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Record W4313829071 · doi:10.1007/s40279-022-01804-x

Resistance Training Induces Improvements in Range of Motion: A Systematic Review and Meta-Analysis

2023· review· en· W4313829071 on OpenAlexafffund
Shahab Alizadeh, Abdolhamid Daneshjoo, Ali Zahiri, Saman Hadjizadeh Anvar, Reza Goudini, Jared P. Hicks, Andreas Konrad, David G. Behm

Bibliographic record

VenueSports Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaAustrian Science Fund
KeywordsMeta-analysisResistance trainingSports medicineRange of motionRange (aeronautics)Physical medicine and rehabilitationTraining (meteorology)MedicinePhysical therapyInternal medicineEngineeringGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Although it is known that resistance training can be as effective as stretch training to increase joint range of motion, to date no comprehensive meta-analysis has investigated the effects of resistance training on range of motion with all its potential affecting variables. OBJECTIVE: The objective of this systematic review with meta-analysis was to evaluate the effect of chronic resistance training on range of motion compared either to a control condition or stretch training or to a combination of resistance training and stretch training to stretch training, while assessing moderating variables. DESIGN: For the main analysis, a random-effect meta-analysis was used and for the subgroup analysis a mixed-effect model was implemented. Whilst subgroup analyses included sex and participants' activity levels, meta-regression included age, frequency, and duration of resistance training. DATA SOURCES: Following the systematic search in four databases (PubMed, Scopus, SPORTDiscus, and Web of Science) and reference lists, 55 studies were found to be eligible. ELIGIBILITY CRITERIA: Controlled or randomized controlled trials that separately compared the training effects of resistance training exercises with either a control group, stretching group, or combined stretch and resistance training group on range of motion in healthy participants. RESULTS: Resistance training increased range of motion (effect size [ES] = 0.73; p < 0.001) with the exception of no significant range of motion improvement with resistance training using only body mass. There were no significant differences between resistance training versus stretch training (ES = 0.08; p = 0.79) or between resistance training and stretch training versus stretch training alone (ES = - 0.001; p = 0.99). Although "trained or active people" increased range of motion (ES = 0.43; p < 0.001) "untrained and sedentary" individuals had significantly (p = 0.005) higher magnitude range of motion changes (ES = 1.042; p < 0.001). There were no detected differences between sex and contraction type. Meta-regression showed no effect of age, training duration, or frequency. CONCLUSIONS: As resistance training with external loads can improve range of motion, stretching prior to or after resistance training may not be necessary to enhance flexibility.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0150.029
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.180
GPT teacher head0.410
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations97
Published2023
Admission routes2
Has abstractyes

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