Test–Retest Reliability of the Magnitude and Direction of Asymmetry in the Countermovement Jump, Drop Jump, and Countermovement Rebound Jump
Bibliographic record
Abstract
This study aimed to investigate the test–retest reliability of three bilateral jump tests to assess asymmetry and determine the consistency of both the magnitude and direction of asymmetry between two testing sessions. Thirty-three participants performed the countermovement jump (CMJ), drop jump (DJ), and countermovement rebound jump (CMRJ—jump 1: CMRJ1; jump 2: CMRJ2) over two sessions. Inter-limb asymmetry was calculated for kinetic metrics, including the mean propulsive force, net braking impulse, and net propulsive impulse. Test reliability was computed using intraclass correlation coefficients (ICC), coefficients of variation (CV), and standard error of measurement. Furthermore, analysis of variance was used to determine the systematic bias between jump types and sessions. Kappa coefficients were utilised to assess the consistency of asymmetry favouring the same limb. Results showed poor to excellent reliability for all jump tests between sessions (ICC range = 0.19–0.99, CV range = 2.80–11.09%). A significantly higher magnitude of asymmetry was revealed for the net braking impulse during the DJ compared to the CMRJ2 (p ≥ 0.014, g ≤ 0.53). When computing the direction of asymmetry between test sessions, Kappa coefficients revealed that levels of agreement were substantial (Kappa = 0.63–0.70) for the CMJ, moderate to almost perfect (Kappa = 0.59–0.94) for the CMRJ1, moderate to almost perfect (Kappa = 0.58–0.81) for the DJ, and slight to moderate for the CMRJ2 (Kappa = 0.19–0.57). These results underscore the variable nature of both the magnitude and direction of asymmetry during jump testing. Thus, practitioners should carefully choose evaluation methods and metrics characterised by low variability to ensure robust asymmetry assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".