Athlete Monitoring & Frequency of Feedback by Mark Langley

A strength coach once defined his job as both imposing stress on athletes and managing it. We try to quantify this physical stress to reduce the incidence of injury and ensure the best possible outcomes of our training plans.

By collecting the right data, you can draw general conclusions about your athletes’ training status.1 You can see if you are pushing them too hard, or if they need to be pushed more. This same idea applies at the individual level, showing whether certain individuals are more or less prepared than their peers. Monitoring can be real-time or lapse behind training by a day, week, or month. You can use it as a force multiplier, using feedback to augment training either within a session or as part of a broader plan.

Most coaches use cherry-picked items of interest as methodologies. The focus is defining them by how they monitor fatigue and frequency of feedback. This is important because they reveal the acute and chronic fatigue effects that hinder training. In general, the best monitoring systems are easy to implement, cost-effective, and report actionable data. I chose the ones here because they allow concurrent methods of monitoring fatigue across different time domains. This allows us to have actionable information between sets, between training days, and across training blocks.

Real-time/Immediate Monitoring: Heart Rate Monitors, Velocity-Based Training

Real-time and immediate feedback provides information that allows us coaches to augment training as it is being conducted to produce the adaptations we’re looking for. If we mean to produce speed/strength in our athletes but select training parameters more suited for hypertrophy, we run the risk of compromising a training day and possibly derailing the training focus of the week or block. If we’re not working by block periodization, it also gives us a tool for more process-oriented training, which works more serendipitously.

For real time and immediate measurements, most parameters are common coaching competencies. Many are kinematic, pertaining to the qualities of the movement. These are things like form, positioning, and stability. If we move more towards the metrics of training, we could look at kinetic feedback such as percentage of 1 repetition maximum, relative volume, velocity, power, and heart rate. Both these elements of feedback are useful to augment training; however, there are limited options when it comes to utilizing the kinetic elements in near-immediacy.

In terms of real-time and immediate feedback, two options are heart rate monitors and velocity-based training (VBT). Heart rate monitors are more useful to routines that lend themselves to aerobic systems. Within the context of resistance training, the best utility I’ve seen uses HR monitors as an indicator of rest intervals if the training goal isn’t to maintain steady state exercise.2 VBT can guide us in real time or review between sets to determine if we’re working at the velocity that matches to our training goal3, whether the intent is to develop absolute strength, speed-strength, speed, or something else.

While coaches base their recommendations on experience, real-time systems give us certainty, as well as a safety net if we can’t observe and wield full control of the training session. A staff of six coaches to train twelve athletes won’t have their attention divided as much as a staff of one and a few interns with a flood of twenty athletes. Both can utilize VBT and HR monitors to their advantage. But in the case of disparate coaching there is the unique leverage that training variables can be controlled with windows beside just X to Y% of your old 1RM.

Daily/Weekly Monitoring Tools: Subjective Questionnaires, Heart Rate Variability, Peak Power

Good coaches rely on feedback from their athletes to determine the efficacy of their programs. That feedback can be as informal as observing how they socialize before practice, or something more formal akin to performance testing. Daily feedback is useful in short-term planning. The easiest example is using a high-low-low-high training method, where heights describe the relative impact of the training stressor. Daily monitoring systems let us gauge if we achieved yesterday’s training stimulus. If yesterday was supposed to be a hard training day, there should be some indications that it was. Likewise, if we mean to subject athletes to a hard training day, it is in the interest of safety and performance to know they are prepared.

We normally talk to our athletes, and this method makes that information more reliable, accurate, and actionable. The information correlates with other indicators like testosterone and cortisol ratios. The metric could be something as simple as subjective questionnaires, surveys asking about muscle soreness, mood, sleep quality, stress, and fatigue. They could be quantified on a scale of 1 to 5, or 1 to 10. Once we have numbers, we can look at the data and identify daily responses from the subjective level. This is a formalized way of just asking your athletes how they’re doing and logging it.

Another metric is heart rate variability (HRV). By quantifying the frequency of different components of a heartbeat, we can classify the volume of different classes to determine if the variability favors sympathetic or parasympathetic stimulation. This gives us a metric of the physiological response that describes many of the same variables of subjective questionnaires. In simple terms, showing how varied the heart is in doing what it does can tell you if the body is responding to stress.4–6 Several systems on the market analyze the HRV response to training. Some equipment packages, such as Bioforce HRV, include a web portal to chart the information online. This displays results as weekly and monthly changes of HRV at given measurement points.

A third metric is daily power measurements. If we’re being pragmatic, vertical jumps can be a useful indicator. This will give us a gauge of neuromuscular status and preparedness for training.7

McLean BD, Coutts AJ, Kelly V, McGuigan MR, Cormack SJ. “Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players.” Int J Sports Physiol Perform. 2010;5(3):367-383.

McLean BD, Coutts AJ, Kelly V, McGuigan MR, Cormack SJ. “Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players.” Int J Sports Physiol Perform. 2010;5(3):367-383.

Some VBT systems like PUSH accommodate this method. Since PUSH is more easily portable than jump ladders or mats, it simplifies the process logistically.

Peak Power (Watts) = [60.7 * jump height (cm)] + [45.3 * body mass (kg)] - 2055

Using the displacement of the athletes and their mass, we can calculate power for the best of three or five trials.8 It also pairs well with pre-training weigh-ins to monitor for dehydration. Graphing athletes’ power or HRV over time shows individual responses to training stress the mornings after hard sessions. This method works both in team and individual athlete settings. It provides information to augment the training session, based on response and recovery from the previous one.

