• Ian Darragh // BSc Sport Science & Health

1RM and Autoregulation - A Discussion on the Consideration of External Stress and Resistance Trainin


Traditionally in a field-based setting, an athlete’s maximum strength within a given movement can generally be represented by the single heaviest load an athlete can lift in a single absolute effort, the result of this effort being commonly referred to the athlete’s 1 repetition max (1RM). In terms of biomechanics, any maximum strength values will be represented very closely to the far left of concentric force/velocity curve. To give an example, when establishing a new 1RM in the deadlift, peak force correlated negatively to peak velocity as loading increased[1].

The 1RM is also commonly used as a point of reference for resistance training load prescription and programme planning. This usually comes in the form of a programme being designed in a way that, on any given day the athlete will be asked to perform exercise X for reps/sets Y at say, 80% of 1RM. From a coaching perspective, the intended intensity of the session can be provided to the athlete clearly and quantifiably, as well as using previous 1RM data to develop linear progressions of load, and periodize training programmes to peak at specific points of a given sport season. Generally beginning with periods of higher volumes at moderate percentages of 1RM and ending with lower volume and closer percentages to an individual’s 1RM.

The simplicity of this system is why the 1RM is so popular as a point of reference. It quantifies the desired training stimulus in a very painless and easy-to-apply fashion. However, bigger-picture issues begin to arise with this style of training as it ignores the general fatigue state of the athlete by not accounting for the effects of external stressors.

Let’s discuss this in anecdote. If I max out on my squat today, hit 100kg and then subsequently decide to go out drinking to celebrate, it’s not entirely likely I’ll be able to squat 100kg the next morning, why? Because the fatigue state induced by the stress applied from drinking alcohol, being out late and squatting the previous day has temporarily reduced my ability to produce maximal force. When we reflect upon this further, it’s essentially telling us our “1RM” at any point in time can be fluctuating, due to a near-constant level of interaction with outside stressors. In turn, this means the accuracy of a prescribed percentage 1RM for training will always have a margin of error. This has the potential to be detrimental in the case of athletes with large training volumes or large levels of external stress, like student athletes.


Acknowledgement that an athlete’s capacity to perform can fluctuate daily has led to the development of alternative measures of training load selection. Auto-regulation is the idea of the athlete or coach regulating day-to-day load based on athlete performance or intrinsic measurements of perceived fatigue. A common example of auto-regulation is the Rate of Perceived exertion (RPE) method. Instead of a specific load, the coach assigns a level of perceived intensity (i.e., lift something that feels 6/10 in terms of “heaviness”) This approach has benefit, as the training load has a closer relationship to the athlete and their physical state, but has some pretty big flaws, firstly it relies mostly on feedback from the athlete, different athlete’s may have individual differences in terms of mental toughness and ability to accurately perceive load, it’s fair to think an elite powerlifter may more accurately attenuate to the desired intensity than an amateur cyclist. It’s interesting to note that some studies have found a positive correlation between RPE and % of 1RM, but it’s appearing not to be as accurate in resistance training as it is with endurance exercise[2,5].

Interestingly, more quantifiable methods of autoregulation are beginning to develop. Mann et al, (2015) discusses the idea of instead, auto-regulating load from Bar velocity data. To provide an example, in the back squat, mean velocity tends to be around 0.3m/s. This comes from evidence that there is an apparently linear relationship between load and velocity[3], but also certain velocities may stay relatively attenuated to specific percentages of 1RM[3-4]. This potentially suggests that the velocity an individual registers, at say, 60% of 1RM, may not change all too significantly even if the literal load 60% represents has changed due to adaptation. This is advantageous from an autoregulation perspective because training load can instead be prescribed with the correlating velocity instead of the literal load connected to the percentage of 1RM.


The benefit here is that, theoretically, velocity-based autoregulation will increase the accuracy of a training session because the desired velocity will occur at weights that are higher or lower on any given day, due to the fatigue state of the athlete.

Personally, this is a pretty interesting concept, but I think we’re a little early on in terms of research. But I certainly think it has a lot of potential, simply because it allows for a very straightforward and quantified method of regulating load and fatigue. One of the biggest limitations experienced with this type of training is the cost of equipment, with accurate measurement tools costing up to around $400, it’s not exactly a model that’s economical for the weekend warrior or amateur athlete just yet.

The purpose of this article was really to highlight, when coaching athletes in resistance training, the need to examine a broader amount of variables. Any given resistance training programme should have room for autoregulation when it’s needed. Will the world of training ever completely swap over to a holistic system such as autoregulation? Unlikely. Despite its flaws, the traditional 1RM approach is cheap, simple and still pretty effective. Its effectiveness can be quite easily increased by acknowledging its flaws and as a coach, giving constant consideration to the physiological state of the athlete, Food for thought.

References:

1. Blatnik, J. A., Goodman, C. L., Capps, C. R., Awelewa, O. O., Triplett, T. N., Erickson, T. M., & McBride, J. M. (2014). Effect of load on peak power of the bar, body and system during the deadlift. Journal of sports science & medicine, 13(3), 511.

2. Day, Meghan L., et al. (2004) Monitoring exercise intensity during resistance training using the session RPE scale. The Journal of Strength & Conditioning Research 18(2), 353-358.

3. Izquierdo, M., et al. (2006) Effect of loading on unintentional lifting velocity declines during single sets of repetitions to failure during upper and lower extremity muscle actions. International journal of sports medicine 27(9), 718-724.

4. Mann, J. B., Ivey, P. A., & Sayers, S. P. (2015). Velocity-Based Training in Football. Strength & Conditioning Journal, 37(6), 52-57.

5. Sweet, Travis W., et al. (2004) Quantitation of resistance training using the session rating of perceived exertion method. The Journal of Strength & Conditioning Research 18(4),: 796-802.

#Conditioning #Stress #Autoregulation

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