Name: Frequency and Hypertrophy: New Science Explained | S2E2 @datadrivenstrength
Uploaded: 2026-09-19T08:17:00-05:00
Duration: 8 min 17 s
Description: Frequency and Hypertrophy: New Science Explained | S2E2

Frequency and Hypertrophy: New Science Explained | S2E2

Data Driven Strength 2026-09-19 | Frequency and Hypertrophy: New Science Explained | S2E2

Is Your Program Holding You Back?

Data Driven Strength 2025-10-04 | Training execution is more than just sets, reps, and RPE.

Not All Advanced Lifters Are The Same

Data Driven Strength 2025-10-02 | As training advancement increases, variables both inside and outside the gym must become more targeted. The “window” for progress becomes smaller.

Similarly, this “window” can also shrink in the face of external challenges like injuries or time constraints.

What Bodybuilders Do Well

Data Driven Strength 2025-09-24 | What do bodybuilders do well that powerlifters can learn from?

What Powerlifters Do Well

Data Driven Strength 2025-09-04 | What do powerlifters do well that bodybuilders can learn from?

What Bodybuilders Can Steal From Powerlifters (And Vice Versa)

Data Driven Strength 2025-08-22 | The biggest benefit of keeping a logbook? It reminds you of what you’re capable of.

What Bodybuilders Can Steal From Powerlifters (And Vice Versa)

Data Driven Strength 2025-08-21 | Work with Josh, Zac, or another DDS Coach: datadrivenstrength.com/coaching

What Is Specificity Actually?

Data Driven Strength 2025-08-13 | It's common to hear the principle of specificity interpreted as "just do more of the task you want to get better at." In my opinion, this approach may be overly reductionist for what the SAID principle can mean in the context of training for strength.

Most applications of the SAID principle only scratch the surface by limiting discussions of specificity to exercise selection and training intensity. However, when you think about the concept on a higher level, the X's and O's of effective programming emerge organically from addressing rate limiters rather than directly mimicking the target task for the sake of it.

Let's say your goal is to improve your 1RM squat. Most assume that training with 'high specificity' means lots of competition squatting with heavy loads. In reality, this example lacks crucial context. 'Specificity' is probably better thought of as directly addressing the rate limiter of the target task. Said another way, training with 'high specificity' is that which addresses YOUR weakest link successfully, rather than assuming that closely mimicking the target task is the most efficient way to do so.

The reality is that limiters exist in hierarchical relationships where primary constraints (muscular torque, neuromuscular efficiency, technical stability) may be governed by secondary factors (work capacity, fatigue management) or tertiary issues (pain, time availability). Some limiters require months to address while others can be resolved in weeks. But this doesn't invalidate specificity—it highlights why: i) proper limiter identification is fundamental to program design, and ii) the most "specific" training prescription often emerges from addressing upstream constraints rather than defaulting to target task practice.

The smartest training programs emerge when you first identify what's actually holding you back, then apply the principles that best address that specific limitation. True specificity is about strategic problem-solving, not a one-size fits all solution.#datadrivenstrength

Going Off Program: The Progress Killer?

Data Driven Strength 2025-08-08 | Don’t be a prisoner to the program.

Structure and programming matters, but that doesn’t mean you should follow your program blindly. At Data Driven Strength, our coaches encourage “training skill”, which allows our clients to strategically deviate from the program to enhance their results.

Going Off Program: The Progress Killer?

Data Driven Strength 2025-08-07 | Sign up for a 14-day free trial for the Evolve Training App: evolvetrainingapp.com

Work with Josh or Zac: datadrivenstrength.com/coaching

Free newsletter with exclusive content: datadrivenstrength.com

This research is WRONG?

Data Driven Strength 2025-08-07 | Looking for research to tell you exactly how many sets to do? I advise against that.

Don’t Commit the Snapshot Fallacy

Data Driven Strength 2025-08-06 | The snapshot fallacy occurs when we assume that short-term study findings directly translate to optimal long-term training prescriptions. This represents a potential misunderstanding of how to extract and apply research insights across different timeframes.

