Development of a Mobile Phone App for Measuring Striking Response Time in Combat Sports: Cross-Sectional Validation Study
Bibliographic record
Abstract
BACKGROUND: TReaction is a mobile app developed to determine strike response time at low cost and with easy application in combat sports. However, the validity and accuracy of the response time obtained by the TReaction app has not yet been evaluated. OBJECTIVE: This study aimed to test the validity and reliability of the TReaction app in measuring motor response time in combat sports. METHODS: A total of two athletes performed 59 strikes to assess the response time upon visual stimulus using the TReaction app simultaneously with a high-speed camera. Accuracy of the measure was verified using a computer simulator programmed to discharge visual stimuli and obtain the response time. Pearson correlation, Student t test for dependent samples, and the Bland-Altman analysis were performed. Accuracy was verified using the intraclass correlation coefficient. Effect size (g) and the typical error of measurement (TEM) were calculated. The significance level was set at P<.05. RESULTS: No significant difference (P=.56) was found between both systems. The methods presented a very strong correlation (r=0.993). The magnitude of differences was trivial (g<0.25), and TEM was 1.4%. These findings indicate a high accuracy between the computer screen and the mobile app measures to determine the beginning of the task and the response time. CONCLUSIONS: Our findings suggest that the TReaction app is a valid tool to evaluate the response time in combat sports athletes.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".