Mental Fatigue Uniquely Influences Drop Landing Biomechanics for Individuals With a Concussion History
Bibliographic record
Abstract
CONTEXT: Induced mental fatigue negatively impacts sport performance and neurocognition. However, it is unclear how induced mental fatigue influences landing biomechanics. The purpose of this study was to examine the influence of mental fatigue on drop landing biomechanics in individuals with and without a concussion history. DESIGN: Crossover design. METHODS: Forty-eight (24 per group) recreationally active individuals were matched on age (±3 y), sex, and body mass index (±1 kg/m2). All participants completed an experimental (30-min Stroop task) and control (30-min reading magazines) intervention on separate days separated by a minimum of 24 hours. Drop landings were performed before and after both interventions. Outcomes included peak vertical ground reaction force (vGRF), vertical loading rate (VLR), knee flexion angle, knee abduction angle, external knee flexion moment, external knee abduction moment, and initial ground contact knee flexion and knee abduction angles. Separate 2 (group) × 2 (intervention) between-within analyses of covariance compared drop landing outcomes. Each group's average pre-Stroop and premagazine outcomes were covariates. RESULTS: There was a significant interaction for vGRF (P = .033, ηp2=.097) and VLR (P = .0497, ηp2=.083). The vGRF simple effects were not statistically significantly (P range = .052-.325). However, individuals with a concussion history displayed a medium effect size for greater vGRF post-Stroop compared with their own postmagazine vGRF (mean difference (95% confidence interval [95% CI] = 0.163 (-0.002 to 0.327) bodyweight (BW), p =.052, ηp2=.081. In contrast, the control group displayed a small effect size (mean difference [95% CI] = 0.095 [-0.069 to 0.259] BW, p =.251, ηp2=.029). Individuals with a concussion history displayed greater VLR post-Stroop compared with controls (mean difference [95% CI], 26.29 [6.19 to 46.40] BW/s, P = .012, ηp2=.134) and their own postmagazine values (mean difference [95% CI] = 32.61 [7.80 to 57.42] BW/s, p =.011, ηp2=.135). CONCLUSION: Mental fatigue leads to greater VLR for individuals with a concussion history. Athletic competition and activities of daily living can increase mental fatigue. Training programs may seek to teach mental fatigue reducing strategies to athletes with a concussion history.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".