Consider a hypothetical 8-week study comparing 10, 15, and 20 sets per week, with the 20-set group achieving superior outcomes. The principle extracted is correct: “more volume leads to better outcomes, on average”. However, the snapshot fallacy lies in assuming that the prescription that optimized outcomes over 8 weeks (20 sets/week) is therefore the optimal way to achieve that same principle over longer periods.

Here's the critical distinction: the study demonstrates that more volume per week led to better outcomes over an 8-week period. But this doesn't necessarily mean that maximizing weekly volume is the best strategy for maximizing volume accumulation over a calendar year. The 15-set group might actually perform more total sets over 12 months because of better sustainability and fewer forced breaks.

This creates a paradox where the principle "more volume leads to better outcomes" remains valid, but the prescription that achieved this principle in the short-term study (20 sets/week) might actually violate that same principle when applied over the timeframe that matters for real-world goals.
The key insight is that principles need temporal context. More volume over what period? A week? A month? A year? The example provides evidence for weekly volume effects over 8 weeks, but extrapolating this to year-round programming represents a fundamental category error.

Short-term optimization and long-term strategy often require different approaches.
This is why the appropriate application of research requires extracting principles and then strategically considering how to best achieve those principles over the timeline relevant to your actual goals, rather than simply copying study protocols. Don't fall for the snapshot fallacy—optimize for the timeframe that actually matters.

#datadrivenstrength

Structure vs. Flexibility, Bodybuilding vs. Powerlifting, and a Huge Announcement | S2E11

Data Driven Strength 2025-07-31 | Sign up for a 14-day free trial for the Evolve Training App: evolvetrainingapp.com

Work with Josh or Zac: datadrivenstrength.com/coaching

Free newsletter with exclusive content: datadrivenstrength.com

00:00 - Introduction & Rocking Chair Discussion
09:30 — Coaching Anecdotes
14:10 — Structure vs. Flexibility in Training
44:59 — What Bodybuilders Can Learn From Powerlifters (& Vice Versa)
1:00:35 — Announcement: Evolve Training App

Why Strength and Hypertrophy Curves Look Different

Data Driven Strength 2025-07-31 | Hypertrophy and Strength seem to have different volume curves. Why is that?

Measurement Error in Research: Friend or Foe?

Data Driven Strength 2025-07-30 | It's common to hear people dismiss the utility of exercise science research because "measurement error makes the results unreliable."

This criticism fundamentally misunderstands how the law of large numbers actually works to our advantage. Think about tracking your own bodyweight: daily fluctuations from hydration status, macronutrient composition, and meal timing create "noise" around your true weight trend. However, when you average these measurements over time, the random fluctuations cancel out, revealing the actual signal—your real progress. Research works the same way, separating the noise of individual-level data from the signal of the group in aggregate.

The law of large numbers ensures that in well-powered studies, random measurement errors tend to cancel each other out, allowing us to obtain unbiased estimates of population means and treatment effects. The precision of these group-level estimates is directly captured in our uncertainty intervals—measurement error isn't undermining the research; it's being systematically accounted for.

It's important to clarify that measurement error is just one source of variation in research. Other sources (e.g., biological variability) still require proper study design to be causally accounted for. An example here could be the use of time-matched non-training control groups. This is the best way to causally identify the summation of all confounding sources of error, which may be required to appropriately answer some research questions.

The reality is that most studies in exercise and nutrition science operate with relatively small sample sizes where measurement errors may not fully cancel out, creating artificial heterogeneity between studies. But this doesn't invalidate research—it highlights why: i) appropriately powered studies with larger sample sizes provide more reliable estimates, and ii) well-conducted meta-analyses help us distinguish the signal from the noise.

Accounting for measurement error isn't a weakness of research—it's actually one of its biggest strengths. Statistical methods are specifically designed to separate signal from noise and draw meaningful conclusions from imperfect data.

Periodization for Hypertrophy

Data Driven Strength 2025-07-29 | Is periodization necessary for maximizing hypertrophy?

