Quantifying the Physical Demands of Tactical First-Person Shooter Gameplay: Muscle Activity and Movement Characteristics During Competitive Valorant
Bibliographic record
Abstract
Competitive gamers play for prolonged hours everyday, resulting in a high prevalence of gaming-related pain and discomfort. However, due to the lack of gaming-specific research, health professionals must make inferences based on analogous research or anecdotal evidence, suggesting the need for additional research to inform evidence-based intervention. The purpose of this study was to quantify the physical demands of playing a competitive first-person shooter. Forty competitive Valorant players were recruited and subsequently stratified based on skill level (20 high skill players and 20 low skill players). Muscle activity was recorded from eight muscles of the upper body, on the mouse side. Markerless motion capture was used to record player hand movement throughout Valorant gameplay. Static load levels for the forearm and shoulder muscles exceeded recommended guidelines and experienced little to no muscular rest, resulting in a nearly 100% duty cycle. Hand kinematics revealed that high skill players moved the mouse further and with greater velocity and acceleration compared with low skill players. These findings indicate that playing competitive Valorant may increase musculoskeletal injury risk and risk may be greater for high skill players.
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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".