Association Of On-court Impact Load And Intensity With Rating Of Perceived Exertion In Basketball
Bibliographic record
Abstract
PURPOSE: Our purpose was to (i) examine the association between wearable sensor-derived loading and rating of perceived exertion (RPE) during on-court practices in men’s basketball, and (ii) compare wearable sensor-derived loading between practices and games. METHODS: Eleven male basketball players, free from musculoskeletal injury, on the McMaster University Basketball team participated in the study. Players were fitted with bilateral inertial measurement units (iMeasureU, Vicon) at the ankle to measure resultant acceleration impacts during 11 practices and 5 games over a 3-week period. Two primary variables were obtained from the sensors relating to (i) total impact load (number of steps x resultant acceleration of impact) and (ii) average intensity (average impact intensity from all steps). Additionally, the Borg-10 RPE questionnaire was collected immediately after each practice and used to quantify RPE and sRPE (RPE x practice time). The association between the sensor and self-reported data were quantified using Pearson correlation coefficients. Additionally, sensor variables from practice were compared to those obtained in games using non-parametric paired t-test. RESULTS: A low and moderate correlation was observed for total impact load on RPE (r = 0.11, p = 0.04) and sRPE (r = 0.45, p < 0.001), respectively, in practices. Alternatively, no significant correlation was observed between average intensity and RPE (r = 0.07, p = 0.55) or sRPE (r = 0.11, p = 0.35). In comparing games to practices, games were found to display a greater average intensity compared to practices (19.6 ± 2.1 g vs. 11.6 ± 2.1 g; p = 0.004). Although games on average had a lower total impact load compared to practices (66 k ± 29 k-g vs. 73 k ± 8 k-g; p = 0.42), this was not significant given the high amount of variability and limited sample size for in-game data (e.g., n = 6 athletes with in-game sensors). CONCLUSIONS: Our results suggest that loading metrics from wearable inertial sensors display low to moderate correlations with practice RPE. Additionally, we found that while games have higher average intensities, the total load may be lower overall. These initial findings highlight the utility of wearable sensors to provide unique insights into practice and competition load that may not be fully captured in RPE alone.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".