Reply to “Discussion of ‘The effects of pre-exhaustion, exercise order, and rest intervals in a full-body resistance training intervention’ – Pre-exhaustion exercise and neuromuscular adaptations: an inefficient method?”
Bibliographic record
Abstract
Dear Editor, we appreciate being able to respond to the discussion raised by Prestes et al. (2015) in their letter to the Editor. We thank the authors for their commentary and agree that raising discussion of methodological issues is a step towards resolution. However, we believe that Prestes et al. may have misinterpreted and misrepresented our study. The primary themes within the authors’ letter appear to dispute volume of resistance training (single vs.multiple sets) and hypertrophic adaptations. For clarity, our original paper (Fisher et al. 2014) neither compared nor measured either of these variables. However, in the interests of open dialoguewe feel readersmight benefit from our responding to the letter to clarify any misinterpretations. A concern regarding the comments by Prestes et al. (2015) arises in their statement that the disparity between Jones’s (1970) original hypothesis and our results might be explained by a “lack of gold standard” methods to measure muscle hypertrophy. We reiterate that we did not measure, and made no claims regarding hypertrophy within our study (Fisher et al. 2014). In addition the rejection of empirical data in favour of a preconceived hypothesis, without identifying genuine methodological issues that might limit the extent to which said data can be respected, appears demonstrative of considerable bias. Prestes et al. (2015) continued stating “for trained subjects, it is important to note that resistance training volume can increase magnitude of muscle strength improvements”. To support this statement Prestes et al. cited a meta-analysis relating to muscular hypertrophy not strength (Krieger 2010). Interestingly this metaanalysis (Krieger 2010) has been critiqued in detail for a lack of control over the numerous variables disparate between the studies included within said meta-analysis (Fisher 2012). In the interests of clarity, Krieger did in fact publish a meta-regression comparing single and multiple sets for strength (Krieger 2009), whichwas later heavily criticised by Carpinelli (2012) for inclusion of poor-quality studies. Furthermore, we should be cautious in validating a belief citing only meta-analyses, which in their very process have considerable limitations (Shapiro 1994; Egger and Smith 1997), without consideration of the studies included. Irrespective of this, we urge Prestes et al. to re-read our paper, which in no way compared single and multiple set training but rather considers the use of pre-exhaustion (PreEx) training and exercise order for equated volume groups; further evidence that our paper (Fisher et al. 2014) may have been misinterpreted and/or misunderstood by the authors of the letter. Prestes et al. may argue that our results might have differed with greater set volumes and indeed this may be true. However, in the absence of evidence to support that PreEx produces greater strength gains with multiple sets this remains speculative. It should be noted that in the absence of evidence regarding a particular training approach (i.e., PreEx), the most logical direction for research is to begin with a simple intervention, which is the approach we took. The authors then discussed PreEx training and cited multiple studies considering acute muscle activation measured by electromyography (EMG), and indeed we thank Prestes et al. for bringing to our attention the study by Junior et al. (2010). However, as previously stated (Fisher et al. 2011), the use of acute EMG at best only infers hypotheses regarding training adaptations or provides evidence regarding the potential role of motor unit recruitment to adaptations evidenced from a training intervention study. In fact the only scientific method to measure a chronic response is with a controlled intervention study, such as our PreEx article. Indeed Prestes et al. (2015) noted themselves that “...muscle strength can increase even without a significant increase in muscle electromyographic activity...”. It would appear Prestes et al. are suggesting that PreEx (compared with traditional methods) may produce greater adaptationwhen exercise is not continued to momentary muscular failure (MMF), a premise to which we agree. In this case, as Prestes et al. noted, it has been proposed that PreEx may allow for greater fatigue-related stimuli to be induced and it seems likely that when not training to MMF the use of PreEx would enhance responses related to metabolic stress as well as motor unit activation. The results of Junior et al. (2010) suggest that future avenues of research utilizing PreExmight consider not training toMMF along with multiple set training. In addition, and because of the higher muscle activation, research might consider perceived exertion anddiscomfort in response to PreEx trainingnot toMMF (which might be higher compared with traditional methods). Irrespective, our study suggests when training to MMF using single sets per exercise PreEx offers no further benefits (Fisher et al. 2014). Prestes et al. (2015) proceeded to discuss muscle hypertrophy mediated through mechanisms of metabolic stress, which, unfortunately, is not directly relevant to our study since we did not measure, discuss, or even infer hypertrophic adaptation or measures ofmetabolic stress in conjunctionwith PreEx training. However, we agree that studies that are indeed designed to investigate hypertrophy should utilise adequate outcome measures such as
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.035 | 0.042 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".