Validity and Reliability of 2-Dimensional Video-Based Assessment to Analyze Foot Strike Pattern and Step Rate During Running: A Systematic Review
Why this work is in the frame
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Bibliographic record
Abstract
CONTEXT: Two-dimensional (2D) video-based analysis is often used by clinicians to examine the foot strike pattern (FSP) and step rate in runners. Reliability and validity of 2D video-based analysis have been questioned. OBJECTIVE: To synthesize the psychometric properties of 2D video-based analysis for assessing runners' FSP and step rate while running. DATA SOURCES: Medline/PubMed, Science Direct, Embase, EBSCOHost/CINAHL, and Scielo were searched from their inception to August 2018. STUDY SELECTION: Studies were included if (1) they were published in English, French, Portuguese or Spanish; (2) they reported at least 1 psychometric property (validity and/or reliability) of 2D video-based analysis to assess running kinematics; and (3) they assessed FSP or step rate during running. STUDY DESIGN: Systematic review. LEVEL OF EVIDENCE: Level 2. DATA EXTRACTION: Studies were screened for methodological (MacDermid checklist) and psychometric quality (COSMIN checklist) by 2 independent raters. RESULTS: Eight studies, with a total of 702 participants, were included. Seven studies evaluated the reliability of 2D video to assess FSP and found very good to excellent reliability (0.41 ≤ κ ≤ 1.00). Two studies reported excellent reliability for the calculation of step rate (0.75 ≤ intraclass correlation coefficient [ICC] ≤ 1.00). One study demonstrated excellent concurrent validity between 2D and 3D (gold standard) motion capture systems to determine FSP (Gwet agreement coefficient [AC] > 0.90; ICC > 0.90), and another study found excellent concurrent validity between 2D video and another device to calculate step rate (0.84 ≤ ICC ≤ 0.95). CONCLUSION: Strong evidence suggests that 2D video-based analysis is a reliable method for assessing FSP and quantifying step rate, regardless of the experience of the assessor. Limited evidence exists on the validity of 2D video-based analysis in determining FSP and calculating step rate during running.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 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 | 0.001 |
| 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 it