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

### Introduction

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 (6

^{th}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 3^{rd}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:

^{-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

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 18

^{th}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]

[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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