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Record W2152525177 · doi:10.1113/jphysiol.2012.235127

Reply from M. J. Gibala, J. P. Little, M. J. MadDonald and J. A. Hawley

2012· article· en· W2152525177 on OpenAlexaff
Martin J. Gibala, Jonathan P. Little, Maureen J. MacDonald, John A. Hawley

Bibliographic record

VenueThe Journal of Physiology · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHeart rateIntensity (physics)MedicineExercise intensityCardiologyInternal medicinePhysicsBlood pressure

Abstract

fetched live from OpenAlex

Thank you for the opportunity to respond to the letter submitted by Gayda and colleagues in response to our recent review published in The Journal of Physiology (Gibala et al. 2012). With regards to their first comment regarding our new ‘practical’ high-intensity interval exercise (HIIE) protocol, we disagree with the assertion that ‘exercise intensity at 60% of peak power cannot be considered high intensity.’ In our efforts to develop a low-volume HIIE protocol that can be applied across different cohorts including clinical populations, we devised a model comprising 10 × 60 s work bouts at an intensity eliciting ∼85–90% of maximal heart rate (HRmax; averaged over the 10 intervals), interspersed by 60 s of recovery. We have found that the percentage of peak power output (PPO; determined using a standard ramp test to volitional fatigue which does not always elicit peak O2 uptake) that approximates the desired target heart rate (i.e. the % of HRmax) varies considerably between subjects and is exercise-mode specific. For example, in the study by Hood et al. (2011) which was conducted on sedentary healthy adults, a workload equivalent to 60% of PPO during upright cycling was sufficient to elicit a training intensity of ∼90% HRmax. However, in our recent study conducted on patients with type 2 diabetes, the intensity required to elicit ∼90% HRmax was ∼95% of PPO determined during recumbent cycling (Little et al. 2011). We agree with the assertion by Gayda and colleagues that ‘acute physiological responses during different HIIE protocols as well as patient's safety, tolerance and comfort should be tested before their implementation into training programs’. Ongoing protocol optimization work in our laboratory reveal that when interval exercise was prescribed as 80% of PPO in coronary artery disease (CAD) patients – most of whom were taking beta-blocker medication – the 10 × 60 s protocol resulted in peak heart rates during the exercise that averaged ∼85% of age-predicted HRmax. Further, the 10 × 60 s protocol was best tolerated and rated as most preferred by CAD patients in comparison with a modified Wingate protocol (repeated 30 s efforts at 100% PPO with 4 min unloaded cycling for recovery), the standard aerobic interval training protocol used by Wisloff and colleagues (2007), or a moderate-intensity continuous exercise (MICE) protocol. It is likely that high-intensity interval training (HIT) does not conform to a ‘one size fits all’ approach and the interval training stimulus needs to be tailored to individuals depending on their initial level of fitness, exercise tolerance, use of prescription medications and other factors. We also concur with the other main comment by Gayda and colleagues that ‘the superiority of this HIIE protocol [our 10 × 60 s ‘hard’/60 s ‘easy’ model]… needs to be demonstrated.’ Indeed, our review concluded ‘One aspect that is unclear from the present literature is the precise intensity and minimal volume of training that is needed to potentiate the effect of the stimulus-adaptation on outcomes such as mitochondrial biogenesis and relevant health markers. To answer such questions, a complex series of studies needs to be undertaken that systematically ‘titrate’ levels of the ‘training impulse’ and determine subsequent cellular, performance and clinical responses after divergent training interventions.’ Specifically with respect to the use of HIIE in patients with cardiovascular risk or cardiovascular disease, the letter by Gayda and colleagues highlights four references from their laboratory that were not cited in our review. Given the relatively broad scope of our review and the fact that Journal guidelines restricted the number of references to 50, it was obviously not possible to cite all relevant work. Moreover, two of the citations listed by Gayda et al. were acute exercise studies (whereas the focus of our review was training adaptations) and the other two citations were a journal abstract and a recent paper published in February 2012 (neither of which we had access to at the time of submission of our original manuscript). We are also aware of the pioneering research conducted by Meyer and colleagues (e.g. Meyer et al. 1998) and have acknowledged this work in a previous commentary (MacDonald & Currie, 2009). We apologize to all authors whose work on interval training we could not cite due to the broad focus of our review and referencing limitations imposed by The Journal.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.026
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.005
Open science0.0030.002
Research integrity0.0260.039
Insufficient payload (model declined to judge)0.0070.011

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.

Opus teacher head0.013
GPT teacher head0.251
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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