Monday, September 2, 2013

GymAware user stories: how to track 1RM without actually testing it

GymAware user stories: how to track 1RM without actually testing it


There are two main methods for estimating 1RM of an exercise: (1) to build up to true 1RM lift and (2) to estimate 1RM from reps-to-(technical)-failure [RtTF] with sub-max weight using various formulas and tables.

One simple formula to be used with reps-to-(technical)-failure [RtTF] is the following:

For example, if I squatted 140kg for 5 reps (6th rep would be impossible or technically flawed) I can estimate my 1RM using the above formula:

The problem with these two approaches is that they demand time and energy to be done. Some lifters following Bulgarian ideology lift to daily 1RM by ramping up the weight from set to set in the main movements and finish-up with couple of sub-max sets. RtTF could also be applied somewhere in the training cycle by performing one open set at the end of the prescribed sets (e.g. after doing 2x5 reps with 80%, try to do 3rd one with as-much-reps-as-possible).

Both of them are viable options for occasional testing days. What we are looking for is a way to estimate 1RMs DAILY and without negatively affecting the normal training process. This is important because regular monitoring could help us individualize training load, durations of the training blocks and tapering, especially if it is combined with a measure of workload (like tonnage, relative volume or what have you)[1].

Testing days are a bit left behind us because, excluding the strength sports (but even there),  athletes nowadays cannot afford a whole day for testing and doing it occasionally to be useful – especially in team sports. What do we need is continuous monitoring of both training loads and training effects without negatively affecting the normal training process. This would give us a feedback and it is up to us then to manage emerging information and to modify the overall training process based on them (without relying too much on predefined set/rep schemes and block distributions and durations).

One quick way to do this is to use ISO pulls. Yet for that we need specialize equipment (like force plates or force gauges). Plus we are not sure how force expression at certain joint angle relates to the dynamic movement for a certain individual. Although this might be used as an indicator of improvement/regression, it is not a best way to estimate 1RM for a dynamic movement.

I am about the present you the most simple and quickest way to do just this by using nothing more than your warm-up sets.

Estimating 1RMs using load-velocity regression

I have wrote previously on the method for estimating 1RM using load-velocity relationship. This could be considered new, or the third way to estimate 1RM (two of them being true 1RM test and reps-to-(technical)-failure as explained). What I would love to do now is to tweak it a little bit and make it applicable for daily monitoring instead for occasional full session testing.

Performing either true 1RM or RtTF testing session with velocity analysis should still be the gold standard and it should be performed occasionally at specific check marks or competitions. Here is why – you need to know your “minimal velocity threshold” (MVT) or in plain English the velocity of your 1RM repetition. 

Interestingly enough, the “minimal velocity threshold” (MVT) is the very similar for 1RM lift and for the last rep in RtTF tests[2].  In other words, the mean velocity of the 1RM repetition will be not be (statistically) significantly different than the mean velocity of the last repetition in the 5RM test.

Hence, to use load-velocity regression one needs to know his MVT for the exercises he wants to monitor. In the following table there are MVTs for bench press and squat movement that one could use as a starting point before estimating his own (or of his athletes) MVT.

To perform 1RM estimation one needs at least 3 data points (more and heavier is better; a.k.a. more reliable) that involve weight used and velocity attained. If you perform multiple reps (is what we all usually do in warm-up sets) then take the best rep. The estimation is very easily done in MS Excel. Here is the example calculus:

Using =TREND function one can easily estimate the weight at 0.3 ms-1 in the squat, which is 164kg in this case. Standard Error of Estimate (SEE)[3] is the measure of the accuracy of predictions. We should aim to minimize this by performing all the sets with same technique (depth, pause at the bottom, etc). Sometimes SEE might be a bit higher on certain days, which might also be indicator of performance in some way (if take into consideration that the weight in the warm-up sets are the same across monitoring period) or just plain proof for lack of focus.

