Monday, December 12, 2011

Finding your sweet spot for muscle gain with HCE

In order to achieve muscle gain, one has to repeatedly hit the “supercompensation” window, which is a fleeting period of time occurring at some point in the muscle recovery phase after an intense anaerobic exercise session. The figure below, from Vladimir Zatsiorsky’s and William Kraemer’s outstanding book Science and Practice of Strength Training () provides an illustration of the supercompensation idea. Supercompensation is covered in more detail in a previous post ().

Trying to hit the supercompensation window is a common denominator among HealthCorrelator for Excel (HCE) users who employ the software () to maximize muscle gain. (That is, among those who know and subscribe to the theory of supercompensation.) This post outlines what I believe is a good way of doing that while avoiding some pitfalls. The data used in the example that follows has been created by me, and is based on a real case. I disguised the data, simplified it, added error etc. to make the underlying method relatively easy to understand, and so that the data cannot be traced back to its “real case” user (for privacy).

Let us assume that John Doe is an intermediate weight training practitioner. That is, he has already gone through the beginning stage where most gains come from neural adaptation. For him, new gains in strength are a reflection of gains in muscle mass. The table below summarizes the data John obtained when he decided to vary the following variables in order to see what effects they have on his ability to increase the weight with which he conducted the deadlift () in successive exercise sessions:
    - Number of rest days in between exercise sessions (“Days of rest”).
    - The amount of weight he used in each deadlift session (“Deadlift weight”).
    - The amount of weight he was able to add to the bar each session (“Delta weight”).
    - The number of deadlift sets and reps (“Deadlift sets” and “Deadlift reps”, respectively).
    - The total exercise volume in each session (“Deadlift volume”). This was calculated as follows: “Deadlift weight” x “Deadlift sets” x “Deadlift reps”.

John’s ability to increase the weight with which he conducted the deadlift in each session is measured as “Delta weight”. That was his main variable of interest. This may not look like an ideal choice at first glance, as arguably “Deadlift volume” is a better measure of total effort and thus actual muscle gain. The reality is that this does not matter much in his case, because: John had long rest periods within sets, of around 5 minutes; and he made sure to increase the weight in each successive session as soon as he felt he could, and by as much as he could, thus never doing more than 24 reps. If you think that the number of reps employed by John is too high, take a look at a post in which I talk about Doug Miller and his ideas on weight training ().

Below are three figures, with outputs from HCE: a table showing the coefficients of association between “Delta weight” and the other variables, and two graphs showing the variation of “Delta weight” against “Deadlift volume” and “Days of rest”. As you can see, nothing seems to be influencing “Delta weight” strongly enough to reach the 0.6 level that I recommend as the threshold for a “real effect” to be used in HCE analyses. There are two possibilities here: it is what it looks it is, that is, none of the variables influence “Delta weight”; or there are effects, but they do not show up in the associations table (as associations equal to or greater than 0.6) because of nonlinearity.

The graph of “Delta weight” against “Deadlift volume” is all over the place, suggesting a lack of association. This is true for the other variables as well, except “Days of rest”; the last graph above. That graph, of “Delta weight” against “Days of rest”, suggests the existence of a nonlinear association with the shape of an inverted J curve. This type of association is fairly common. In this case, it seems that “Delta weight” is maximized in the 6-7 range of “Days of rest”. Still, even varying things almost randomly, John achieved a solid gain over the time period. That was a 33 percent gain from the baseline “Deadlift weight”, a gain calculated as: (285-215)/215.

HCE, unlike WarpPLS (), does not take nonlinear relationships into consideration in the estimation of coefficients of association. In order to discover nonlinear associations, users have to inspect the graphs generated by HCE, as John did. Based on his inspection, John decided to changes things a bit, now working out on the right side of the J curve, with 6 or more “Days of rest”. That was difficult for John at first, as he was addicted to exercising at a much higher frequency; but after a while he became a “minimalist”, even trying very long rest periods.

Below are four figures. The first is a table summarizing the data John obtained for his second trial. The other three are outputs from HCE, analogous to those obtained in the first trial: a table showing the coefficients of association between “Delta weight” and the other variables, two graphs (side-by-side) showing “Delta weight” against “Deadlift sets” and “Deadlift reps”, and one graph of “Delta weight” against “Days of rest”. As you can see, “Days of rest” now influences “Delta weight” very strongly. The corresponding association is a very high -0.981! The negative sign means that “Delta weight” decreases as “Days of rest” increase. This does NOT mean that rest is not important; remember, John is now operating on the right side of the J curve, with 6 or more “Days of rest”.

