Cluster Sets to Prescribe Interval Resistance Training: A Potential Method to Optimise Resistance Training Safety, Feasibility and Efficacy in Cardiac Patients
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it