Short answer: Probably not.

Periodization is mainly useful when you’re trying to juggle multiple fitness characteristics. But when it comes to hypertrophy, most lifters aren’t held back by things like work capacity or strength expression. So in many cases, periodization might just overcomplicate things without offering much benefit.

Volume for Strength: SOLVED?

Data Driven Strength 2025-07-25 | Programming for powerlifters is a combination of “building the athlete up” and practicing the specific skills of the squat, bench press, and deadlift.

This often means training each main lifts 1-4 times per week with a few sets per session and smart organization of the training week to manage fatigue. Additional volume on the main lifts is unlikely to move the needle a ton, so the rest of the training time can be spent on hypertrophy-friendly exercises, rep ranges, and RPE’s.

We Read 100+ Studies on Strength Training Volume. Here’s What You Need to Know

Data Driven Strength 2025-07-24 | More from DDS: data-drivenstrength.kit.com/profile

Hypertrophy and Strength are Different Adaptations

Data Driven Strength 2025-07-24 | Do you ever see someone talking about optimizing hypertrophy and supporting their methods using data on strength gains? Or, maybe they’re talking about optimizing strength gains and using hypertrophy data to support their points? Keep in mind that these are different adaptations that regularly diverge in the research.

Is periodization necessary for strength?

Data Driven Strength 2025-07-22 | Is periodization necessary for strength?

Not always. It’s not magic — it’s just a tool to help organize training over the long haul.

Beginners can usually chase size and strength at the same time with great results. But advanced lifters often benefit from periodization to ensure they have dedicated phases for building muscle and phases for expressing strength.

Confused About Strength Training? This Makes It Easy

Data Driven Strength 2025-07-18 | Hot take: Specificity is king for powerlifting training

Your training should match your long-term goal (1RM strength), target your strength limiter for each lift (the “weak point”), and drive any intermediate adaptations you need — usually hypertrophy or work capacity.

Confused About Strength Training? This Makes It Easy.

Data Driven Strength 2025-07-17 | More from DDS: data-drivenstrength.kit.com/profile

An Easy Way for Powerlifters to Make More Progress

Data Driven Strength 2025-07-17 | A big mistake I used to make: telling my powerlifters clients to be overly strict with accessory movements.

Most accessories are programmed to drive hypertrophy. And to get more out of them, it often makes sense to push closer to failure.

This is especially true for movements that are hardest in the squeezed or contracted position (think chest flys, tricep extensions, rows, etc.). For these, I find it helpful to think in terms of “partials in reserve” instead of full reps.

A common question: how do you track that?

I just add a note in @evolvetrainingapp that says “partials in reserve.” That way, I’m logging it accurately and consistently — I’m just changing how I define RPE for that lift.

Will time off hurt your progress?

Data Driven Strength 2025-07-15 | Worried that time off will hurt your progress?

A week or two off per year usually isn’t a big deal — so lean into what you enjoy. For some, time off feels refreshing. Others genuinely like training while traveling.

If you’re a powerlifter with a meet on the calendar, it just takes a little planning. You might adjust your prep so the trip overlaps with a deload week.

Equivalence Fallacy

Data Driven Strength 2025-07-11 | It’s not uncommon to see “no significant differences” interpreted as evidence that two interventions are equivalent. But a lack of significance for a given comparison doesn’t confirm sameness—it just tells us we don’t have strong enough evidence to say there’s a difference.

I like to call this the equivalence fallacy: assuming that a null result implies equivalence, rather than recognizing it may purely reflect imprecise estimates.

In exercise science especially, where studies often have small sample sizes and large uncertainty intervals, many results are simply ambiguous. It’s entirely possible for the difference between two interventions to be compatible with zero—yet correspond with a “real” difference.

True affirmation of equivalence requires a directly testing the compatibility of the difference observed with a margin of whats considered practically equivalent.

So before concluding two approaches yield the same outcome, it’s worth asking:
i) Was the study adequately powered to detect a meaningful difference?
ii) How wide are the uncertainty intervals?
iii) Was equivalence explicitly tested—or just inferred?