The last graph above suggests that taking 12 or more “Days of rest” shifted things toward the end of the supercompensation window, in fact placing John almost outside of that window at 13 “Days of rest”. Even so, there was no loss of strength, and thus probably no muscle loss. Loss of strength would be suggested by a negative “Delta weight”, which did not occur (the “Delta weight” went down to zero, at 13 “Days of rest”). The two graphs shown side-by-side suggest that 2 “Deadlift sets” seem to work just as well for John as 3 or 4, and that “Deadlift reps” in the 18-24 range also work well for John.

In this second trial, John achieved a better gain over a similar time period than in the first trial. That was a 36 percent gain from the baseline “Deadlift weight”, a gain calculated as: (355-260)/260. John started with a lower baseline than in the end of the first trial period, probably due to detraining, but achieved a final “Deadlift weight” that was likely very close to his maximum potential (at the reps used). Because of this, the 36 percent gain in the period is a lot more impressive than it looks, as it happened toward the end of a saturation curve (e.g., the far right end of a logarithmic curve).

One important thing to keep in mind is that if an HCE user identifies a nonlinear relationship of the J-curve type by inspecting the graphs like John did, in further analyses the focus should be on the right or left side of the curve by either: splitting the dataset into two, and running a separate analysis for each new dataset; or running a new trial, now sticking with a range of variation on the right or left side of the curve, as John did. The reason is that nonlinear relationships tend to distort the linear coefficients calculated by HCE, hiding a real relationship between two variables.

This is a very simplified example. Most serious bodybuilders will measure variations in a number of variables at the same time, for a number of different exercise types and formats, and for longer periods. That is, their “HealthData” sheet in HCE will be a lot more complex. They will also have multiple instances of HCE running on their computer. HCE is a collection of sheets and code that can be copied, and saved with different names. The default is “HCE_1_0.xls” or “HCE_1_0.xlsm”, depending on which version you are using. Each new instance of HCE may contain a different dataset for analysis, stored in the “HealthData” sheet.

It is strongly recommended that you keep your data in a separate set of sheets, as a backup. That is, do not store all your data in the “HealthData” sheets in different HCE instances. Also, when you copy your data into the “HealthData” sheet in HCE, copy only the values and formats, and NOT the formulas. If you copy the formulas, you may end up having some problems, as some of the cells in the “HealthData” sheet will not be storing values. I also recommend storing values for other types variables, particularly perception-based variables.

Examples of perception-based variables are: “Perceived stress”, “Perceived delayed onset muscle soreness (DOMS)”, and “Perceived non-DOMS pain”. These can be answered on Likert-type scales, such as scales going from 1 (very strongly disagree) to 7 (very strongly agree) in response to self-prepared question-statements like “I feel stressed out” (for “Perceived stress”). If you find that a variable like “Perceived non-DOMS pain” is associated with working out at a particular volume range, that may help you avoid serious injury in the future, as non-DOMS pain is not a very good sign (). You also may find that working out in the volume range that is associated with non-DOMS pain adds nothing in terms of muscle gain.

Generally speaking, I think that many people will find out that their sweet spot for muscle gain involves less frequent exercise at lower volumes than they think. Still, each individual is unique; there is no one quite like John. The relationship between “Delta weight” and “Days of rest” varies from person to person based on age; older folks generally require more rest. It also varies based on whether the person is dieting or not; less food intake leads to longer recovery periods. Women will probably see visible lower-body muscle gain, but very little visible upper-body muscle gain (in the absence of steroid use), even as they experience upper-body strength gains. Other variables of interest for both men and women may be body weight, body fat percentage, and perceived muscle tone.


js290 said...

Our results suggest that compared with continuous 15-week training, 3-week detraining does not inhibit muscle adaptations.

Ned Kock said...

Thanks as always js290, very pertinent article indeed! The findings mirror, and add to, the example in this post. Even though the post is based on semi-fictitious data.
In fact, the message of the post is that one can come off detraining to become a minimalist, and end up getting very close to one’s maximum muscular potential (i.e., the deadlift).

gwarm said...

Doesn't the book Science and Practice of Strength Training say, "In general, the theory of supercompensation is too simple to be correct. Over the last few years it has lost much of its popularity." Google Books

Ned Kock said...

Hi gwarm. The criticism centers on the idea that the supercompensation curve is not static for an individual, but can vary based on a number of factors – e.g., nutrition status, stress etc. And that seems to be a valid point.

The general shape of the curve, however, has been strongly supported by empirical research, as far as I can tell.

David Isaak said...

Nice post.

Here's a question: What do you think of the concept of "muscle confusion" (working the same muscles in different ways) for strength trainig and muscle gain.

Some folks swear by it, but most of the articles one can find nowadays spend their time vehemently dismissing it on the grounds that "science proves that volume overload is the only thing that matters."

Yet it seems to me that working muscles in different patterns and in different combinations would cause new patterns of neural recruitment, which might have benefits.

