Intrarater and Interrater Reliability and Agreement of a Method to Quantify Lower-Extremity Kinematics Using Remote Data Collection
Bibliographic record
Abstract
CONTEXT: To assess the reliability of a remote 2D markerless motion tracking method (Kinovea) to quantify knee and hip angles during dynamic tasks. METHODS: Fourteen healthy adults performed body weight squats and lateral lunges while video recording themselves at home. Knee and hip angles were quantified in the sagittal plane for the squats and in the frontal plane for the lateral lunges. Two students each performed the video analysis procedure twice, 2 weeks apart. Intraclass correlation coefficients were used to calculate the intrarater and interrater reliability for angles at maximum depth. The intrarater and interrater agreement over the joint angle-time signals were quantified using a validation metric; an acceptable agreement threshold was set at a validation metric of 0.803 or higher. Standard error of measurement (SEM) was also calculated. RESULTS: Reliability was good to excellent (intraclass correlation coefficients = .80-.98) for all angle comparisons at maximum depth. The agreement over the entire joint angle-time signal was acceptable for all squat variables except for the interrater hip angle comparison (validation metric = 0.797). None of the lateral lunge variables met the threshold of acceptable agreement. The mean SEM across participants for all joint angle-time signal and for maximum depth was acceptable (<5°) for all measurements (SEM = 1.2°-4.9°). CONCLUSIONS: Overall, the reliability, agreement, and SEM quantified in this study support the integration of remote methods to quantify lower-extremity kinematics into research and clinical practice.
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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 | 0.058 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".