In most cases, individual studies aren’t equipped to answer these questions definitively. That’s why looking at the broader body of evidence, including well-conducted meta-analyses, is key to reaching more reliable conclusions.

Understanding the difference between absence of evidence and evidence of absence is critical for thoughtful interpretation.

#datadrivenstrength

Evidence-Based Training Volume: Hypertrophy vs. Strength

Data Driven Strength 2025-07-11 | Why don’t hypertrophy and strength dose-response curves line up?

Zac breaks down the rationale for swelling or sarcoplasmic hypertrophy explaining the difference.

But are there other potential explanations? Watch our latest YouTube video for the full breakdown.

Evidence-Based Training Volume: Hypertrophy vs. Strength

Data Driven Strength 2025-07-10 | More from DDS: data-drivenstrength.kit.com/profile

0:00 - Intro
1:09 - The Debate: Why do Hypertrophy and Strength Curves Disagree?
4:12 - Our Perspective on Why the Curves Seem to Disagree
7:04 - Why You Should Avoid Hyper-Specific Conclusions

Myo-Reps Vs. Drop Sets

Data Driven Strength 2025-07-10 | Methods like drop sets and myo-reps (created by @borgefagerli) are great tools. Watch until the end to hear our practical recommendations!

New approach to RIR, Advanced Training Methods, Research Interpretation Errors, and More | S2E9

Data Driven Strength 2025-07-08 | More from DDS: data-drivenstrength.kit.com/profile

00:00 - Introduction
00:05:17 - Training Flexibility vs. Rigidity
00:25:45 - New Rest-Pause Study
00:43:55 - Recommendations: Rest-Pause & Drop Sets
00:55:49 - How to Individualize Training
01:27:32 - An Improved Approach to RIR
01:40:34 - A Guide to Interpreting Research (Common Fallacies)

“Optimal” exercise selection

Data Driven Strength 2025-07-08 | These days, some say there’s too much focus on “optimal” exercise selection and range of motion. Maybe that’s true—but don’t forget there was a time when those things were probably undervalued.

How many sets to maximize strength gains? [66 studies analyzed]

Data Driven Strength 2025-07-05 | A common misinterpretation of meta-regression is that you simply fit the line to the dots. Check out what’s actually going on under the hood!

How many sets to maximize strength gains? [66 studies analyzed]

Data Driven Strength 2025-07-04 | More from DDS: data-drivenstrength.kit.com/profile

0:00 - Introduction & The Value of Within-Study Slopes
0:43 - Unpacking the Primary Dose-Response Findings
1:46 - Decoding the Reciprocal Model and Its Components
3:03 - Positive Dose-Response with Diminishing Returns
4:14 - Beyond "Fitting a Line to Dots": The Meta-Regression Process
10:39 - Training Status: Does it Influence the Effects of Volume?
14:02 - Interpreting the Diminishing Returns

Extreme Comparisons

Data Driven Strength 2025-07-04 | Whenever a new area of research starts to gain traction, its common to see some very eye-catching results. The flashy headlines often result in the over communication of these findings on social media, sometimes causing a meaningful shift in the public perception of a given topic.

In reality, its crucial to keep in mind that early research on a given topic is usually designed to find ANY signal amongst the noise. This usually comes in the form of very “extreme” comparisons (e.g., 1 set vs 20 sets). Studies with these more polarized designs try to determine if a given line of research is worth pursuing further or that its better to cut bait early.

After these initial “proof-of-concept” studies are performed, researchers usually shift their emphasis from internally to externally validity, which inherently comes with training protocols that have much more overlap (e.g., lying to seated hamstring curls on top of a full lower body training program). Purely due to the nature of the differentiation between the interventions, this usually shrinks the expected effect back to more realistic magnitudes.

The lesson here is that early studies on a given topic can help to set a rough “ceiling” for the expected differences between approaches, but as interventions grow closer to one another as they would in practice, the expected differences often will become more modest over time. Don’t let flashy communication of research areas in their infancy get the best of you.