Certainly some of the "primal" exercises (swinging around not-quite full jugs of water as opposed to dumbbells, for example, or carrying things that aren't precisely balanced) seems to result in surprising levels of DOMS compared to an intense but familiar workout.

Any thoughts?

Ned Kock said...

Hi David. Art DeVany has long championed the idea of working out in a random (and minimalist) way, and he does seem muscular, particularly for someone his age.

Jack LaLanne recommended changing workouts every month. The underlying reason is the same as that put forth by the “muscle confusion” proponents, although the latter are more extreme in their approach to variety.

Theoretically, there are benefits and problems with resorting to “muscle confusion”. On the positive side, you have enough variety to prevent habituation. On the negative side, if you are having DOMS after every session, and the sessions are relatively frequent, you will probably experience no muscle gain. But the “muscle confusion” will tone you up.

That is, recovery may be inhibited. In the worst case scenario, one may end up getting a debilitating injury. This sometimes happens with older folks who start emphasizing eccentric exercises too much; with negative reps. After a while they develop a serious injury, needing months off the gym to recover.

The reality is that you will see different approaches working for different people at different points in time. For young men who have a natural tendency toward a mesomorphic type, “muscle confusion” may work quite well. But then almost anything would work for them.

I have a student who did what I have been doing, as described in the post linked above, “My transformation”, and he has become a “monster” – lean and very muscular. My body’s response was different: I am now a thin man who looks like he does some manual labor for a living. My student and I differ in age, and we also have different genetics.

People are built differently, and that affects their results. The best way forward is to know thyself … through numbers!

js290 said...


Perhaps another relevant link that explains the difference in results between you and your student: High responders to resistance exercise training demonstrate differential regulation of skeletal muscle microRNA expression

Historically bodybuilders like Sandow and Hackenschmidt had known about the need for recovery. And, you see this in more modern HIT literature from people like Arthur Jones, Mike Menzter, Doug McGuff, et al.

If the idea of muscle motor unit recruitment is correct, then "muscle confusion" isn't necessary. For one, the body is efficient and will only use the least amount of energy necessary, i.e. recruit the least number of muscles necessary. As one trains a skill more and more, his body becomes more and more efficient. So it's easy to believe muscle confusion is necessary.

Secondly, however, another way to think about it is to load the body in such a way that as many motor units are recruited in the performance of the exercise, i.e. lift heavier weights with increasing levels of effort. The progress will be asymptotic as at some point we'll hit our genetic potential.

Lerner said...

it would be interesting, if you have data from a large number of individuals, to see what inter-subject patterns show up... especially correlated with a meso, endo, ecto type of classification vis-a-vis rep schemes.

David Isaak said...

Great answers, Ned (and js290 and Lerner).

Somatotyping has always been a quantitative problem, and it was one that Sheldon was still working on when he died. Carter formalized some of the methods of Heath, so now it is possible to do consistent measurements rather than ranking perceptions.

There are some dandy excel spreadsheets for calculating the somatotype--some free. I, for example, discovered I was

Endo component: 2.92
Meso component: 6.08
Ecto component: 2.11

Measurement is critical, as most people rank anyone overweight as largely Endomorphic, but according to Sheldon's theories it is actually quite easy for mesomorphs to become quite overweight. (And they lose fat quickly as well.)

It's not entirely clear that Sheldon's categories are scientifically meaningful in the first place, but they have a lot of intuitive appeal.

Lerner said...

Yep David, I suppose the devil in somatotyping is in the details/quantification. Yet it seems so obvious - everybody must have known someone who can gain muscle just from carrying the potato chip bag from the store to their car. Or you can raise a thoroughbred racehorse in a stall with zero exercise and it will still likely develop a lot of muscle - but a greyhound probably never will.

This also relates to the theory of satellite cell recruitment. I recall a study where the heaviest responders (in terms of muscle gaining) also did best at activation and proliferation of satellite cells, while non-responders showed quite the opposite tendency.

So overcoming a plateau might depend tremendously on what rep schemes would most effectively activate satellite cells - and that I suspect varies by somatotype or some other sort of typing which gets at the same inborn differences.

Oh yeah, and creatine might help with that (as well as suppressing myostatin). So there's a practical possibility, apart from merely discussing pathways for learning's sake.

David Isaak said...


I think that causality is backwards in most of our thinking about somatotypes. That is, we say, Hey, of course that guy is great at recruiting satellite cells (or adding mass, or whatever)--after all, he's a mesomorph.

It's probably something more in the nature of "He looks like a mesomorph because he easily recruits satellite cells" (or whatever).

But you're right--the real underlying question is what switched we can flip to get to whatever our genetic potential might be. And that might be anything from supplements to Ned's "sweet spot" to enhancing testosterone through other means, of which there may be many more than we suppose*.

(*cf. Porn and T levels: )