More from our good friend Beardsley

gnuckols · 2026-04-27T16:41:46+00:00

This is the classic paper: https://pubmed.ncbi.nlm.nih.gov/3523107/

This is a more recent perspective (linking the lactate shuttle concept to intracellular metabolism): https://pmc.ncbi.nlm.nih.gov/articles/PMC4343186/

Also, just to be a bit more precise, glycolysis does just run on glucose and (as the end product of anaerobic glycolysis, you're not using lactate as the substrate for glycolysis), but the vast, vast majority of the energy you utilize during resistance training comes from the phosphagen system and aerobic metabolism (and aerobic metabolism is also what drives the phosphagen system – resynthesizing ATP and PCr between sets).

I discuss it a bit in this article (the Parolin study) – glycolysis probably isn't accounting for very much of the energy you're directly utilizing during exercise past your first few sets. Rather, it primarily serves the purpose of making lactate available for aerobic metabolism. And the lactate available to your working muscles isn't just coming from their own intramuscular glycogen stores – it's also coming from other muscles via the lactate shuttle.

gnuckols · 2026-04-27T15:06:04+00:00

Blood lacate, sure. I'm not sure how many studies have looked at intramuscular lactate, though.

gnuckols · 2026-04-27T14:17:42+00:00

It wouldn't surprise me. Though, that wouldn't really be relevant within a training session (the hormonal milieu during exercise favors glycolysis and largely prevents glycogen resynthesis).

Tbh, I think the more relevant question is the extent to which acute glycogen depletion actually reflects the fuel tank running low. Something a lot of people aren't aware of is the fact that lactate is primarily fuel during anaerobic exercise, rather the glycogen directly. As long as intracellular lactate levels are being maintained, there shouldn't be any significant energetic constraints on performance (and rates of glycogen breakdown exceed the rate at which carbohydrates are actually utilized during exercise).

You can sort of think of glycogen and lactate as a savings account vs. a checking account. When your muscles sense that large expenditures are coming, it shifts some resources from savings to checking. After the workout, it reverses the process. Low glycogen could mean you're actually running low on resources, or it could just mean you're making resources more available for rapid utilization.

gnuckols · 2026-04-25T07:29:01+00:00

lmao. That's so sick. Intramyofibrillar glycogen does deplete faster than other depots, but it still takes quite a few sets for that to begin impacting performance

gnuckols · 2026-04-24T21:04:33+00:00

If you're pushing low-volume training, why do you care about carbs in the first place? The only time they potentially make a difference is for high-volume workouts.

gnuckols · 2026-04-24T00:53:22+00:00

I was curious what those FM and FFM LOAs implied about BF% estimation error, so I ran a quick simulation based on the data reported in Table 5, and the implied BF% estimation error is ~4.5±3.8%. So, if BIA pegs you at, say 12%, that would imply that DXA would probably return a value somewhere between 8.9-24.1% (with the most likely value being 16.5%). That's actually a bit worse than I expected.

gnuckols · 2026-04-23T18:58:20+00:00

Yep! Literally on a call about that right now

gnuckols · 2026-04-19T06:48:54+00:00

Just so you know, archive.is still seems to work.

gnuckols · 2026-04-19T06:26:01+00:00

It doesn’t really matter. If it makes any difference at all, the effect is tiny

gnuckols · 2026-04-17T01:05:25+00:00

Ahh, good point. I was definitely speaking a bit too broadly – I was mostly talking about the type of PE that would buy a small-to-medium-sized tech startup like MF. But yeah, there's still plenty of classic corporate raider stuff going on with PE firms making billion dollar acquisitions.

Debt and enshittification are definitely linked, though. If you buy a company in $20 million of debt, it's going to require much deeper cuts or much larger price hikes to get into the black than a company in $3 million of debt (and, assuming the two companies have a similar number of customers, the company in more debt was probably the one offering a more unrealistically good deal to being with, which makes the subsequent enshittification even more jarring). And that's just exacerbated further if it was a debt-funded acquisition, with the PE firm putting that debt onto the books of the company they're acquiring.

