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Record W4318481861 · doi:10.1123/jsr.2022-0252

Mental Fatigue Uniquely Influences Drop Landing Biomechanics for Individuals With a Concussion History

2023· article· en· W4318481861 on OpenAlexaff
Eric J. Shumski, Tricia M. Kasamatsu, Kathleen Wilson, Derek N. Pamukoff

Bibliographic record

VenueJournal of Sport Rehabilitation · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsWestern University
Fundersnot available
KeywordsBiomechanicsMedicinePhysical medicine and rehabilitationPhysical therapyRepeated measures designRehabilitationConcussionPoison controlInjury preventionMathematics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.371
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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