Training: How Much is Too Much? How Can You Tell?
Updated: Jun 3
During the pandemic there were basically 2 types of cyclist: those that did and those that did not train. If you are getting back on the bike, perhaps because of recent illness or injury, it’s tempting to “make up for lost time” and train really hard. Be warned though, as training errors such as “too much too soon” are a leading cause of injury and illness in cyclists. So how do you know what “too much” or “too soon” actually look like?
It’s a key question because there is still this deep held belief amongst cyclists “that if I just train harder than everyone else I will be the best”. This is simply not true and in fact training too hard harms performance! That’s not to say don’t train hard, simply that there is an optimal amount of load at any given time for an individual, and a harmful amount (conversely inadequate load prevents achievement of your performance goals). So how do you know if your training load is optimal? Suboptimal? Harmful?
The short answer is to engage the services of a professional coach who can work this out for you. The other option is to read on as I share some of the scientific insights into how this question is answered using modern data and how you can apply these insights to your own training. The third option is to copy someone else's program (or download one) where the loads have been designed around their body and "see how it goes" (not recommended!).
Scientific research has, over the past decade, proven what good coaches have known for a long time – that a sudden increase in training load often results in injury or illness a few weeks later (Gabbett 2020). This jump in training load is known as a “spike” in training or a “ramp rate” that is too steep (too much too soon). The athlete often felt like they “could handle it”…. at least that’s how it appeared at first. But the damage most often shows up at least a week after the training spike, more often 3-6 weeks later. The science is now at the point of having developed mathematical models to help predict the chance of injury (albeit not for cycling as yet and of course no model is ever 100% perfect at prediction). These models correlate well with the experience of coaches, the actual incidence of injuries, and with the metrics available in some of the training software used by cyclists such Training Peaks, Todays Plan, Golden Cheetah and so on.
How does it work? The underlying assumption looks like this:
Performance (“Form”) = Fitness (Chronic Training Load) – Fatigue (Acute Training Load)
If you are familiar with cycling software you might recognise this as a collection of abbreviations:
Form or TSB (“Training Stress Balance”) = CTL – ATL
As the “equation” indicates, if you have been training regularly and your chronic workload is greater than your acute workload, it means you have freshened up and overcome a good portion of fatigue by having had a week somewhat easier than you are used to, which increases your chance of being in good racing form. On the other hand, if the acute workload is greater than the chronic, you’ve just had a hard training week, and your training stress balance will be negative indicating that your level of fatigue may be sufficient to impair your performance on race day.
Why does this matter and how can you use it? Firstly, research has demonstrated that both too much and too little training can cause injury or illness. Too little training for example, would suggest that your chronic workload (“fitness”) is not enough for the racing or event you plan to undertake. Research also demonstrates that highly trained athletes, who have had seasons of higher training loads, are more injury resistant (Gabbett 2020) providing that they progressed to the high training load gradually. How can you measure this in your individual situation right now, and based on your own metrics define what a training spike or too fast a progression looks like?
You’ll need to start measuring your training load, which can be tricky. There are 2 types of load:
External Workload: this can be measured in many ways, for example how many km you rode today, vertical metres climbed, VAM (how many metres you climbed / hour), time spent at >90%V02max, kJ burnt in today’s session and countless others. The total external load is a combination of both volume and intensity, and various metrics have been developed to measure this although no one metric captures it all.
Internal Workload: this is a measure of how hard you feel the session was. Typically, a metric is created for this by rating the session out of 10 for your perceived difficulty in completing the workout:
Training Stress Score (TSS) = Rate of Perceived Exertion (“RPE”) * Time (length of the session in minutes)
Internal load also includes things like life stress (again tough to measure but a highly stressful week always harms performance), and your internal physiological responses to life stress or physical training loads.
Both types of load are important. For external load training software can be helpful, otherwise you may need to select key features such as total ride time, or perhaps time in each training zone to create an external load metric. For internal load you need to rate the difficulty for each of your sessions and create the "TSS" metric as above. Once you start recording your data for every session then a calculation can be made to work out your acute workload and chronic workload. In research acute load is the sum of training metrics for the past 1 week, and chronic load the average weekly load from the past 4 weeks (although in coaching software an exponentially weighted 6-week average is more common, indicating that the metric takes biases the most recent training weeks more than the earlier training weeks).
The acute:chronic workload ratio (ACWR) is then calculated (Hulin et al 2013). Obviously you need to have at least 4 weeks of training data to get this metric, so if you are starting back on the bike after a break then start really slowly. Avoid increasing more than 10-20% per week, especially in the first 4 weeks.
Once you get your first ACRW you can then begin to assess whether your training is too much or just right. Here’s the simple version of what you are looking for:
Ideally the ACWR sits between 0.8 – 1.3.
A ratio <1 would suggest you are having a recovery (easy) week or tapering for your race. This is good as every few weeks you should have a lighter deload week to freshen up and unsure your training is sustainable. It’s also a key part of freshening up for an important race. See my post here for how Grand Tour riders incorporate this into their seasons.
A ratio >1 would suggest that your last training week was a loading week to build fitness.
A ratio >1.5 has been associated with a significant increase in injury / illness risk.
If you use training software, then the “ramp rate” (how much the weekly load increased by) is also monitored. For experienced high-performance athletes who are constantly training near their capacity, the ramp rate is kept lower than for less experienced riders. A common exception regarding steep ramp rates is when you first start using a power meter and collecting data, in which case your starting value will 0 and there will be an initial period of constant positive ramp values which in time will level out. The ramp rate may be negative on occasion (for example after a big deload week or when you have time off (e.g. from illness or injury)) which is perfectly normal.
Managing your workload and exactly how to go about progressing it sensibly, sustainably, and optimally for performance is a real science and makes full use of modern data collection on and off the bike. Hopefully this overview has provided some insights. For a professional approach, engage the services of a cycle coach such as David Wadsworth from Cycle Physio Coaching, who additionally has the sports medicine background to truly appreciate the impact of training errors on injury and illness.
Gabbett T (2020): The Training-Performance Puzzle: How Can the Past Inform Future Training Directions? J Athl Training 55:874-884.
Hulin BT et al (2013): Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. BJSM 708-712.