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Record W4386845105 · doi:10.1080/1091367x.2023.2258869

Are We Getting the Full Picture? A Systematic Review of the Assessment of Resistance Training Behavior

2023· review· en· W4386845105 on OpenAlexaff
Justin Kompf, Ryan E. Rhodes

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2023
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsResistance trainingPsychologyApplied psychologyPsychological interventionSystematic reviewResistance (ecology)Inclusion (mineral)Strength trainingConstruct validityConstruct (python library)Aerobic exercisePhysical therapyPsychometricsMEDLINEClinical psychologyMedicineSocial psychologyComputer science

Abstract

fetched live from OpenAlex

The measurement of resistance training (RT) is often based on adaptations of aerobic physical activity measures which may not contain the elements necessary to assess RT. The purpose of this systematic review was to examine what measures are used to assess RT and appraise their composition. Specifically, the inclusion of frequency, duration, intensity, the use of major muscle groups, reps, and sets. Search terms included “resistance training,” “strength training,” OR “weight training” AND “behavior” OR “participation.” Studies were evaluated based on whether RT measures assessed the key components of participation. In the 92 included studies, only one of the six components of the RT recommendations was assessed on average. This was almost exclusively frequency. Many assessments of RT were adapted from aerobic measures without rigorous validity evidence. Construct validity in RT measurement may lead to improved public health recommendations, surveillance of behavior, and precision in determining effective interventions and correlates.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.041
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.245
GPT teacher head0.458
Teacher spread0.212 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations4
Published2023
Admission routes1
Has abstractyes

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