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High-intensity Interval Training Enhances Corticospinal Excitability And Motor Learning

2024· article· en· W4402662700 on OpenAlexaff
Jess Gibson, Austin Hurst, Emily Rogers, Derek S. Kimmerly, Jack P. Solomon, Shaun G. Boe

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2024
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHigh-intensity interval trainingIntensity (physics)Physical medicine and rehabilitationInterval (graph theory)Motor learningTraining (meteorology)NeurosciencePsychologyMedicineMathematicsPhysical therapyPhysics

Abstract

fetched live from OpenAlex

High intensity interval training (HIIT) is effective in modulating brain excitability. This effect on neuroplasticity is thought to promote the learning and retention of motor skills. However, it is unclear whether the timing of HIIT with respect to the training of the motor skill can aid in its acquisition and retention. PURPOSE: To examine the effect of HIIT on motor learning when performed before vs after multiple sessions of motor task practice, and to examine the effect of HIIT on corticospinal excitability (CSE) and intracortical networks. METHODS: 28 participants (16 F) performed 4 sessions of HIIT (3 sets of 3 min at 90% maximal power output (POmax) separated by 2 min at 50% POmax). During session 1 participants performed a maximal graded exercise test to determine their POmax, which was used to determine workload for the HIIT sessions. HIIT-mediated changes in CSE and intracortical networks were assessed by transcranial magnetic stimulation, a form of non-invasive brain stimulation, during session 2. During sessions 3-5, either before or after HIIT, participants trained on a complex motor skill requiring them to reproduce trajectories on a touchscreen, where learning was confirmed via a decrease in performance error across training sessions. RESULTS: Using Bayesian linear mixed effects modeling a credible difference in performance was observed between the HIIT groups by the third session, where the group who performed HIIT after task practice showed improved learning when compared to the group performing HIIT before (probability distribution (pd) = 95.4%, d = 0.22; Fig 1). Further, HIIT was shown to drive an increase in CSE both immediately after (pd = 98.44%, d = 0.30) and 30 minutes after the exercise session (pd = 99.25%, d = 0.30; Fig 1). CONCLUSION: This study supports the use of HIIT for motor learning through consolidation of a motor skill, and that HIIT-mediated changes in CSE and intracortical networks are a likely mechanism underlying the effect.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.301
Teacher spread0.280 · 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 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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