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 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)
.
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
.
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)
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
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
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
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.
