Real-Time Biofeedback of Performance to Reduce Braking Forces Associated With Running-Related Injury: An Exploratory Study
Bibliographic record
Abstract
BACKGROUND: The high rate of running-related injury may be associated with increased peak braking forces (PBFs) and vertical loading rates. Gait retraining has been suggested by some experts to be an effective method to reduce loading parameters. OBJECTIVES: To investigate whether PBF could be decreased following an 8-session gait retraining program among a group of female recreational runners and which self-selected kinematic strategies could achieve this decrease. METHODS: In this exploratory study, 12 female recreational runners with high PBFs (greater than 0.27 body weight) completed an 8-session gait retraining program with real-time biofeedback of braking forces over the course of a half-marathon training program. Baseline and follow-up kinetics and kinematics were analyzed with a repeated-measures analysis of variance. RESULTS: There was an average reduction of 15% in PBF (-0.04 body weight; 95% confidence interval [CI]: -0.07, -0.02 body weight; P = .001; effect size, 0.62), accompanied by a 7% increase in step frequency (11.3 steps per minute; 95% CI: 1.8, 20.9 steps per minute; P = .024; effect size, 0.38) and a 6% decrease in step length (-5.5 cm; 95% CI: -9.9, -1.0 cm; P = .020; effect size, 0.40), from baseline to follow-up. CONCLUSION: The gait retraining program significantly reduced the PBF among a group of female recreational runners. This was achieved through a combination of increased step frequency and decreased step length. Furthermore, the modified gait pattern was incorporated into the runners' natural gait pattern by the completion of the program. Based on these results, the outlined gait retraining program should be further investigated to assess whether it may be an effective injury prevention strategy for recreational runners. This study was registered with ClinicalTrials.gov (NCT03302975). LEVEL OF EVIDENCE: Prevention, level 4. J Orthop Sports Phys Ther 2019;49(3):136-144. Epub 7 Dec 2018. doi:10.2519/jospt.2019.8587.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".