MétaCan
Menu
Back to cohort

Vertical Takeoff Acceleration As A Predictor Of Single Leg Jump Performance

2024· article· en· W4402661714 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2024
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTakeoffAccelerationJumpVertical jumpEnvironmental scienceMechanicsAeronauticsAerospace engineeringEngineeringPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Single leg jump tests are important assessment tools for the evaluation of lower extremity joint function after sports injury. The maximum height or distance achieved can indicate readiness to return to sport after injury or predict performance in some sports. Jump height and distance are often measured in lab settings using force plates. Inertial measurement units (IMUs) are a more practical tool that can be used in clinical and field settings to measure acceleration. To maximize jump height and distance an individual must accelerate their mass vertically. However, it is not known if vertical acceleration at takeoff can predict single leg jump height and distance in a healthy recreational population. PURPOSE: To investigate whether vertical takeoff acceleration (VTA) can predict single leg jump distance and height in a healthy recreational population. METHODS: Healthy participants free of lower extremity injuries completed single-leg jumps for distance (SLJD) and for height (SLJH) in neutral cushioned running shoes with a pair of insole-embedded IMUs. Participants were required to perform each jump three times, aiming to maximize their jump height and distance. VTA was measured with the insole-embedded IMUs at 416 Hz. Primary outcomes were the average jump height and distance over the three jumps. A simple linear regression analysis was conducted to investigate the predictive relationship between VTA and single-leg jump for distance and height. RESULTS: One hundred and eighty-seven participants (89 females, 98 males; 41.8 ± 12.07 years, 69.87 ± 11 kg) completed the jump assessment. The linear regression analysis revealed a strong positive linear relationship between the average vertical takeoff acceleration and SLJH performance (r2 = 0.74, p < 0.01), but no significant association between the average vertical takeoff acceleration and SLJD performance (r2 < 0.01, p = 0.15). There was a positive associations between normalized VTA and SLJH ( r2 ≥ 0.5, p < 0.01), but not for SLJD. CONCLUSION: The vertical takeoff acceleration can be a reliable metric for predicting single leg jump height, but not single leg jump distance in clinical settings. As the SLJD test is a combination of vertical and horizontal trajectories, performance on this test may be better predicted by horizontal acceleration.

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.

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 score0.439
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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