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Record W4379469385 · doi:10.2196/42769

Gait Stability Characteristics in Able-Bodied Individuals During Self-paced Inclined Treadmill Walking: Within-Subject Repeated-Measures Study

2023· article· en· W4379469385 on OpenAlexvenueno aff
Chenmiao Lu, Rawan Al-Juaid, Mohammad Al-Amri

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
FundersInternational Society of Biomechanics
KeywordsTreadmillBicepsPhysical medicine and rehabilitationGaitKinematicsPower walkingPreferred walking speedJoint stabilityRepeated measures designBiomechanicsPhysical therapyMedicineMathematicsAnatomyPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: Inclined walking is a challenging task that requires active neuromuscular control to maintain stability. However, the adaptive strategies that preserve stability during inclined walking are not well understood. Investigating the effects of self-paced inclined treadmill walking on gait stability characteristics and the activation patterns of key lower limb muscles can provide insights into these strategies. OBJECTIVE: The aim of this study was to investigate the effects of self-paced inclined treadmill walking on gait stability characteristics and the activation of key lower limb muscles. METHODS: Twenty-eight able-bodied individuals (mean age 25.02, SD 2.06 years) walked on an augmented instrumented treadmill for 3 minutes at 3 inclination angles (-8°, 0°, and 8°) at their preferred walking speed. Changes in gait characteristics (ie, stability, walking speed, spatial-temporal, kinematic, and muscle forces) across inclination angles were assessed using a repeated measures ANOVA and the Friedman test. RESULTS: The study revealed that inclined treadmill walking has a significant impact on gait characteristics (P<.001). Changes were observed in spatial-temporal parameters, joint angles, and muscle activations depending on the treadmill inclination. Specifically, stability and walking speed decreased significantly during uphill walking, indicating that it was the most challenging walking condition. Uphill walking also led to a decrease in spatial parameters by at least 13.53% and a 5.26% to 10.96% increase in temporal parameters. Furthermore, joint kinematics and peak activation of several muscles, including the hamstrings (biceps femoris, long head=109.5%, biceps femoris, short head=53.3%, semimembranosus=98.9%, semitendinosus=90.9%), gastrocnemius (medial gastrocnemius=40.6%, lateral gastrocnemius=35.3%), and vastii muscles (vastus intermedius=12.8%, vastus lateralis=16.7%) increased significantly during uphill walking. In contrast, downhill walking resulted in bilateral reductions in spatial-temporal gait parameters, with knee flexion increasing and hip flexion and ankle dorsiflexion decreasing. The peak activation of antagonist muscles, such as the quadriceps, tibialis anterior, and tibialis posterior, significantly increased during downhill walking (rectus femoris=97.7%, vastus lateralis =70.6%, vastus intermedius=68.7%, tibialis anterior=72%, tibialis posterior=107.1%). CONCLUSIONS: Our findings demonstrate that able-bodied individuals adopt specific walking patterns during inclined treadmill walking to maintain a comfortable and safe walking performance. The results suggest that inclined treadmill walking has the potential to serve as a functional assessment and rehabilitation tool for gait stability by targeting muscle training. Future research should investigate the effects of inclined treadmill walking on individuals with gait impairments and the potential benefits of targeted muscle training. A better understanding of the adaptive strategies used during inclined walking may lead to the development of more effective rehabilitation interventions for individuals with lower limb injuries.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.103
GPT teacher head0.458
Teacher spread0.354 · 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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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