Selecting Resistance Training Exercises for Novices: A Delphi Study with Expert Consensus
Bibliographic record
Abstract
Resistance training (RT) is a form of exercise that provides numerous health benefits. One barrier to participation may be the technical demands associated with some exercises. While recommendations for RT, including the number of repetitions, sets, rest, and training loads are established, recommendations for exercise selection have not been addressed. We used a Delphi-type method in three iterative surveys. In the first survey, 17 experts rated the technical complexity of 77 different strength training exercises as having low, moderate, or high technical demands. A second survey was generated based on the first, such that exercises receiving a majority high complexity vote were removed. In the second survey, experts rated the remaining exercises as either appropriate or too advanced for a novice. Exercises were deemed appropriate if 70% agreement was reached. Lastly, experts rated exercises as being appropriate for adults over the age of 60. Experts agreed that 41 different exercises were appropriate for novices and that 32 of the exercises were appropriate for novice adults over the age of 60. Our findings provide recommendations for program design to compliment already established recommendations for RT of repetitions, sets, rest periods, and training loads.
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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 | 0.136 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".