Training junior tennis players to increase knee flexion improves their service performance
Bibliographic record
Abstract
This study aimed to assess whether training to increase knee flexion during the tennis serve improves performance and to explore the associated biomechanical changes across the body. Twenty junior tennis players were randomly allocated into control (standard in-season training) and training groups (received training to increase knee flexion during serve). Inertial sensors tracked full body and racket kinematics during five serves performed in pre- and post-training assessments. Racket velocity, impact height, and lower- and upper-body kinematics were compared. Training increased serve knee flexion by 31° (p < 0.001), leading to a 1.38 km/h increase racket velocity (p = 0.036) without affecting impact height (p = 0.331). Additionally, training increased: range of front leg knee extension (MD = 23.46°, p < 0.001) and extension velocity (MD = 54.28°/s, p = 0.008), hip range of motion (front: MD = 53.60°/s, p = 0.003; back: MD = 57.28°/s, p = 0.015), pelvis upward velocity (MD = 0.27 m/s, p < 0.001), and trunk contralateral flexion velocity (MD = 23.18°/s, p = 0.025). No main effects were found for shoulder internal rotation (p = 0.304) and elbow extension (p = 0.214) velocities. No changes were observed in the control group other than a decreased trunk contralateral flexion velocity (MD = −28.98°/s, p = 0.007). Specific training can, therefore, increase serve knee flexion. This study highlights that specific training to increase knee flexion can enhance serve performance by increasing racket velocity, without increasing upper limb joint contribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".