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Record W4402467655 · doi:10.1101/2024.09.10.24313424

EFFECTS OF LIFESTYLE ACTIVITY LEVEL ON ANTICIPATORY LOCOMOTOR ADJUSTMENTS FOR PEDESTRIAN CIRCUMVENTION

2024· preprint· en· W4402467655 on OpenAlexafffund
Joris Boulo, Margaux Simon, Bradford J. McFadyen, Andréanne K. Blanchette

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
FundersNatural Sciences and Engineering Research Council of CanadaRéseau Provincial de Recherche en Adaptation-Réadaptation
KeywordsPedestrianPsychologyEnvironmental scienceTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Navigating public environments requires adjustments to one’s walking patterns to avoid stationary and moving obstacles. It is known that physical inactivity induces alterations in motor capacities, but the impact of inactivity on anticipatory locomotor adjustments (ALA) has not been studied. The purpose of the present study was to compare ALAs and related muscle co-contraction during a pedestrian circumvention task between active (AA) and inactive young adults (IA). Thirteen AA and thirteen IA were placed in a virtual environment simulating a public park. Participants circumvented virtual pedestrians walking towards them. Walking speed, onset of deviation, clearance, foot placement strategies and muscle co-contraction were analysed. IA exhibited slower walking speeds compared to the AA during circumvention condition but not during unobstructed walking condition. The distance at the onset of trajectory deviation was larger for IA. Both groups increased co-contraction for pedestrian circumvention at the ankle and left hip and IA displayed greater ankle co-contraction overall. No significant group differences were observed in minimum clearance. This study suggests that an inactive lifestyle influences ALAs by inducing a cautious behavior during pedestrian circumvention.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.358
Teacher spread0.305 · 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.

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

Citations1
Published2024
Admission routes2
Has abstractyes

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