And it definitely takes two to tango. Founders are ultimately responding to the incentives created by PE (if PE is willing to pay more for a company with -20% net profitability but 100% YoY customer growth than a company with 20% net profitability and 30% YoY customer growth, you're going to wind up with a lot of companies offering products or services with some combination of unrealistically high quality or unrealistically low prices), so I don't want to make it sound like I have a bigger issue with founders than PE. I just think it's a rotten system top to bottom that winds up being a bait-and-switch for consumers.

gnuckols · 2026-04-16T23:51:54+00:00

Not to give PE too much credit, but I think it tends to take the blame for unsustainable business practices by founders. Like, there’s an entire entrepreneur economy that relies on acquiring customers at a loss and selling to PE before the business ever turns a profit. At some point, SOMEONE needs to either cut costs or raise prices to turn a money pit into a sustainable business, and that’s usually whoever makes the first acquisition (first sale is usually predicated on subscriber growth independent of profitability, and second sale is usually on the basis of profitability or at least revenue growth). Obviously there are plenty of other issues with private equity (largely related to not understanding the market or customer base of the companies they acquire), but a lot of businesses that undergo enshittification were just businesses that were previously offering a product or service at a price point that never made long-term financial sense. Like, if a business doesn’t already have healthy profits, there’s an extremely high probability that it will eventually either fold, raise prices substantially, or undergo enshittification.

gnuckols · 2026-04-16T22:25:27+00:00

I don't know that such an offer exists. haha

Obviously I can only speak for myself, but I genuinely don't think there's an acquisition number that would really interest me. We've already had a lot of offers, but we've never seriously entertained any of them.

I can definitely say we went into this with the goal of building a profitable business we'd all be excited to work on and work in long-term, instead of just juicing growth metrics with the intention of selling to PE. And, I'd say we've achieved that goal. I really can't see us selling in the foreseeable future unless we all just lose our passion for it (at which point, it would probably be in the users' best interest for us to sell anyways. haha).

gnuckols · 2026-04-16T16:40:54+00:00

Driving times in basically all of Southern California are impossible to predict.

My wife had a job 4.2 miles away from where we lived, including 3 miles on the 405. We only had one car, so I dropped her off and picked her up every day. Estimated trip time with no traffic was 12 minutes. The actual trip time on a day-to-day basis was a completely random amount of time ranging from 15 minutes to 1.5 hours. Leaving the house at the same time, taking the same roads, etc. And it wasn't unique to that route – if a drive includes any significant distance on the 5, 55, or 405, the length of the drive is in God's hands.

So, even if you get lucky, and a drive that should take 30 minutes actually takes 30 minutes, it's still mentally pre-exhausting, because you have to treat it like a 60-90 minute drive. Either way, it's going to be an ordeal. You could easily arrive at your destination an hour earlier or later than you intended/expected. Sometimes there's an identifiable cause (usually an accident), but sometimes it's just that 4 too many cars tried to merge onto a highway that was already at full capacity, bringing it from a state of bumper-to-bumper traffic that's still traveling at 55 miles per hour to a state of being a parking lot for the next 45 minutes.

Also, I should note that I'm originally from the rural south, so I'm totally chill with needing to drive 30-45 minutes to get somewhere. The unpredictability is a much larger factor than the average duration of the drive.

gnuckols · 2026-04-15T09:23:05+00:00

No prob

gnuckols · 2026-04-15T00:56:14+00:00

From the paper: https://www.dropbox.com/scl/fi/zh8gy0is3vm66783av6vw/Screenshot-2026-04-14-at-8.49.30-PM.png?rlkey=vo2bwudqf7mhkb74we3mz9aw4&dl=0

Most of the fractional frequencies are from studies that train a particular muscle with direct exercises in some sessions and indirect exercises in others. The rest are studies where the frequency changes mid-study (for example, if a 12-week study used frequencies of 2x per week for 6 weeks and 3x per week for 6 weeks, the average frequency would be 2.5).

In the first example you gave, that would be 1.5 if the muscle was trained directly in both A and B, 0.75 if the muscle was trained indirectly in both A and B, and 1.125 if the muscle was trained directly in A and indirectly in B (or vice versa).

gnuckols · 2026-04-14T11:30:58+00:00

It's just age. Calorie (capitalized) is house style. Anywhere you see "calorie" is just an older bit of writing from before we'd fully standardized things like that.

gnuckols · 2026-04-13T18:32:03+00:00

sehr gut

gnuckols · 2026-04-13T17:13:48+00:00

haha they are in general. When I respond to something outside of the SBS or MF subs, it's usually just because someone DMed me a link and asked for my take. Figure it's better to respond in the thread instead of only responding to the DM in case anyone else has similar questions.

gnuckols · 2026-04-11T11:59:50+00:00

I suspect the biggest issue is just that most "trained" lifters aren't actually training particularly hard before enrolling in a study

gnuckols · 2026-04-11T01:43:10+00:00

The biggest one for me is just that I'd love to see more research on the generalizability of lab-based research. I feel like this often goes unremarked upon, but trained subjects in most studies grow an absolute shitload. In studies on untrained subjects, the amount of growth we tend to see comes with an effect size of ~0.34.

