hard and easy days, and adjusting training from week to week are sound training principles that allow sustained progress and adherence. If an athlete just trains on ‘feel’, or believes that every session has to be hard, then burnout, staleness and injury can occur. By monitoring training load, the athlete can see if they have been following the plan, or whether they have become stuck in a rut.
Common ways to measure training load
Here are some common measurement tools with their respective advantages and disadvantages.
Remember though that these tools are designed to measure a single component of fitness. Adjusting your training to improve these measures (rather than what counts in a race – crossing the line first), is the most common error amongst many athletes.
Heart rate (HR)
This is a very simple method that requires no equipment except a HR watch. HR rises with a corresponding increase in effort and work. Therefore, if all else is equal, a workout with a higher average heart rate requires more effort and work done per unit of time4. HR is very useful for comparing like-forlike workouts over time such as running the fastest possible mile with a given heart rate. You can also work it the other way round; if you can run the same mile at the end of a training block at the same pace you used at the start and find your average HR is lower, this indicates that your heart has become stronger.
There are several disadvantages of using HR however, some of which are due to misunderstanding of application. HR should be an indicator not a dictator – ie you should use it to help you pace and judge how you feel based on times and distances. The main error is in estimating your maximum heart rate using formulae and then planning sessions around percentages of this fictitious maximum. Instead, use something like running the fastest mile you possibly can to get a closer (and real) approximation of what your maximum heart rate actually is. Also, hot weather, your hydration status and stress can all skew HR. Therefore it should not be used as the sole indicator of your training load5.
This is a simple and effective method of measuring load, made easier with technology. Distance is great for single discipline sports such as running or cycling but less effective when comparing load across disciplines.
Blood lactate testing became very popular twenty years ago when portable lactate testers became accessible, and is still used in swimming. However, there may be many inconsistencies in lactate results, which can arise from outside factors such as carbohydrate ingestion, preceding exercise loadings and levels of muscle damage caused by unusually hard exercise6. Also, there’s quite a lot of measurement error that can occur when using just a pin prick of blood, and this error can swamp any potential changes in exercise intensity.
GPS is useful for measuring distance and changes of pace and speed. In particular, it’s very useful to assess and monitor how you change according to terrain and difference portions of the session7. For example, do you run even 1km splits or start slower and finish faster? Disadvantages include interpreting this data and using it to adjust your subsequent sessions. Also, if you are doing short sprints or have lots of changes of direction, GPS data is less accurate8.
Power output (watts) is used extensively by cyclists and to a smaller extent by rowers. However, it has little transfer to other sports where accurate power measurement is very difficult. Power output can quantify the work done in each session and is useful in conjunction with distance covered and speed. It allows the cyclist to see if they are adapting to a training programme.
Problems with measuring training loads
It’s important to understand that the body is a complex organism and many factors outside of training will affect how each athlete adapts. These include: gender, age, training history, illness, stress, diet, hydration, sleep, work, travel, climate etc. Suppose we have two athletes with similar 5km performance; a similar training programme may produce very different results due to these, or other factors. In other words, monitoring training load is very useful, but is only part of the process. There are three main problems with measuring training loads:
- Reliability – Is the measurement accurate at all times and with all users? A set of scales should be easy enough to use and give accurate results for mass. However, is the same accuracy present in devices that analyse your body fat percentage? The more complex the measurement, the more room for error. Do pedometers on phones measure distance covered as well as they do steps? Be careful on relying on apps!
- Validity – Is the measurement relevant to your sport? Is body fat percentage important to you as a cyclist or runner? If so, then those with the lowest body fat percentage would be the fastest. But, is Mo Farah’s body fat % low because he trains so much, or can he run fast because he is lean? It is obvious that no runner with 25% bf is going to make an olympic final, but would 6% vs 7% be the deciding factor as both runners are very lean? A more obvious example would be using a heart rate monitor to measure your gym session. It is irrelevant for what you are trying to achieve, unless you are doing a cardiovascular circuit.
- Transfer – Many of the measurements are modality specific. Heart rate is useful for endurance work, but this changes according to whether you are running, swimming or cycling. 168bpm means different intensities for all three of these activities. Measuring kilometres covered is highly relevant for cycling, but useless for run speed sessions. 10km would be a tough swim session, but only a warm up for cycling.
Trying to use the same tool to measure different things is common because it is easy but may not be appropriate. The alternative is often to have different tools for everything, but then you are swamped with data and it can quickly become overwhelming.
A simple alternative
Given the complexities and problems measuring training load (box 1), are there any other simpler methods? A measurement tool that I use is ‘session rating of perceived exertion’ (sRPE). This concept was first described in 1998 by researchers in Milwaukee, when trying to quantify training load and identify correlations with overtraining9.
