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Record W4414273448 · doi:10.1101/2025.09.17.676841

Neuromuscular, Cardiovascular, and Cognitive Fatigue in Motor Learning: A Systematic Review

2025· review· en· W4414273448 on OpenAlexafffund
Abdellah Hassar, Martin Simoneau, Jason Bouffard

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCentre for Interdisciplinary Research in RehabilitationUniversité Laval
KeywordsNeurophysiologyCognitionMotor learningFacilitationMuscle fatigueDreyfus model of skill acquisitionExertionMotor skill

Abstract

fetched live from OpenAlex

BACKGROUND: Fatigue is multifactorial and task-dependent, arising from the interplay between performance and perceived fatigability. Fatigue-related changes in sensorimotor control and neural activity may alter skill acquisition and retention. OBJECTIVE: To synthesize behavioral and neurophysiological evidence on how cognitive, cardiovascular, local neuromuscular, and mixed fatigue influence motor-skill acquisition, retention, and transfer. METHODS: A systematic search was conducted within PubMed, Web of Science, and Embase. Thirty-eight articles, corresponding to 43 extracted experiments, met inclusion criteria. Data were extracted on fatigue protocol, learning taxonomy, learning stage, retention/transfer context, fatigue asssessment, and neurophysiology. Methodological quality was appraised with a modified Downs and Black checklist. RESULTS: During acquisition, 28 of 43 experiments (65.1%) showed detrimental effects, 4 (9.3%) showed facilitatory effects, 7 (16.3%) showed neutral effects, and 4 (9.3%) were mixed or inconclusive. Retention after recovery and retention under sustained or renewed fatigue were assessed in 36 and 7 experiments, respectively. Retention findings were variable and depended on fatigue modality, task demands, and testing context. Neurophysiological measures were scarce, being included in only six experiments, and sex-specific analyses or other participant-level moderators were rarely examined. CONCLUSIONS: Fatigue does not uniformly impair motor learning. Local neuromuscular fatigue appears most likely to disrupt acquisition and sometimes retention, whereas cardiovascular exertion may support learning or consolidation in some contexts when acute fatigue has partly recovered. Evidence for cognitive fatigue remains limited and inconsistent. Future work should use standardized fatigue assessment, longer retention windows, state-matched testing, and integrated neurophysiology to clarify mechanisms and guide training under fatigue.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.267
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes2
Has abstractyes

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