One way to use SEE is to provide confidence intervals for the 1RM estimate. For 90% level of confidence[4] that means multiplying SEE with 1.645. For the example above we are 90% certain that 1RM lies within:

 If you plan graphing your scores you can use confidence intervals as error bars on the graphs.

On the following table is an example of the training program for squat and tracking of the warm-up sets for estimating daily 1RM. The numbers come from my self-experiment with higher frequency lifting

On the following picture is a visual representation of daily estimated 1RMs with 90% CI (error bars), cycle 1RM (dotted horizontal line) with its SWC[5] and tonnage (vertical bars) over the duration of the cycle

As you can see from the graph of the daily 1RM is how often it is above/below pre-cycle 1RM (160kg in this case). This is pretty usual variability of the daily readiness and it should be taken into account.

One solution might be to utilize RPE scales alongside rep ranges to prescribe for intensities taking into account daily variability in readiness. One example of such a system is the one by Mike Tuchscherer

Another solution might be to “correct” pre-cycle 1RM by using estimates from the warm-up. In the example above, I had real problems finishing work-out on April 18th because my daily 1RM was 87% of pre-cycle 1RM. Since I planned performing 7x3x80% of 160kg (which is 130kg) I have performed 7x3 with around 90% (130kg / 138kg taking into account large SEE). After that workout I had some really bad knee soreness/pain that lasted for couple of weeks. Dumb decision definitely. What should I have done instead is to use daily 1RM to prescribe percent based training.

Third option to self-regulate would be to completely ditch the percentages and weights to prescribe intensity and rather prescribe training in form of velocities. This is completely novel approach that might yield some potential benefit besides auto-regulation. One of those benefits might be usage of the immediate feedback using GymAware which might yield higher and more reliable effort and hence adaptation stimulus. One aspect of velocity-based approach might involve prescribing the velocity of first rep (e.g. first rep at 0.5ms-1) and velocity threshold (e.g. perform reps until reaching 0.4ms-1) for a certain amount of time with prescribed rest periods.

Velocity-based approach is of great interest of mine. Unfortunately, there are not much of information on such an approach.

One aspect of utilizing daily 1RM estimate might be to allow certain drop during concentrated training block and decide on the duration of such a block and duration of the taper. Knowing how one lifter reacts to certain training load[6] allows for individualization of the training planning and programming, instead of relying on pre-made solutions.

With the above examples in Excel it is quite easy to start estimating daily 1RMs using warm-up sets within your training or with your athletes. Information like this gives you precious feedback to modify and individualize training prescription.

I have provided the simple Excel workbook HERE  that I have used to calculate and graph daily estimated 1RMs.

[1] Interested readers might look forward into Banister’s impulse-response model. Great read on the mathematical modeling of athletic training and performance is a paper by David Clark and Philip Skiba.

Clarke DC, Skiba PF. Rationale and resources for teaching the mathematical modeling of athletic training and performance. Adv Physiol Educ 37: 134–152, 2013

[2] Izquierdo M, Gonzalez-Badillo JJ, Häkkinen K, Ibañez J, Kraemer WJ, Altadill A, Eslava J, Gorostiaga EM. 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. Int J Sports Med ; 27: 718–724, 2006. [Pubmed Abstract]

[3] Read more on SEE here and here

[4] Level of confidence is used to describe the percentage of instances in which we will capture the true value. In the case of 90% that means we are 90% confident that the true value resides within the confidence interval. See more in Statistics in Kinesiology and Understanding the New Statistics.

[5] SWC stands for Smallest Worthwhile Change and for this example I have took 0.3 x standard deviation of daily estimated 1RMs over the duration of the cycle (although it should be data from competition). Using SWC and TE (typical error, in this case SEE) one could assess the individual and interpret changes in performance. Read more on these concept by statistics wizard Will Hopkins here, here and here.

[6] The problem in estimating impulse with Banister impulse-response model might be in deciding what represents it. Should one keep track of all tonnage for specific lifts, relative volume, or relative intensity? It is up for coaches to figure out what type of workload statistic gives the best predictions in 1RM response. See referenced paper by Clarke and Skiba on mathematical modeling.

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