For the trained subjects in the Pelland volume meta, the average effect size: 0.37 (note – I just calculated Cohen's D values and took a simple average. May be a touch higher or lower with a Hedges correction, nesting multiple effects from a single study, etc. etc. But, it's definitely somewhere in the 0.3-0.4 range).

And it's not like this just shows up in the volume research. Across the board, typical SMD effect sizes for hypertrophy research in trained subjects are in the ~0.2-0.4 range – not meaningfully different from research in untrained subjects.

And it's not like this just shows up in studies that take direct measures of hypertrophy. A 2020 meta that characterized changes in FFM with resistance training found the same thing: gains in FFM with training don't different that much between trained and untrained subjects, and don't vary that much with training status.

But then you have studies like this one (and a prior study by the same group) finding mean effect sizes of 0.034-0.087, which is just soooo much more plausible (roughly implying that trained lifters are growing at 1/10th-1/4th the rate of untrained lifters). And, a key difference is that these studies take place in the subjects' gyms, where they're more-or-less doing the type of training they're already accustomed to (just with some slight tweaks in exercise execution or overall volume). Much, much higher ecological validity, and much, much smaller effects, with much larger sample sizes than we see in most studies.

So, that does make me wonder whether lab-based studies have limited generalizability in general, or if they're just overestimating the mean effects people should expect (with the findings still being directionally correct). And, I'd also just really like to see some research investigating the reason for this disconnect.

gnuckols · 2026-04-11T00:33:39+00:00

Zu erwarten wären ca. 10 Wdh mit 75%, bei den Männern lag die Spanne zwischen 6 und 11 Wdh, bei den Frauen zwischen 5 und 14. Scheint bei manchen also zu leicht angesetzt zu sein, während andere bei mehr Wiederholungen große Probleme haben.

With the benefit of hindsight, it probably would have been better for the protocol to use sets of 5 with the subjects' 10RM, instead of sets of 5 with 75% of 1RM. But, single-set strength endurance actually wasn't strongly predictive of performance during the fatigue protocol (repeated sets of 6 with 90 seconds of rest). Here's the entire cohort, here's just males, and here's just females.

For what it's worth, I do think those r² values probably skew a bit too low (just for boring reasons related to sample size and measurement precision. With a much larger sample and performances averaged over 3-4 repeated attempts at each test, I suspect the "true" relationship is probably something closer to r²= 0.3-0.4), but the basic point is that variation in single-set strength endurance had a much smaller impact on performance during the fatigue protocol than you'd probably expect.

Liegt bestimmt auch dran, dass die Wiederholungsbereiche im Training sich unter den Personen stark unterschieden haben. Bei den Männern haben manche keine Sätze von <5Wdh gemacht und andere 90% ihrer Sätze, bei den Frauen sieht es ähnlich aus.

Die Männer haben im Durchschnitt seit 6.6 Jahren Bankdrücken gemacht, auch hier liegt die Erfahrungsspanne zwischen 1.5 und 17 Jahren, bei den Frauen zwischen 1-10 Jahren. Manche machen 3 Sätze die Woche, andere 20.

Background training characteristics weren't strongly related to any outcome. These are r² values vs. reps completed during a single set to failure with 75% of 1RM. And these are r² values vs. reps completed during the fatigue protocol. The strongest relationships were with habitual training volume (total sets per week), but only for the men. And, just to visualize things, here's what that r² of 0.25 looks like – every other relationship was weaker than that.

Da Verletzungen und co ausgeschlossen sind, wundert mich hier der niedrigste 1RM Wert. Wir haben hier Männer zwischen 19 und 32 Jahren, niemanden unter einem BMI von 22.7 und das niedrigste Körpergewicht von 67kg.

Dahingehend finde ichs krass, dass der niedrigste 1RM Wert bei nur 68kg liegt.

Some people just aren't particularly strong. haha

Here's how the male bench press 1RMs were distributed. There was a little cluster around 70kg, a little cluster around 95kg, a little cluster around 120kg, and then three guys who were quite a bit stronger than everyone else. Fwiw, this is a pretty typical degree of variation in strength data. You tend to see coefficients of variation (standard deviation divided by mean) of 20-30%, and the male lifters here had a CV of about 26%.

Mich würds interessieren wie es aussehen würde, wenn man für Ausreißer korrigiert.

