Examining the Independent and Interactive Carryover Effects of Cognitive and Physical Exertions on Physical Performance
Bibliographic record
Abstract
ObjectiveThis study compared the effects of prior cognitive, physical, and concurrent exertion on physical performance.BackgroundFatiguing cognitive and physical exertions have been shown to negatively affect subsequent task performance. However, it is not clearly understood if concurrent physical and cognitive effort may exaggerate the negative carryover effects on physical task performance when compared to cognitive or physical exertion alone.MethodTwenty-five participants completed four isometric handgrip endurance trials on different days. The endurance trials were preceded by four, 15-minute experimental manipulations (cognitive, physical, concurrent, control). Electromyography (EMG) and force tracing performance were monitored, with handgrip strength measured pre and post. Subjective ratings of mental and physical fatigue, as well as affect, motivation, and task self-efficacy, were also assessed.ResultsHandgrip strength decreased following both physical (-14.4% MVC) and concurrent (-12.3% MVC) exertion manipulations, with no changes being observed for the cognitive and control conditions. No differences were observed across conditions for endurance time, EMG, nor tracing performance. When compared to the control conditions, perceptions of mental and physical fatigue were higher following the experimental manipulation. Endurance trial self-efficacy was lower for the mental, physical and concurrent conditions compared to control.ConclusionThe concurrent condition resulted in similar decreases in strength as the physical fatigue condition, but otherwise resulted in similar carryover effects on endurance performance across all conditions. Further study is required at higher exposure levels, or for longer exposure durations, to further probe the influence of concurrent physical and cognitive effort on task performance.ApplicationConcurrent cognitive and physical effort resulted in similar physical performance decrements to physical effort 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".