Cluster Sets to Prescribe Interval Resistance Training: A Potential Method to Optimise Resistance Training Safety, Feasibility and Efficacy in Cardiac Patients
Bibliographic record
Abstract
The integration of resistance training for cardiac patients leads to important health outcomes that are not optimally obtained with aerobic exercise; these include an increase in muscle mass, maintenance of bone mineral density, and improvements in muscular fitness parameters. Despite the proliferation of evidence supporting resistance exercise in recent decades, the implementation of resistance training is underutilised, and prescription is often sub-optimal in cardiac patients. This is frequently associated with safety concerns and inadequate methods of practical exercise prescription. This review discusses the potential application of cluster sets to prescribe interval resistance training in cardiac populations. The addition of planned, regular passive intra-set rest periods (cluster sets) in resistance training (i.e., interval resistance training) may be a practical solution for reducing the magnitude of haemodynamic responses observed with traditional resistance training. This interval resistance training approach may be a more suitable option for cardiac patients. Additionally, many cardiac patients present with impaired exercise tolerance; this model of interval resistance training may be a more suitable option to reduce fatigue, increase patient tolerance and enhance performance to these workloads. Practical strategies to implement interval resistance training for cardiac patients are also discussed. Preliminary evidence suggests that interval resistance training may lead to safer acute haemodynamic responses in cardiac patients. Future research is needed to determine the efficacy and feasibility of interval resistance training for health outcomes in this population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".