Wheelchair Basketball Competition Heart Rate Profile According to Players’ Functional Classification, Tournament Level, Game Type, Game Quarter and Playing Time
Bibliographic record
Abstract
Heart rate is a popular parameter observed in team sports to plan training sessions with regard to load and sport specificity. Wheelchair basketball is an intermittent team game for physically impaired players. The study aim was to define heart rate profile of wheelchair basketball players in terms of their functional classification (category A: 1.0-2.5 points, category B: 3.0-4.5 points), tournament level (championships, friendly games), game type (close, balanced, unbalanced), game quarter (1st, 2nd, 3rd, 4th) and playing time (40-59%, 60-79%, 80-100% in a quarter). Heart rate of 18 wheelchair basketball players was monitored in 22 games in four different tournaments, i.e. European Championships 2017, World Championships 2018, two friendly international tournaments of national teams (2017, 2018). Heart rate (HRmean, HRpeak, %HRpeak, HRR, %HRR) was monitored by Polar Team Pro (Kempele, Finland) during playing time on the court. Timeouts, quarter breaks, a half break, time on a bench were not taken into account in HR monitoring. The Kolmogorov-Smirnov test, the Mann-Whitney U test and the Kruskall-Wallis test were used. Fourteen players divided according to the classification into category A and B were included in the final calculations (n = 457 cases). Significantly higher HRmean, %HRpeak, HRpeak and %HRR were noted among category B players, and higher %HRpeak and %HRR among category A players at the highest tournament level compared to friendly games. There were significant differences in %HRR and the percentage of time spent in HR zone I between the players with different playing time (40-59% versus 60-79%) in category B. No significant differences in HR were noted between four quarters. Among category A players, differences in HR in zone II were observed. Among category B players, statistically significant differences in % HRpeak, the percentage of time spent in HR zone I, II, III and %HRR between close, balanced and unbalanced games were found. In conclusion, the intermittent nature of wheelchair basketball was confirmed. Monitoring heart rate in a game could be helpful in creating exercises with proper loads for better physical preparation of wheelchair basketball players. High intensity training sessions would be more beneficial in preparing players for game demands.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".