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Record W2337884231 · doi:10.1055/s-0035-1565140

An Acute Bout of Barefoot Running Alters Lower-limb Muscle Activation for Minimalist Shoe Users

2016· article· en· W2337884231 on OpenAlexaff
Nicholas J. Snow, Fabien A. Basset, James M. Byrne

Bibliographic record

VenueInternational Journal of Sports Medicine · 2016
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsMemorial University of NewfoundlandUniversity of British Columbia
Fundersnot available
KeywordsBarefootElectromyographyBicepsVastus medialisMedicinePhysical medicine and rehabilitationTreadmillTibialis anterior muscleAnklePhysical therapyAnatomySkeletal muscle

Abstract

fetched live from OpenAlex

Despite the abundance of barefoot running-related research, there have been no electromyography studies evaluating the effects of this mode of exercise on habitual users of minimalist footwear. The present study investigated differences in muscle activation during acute bouts of barefoot and shod running, in minimalist shoe users. 8 male participants ran on a motorized treadmill for 10 min under both conditions, at 70% maximal aerobic speed. Electromyographic data were sampled from the biceps femoris, gluteus maximus, gastrocnemius medialis, tibialis anterior, and vastus lateralis during both swing and stance. Root-mean-square analysis of electromyographic data was conducted to compare muscle activation between conditions. During stance, barefoot running resulted in greater muscle activity in gastrocnemius medialis and gluteus maximus, and lower muscle activity in tibialis anterior. During swing, barefoot running resulted in increased muscle activity in vastus lateralis and gastrocnemius medialus. These results indicate that, for minimalist shoe users, an acute bout of barefoot running results in significantly different lower-limb muscle activity. Increased activation in the above muscles presents a possible mechanism for injury, which should be considered during exercise prescription.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.016
GPT teacher head0.278
Teacher spread0.262 · 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 designBench or experimental
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

Citations11
Published2016
Admission routes1
Has abstractyes

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