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Record W4401459358 · doi:10.1186/s13102-024-00841-5

Effects of slow dynamic, fast dynamic, and static stretching on recovery of performance, range of motion, balance, and joint position sense in healthy adults

2024· article· en· W4401459358 on OpenAlexaff
Abdolhamid Daneshjoo, Elham Hosseini, Safoura Heshmati, Mansour Sahebozamani, David G. Behm

Bibliographic record

VenueBMC Sports Science Medicine and Rehabilitation · 2024
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDynamic balanceSense (electronics)Balance (ability)Position (finance)Joint (building)Range (aeronautics)Motion (physics)Range of motionDynamic rangeControl theory (sociology)Computer sciencePhysical medicine and rehabilitationMaterials scienceMedicineEngineeringComputer visionPhysical therapyArtificial intelligenceStructural engineeringEconomicsMechanical engineeringComposite materialElectrical engineering

Abstract

fetched live from OpenAlex

INTRODUCTION: Considering the effects of fatigue on athletic performance and the subsequent increase in the probability of injury, the purpose of this study was to compare the effects of slow dynamic, fast dynamic, and static stretching on the recovery of performance, range of motion (ROM), balance, and joint position sense. METHODS: Fifteen collegiate healthy females were involved in four separate sessions of slow dynamic stretching (SDS), fast dynamic stretching (FDS), static stretching (SS), and control condition (CC; without stretching), in a random order with at least 48 h of rest between sessions. After warming up, the individuals performed ROM, balance, joint position sense (JPS) maximum voluntary isometric contraction (MVIC) force as well as countermovement (CMJ) and squat jump (SJ) as pre-tests. After performing the knee fatigue protocol of 4 sets of knee extension and flexion at 60% of 1 repetition maximum (RM) to exhaustion (CC; without stretching) or stretching programs (SDS or FDS or SS), the subjects repeated all the tests at post-test 1 (after 5 min) and post-test 2 (after 60 min). RESULTS: A significantly lower JPS error was detected with SDS while JPS error increased in the SS and control conditions (p < 0.0001). MVIC force significantly increased with SDS and FDS but decreased in control and SS conditions (p < 0.0001). Moreover, a significant decrease in CMJ and SJ height in SS and control conditions was revealed (p < 0.0001). Also, a significant decrease in balance with the control condition was revealed. But only SDS minimized fatigue-induced balance decrements (p < 0.0001). Additionally, the control condition experienced a significant decrease in knee extensor ROM, which contrasted with the significant increase in the quadriceps flexibility with the stretching conditions. CONCLUSIONS: The present results support the idea that SDS may increase quadriceps MVIC force, knee extensor ROM and knee JPS. So according to the present results, it is suggested that the SDS could be implemented and incorporated into a regular recovery program.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.004
GPT teacher head0.262
Teacher spread0.258 · 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 designObservational
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

Citations13
Published2024
Admission routes1
Has abstractyes

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