Strategic Planning in the Big Picture: Training Load and sRPE

When accounting for athlete fatigue and induced stress on a larger scale, the usable metrics are almost unlimited. If we strictly play accountant, we can identify intensity, volume, work, and relative volume. There are many more, but few will capture a cohesive picture of training stress. One common flaw of strategic training plans is accounting for what counts as training and what does not. As such, work in the weight room is calculated and methodical, but conditioning and agility paradoxically have little influence on either residual fatigue or training adaptations.

The problem in analyzing athlete fatigue on such a large and diverse scale is finding the common thread that makes one event comparable to another. One way to resolve and smooth differences in skill practice, conditioning, and strength training is quantifying them in a universal way. Time is a component common to all, but one minute of training can obviously have a qualitative difference from another. The rating of perceived exertion (RPE) is a subjective measure that allows us to transcend different activities. Using these two components, we can create a metric that indicates training stress.

The concept of RPE is fairly simple. Athletes rate the difficulty of a training session (scale of 1 to 10—simplified, 1 to 20—Borg, or whatever is preferred) shortly afterward. This RPE can be multiplied against the duration (such as how many minutes the session took) to give us training load—an artificial variable.7,10 Training load can be graphed against time to give us a picture of whether or not we’ve imposed the planned amount of stress. If we planned a light day but it was perceived as intermediate, that information can guide our training plan. If something supposed to be close to a peak training load was perceived as moderate, we could amend the plan. These are singular examples though. If our intent is to progressively increase our training load and then cycle back for another progressive increase or an intricate periodization scheme, we can visualize the training stress rather than basing it on informal feedback.

Using a team average, we can see the training load on a larger scale and how the team generally perceives the training session to see if it matches with the level of difficulty we planned.9

Clarke N, Farthing J, Norris S, Arnolod B, Lanovaz J. “Quantification of training load in Canadian Football: Application of Session-RPE in collision-based team sports.” J Strength Cond Res. 2013;27(8):2198-2205. doi:10.1519/JSC.0b013e31827e1334. 

Clarke N, Farthing J, Norris S, Arnolod B, Lanovaz J. “Quantification of training load in Canadian Football: Application of Session-RPE in collision-based team sports.” J Strength Cond Res. 2013;27(8):2198-2205. doi:10.1519/JSC.0b013e31827e1334. 

There are caveats. One is that a newer athlete or transfer will perceive training as harder than those who have grown accustomed to the grind after some years of experience. Another is that this shouldn’t be taken as immediately actionable information. Some level of collection is required before actionable information can be gleaned.

Don’t Be a Coach Accountant

One factor to consider is ease of implementation. It’s important to determine how these systems can be integrated into the training process so that they don’t drain time away from your actual coaching. Interfaces like the PUSH portal simplify integrating that system, and other systems can be integrated using web forms, tracking boards, and rosters. Finally, if data isn’t actionable, it isn’t useful. If you can’t see trends or draw conclusions based on the information you obtain, you can’t use it to augment training in a meaningful way and you’re wasting your time. When in doubt: keep it simple, stupid.


Mark Langley is a NSCA-certified strength and conditioning specialist. He started his career as a paratrooper in the US Army. Since then he has worked at the collegiate and private levels training athletes in several sports. His research includes sensor validity, reliability testing, and energy homeostasis. Mark is working on his master's degree in exercise physiology.

REFERENCES

1. Brookreson N. Athlete Monitoring: Practical Applications and Implications for Training. Boone, NC, 2015.

2. Piirainen JM, Tanskanen M, Nissilä J, et al. “Effects of a heart rate-based recovery period on hormonal, neuromuscular, and aerobic performance responses during 7 weeks of strength training in men.” J Strength Cond Res. 2011;25(8):2265-2273. doi:10.1519/JSC.0b013e3181ecd050.

3. Mann B. Developing Explosive Athletes: Use of Velocity Based Training in Training Athletes. 2nd edition. Ebook,. 2013.

4. Berkoff DJ, Cairns CB, Sanchez LD. “Heart rate variability in elite American track-and-field athletes.” Am J Physiol Circ Physiol. 2007;21(1):227-231. doi:10.1519/R-20135.1.

5. Tian Y, He Z, Zhao J, et al. “Heart rate variability threshold values for early-warning nonfunctional overeaching in elite female wrestlers.” J Strength Cond Res. 2013;27(6):1511-1519.

6. Morales J, Álamo J, García-Massó X, et al. “The Use of Heart Rate Variability in Monitoring Stress and Recovery in Judo Athletes.” J Strength Cond Res. 2014;28(7):1896-1905.doi:10.1519/JSC.0000000000000328.

7. McLean BD, Coutts AJ, Kelly V, McGuigan MR, Cormack SJ. “Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players.” Int J Sports Physiol Perform. 2010;5(3):367-383.

8. Sayers SP, Harackiewicz D V, Harman EA, Frykman PN, Rosenstein MT. “Cross-Validation of Three Jump Power Equations.” Med Sci Sports Exerc. 1999; Apr 31(4):572-7.doi:10.1097/00005768-199904000-00013.

9. Clarke N, Farthing J, Norris S, Arnolod B, Lanovaz J. “Quantification of training load in Canadian Football: Application of Session-RPE in collision-based team sports.” J Strength Cond Res. 2013;27(8):2198-2205. doi:10.1519/JSC.0b013e31827e1334.

10. Kraft JA, Green JM, Gast TM. “Work Distribution Influences Session RPE Response during Resistance Exercise Matched for Total Volume.” J Strength Cond Res. 2014:1-16. doi:10.1519/JSC.0000000000000342.