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Record W4403100094 · doi:10.58739/jcbs/v14i3.91

Relationship Between Functional Movement Screen, Lower Quarter Y-Balance Test and Physical Performance Tests in Athletes

2024· article· en· W4403100094 on OpenAlexaboutno aff
P. S. S. Rao, Bisht Kanika

Bibliographic record

VenueJOURNAL OF CLINICAL AND BIOMEDICAL SCIENCES · 2024
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesBalance (ability)Test (biology)Movement (music)Balance testFunctional movementQuarter (Canadian coin)PsychologyPhysical medicine and rehabilitationPhysical therapyMedicineArtHistory

Abstract

fetched live from OpenAlex

Context: The Functional Movement Screen (FMS) and lower quarter Y-Balance Test (LQ-YBT) are examples of functional screening tools used to identify physical dysfunction or functional asymmetries. Limitations in flexibility, agility and power may also have negative consequences on performance in fundamental movements in sport. Due to the time constraints of physical therapists and athletic trainers providing care towards athletes during rehabilitation and treatment hours, it is not possible to perform multiple screening tests/tools prior to an athletic season to determine if athletes have poor mobility and fundamental movements that may alter sport performance. Understanding associations between movement performance and global screening tools (FMS and LQ-YBT) could provide a foundation for prevention programs and performance enhancement for athletes. Therefore, this study was performed to identify how FMS composite score and LQ-YBT relate to the measures of physical performance (flexibility, agility, and jump) in athletes. Settings and Design: A Cross-sectional study conducted on university athletes. Methods and Material: Demographic and Anthropometric data of 50 athletes (32 males,18 females) in the age group of 18 – 30 years were collected at the beginning of the test after obtaining informed consent. The seven FMS tasks, LQ-YBT, Sit and Reach Test, ProAgility Test and Standing Long Jump were performed sequentially in the order. A total of 3 trials were performed in each test and the average of three trials were recorded for statistical analysis. Statistical analysis used: Pearson correlation was used to assess the association between FMS C.S, LQ-YBT and other physical performance measures and p < 0.05 was utilized for statistical significance. Results: FMS CS and LQ-YBT showed significant positive correlation with standing long jump performance (p<0.05) but not with agility. LQ-YBT correlated significantly with sit and reach test (p<0.05). Conclusions: In athletes, the observed significant correlation between the FMS composite score (FMS CS) and the Lower Quarter Y Balance Test (LQ-YBT) with standing long jump performance suggests their potential utility in predicting jump capabilities. However, their lack of correlation with agility indicates limited applicability in that domain. Notably, the LQ-YBT's significant correlation with the sit and reach test underscores its potential as an indicator of flexibility. Key message: Utilizing the FMS or LQ-YBT autonomously or in pair might help sports medicine and strength and conditioning experts in their capacity to recognize people with an expanded gamble of injury during sports participation through distinguishing proof of physical or functional movement deficiencies. Keywords Agility, Flexibility, Functional Asymmetries, Power, Quality of Movement

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)0.004
metaresearch head score (Gemma)0.002
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 score0.018
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.224
GPT teacher head0.539
Teacher spread0.316 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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