#datadrivenstrength

Principles apply to everyone

Data Driven Strength 2025-07-01 | Principles apply to everyone — or at least, that’s the safest starting point.

Individualization matters, but it should flow from how someone’s unique context fits within those principles.

Variance Demon

Data Driven Strength 2025-07-01 | In today’s era of fast-paced science communication, it can be common to feel the “pendulum effect” with each new study published or hitting the preprint server. One study comes to a given conclusion, while the next study appears to say the opposite. This can be understandably frustrating for someone simply looking to improve their knowledge and refine their training practices.

Deciphering the causal factor(s) that lead studies with similar designs to come to different conclusions is an extremely difficult process. Often, we attribute these differences to some characteristics of the study design (e.g., exercise selection, rep range, training status, etc.). However, its possible to create examples where, despite knowing that two studies represent the same “known effect”, they appear to differ substantially purely due to statistical noise.

The “variance demon” is a metaphor I like to use to describe this process. The demon lurks in the shadows “pulling” effect estimates away from what is true, adding noise to the signal we’re attempting to detect. The worst part is we can’t know when this is happening in any individual study. This is where understanding the collective body of evidence is crucial. Some studies are better equipped to deliver results that generalize, but often it requires pooling the studies on a given topic to separate signal from noise.

Beware of false attributions, and realize the variance demon is always lurking in the background.

#datadrivenstrength

Periodization for Hypertrophy: What Actually Matters

Data Driven Strength 2025-07-01 | Periodization for hypertrophy should be guided primarily by practical rather than physiological reasons.

Volume and Strength: New Science Explained | S2E10

Data Driven Strength 2025-06-30 | More from DDS: data-drivenstrength.kit.com/profile

00:00- Podcast Introduction & Episode Focus
06:05 - The Deceiving Nature of Scientific Data
10:10 - Episode Outline and Goals
16:15 - Acknowledging the Research Team
18:20 - Historical Periodization Narratives
24:25 - Contrasting Strength Training Approaches
32:30 - Evolving Views on Training Volume
38:35 - Prior Meta-Analyses on Volume and Strength
54:50 - Limitations of Past Research
01:03:00 - Novel Volume Quantification Methods
01:13:10 - Examples of Indirect Exercises
01:17:15 - Meta-Analysis Inclusion Criteria
01:19:15 - Smallest Detectable Effect Size
01:23:20 - Model Adjustments for Precision
01:29:25 - Optimal Volume Approach & Total Studies
01:33:30 - Understanding Within-Study Slopes & Shrinkage
01:45:40 - Plateau & Precision of Estimates
01:51:45 - Common Rep Ranges & Rest Periods
01:55:50 - Specificity in Strength Assessments
02:01:55 - Training Specificity and Volume
02:08:00 - Volume Impact on Short-Term Strength Gains
02:12:05 - Hypertrophy vs. Strength: Causal Questions
02:22:15 - Qualitative Data Agreement
02:30:25 - Analyzing Paired Matched Effects
02:38:30 - Reliability vs. Agreement Plot Analysis
02:44:35 - Muscle Size and Strength Compatibility

Why Research Results Can Lead You Astray [False Attribution Fallacy]

Data Driven Strength 2025-06-27 | More from DDS: data-drivenstrength.kit.com/profile

Why Research Results Can Lead You Astray [False Attribution Fallacy]

Data Driven Strength 2025-06-26 | More from DDS: data-drivenstrength.kit.com/profile

0:00 Intro
2:44 The False Attribution Fallacy
4:18 Sampling Variance
5:36 Measurement Error
7:00 Biological Variability
7:43 Variance as the True Explaining Factor
8:18 Example: Proximity to Failure Meta-Analysis
10:04 Sub-Analyses as Hypothesis Generating
11:03 Confounding Variables

Partials in Reserve Vs. Lengthened Partials

Data Driven Strength 2025-06-25 | There’s two training strategies growing in popularity.

