ACCURACY OF SELF-REPORTED FOOT STRIKE PATTERN IN INTERCOLLEGIATE AND RECREATIONAL RUNNERS DURING SHOD RUNNING.
Bibliographic record
Abstract
BACKGROUND: Clinicians are interested in the foot strike pattern (FSP) in runners because of the suggested relationship between the strike pattern and lower extremity injury. PURPOSE: The purpose of this study was to assess the ability of collegiate cross-country runners and recreational runners to self-report their foot strike pattern during running. STUDY DESIGN: Cross-sectional Study. METHODS: Twenty-three collegiate cross-country and 23 recreational runners voluntarily consented to participate. Inclusion criteria included running at least 18 miles per week, experience running on a treadmill, no history of lower extremity congenital or traumatic deformity, or acute injury three months prior to the start of the study. All participants completed a pre-test survey to indicate their typical foot strike pattern during a training run (FSPSurvey). Prior to running, reflective markers were placed on the posterior midsole and the vamp of the running shoe. A high-speed camera was used to film each runner in standing and while running at his or her preferred speed on a treadmill. The angle between the vector formed by the two reflective markers and the superior surface of the treadmill was used to calculate the foot strike angle (FSA). To determine the foot strike pattern from the video data (FSPVideo), the static standing angle was subtracted from the FSA at initial contact of the shoe on the treadmill. In addition to descriptive statistics, percent agreement and Chi square analysis was used to determine distribution differences between the video analysis results and the survey. RESULTS: The results of the chi-square analysis on the distribution of the FSPSurvey in comparison to the FSPVideo were significantly different for both the XCRunners (p < .01; Chi-square = 8.77) and the REC Runners (p < .0002; Chi-square = 16.70). The cross-country and recreational runners could correctly self-identified their foot strike pattern 56.5% and 43.5% of the time, respectively. CONCLUSION: The findings of this study suggest that the clinician cannot depend on an experienced runner to correctly self-identify their FSP. Clinicians interested in knowing the FSP of a runner should consider performing the two-dimensional video analysis described in this paper. LEVEL OF EVIDENCE: 3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".