Although some of the tools and technology have changed since, these researchers faced the same problems already identified about load measurement. The aim of their research was to try and identify how much training could be done before an increase in rates of illnesses occurred within athletes. What they found was that each athlete had a unique ‘training threshold’, and that if they trained above this threshold, illnesses were far more likely to occur. Indeed, retrospective analysis showed that 84% of illnesses could be explained by a preceding spike in an athlete’s training load (TL) above their individual threshold10!
Subsequent research has refined the detail of the sRPE; we shall look at three key variables that arose from their research:
- Training load (TL) – this is the sRPE (see figure 2) x duration in minutes of the session. Each session is measured and a daily/ weekly total score is produced
- Standard Deviation (SD) – this is how much difference there is between each session compared to the average session score.
- Training Monotony (TM) – this is the average daily training load (number 1 above) divided by SD (number 2 above).
Figure 2: Modified Borg scale of session RPR (SRPE)
Training load uses a modified scale of Borg’s rating of perceived exertion as the basis of sRPE. Athletes can gauge quite well how hard their session was with only a small amount of practice. It is best done 30 minutes after a session has finished, to allow a more reflective and accurate approach to be taken. This needs to be measured after every training session and recorded. A daily and weekly total then needs to be calculated.
Standard deviation is a statistical tool that is necessary to allow us to calculate the ‘training monotony’. I was a bit concerned about the maths at the start, but setting it up on the spread sheet was easy enough using the inbuilt formulae. There are also a number of easy-touse online standard deviation calculators.
Training monotony is a very important number that shows how much or how little variation occurs within training. The dangers of monotonous training were first found in race horses but have since been found in endurance athletes too11 12 13. There is a psychological component to overtraining and doing the same type of training too often, with little variation appears to be a big factor.
Your TM figure should be as close to 1.0 as possible. This shows that you have lots of variation between your days. On paper you may plan a variety of sessions, but each day and potentially each week could end up being very similar in training load and therefore you have training monotony.
The big advantage of sRPE is that you can compare efforts and loads across a wide range of different types of sessions such as swimming, running, weight training and cycling. This allows you to take an overall look at the total work done in a week, rather than adding up different forms of data from individual sessions and trying to make sense of it all. This is especially useful in multi discipline athletes such as triathletes (as you will see in the case study overleaf).
CASE STUDY: Jenny Callaghan
Twenty-three year old Jenny Callaghan is a modern pentathlete who has recently started full-time work. Previously she was able to rest in between sessions and manage her week around training. Now she has to train before and after work, and at weekends. Concerned with how this may add to the overall load, we decided to try using sRPE to monitor load and variety. Here is some actual sRPE data from two consecutive weeks of training, and comments about how we have adapted as a result:
The main difference between week 1 and week 2 was the fencing competition on the 2nd Saturday which she won. The idea was to use this as a “tough” training environment, but in the end she won comfortably. Other differences can occur with seemingly small changes. For example, by adding 10 extra minutes to her Monday weights, with a small increase in intensity, the actual training load increased by 46%! A similar thing happened with the week 1/2 Tuesday swimming sessions, where there was in an increase of 55% on the week 2 Tuesday.
Looking at these two increases, what should happen is that, a corresponding decrease should take place on the Wednesday. Instead, she carried on as normal and actually increased the workload due to a tough swimming session – going from TL 390 to TL 730 a whopping 87% increase. However, going into the competition she did reduce training somewhat over the Thursday and Friday in week 2 compared to week 1 and got the desired result.
The training monotony is too high for both weeks (1.93 and 2.09); we need to move this closer to 1.0. There is only one really hard day with a TL of over 700. There are several in the 600s, but none in the zeros or 100s. The ‘rest day’ of riding in week 1 did not actually provide enough rest (a TL of 270) due to the amount of time spent on the horse. We will be looking closely at how to add more variety, and be reinforcing the need to ‘rest’ – ie that easy means easy. Overall, there are three key points for readers to note from this case study:
- Small changes can make a big difference in total.
- The results must be recorded immediately and looked at; so that changes can be made to the following day’s training.
- Training monotony can creep up on you if you are not careful. Whilst individual sessions are different, the TL needs to differ day to day too.
|Day||Session||Duration (mins)||SRPE||Session TL||Daily TL|
|Average Daily TL||294.29|
|SD Daily TL||140.65|
|Day||Session||Duration (mins)||SRPE||Session TL||Daily TL|
|Average Daily TL||427.86|
|SD Daily TL||222.26|
Technology is developing rapidly and so is its availability to recreational athletes. It is easy to get caught up in measuring things without understanding if and how they affect your overall goal. Using sRPE is a simple tool that allows you to get an overall picture and ensure that you have plenty of variety in your training. sRPE should be used to compare results on an individual basis, rather than using it to compare between athletes. There is no ‘one size fits all’ training plan. Athletes respond differently and so the loads will differ for optimal training. Alternating easier and harder days is a fundamental training principle, with hard being hard and easy being easy. This will allow you to train effectively over longer periods of time which then leads to better results!
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