Effects of Exergaming Tennis on Players’ Tennis Skills and Mental State Compared to Regular Tennis in Adult Players: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Exergaming, which combines physical activity with interactive gaming, has been shown to improve motor skills and fitness. However, exergaming's potential in complex, open-skill sports such as tennis, which require real-time coordination, decision-making, and technical precision, remains underexplored. Furthermore, only a few studies have evaluated the impact of exergaming on both technical skill development and psychological outcomes such as motivation and confidence, especially among novice players. This study addresses these gaps by comparing the combination of exergame-based tennis training and on-court tennis training (EBTT+OCTT) with on-court tennis training alone (OCTT×2) in improving technical skills, grip strength, confidence, and motivation. OBJECTIVE: This study aims to assess the effect of exergaming tennis in developing tennis technical skills, grip strength, confidence level, and motivation level. METHODS: and 26 (SD 7) years, respectively. The EBTT+OCTT group had 1 weekly session of OCTT and 1 session of exergaming training using Virtual Tennis (PlayStation 3 Move), while the OCTT×2 group completed 2 weekly on-court sessions. Outcomes included tennis technical skills such as groundstroke technique, service speed, and service accuracy (assessed using the Hewitt tennis test), grip strength (using a handheld dynamometer), confidence (using the Sports Confidence Inventory), and motivation (using the Sport Motivation Scale). Mean differences (MDs) between pre- and postintervention were collected and analyzed using repeated-measures ANOVA (α=.05) and Pearson correlation analysis. Effect sizes were calculated using partial eta squared (ηp²), where values ≥0.14 indicated large effects. RESULTS: =.31. In terms of motivation, EBTT+OCTT retained intrinsic motivation (IM) better than OCTT×2, with significant reductions in IM, mainly IM-to-know (MD=7, SD 2.95) and IM-to-accomplish (MD=5, SD 3.77) observed in the OCTT×2 group (P<.001). Grip strength, confidence, and motivation levels (except amotivation) showed positive correlations with tennis technical skills (r=0.39-0.80). CONCLUSIONS: EBTT+OCTT and OCTT×2 significantly improve tennis skills and confidence levels in novice players, although no significant differences were found between the two. However, EBTT+OCTT appeared to better sustain IM. Thus, EBTT+OCTT may serve as a supplementary tool for novice players to better learn tennis.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".