Evidence map›Paper›PMID 42327294›Full record

ArticlebioRxiv : the preprint server for biology2026

Predictive Neuromechanical Simulation Explains Gait Biomechanics in Obesity.

Chi-Whan Choi, Vincent Ton, Simone V Gill, Seungmoon Song

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Chi-Whan ChoiSargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA.ORCID 0000-0002-1467-4802
Vincent TonDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA.ORCID 0009-0003-3226-0456
Simone V GillSargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA.ORCID 0000-0003-4019-1609
Seungmoon SongDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA.ORCID 0000-0002-4358-5948

Funding

Simulation framework to develop ankle exoskeleton gait assistance for older adultsR00AG065524 · NIA · NORTHEASTERN UNIVERSITY · PI SONG, SEUNGMOON · 2022 to 2024
$727k
NIA NIH HHS R00 AG065524
6 · The paper itself

Abstract

Individuals with obesity exhibit gait adaptations including reduced early-stance knee flexion, altered muscle coordination, slower preferred walking speeds, and shorter step lengths. Although these features are well documented, the mechanisms by which obesity-related physiological changes produce these patterns and influence knee joint loading relevant to osteoarthritis (OA) remain unclear. This study used predictive neuromechanical simulation to examine how musculoskeletal changes and movement objectives interact to generate obesity-associated gait patterns and tibiofemoral loading. Predictive simulations were performed using a reflex-based neuromechanical walking model. A baseline non-obese model (1.8 m, 80 kg) was modified to represent obesity-related changes in segment mass distribution and muscle strength (1.8 m, 140 kg), including more apple-like and more pear-like body mass distributions. Control parameters were optimized to generate stable walking while minimizing muscle effort and tibiofemoral joint loading. Objective weightings were identified by matching simulated knee kinematics to experimental observations at a typical walking speed. Using the selected weightings, we compared joint kinematics, kinetics, and muscle activations, and simulations were performed across walking speeds to evaluate optimal walking speed, step length, muscle effort, and knee loading. The baseline model best matched reference knee kinematics using a muscle-effort objective alone, whereas the obese model required a combined objective penalizing both muscle effort and knee loading. This formulation reproduced key gait features, including reduced early-stance knee flexion, reduced vastii activation with increased plantarflexor activation, slower optimal walking speeds, and shorter step lengths. Variations in body mass distribution produced moderate but consistent effects on gait mechanics relative to larger effects of increased body mass. Obesity-related changes in body mass and muscle strength alone did not reproduce observed gait patterns, but incorporating an objective that penalizes knee loading generated multiple characteristic features. Predictive neuromechanical simulation provides a framework for identifying candidate mechanisms linking obesity, gait biomechanics, and knee joint loading.

Indexed as

Gait biomechanicsKnee joint loadingNeuromechanical simulationsObesity

Identifiers

PMID42327294
PMCPMC13277856

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.