Interview with Mike McGuigan

Interview with Mike McGuigan

It was a great pleasure to interview professor Mike McGuigan from AUT (New Zealand). I have been reading his work for years and was lucky enough to correspond with him on a regular basis lately. Mike is always happy to chat and share his viewpoints and insights, which he also did with this interview.

Mike and I share the interests in Velocity Based Strength Training, Strength/Power profiling, monitoring and data analysis/visualization so I picked his brain on those topics.  

  

Mladen: Mike, although I am pretty sure that most of the readers are familiar with your work, can you provide some information on who you are, what you do and what are your future plans and interests?

Mike:  Thanks Mladen.  It’s a pleasure to answer a few questions and I really enjoy reading your blog.  I’m currently a Professor in Strength and Conditioning at AUT University in the Sports Performance Research Institute. Prior to this, I was a Power Scientist with High Performance Sport New Zealand working across a range of different sports (mainly Athletics, Rugby Union, Rowing and Netball).  I have had a number of different academic roles in the US and Australia since graduating from Southern Cross University. A definite highlight was doing my postdoc with William Kraemer from 2000-2001.   My current role with AUT mainly involves Masters and PhD supervision and my own research in the areas of strength and power development and monitoring training. I am fortunate to be able to supervise a number of students working in elite sport environments. I also continue to work closely with Netball and have a role with our national team as their Sports Scientist/Research and Innovation coordinator.  This works really well as it allows me to continue to work closely with elite sport and continuing to pursue my passion of applied research. I also do editorial work for various journals such as Journal of Strength and Conditioning Research, Journal of Australian Strength and Conditioning and Journal of Sports Science and Medicine which keeps me up to date with the latest applied research.   

Mladen: You told me that you like discussing the links between research and practice, so let’s start with that. In my opinion (and in opinion of many others) research is solely focused on finding average effects in the population by using sample sizes with appropriate power. Usually the outliers are problematic as well as non-normal distributions. On the flip side, coaches are not interested in averages, but individual athletes (or should I say cases). How is research in sport evolving to take this into account (effect ranges, magnitude-based inferences, single case studies, etc)?

Mike:  I agree with you but I have seen a real shift in recent years with an increased awareness and understanding of how statistics can be used more effectively in sporting environments.  I have been greatly influenced (as have many others) by Will Hopkins over the years.  I was fortunate enough to have Will as a lecturer during my undergraduate degree so I was exposed to his ideas very early on. I think most people are now aware of his website “A New View of Statistics” and it is a fantastic resource for application of statistics in sport.  An exciting development is the increasing acceptance in academic journals (e.g. International Journal of Sports Physiology and Performance) of these types of approaches which will only encourage more researchers to apply these methods. However we still have challenges with some journals, editors and reviewers who would prefer to see more traditional approaches in journal articles. I definitely think though that a “case” based approach is more appropriate when working in and researching sporting performance. Concepts such as smallest worthwhile change and individual responses to training interventions are more meaningful for practitioners.   In a new book coming out later this year from Human Kinetics called High Performance Training for Sport (edited by David Joyce and Dan Lewindon), I have contributed a chapter “Evaluating Athletic Capacities” where I have attempted to explain some of these concepts and how practitioners can use them in their practice.

Mladen: You recently published a paper in Strength and Conditioning Journal regarding strength and power profiling of the athletes. Why is this important and how is it influencing training prescription and individualization, especially in team settings?

Mike: Again I have been really fortunate to work with some great practitioners and scientists over the years and this paper was a collaboration with two of those individuals, Stu Cormack and Nic Gill. One of the problems we have in our field is how do we appropriately test different physical capacities and then use that information to inform our programme design.  I am sure a lot of readers are familiar with situations where fitness testing occurs but then very little is done with the information and in some cases coaches and athletes never see the information.  I believe this has been a significant barrier to getting more buy in from coaches for sports science. Rob Newton has been my main influence in this area and the work we did in collaboration with Stu at West Coast Eagles (Australian Rules Football) was always driven by this principle – how can we use this testing information to make a training programme better? This is also something that Nic does really well with the All Blacks. Other former students such as Jeremy Sheppard, Sophia Nimphius and Travis McMaster are doing great work in this area also. Hopefully in the paper we have been able to give readers an idea of how they can implement strength and power profiling and use that information to make adjustments to the training programme.   

Mladen: I still wonder why did you choose to present profiles graphically using radar chart ~ they are definitely over-rated :). What are your thoughts regarding the importance of visualization of the data to convey information to the coaches?

Mike:  I began using radar plots after being introduced to them by Tim Doyle and Rob Newton back in 2004.  Of all the various presentation methods I have used for reporting data for coaches and athletes over the years, they have consistently gotten positive feedback and seemed to have been easily understood.  I really like the first sentence of the reference “The test of a graph's usefulness is its ability to communicate efficiently and effectively”.  Clear visualization of data is critical for conveying information to coaches and I would see radar plots as being one potential tool that can be used to do this. However, I don’t think you necessarily have to stay with a method of presentation. For example, with netball we have moved away from using radar plots as we felt after four years they were losing their impact with the coaches and players.  So as a practitioner you need to be open to using presentation tools that will most effectively relay the information and this may vary depending on the environment you are working in. These principles are also important for educators as we need to be able to clearly present complex ideas in a way that students can understand.    

