Validity and Reliability of 2-Dimensional Video-Based Assessment to Analyze Foot Strike Pattern and Step Rate During Running: A Systematic Review
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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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 | 0.026 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".