Using Biomechanical Devices in Elite Baseball Pitchers: A Preliminary Feasibility Study
Bibliographic record
Abstract
Background: Baseball clinicians, coaches, and players have sought to leverage biomechanical devices to inform training and prevent injury. However, discerning feasibility is sparse for novel biomechanical devices to inform adoption of these technologies. The objective of this study was to investigate the uptake (i.e., proportion of initial use) and adherence (i.e., proportion of continued use) of wearable and portable biomechanical devices among collegiate baseball pitchers during a single summer training season. Methods: Uptake, adherence and 95% confidence intervals (95% CI) were calculated for a commercially available biomechanical pitching sleeve during practice, and use of a force plate during a countermovement jump task for a minimum of one time per week per the established protocol. Potential non-response bias analyses were assessed descriptively by comparing medical history, clinical measures, and pitching patterns among athletes that continued and discontinued using the biomechanical devices. Results: Twenty-two pitchers participated. The uptake for initiating force plate use was 0.32 (95%CI: 0.14, 0.55); uptake for the pitching sleeve was 0.55 (0.32, 0.76). Adherence for force plate use was 0.46 (0.31, 0.70), compared to 0.13 (0.09, 0.17) for the pitch sleeve. Potential non-response bias analysis revealed pitchers with no previous upper extremity injury in the prior season (n=14) were more likely to use the pitching sleeve beyond the first session (43%) than those who had reported a previous season UE injury at study baseline (13%). Conclusions: Variable uptake and adherence was observed across devices and players. Addressing barriers/facilitators to increase uptake and adherence is necessary to inform future studies on the effect of these devices on preventing injury using training load, kinetic, and kinematic data monitoring.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".