ONE-WEEK RELIABILITY OF JUMP TESTS USING AN INSOLE-EMBEDDED IMU
Bibliographic record
Abstract
PURPOSE: Inertial measurement units (IMUs) have become valuable in biomechanical research and in the clinic because of their ability to measure movements in the field. Jump tests are often used to assess lower extremity power capacity, muscle strength, speed, and performance. However, the reliability of insole-embedded IMUs for jump tests has not been examined. We aimed to test the reliability of IMUs for the most common jump tests performed in clinical and on-field assessments. METHODS: Eight healthy participants were fitted with two insole-embedded IMUs and standardized footwear in a lab setting. Jump tests included: single-leg jumps for distance (SLJ-D), height (SLJ-H), and reactive strength index (SLJ-RSI); consecutive countermovement jump (CCMJ); and cyclic jump (Cyc-J). Outcomes included: RSI, average jump height (AJH), maximum jump height (MJH), and maximum jump distance (MJD). Participants repeated the jumps over two test sessions separated by 7 days. Intraclass correlation coefficient (ICC), standard error of measurement (SEM), and minimum detectable change (MDC) were used to analyze the reliability of each outcome for the five jump tests. ICC was categorised as: poor (<0.5), moderate (0.5 ≤ 0.7), high (0.7 ≤ 0.9) and excellent (>0.9). RESULTS: Seven participants (3 females, 4 males) were included in the analysis. One participant was excluded based on improper completion of the jump protocol. We found that all outcomes from the five jump movements presented high (ICC > 0.7) or excellent (ICC > 0. 9) reliability (Table 1). CONCLUSION: The insole-embedded IMUs presented high and excellent reliability in the most common jump tests. Clinicians can use the MDC values presented to assess for true change between testing sessions. These findings support the use of insole-embedded IMUs as a tool to monitor jump performance and evaluate the risk of lower extremity injuries in the field. Table 1. Biomechanical outcomes of five common jump tests (n = 7) - Jump Test Outcome Mean ± SD Pooled SD ICC SEM MDC Limb Session 1 Session 2 SLJ-RSI RSI L 0.95 ± 0.25 0.93 ± 0.23 0.070 0.969* 0.012 0.034 R 0.97 ± 0.23 0.93 ± 0.20 0.063 0.852 0.024 0.068 RSI-AJH (cm) L 8.87 ± 4.26 7.91 ± 3.76 1.159 0.943* 0.277 0.767 R 9.38 ± 4.29 8.54 ± 3.98 1.195 0.927* 0.323 0.895 RSI-MJH (cm) L 10.76 ± 4.17 9.47 ± 3.90 1.165 0.909* 0.351 0.974 R 11.21 ± 4.19 10.05 ± 4.44 1.247 0.914* 0.366 1.014 SLJ-D MJD (m) L 1.54 ± 0.23 1.61 ± 0.22 0.065 0.825 0.027 0.076 R 1.52 ± 0.22 1.60 ± 0.24 0.066 0.922* 0.018 0.051 SLJ-H H-MJH (cm) L 16.59 ± 4.39 16.44 ± 4.36 1.263 0.928* 0.339 0.939 R 16.8 ± 3.10 16.96 ± 3.21 0.910 0.934* 0.234 0.648 CCMJ RSI 1.39 ± 0.56 1.41 ± 0.52 0.156 0.870 0.056 0.156 AJH (cm) 27.06 ± 8.21 26.28 ± 8.11 2.355 0.982* 0.316 0.876 MJH (cm) 29.95 + 8.53 28.57 + 8.33 2.430 0.934* 0.624 1.730 Cyc-J RSI 1.78 ± 0.46 1.82 ± 0.35 0.118 0.813 0.051 0.141 AJH (cm) 15.85 ± 6.59 14.89 ± 6.65 1.911 0.970* 0.331 0.917 MJH (cm) 19.67 ± 8.06 18.16 ± 7.27 2.216 0.913* 0.654 1.812 L = Left limb; R = Right limb; cm = centimeter; m = meter * = excellent (ICC > 0.9).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".