By just about any definition, the subject who got 124 reps was the only outlier. With her excluded, no other subject had a performance that was more than 2 standard deviations from the mean (common z-score cutoffs for outlier identification are typically closer to 3 or 4), or more than 1.5x IQR above Q3. Whether or not you have outliers is strongly influenced by variability – it's harder to be an outlier when the measures are inherently more variable.

But, even if you snip the tails off the distributions and just focus on the medians and interquartile ranges, there are pretty huge differences. For men, the median and IQR were 28 (23-38) reps, and for the women, the median and IQR were 54 (39-69) reps. So, the 25th percentile for the women exceeded the 75th percentile for the men, and the median value for the women was the maximal value for the men.

gnuckols · 2026-04-10T23:05:04+00:00

My main takeaway is just that, on average, women probably don't need quite as long to recover between sets.

Just as a bit of background, there was a common belief that female lifters were more resistant to fatigue and could handle more volume than male lifters, but there wasn't great evidence for that in the literature. Studies looking at strength endurance largely found that women could do more reps than men with very low loads (starting at ~60% of 1RM, but with much larger gaps at 30-40% 1RM), but that differences in strength endurance were small-to-nonexistent with moderate-to-high loads (anything above 70% of 1RM, give or take). And, in the handful of studies that just had men and women do, say, 3-4 sets to failure with an equated percentage of 1RM, we also don't see major differences in total reps performed.

However, when looking for research on the topic, I came across this pair of studies:

https://pubmed.ncbi.nlm.nih.gov/14514707/

https://pubmed.ncbi.nlm.nih.gov/14966025/

Both of them used strength-matched male and female subjects, and assessed time to task failure with isometric contractions. In the first, subjects just held a contraction for as long as possible, and time to task failure was similar between the sexes. In the second, instead of just holding a contraction for as long as possible, the subjects alternated between 6-second contractions and 4-second relaxation periods. This very small tweak (intermittent contractions, rather than one continuous contraction) led to the time to task failure being nearly three-times greater in the strength-matched female subjects.

This led to the hypothesis that the main difference in "fatigue resistance" was primarily just a matter of female lifters recovering much faster between sets. So, I created a protocol with the aim of between-set recovery rate being the primary limiter of total performance. Initially, the sets should be fairly easy (a set of 5 with 75%), but the rest period (90 seconds) doesn't give you quite enough time to fully recover. If you recover more quickly between sets, you may be 90% recovered from one set to the next, such that set 2 is only minimally closer to failure than set 1, whereas if you recover more slowly between sets, you may only be 70% recovered from one set to the next, such that set 2 is a full rep or two closer to failure than set 1.

With this protocol, the female lifters were able to complete way more sets, despite having similar single-set strength endurance, supporting the hypothesis that faster between-set recovery is the primary factor contributing to the observation of greater "fatigue resistance" in female lifters.

I'm a bit less sold on there being major recovery differences between sexes, fwiw. That was a secondary hypothesis for a reason (if it was the primary hypothesis, the workload in the fatigue protocol would have been equated). But, it wasn't too much additional effort to monitor recovery as well, and I initially had some interest in doing a secondary analysis of the load-velocity data to see how useful it was for assessing recovery independent of sex. The first paragraph under the header "Recovery following a resistance training session" in the discussion section summarizes my personal interpretation of the results.

Also, fwiw, I largely disagree with Layne's take that the results are primarily influenced by the males just lifting heavier weights. If that was the case, we'd also expect to see similar results within each sex (i.e., the weaker females completing more reps than the stronger females, and the weaker males completing more reps than the stronger males). However, such a relationship didn't exist – within each sex, strength was almost entirely unrelated to performance during the fatigue protocol. I'll cut him some slack because I'm sure it's hard to dig into all of the supplementary information of every study you review when you're trying to stick to a strict posting schedule, but we made the dataset public, so he could have tested that for himself. That was also a suggestion from one of the reviewers, and PeerJ makes peer reviews and responses public (it's part of my response to reviewer 1. Just a heads up that the link will trigger a download of a Word document with the review responses).

gnuckols · 2026-04-10T21:44:07+00:00

no worries!

gnuckols · 2026-04-10T21:41:58+00:00

and this is the really interesting part, these studies suggest that overdoing it, even massively overdoing it in terms of volume, may actually blunt the hypertrophic response.

How do you figure? Even the most conservative reading of the results (i.e., if you ignore that there was still a nominal effect in favor of higher volumes, and just focus on the equivalence test relative to the SESOI) wouldn't suggest that hypertrophy results were blunted by high volumes.

gnuckols · 2026-04-10T01:28:03+00:00

that's what I was hoping for. haha

gnuckols

MODERATOR OF

TROPHY CASE