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Record W2417185217 · doi:10.1111/sms.12692

Effects of the foot strike pattern on muscle activity and neuromuscular fatigue in downhill trail running

2016· article· en· W2417185217 on OpenAlexaff
Marlène Giandolini, Nicolas Horvais, Jérémy Rossi, Guillaume Y. Millet, Jean-Benoı̂t Morin, Pierre Samozino

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2016
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Calgary
FundersUniversité Savoie Mont Blanc
KeywordsMedicineMuscle fatigueHeelPeripheralPlantar flexionElectromyographyPhysical medicine and rehabilitationTibialis anterior muscleInternal medicineAnkleCardiologyAnatomySkeletal muscle

Abstract

fetched live from OpenAlex

Minimizing musculo-skeletal damage and fatigue is considered paramount for performance in trail running. Our purposes were to investigate the effects of the foot strike pattern and its variability on (a) muscle activity during a downhill trail run and (b) immediate and delayed neuromuscular fatigue. Twenty-three runners performed a 6.5-km run (1264 m of negative elevation change). Electromyographic activity of lower-limb muscles was recorded continuously. Heel and metatarsal accelerations were recorded to identify the running technique. Peripheral and central fatigue was assessed in knee extensors (KE) and plantar flexors (PF) at Pre-, Post-, and 2 days post downhill run (Post2d). Anterior patterns were associated with (a) higher gastrocnemius lateralis activity and lower tibialis anterior and vastus lateralis activity during the run and (b) larger decreases in KE high-frequency stimulus-evoked torque Post and larger decrements in KE MVC Post2d. High patterns variability during the run was associated with (a) smaller decreases in KE Db100 Post and MVC Post2d and (b) smaller decreases in PF MVC Post and Post2d. Anterior patterns increase the severity of KE peripheral fatigue. However, high foot strike pattern variability during the run reduced acute and delayed neuromuscular fatigue in KE and PF.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.238
Teacher spread0.221 · 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

Citations33
Published2016
Admission routes1
Has abstractyes

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