Mladen: One thing that bothers me and I have already wrote about it HERE is the concept of peak power in load~velocity (or load~power) profiling and the concept that training at that intensity will magically improve “power”. I believe that these are “mental constructs” and that power is load-specific and measurement-specific (we can see a lot of discrepancies between research regarding methods and thus results) and that goal of training should be improving movement velocity at certain (for a given sport, specific) load (which will result in improved power at that load anyway)? What are your thoughts about it and why are researchers still trying to find this ‘magic bullet’?

Mike:  Excellent question and I know that this is an issue that bothers a lot of people.  The first thing I would say on this point is that there is no “magic bullet”.  The research that has been done by Rob Newton and many others has shown that it is more effective to train across a range of loads. I would encourage people to read Prue Cormie’s review in Sports Medicine on this area. I do think that measures such as peak power and velocity can be useful and we have tended to use fixed loads such as bodyweight only and 20-60kg (depending on exercise and athlete/sport) for testing purposes. Another advantage of using fixed loads in testing for team sports is it makes things a lot more efficient.  If you are testing a squad of say 30 athletes then expecting to do individualized load profiles based in %RM is going to be a challenge.  The final thing I will say about this area is that if you want to improve power in the majority of your athletes then get them stronger!  There is an overwhelming body of literature that shows this. 

Mladen: Talking about profiling, what are your thoughts regarding injury prediction and reduction? What about gait analysis (foot pressure mapping), asymmetries between limbs, manual testing (e.g. groin squeeze)? Are they cause or the effect of injury? What is the next step in applied research regarding those?

Mike:  I definitely think the profiling can make an important contribution to injury prediction and reduction. However it’s important to recognize that we can’t be experts in all of the areas.  I think the key here is to have a multidisciplinary approach to athlete preparation with professionals such as physiotherapists, strength and conditioning coaches, and performance analysts working closely together. The current research makes a strong case for high levels of integration of fitness testing data with injury and medical screening.  For example, Mike and Meg Stone and their team at East Tennessee State University are starting to publish some really interesting multidisciplinary work using sport performance enhancement groups.  I think we will start to see more of this type of approach and research studies in this area being published. It is vital that the tests employed are understood by the entire performance team, that the results yielded provide information of real value in assessing the status of the athlete and that this information is communicated effectively.

Mladen: What are your thoughts on the novel velocity-based strength training prescription and monitoring? Do you plan any studies on the topic?

Mike:  I think this is a very interesting area and there is some good evidence supporting its use. I know this is something you have also discussed on your blog and it is a training approach that practitioners are starting to consider and implement.   With the advent of more affordable monitoring technologies its use will only increase.  My colleague John Cronin did a study with one of his students where they investigated the effect of instantaneous velocity feedback during resistance training and showed some positive performance benefits.  This has implications for both training prescription and monitoring. What we need now are more training studies with high level athletes and this is an area I am very interested in exploring further – it would definitely make for a great PhD!

Mladen: When it comes to monitoring readiness in strength related sports, what seems to correlated with performance the most: HRV, grip strength, subjective indicators, vertical jump, reactive strength? Besides, where does the concept of training when you are in the highest readiness yield highest adaptation comes from? Waiting to train hard when we are in the best shape might be self-limiting. What about self-fulfilling prophecy: where athletes know that their monitoring metrics are down and thus expect lousy performance? Do we need to approach these using “single blind” approach?

Mike:  It really depends on which research study you read :).  This is another fascinating area and I don’t think we are at a point where we have any definitive answers. I wonder sometimes whether we are overcomplicating things here with this concept of training readiness.  The problem here from a research perspective is it is really difficult to design a good study to answer this question.  As you suggest there is actually no evidence to suggest that training when you are in a state of “readiness” results in more effective training adaptations.  Having said that though, I do think monitoring of athletes provides useful information (already discussed very well by Stu Cormack in your blog previously).    Perhaps with training readiness we just need to keep it simple and go with actually just asking our athletes how they feel before they start training?  This is what good practitioners do anyway and they make adjustments throughout the training session as needed based a variety of sources of information. This also comes back to an earlier question and the issue of individual differences.  Perhaps for some athletes knowing their performance metrics are down at the start of a session could be a problem whereas for others it could result in greater effort during the session?  By incorporating additional tools such as monitoring training velocity during specific sessions for individual athlete, we can then look at whether is it possible to optimize the training stimulus. Current evidence would suggest that individualizing the training programme is going to be more effective than having a standard training programme given to a squad of athletes which doesn’t take into account individual differences.

Mladen: Thank you Mike for sharing great insights and good luck with the future projects.

Mike: Thanks Mladen.  Keep up the great work and I look forward to reading more of your work online!

