Evidence map›Paper›PMID 41463620›Full record

ArticleBioengineering (Basel, Switzerland)2025

Finite Element-Based Biomechanical Evaluation of Patient-Specific Insoles for a Pediatric Patient with Hereditary Spastic Paraplegia Using the Taguchi Method.

Dhifaf Muhi Alsaleh, Fuat Bilgili, Meral Bayraktar, Yunus Ziya Arslan

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Dhifaf Muhi AlsalehDepartment of Mechanical Engineering, Faculty of Mechanical Engineering, Yildiz Technical University, Istanbul 34349, Türkiye.ORCID 0009-0007-5077-3887
Fuat BilgiliDepartment of Orthopedics and Traumatology, Istanbul Faculty of Medicine, Istanbul University, Istanbul 34093, Türkiye.
Meral BayraktarDepartment of Mechanical Engineering, Faculty of Mechanical Engineering, Yildiz Technical University, Istanbul 34349, Türkiye.ORCID 0000-0002-8779-0476
Yunus Ziya ArslanDepartment of Robotics and Intelligent Systems, Institute of Graduate Studies in Science and Engineering, Turkish-German University, Istanbul 34820, Türkiye.ORCID 0000-0002-1861-9368

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Customized foot orthoses are widely used to manage plantar pressure and improve structural support in children with hereditary spastic paraparesis. However, the combined biomechanical effects of insole design parameters remain insufficiently quantified. This study employed a patient-specific three-dimensional finite element model to evaluate the influence of four design factors (arch height, heel cup depth, insole thickness, and material type, namely ethylene-vinyl acetate [EVA], thermoplastic polyurethane [TPU], and rubber) on four biomechanical metrics: plantar pressure distribution, von Mises stress, strain, and total deformation. Nine orthotic configurations, defined by a Taguchi L9 orthogonal array, were simulated under a vertical ground reaction force equal to 1.1× body weight. The configuration with an arch height of 42 mm, heel cup depth of 20 mm, thickness of 10 mm, and EVA material achieved the lowest peak plantar pressure (0.087 MPa). Arch height was the dominant factor for plantar pressure (79.4% of variance), deformation (68.1%), and strain (48.2%), while heel cup depth was most influential for stress (40.2%). Material type contributed minimally to plantar pressure and deformation but had a greater effect on stress (11.6%) and strain (15.0%). Thickness played a secondary role, particularly in deformation (19.9%) and strain (22.3%). These findings demonstrate the feasibility of using finite element modeling combined with the Taguchi method to systematically evaluate and optimize orthotic design parameters. Specifically, the study demonstrates that optimized personalized insoles can substantially reduce peak plantar pressure and improve load distribution in a pediatric patient with HSP, pes planovalgus, and flexed-knee gait, providing a potentially effective noninvasive intervention to prevent secondary complications and improve gait mechanics.

Indexed as

ANOVAfinite element analysishereditary spastic paraparesisMinitabpatient specific foot insolepes planovalgusTaguchi method

Identifiers

PMID41463620
PMCPMC12730074

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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 OpenQuestion graph.