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Reliability of Kinematic and Kinetic Measures of Jump Performance Using Accelerometry

2011· article· en· W2808166359 on OpenAlexaboutno aff
Kevin McCurdy, Jack W. Ransone, Mike Guerrero, Brian K. Miller, Jesse Walker

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2011
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsCountermovementJumpConcentricMathematicsVertical jumpKinematicsForce platformJumpingGround reaction forceStretch shortening cycleMechanicsGeometryPhysicsGeologyClassical mechanics

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to determine the reliability of kinetic and kinematic variables measured from bilateral and unilateral countermovement and drop jumps using an accelerometer. METHODS: Twenty six subjects with mean age, height, and weight of 21 ± 1.2 years, 179.3 ± 9.9 cm, and 82.18 ± 16.2 kg, respectively completed the study. After a 5-minute warm up and dynamic stretches, the following jumps were completed in random order with a 30-second rest period between each trial: bilateral countermovement vertical jump (BCV), bilateral countermovement horizontal jump (BCH), bilateral 40 cm drop vertical jump (BDV), bilateral 40 cm drop horizontal jump (BDH), unilateral countermovement vertical jump (UCV), unilateral countermovement horizontal jump (UCH), unilateral 20 cm drop vertical jump (UDV), and unilateral 20 cm drop horizontal jump (UDH). An accelerometer (Inform Sport Training Systems, Victoria, BC. Canada), worn at the waist, measured the following jump kinematics: jump height, ground contact time, concentric time, time to peak concentric force, time to peak power, and reactive strength (jump height divided by ground contact time). Kinetic measures included mean concentric force, peak concentric force, and peak power. Jump distance during the horizontal jumps was measured using a tape measure. After completing all jumps in one session, a minimum of 48 hours of rest was allowed before repeating the jumps in a second session. The best of 3 trials was recorded for analysis. The ground contact recorded for analysis was taken from the highest reactive strength found during the drop jumps. The Pearson r was used as a measure of relative reliability (between-subject variation) and the coefficient of variation (cv), expressed as a percent, determined absolute reliability (within-subject variation) after test-retest procedures. RESULTS: Relative reliability was high ( 0.69 ≤ r ≤ 0.93) for all of the bilateral vertical jump variables while jump height, mean concentric force, and peak concentric force during the BCV and jump height during BDV was found to produce moderate to high absolute reliability with a range between 3.7–9.4%. Relative reliability ranged from moderate to high ( 0.5 ≤ r ≤ 0.96) for the unilateral vertical jump variables while the CV for jump height (8.3%) and peak concentric force (9.2%) during the UCV demonstrated moderate absolute reliability. High relative (r = 0.96 and 0.94) and absolute reliability (cv = 3.3 and 4.4%) was found for jump distance during the BCH and BDH, respectively while reactive strength during the BDH produced moderate relative (r = 0.67) and low absolute (cv = 15.3%) reliability. Similar results were found for the unilateral horizontal jumps. Jump distance demonstrated high relative (r = 0.91 and 0.91) and absolute (cv = 4.8 and 6.3%) reliability during the UCH and UDH, respectively while reactive strength during the UDH demonstrated moderate (r = 0.62) and low (cv = 19%) reliability. Conclusion: The best overall reliable measures were jump height and distance while reactive strength demonstrated moderate to low reliability. All other variables, except for ground time during the unilateral jumps, produced high and significant relative reliability but low absolute reliability. Practical Application: The data indicate that the variables can be used to rank subjects but, beside jump height and distance, do not provide enough within-subject accuracy to monitor improvement.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.153
GPT teacher head0.363
Teacher spread0.210 · 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 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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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