1. “Partials in Reserve” — doing full range of motion reps to failure, then squeezing out partials

2. “Lengthened Partials” — only doing the lengthened portion of the range of motion

Both of these strategies allow you to train harder in exercises where the hardest portion of the range of motion is the shortened (”squeeze”) position. But which is more effective?

A recent study by @coachstian provides some initial insight. The results leaned slightly in favor of lengthened partials, but not enough to take to the bank.

My opinion is that either strategy probably stimulates a bit more hypertrophy than just training to full range of motion failure in shortened-challenge exercises. I’d lean into preference when choosing between the two for now.

Periodization for powerlifters

Data Driven Strength 2025-06-24 | Periodization for powerlifters can be viewed as a sliding scale.

At times, long term development is prioritized, and strength expression is less of an immediate concern. Other times, the opposite is true.

There’s a Big Problem With the Reps in Reserve Scale

Data Driven Strength 2025-06-20 | Don’t always use reps in reserve.

For exercises where the hardest part of the exercise is the end of the concentric (the shortened position), consider squeezing out partials after failing the full range of motion.

Then, you can rate the set difficulty based on partials in reserve instead of reps in reserve.

Mechanisms vs Outcomes

Data Driven Strength 2025-06-20 | In my opinion, the heated debate of whether we should use mechanistic or outcome data to guide our training decisions somewhat misses the forest for the trees.

In reality, these data should be used synergistically, leveraging the unique strengths and weaknesses they provide.

#datadrivenstrength

There’s a Big Problem With the Reps in Reserve Scale

Data Driven Strength 2025-06-19 | More from DDS: data-drivenstrength.kit.com/profile

00:00 - Defining the task for RIR/RPE
00:50 - The evolution of autoregulation
02:18 - RPE vs. RIR explained
03:00 - Introducing Partials in Reserve
04:20 - What defines a "rep" in different movements
06:40 - Hypertrophy benefits of Partials in Reserve
07:45 - Chest fly demonstration of partials
09:00 - Standardizing RIR across exercises

Higher frequencies

Data Driven Strength 2025-06-17 | The programming optimization game for strength is different than hypertrophy. For strength, there are specific tasks that must be executed to an objective standard across individuals. Therefore, creative solutions are sometimes needed to overcome a barrier and improve performance.

Muscle Growth Hack?

Data Driven Strength 2025-06-13 | More from DDS: data-drivenstrength.kit.com/profile

Can this training method ENHANCE muscle growth? (new rest-pause training research)

Data Driven Strength 2025-06-12 | More from DDS: data-drivenstrength.kit.com/profile

00:00 - Extended set techniques
04:14 - A 2025 study on rest pause training
12:00 - Study findings
13:44 - Practical applications for your training
17:54 - Recommendations

Why a higher frequency all else equal would help?

Data Driven Strength 2025-06-11 | Should powerlifters use high frequencies?

Powerlifting is a measure of maximal force output, and higher frequencies may allow your training to be higher quality and therefore a higher average force output.

Just like any other variable, frequency training needs to be balanced within the constraints of the athlete such as their ability to tolerate the training program.

More from DDS: data-drivenstrength.kit.com/profile

Following a program but not getting anywhere? This is why

Data Driven Strength 2025-06-06 | More from DDS: data-drivenstrength.kit.com/profile

Auto-regulation

Data Driven Strength 2025-06-06 | Auto-regulation is a foundational component of program design for strength. While it may be difficult to demonstrate the fully utility of auto-regulation in the context of a typical study (e.g., 8 weeks), in practice, the potential advantages are clear.

More from DDS: data-drivenstrength.kit.com/profile

Following a program but not getting anywhere? This is why

Data Driven Strength 2025-06-05 | More from DDS: data-drivenstrength.kit.com/profile

00:00 - Intro
00:54 - Rethinking How Individualization Works
04:30 - Pillar 1: Goals & Priorities
08:47 - Pillar 2: Leverages
12:43 - Pillar 3: Training History
15:04 - Pillar 4: External Factors
21:17 - Pillar 5: Personality
23:10 - Practical Application