ArticleOrthopedic research and reviews2023
3D Printing of Individual Running Insoles - A Case Study.
Article in Orthopedic research and reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Impact of Material Stiffness on Medial Arch Support and Stress Redistribution in 3D-Printed Insoles for Adult Acquired Flatfoot Deformity.Bioengineering (Basel, Switzerland) · 2026Article
- Clinical Evaluation of Novel Custom 3D-Printed Meshed-Silicone Orthotics Utilizing Standing Foot Scans and Dynamic Gait Data.Biomedical engineering and computational biology · 2025Article
- PA12 Surface Treatment and Its Effect on Compatibility with Nutritional Culture Medium to Maintain Cell Vitality and Proliferation.Bioengineering (Basel, Switzerland) · 2024Article
- Literature Review of an Anterior Deprogrammer to Determine the Centric Relation and Presentation of Cases.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The study's starting point is to find a low-cost and best-fit solution for comfortable movement for a recreational runner with knee pain using an orthopedic device. It is a case study. The research aims to apply digitization, CAD/CAM tools, and 3D printing to create an individual 3D running insole. The objective is to incorporate flexible shape optimization would provide comfort reductions in foot plantar pressures in one subject with knee pain while running. The test hypothesis was if it is possible to make it from one material. For this purpose, we created a new digital workflow based on the Decision Tree method and analyzed pain and comfort scores during user testing of prototypes. Patient and Methods: The input data were obtained during a professional examination by a specialist doctor in the orthopedic outpatient clinic in the motion laboratory (DIERS 4D Motion Lab, Germany) with the output of data on the proband's complex movement stereotype. Surface and volumetric data were obtained in the biomedical laboratory with the 3D scanner. We modified the digital 3D foot models in 3D mesh software, developed the design in SW Gensole (Gyrobot, UK), and finally incorporated the internal structure and the surface layer of the insole data of the knowledge from the medical examination, comfort analyses, and scientific studies findings. Results: Four complete 3D-printed prototypes (n=4) with differences in density and correction elements were designed. All of them were fabricated on a 3D printer (Prusa i3 MK3S, Czech Republic) with flexible TPU material suitable for skin contact. The Participant tested each of them five times in the field during a workout and final insoles three months on the routine training. Conclusion: A novel workflow was created for designing, producing, and testing full 3D-printed insoles. The product is fit for immediate use.
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Registered trials
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.