Three I’s of intensity in strength training

Three I’s of intensity in strength training

This is a short rant regarding the confusion that exist regarding the topic of intensity in strength training. Without going into too much of details (I have spoken about this problem before HERE and HERE) my opinion is that Intensity (with the big “I”) has THREE interrelated components:

INTENSITY – This is weight on the bar [absolute], usually expressed as percentage of your maximum lift [% 1RM] or expressed as weight with which you can do certain number of maximum reps [5RM, 10RM, etc]. Example might be squat with 150kg, or bench with 80% 1RM or press with 10RM weight which might be 50kg [10RM = 50kg]. 

INTENT – Is lifters’ [well duh] intent to lift each rep with maximum acceleration and speed. For example, using 80% 1RM one athlete might use maximum intent and lift it with 0.4 ms^-1 mean velocity or he might use sub-max intent and lift it with 0.2 ms^-1. With the same intensity [% 1RM] using different levels of intent will yield different levels of force, power, velocity & acceleration and TUT (Time Under Tension). Sometimes this takes form of Tempo prescription.

INTENSIVENESS -  This is how close to a failure [RM; repetition maximum] you are. If the intensity is 80% 1RM one might perform 3 reps [pretty away from failure] or perform 7 to 8 reps [which is point of failure]. There are couple of ways of controlling and prescribing intensiveness. One is to prescribe intensity with nRM [e.g. 5RM weight, or 10RM] and prescribe reps with that weight, something along these lines 8[10], which means do 8 reps with the weight you could use to do 10 reps. In other words reps in the tank, in this case 2. You can also prescribe reps in the tank for a given intensity (e.g. lift this weight until you have two left in the tank). Another way is using RPE (Rate of Perceived Exertion/Exhaustion) which is another way to quantify/quality reps left in the tank. One novel way of controlling and prescribing intensiveness is by monitoring velocity of the lifts (especially the stop velocity), taking into account that intent in every rep is maximal. 

Coaches, and especially researchers should take into account all three parts of prescribing and quantifying Intensity (Intensity together with Volume provide LOAD; but I will leave different ways of quantifying Volume for another rant) to avoid confusion and provide precision. This could be done with monitoring velocity of the lifts as well and I urge researchers reading this to start using velocity data to supplement usual statistics reported (like 1RM, 5RM, % 1RM, etc). 

One thing to consider also is that all these three “components” are related not only to kinetic and kinematic performance [external], but also to MU recruitment, fatigue, etc. Here is a short summary:

INTENSITY – Highly related to force output (both mean and peak) and MU recruitment. Inversely related with velocity taking into account maximum intent.  

INTENT – Related to peak force output (not sure about average force), acceleration and velocity along with MU recruitment

INTENSIVENESS – Related to end velocity (taking maximum intent into account) and MU recruitment. Highly related to fatigue, both metabolic and NMF. Related to volume parameters as well. 

The tricky part is the complexity of their interaction at both micro (single set and sets for an exercise) to mezo (single workout to a single week) to macro (couple of weeks to couple of months) training effects and training prescriptions. Another complexity is that we still don’t know what drives adaptation (gain in strength, hypetrophy) regarding these parameters (i.e. mean/peak force, MU recruitment, volume, velocity…).

Hence the importance of acknowledging these three components, prescribing it and reporting it with studies. 

UPDATE: Percent-based to velocity-based converter 2.0

I have updated Percent-based to velocity-based converter to include conversion from velocity-based to percent-based approach using load-velocity profile and %1RM-max reps profile. This is important since it allows coaches to get easier 'grip' on velocity-based approach and how it could be used and converted.

Here is the screen shot

You can download the new version HERE. Please refer to video on this page on how to use it.

Percent-based to velocity-based converter

Percent-based to velocity-based converter

“Nothing Is More Practical Than A Good Theory” – Kurt Lewin[1]

In the following video I am explaining how to use Excel converter to convert percent-based programs to velocity-based programs based on lifters load-velocity profile for a given movement.

I am covering some important ‘rules’ and relationships of velocity-based approach to strength training, like load-velocity profiles, minimal-velocity threshold, “reps-in-tank velocity relationship” (not sure if I should patent this one?) – So before watching the following video please refresh your understanding by reading the following posts:

Estimating 1RM using load-velocity relationship

Velocity Loss as an Indicator of Neuromuscular Fatigue during Resistance Training

Velocity-based strength training: Short Q&A with Mario Marques

Using PowerTool/GymAware: short video and explanation

Does Speed Work work? My response to Mike Tuchscherer’s article. Part 1

Does Speed Work work? My response to Mike Tuchscherer’s article. Part 2

Is power/speed reading in clean and snatch counterproductive (and other rant)?

Percent-based training vs. Auto-regulatory? Can they be complementary?

Some great findings and ideas from velocity-based strength training

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

Here is the Excel converter I used in this video [DOWNLOAD].

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[1] In the video I stated that Volta said this, but I cannot find it anywhere online. Apparently Kurt Lewin said it. 

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

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:

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 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.

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[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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