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Record W4285727672 · doi:10.1177/15598276221115662

Selecting Resistance Training Exercises for Novices: A Delphi Study with Expert Consensus

2022· article· en· W4285727672 on OpenAlexaff
Justin Kompf, Ryan E. Rhodes, Sohee Lee

Bibliographic record

VenueAmerican Journal of Lifestyle Medicine · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMedicineDelphi methodDelphiResistance trainingTraining (meteorology)Rest (music)Physical therapySelection (genetic algorithm)Medical educationApplied psychologyComputer sciencePsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.120
GPT teacher head0.449
Teacher spread0.329 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations5
Published2022
Admission routes1
Has abstractyes

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