Reliability Of Joint Moments During Treadmill Running At Varying Speeds And Timepoints
Bibliographic record
Abstract
Joint moments at the ankle, knee, and hip explain the forces acting on these joints during the stance phase of running and have been associated with the development of certain running-related injuries (Ceyssens et al., 2019). While the reliability of kinematic data has been well-studied, the reliability of joint moment data during treadmill running at varying speeds remains unexplored (McGinley et al., 2009). PURPOSE: This study aimed to investigate the short and long-term reliability of joint moment data (ankle, knee, and hip) during treadmill running at different speeds. METHODS: Fourteen healthy recreational runners (8 males, 6 females; age 27.6 ± 4.8 years; BMI: 22.4 ± 2.53 kg/m2) completed 30-second running trials at three speeds (2.5, 3.0, and 3.5 m/s) on an instrumented treadmill across three sessions (baseline, 1 week, and 3 months). Joint moments normalized to body mass were calculated for peak moments at each joint: Peak Ankle Dorsiflexion, Peak Ankle Eversion, Peak Knee Flexion, and Peak Hip Adduction. Reliability metrics, including the Intraclass Correlation Coefficient (ICC), Minimum Detectable Change (MDC), and Typical Error (TE), were calculated to analyze their short and long-term reliability. RESULTS: Moderate to strong reliability with low TE and MDC values were observed across all variables. Short-term reliability was superior to long-term reliability, with speed having no significant effect. The knee and hip joints demonstrated lower reliability compared to the ankle joint. CONCLUSION: The lower reliability at the knee and hip joints may be due to greater soft tissue artefact more proximally (Reinschmidt et al., 1997). Reduced reliability over the long term may be attributed to natural variability in a runner's gait pattern. Clinically, establishing a minimum detectable change is crucial for accurately assessing true joint moment changes and evaluating interventions, such as gait modifications, aimed at reducing injury risk.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".