Toolbox: Heart Rate Recovery
While power-based training and dissecting every micro-watt in multiple permutations appears to be the dominant “new wave,” do not forget that there are other ways to monitor fatigue and predict performance that have been around for a long time and that can be much simpler, cheaper, and potentially just as effective…
Cyclists are possibly the luckiest of modern athletes in terms of performance monitoring, in that we are the main sport in which we can record the actual power output of our efforts. This is partly due to the bicycle being a stable platform compared to speedskating boots or running shoes. Also, the pedal stroke is relatively continuous, unlike more “cyclical” or “intermittent” efforts such as running, speedskating, or rowing. Therefore, we have been blessed with a plethora of options for power monitors.
With the popularization of power monitors have come a quantum advance in training methodology based on wattage output. Driving the advance has been Dr. Andy Coggan and TrainingPeaks’ development of parameters such as Training Stress Scores (TSS), Acute Training Load (ATL), Chronic Training Load (CTL), and Training Stress Balance (TSB). The objective with these types of analyses is to quantify our training load that we’re imposing on our bodies, and to help us determine whether we are well-rested, undertrained, or overtrained.
There is no doubt that power-based training can be very “power”-ful and beneficial, but it cannot be denied that power monitors are still in their infancy in their adoption by the general peloton. This is partly due to the expenses involved, and also due to their real or perceived complexity in both use and analysis.
Is there other ways to monitor performance? Absolutely – there’s one that should be very close to all of our hearts, because it’s the simple and seemingly passé heart rate monitor (HRM).
Sometimes perceived or advertised as a poorer cousin to the power monitor, the HRM is a powerful tool in its own right. Yes, heart rate response to exercise can be acutely affected by some factors not related to your true physiological state, such as caffeine and other stimulants.
Other modulating factors, such as hydration status, fatigue, emotional stress, sleep quality, etc., have been touted as making HRMs unreliable because of day-to-day variability. However, it can equally be argued that heart rate is valuable as a performance monitor specifically BECAUSE it can be affected by your physiological state. In other words, whether you have a power monitor or not, paying careful attention to your heart rate response to different efforts can give you a very good insight into your readiness to train or race.
Heart Rate Recovery
One of the simplest, and most powerful, ways of using heart rate is to test your day-to-day recovery. This is generally done by taking your resting heart rate after waking and while lying in bed. This is often also supplemented by taking your heart rate immediately after first standing up from bed. Abnormal spikes in one or both heart rates can indicate poor recovery.
Heart rate can also be used during exercise itself, both within a single workout to see whether you should continue with another interval, and also to monitor training status over a period of time. In both uses, the variable of interest is your Heart Rate Recovery (HRR). Simply put, how rapidly your heart rate drops back towards baseline after a hard effort provides an excellent index of your overall “freshness” both during a workout and also over time. The general idea is that, the faster or greater your drop in heart rate, the better your recovery.
Lambert et al. 2010
The utility of HRR in tracking long-term training responses was the basis for a study from Tim Noakes’s research group at the University of Cape Town in South Africa (Lamberts et al. 2010). The questions were quite simple and straightforwards: 1) does HRR (how much your heart rate drops) improve with 4 weeks of high intensity training? 2) are there differences between cyclists in their HRR response? 3) If differences exist, can they correlate with differences in performance following training?
Here’s the basic experimental setup:
• 14 trained male cyclists took part in the study.
• Subjects completed a VO2max test, a Peak Power Output test, and a 40 km TT, all done on a CompuTrainer with their own bicycles, before and after 4 weeks of training.
• A 10 day tapering phase was done between the 4 weeks of training and the post-testing. Subjects avoided other heavy training and racing throughout the study period.
• The high intensity training consisted of eight workouts spaced over 4 weeks. Each workout consisted of 8×4 min at 85% of peak power output, with 90 s of self-paced pedaling in between. This workload was maintained constant throughout the 4 weeks so that the absolute training stress remained the same.
• The 40 km TT was chosen to simulate real-life racing, as opposed to riding at a fixed workload to voluntary exhaustion. The subjects received no external feedback during the TT except for distance completed.
• Over each high intensity training session, HRR = HR(end) – HR(60), where HR(end) was the HR immediately upon completion of the 8th interval, and HR(60) was the HR at 60 s after this 8th interval.
• After the study was completed, the subjects were categorized as HRR-INC and HRR-DEC based on whether their HRR increased over the 4 weeks (indicating improved recovery) or decreased (indicating impaired recovery). This was not done until the entire study was complete in order to remove experimental bias.
So after all 14 subjects completed the data collection, the authors observed two distinct patterns in HRR responses over the four weeks of training. In 8 subjects (HRR-INC), the magnitude of HRR increased/improved each session in a fairly linear pattern, from HRR at 34 bpm after Session #1 to 46 bpm after Session #8.
However, six subjects (HRR-DEC) demonstrated an pattern of an initial slight to good improvement (from 39 bpm after Session #1 to 47 bpm after Session #6), but then a big drop back towards baseline in Sessions #7 (44 bpm) and #8 (40 bpm). It appeared as if the workload became too intense for this group, and the decrease in HRR would support the idea that they may be at or beyond the point of over-reaching or overtraining.
Some caveats before we explore things further. Upon separating the subjects into the two HRR groups, there arose differences between the groups for age, body mass, and body fatness. While the absolute VO2max and PPO were similar across the two groups, when normalized to body mass, these values were higher in the HRR-DEC group.
• Both groups ended up training at similar workloads of ~80% VO2max.
• Neither absolute nor relative VO2max changed in either group post-training.
• Relative peak power output improved in both groups, but absolute peak power output improved only in the HRR-INC group.
• The average power over the 40 km TT post-training improved in both groups, but was slightly greater in the HRR-INC than in the HRR-DEC.
• Both groups improved their 40 km TT times post-training fairly dramatically, with the HRR-INC group going from 66.17-64.26 min and the HRR-DEC from 64.51-63.58 min. There was a close to significantly greater improvement in the HRR-INC group.
• When all the subjects were grouped together, there was a significant correlation between HRR response and improvement in 40 km TT (time, absolute and relative mean power), with the highest improvements going to those with the highest HRR responses to the four weeks of training.
Recovering the Data
Some interesting, though initial, ideas emerge from this data, so let’s try to answer each of the three initial objectives.
Firstly, there appears to be an improvement in about half of the subjects in their HRR over the four weeks of high intensity training. It suggests that these subjects adapted well to the training and improved their fitness and performance as a result.
Secondly, this improvement in HRR is not universal, as there appears to be a second grouping of responses to the high intensity training. About half of the subjects initially improved their HRR. But then, rather than plateauing, their responses got worse in the latter sessions, such that their HRR returned to what was baseline by the end. There are lots of potential explanations for the differences between the two groups, but one explanation may be that the final two workouts were too much in volume for this group, and that they may have been better off if they had stopped after six sessions of high intensity training.
Finally, both groups improved their fitness and 40 km TT performance overall, but there was some tendency, though not statistically significant, for a greater improvement in the HRR-INC group. As noted above, it may be because fitness was starting to stagnate in the HRR-DEC group, though this remains speculative.
A few interesting trends and ideas emerge from this study. Perhaps the most important of all is that, while power monitors are good and useful tools, they are not the only game in town. Heart rate-based training is neither passé nor irrelevant. Rather, carefully understanding your individual responses to training load, whether it be via power, heart rate, or subjective perceptions, remains the most important skill to have for an athlete.
Ride safe and have fun!