Evidence map›Paper›PMID 35945533›Full record

ArticleBiomedical engineering online2022

Analysis of skeletal characteristics of flat feet using three-dimensional foot scanner and digital footprint.

Tomoko Yamashita, Kazuhiko Yamashita, Mitsuru Sato, Masashi Kawasumi, Shingo Ata

Open access · goldAbstract read
In one paragraph

Article in Biomedical engineering online, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 35% of its field
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

4 citing papers in PubMed, 9 citations in OpenAlex.

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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

5 authors at 4 institutions in 1 country.

Tomoko YamashitaGraduate School of Engineering, Osaka City University, 3-3-138 Sugimoto, Sumiyoshi, Osaka, 558-8585, Japan. d19tb552@st.osaka-cu.ac.jp.
Kazuhiko YamashitaDepartment of Clinical Engineering, Faculty of Human Care at Makuhari, Tohto University, Chiba, Japan.
Mitsuru SatoSchool of Nursing and Rehabilitation Sciences, Showa University, Kanagawa, Japan.
Masashi KawasumiSchool of Science and Technology for Future Life, Tokyo Denki University, Tokyo, Japan.
Shingo AtaGraduate School of Engineering, Osaka City University, 3-3-138 Sugimoto, Sumiyoshi, Osaka, 558-8585, Japan.
Osaka City University · JPSeisa Dohto University · JPShowa University · JPTokyo Denki University · JP

Funding

Japan Society for the Promotion of Science 18K12161Japan Society for the Promotion of Science,Japan 18H03559
6 · The paper itself

Abstract

backgroundFlat feet increase the risk of knee osteoarthritis and contribute to frailty, which may lead to worse life prognoses. The influence of the foot skeletal structure on flat feet is not yet entirely understood. Footprints are often used to evaluate feet. However, footprint-based measurements do not reflect the underlying structures of feet and are easily confounded by soft tissue. Three-dimensional evaluation of the foot shape can reveal the characteristics of flat feet. Therefore, foot shape evaluations have garnered increasing research interest. This study aimed to determine the correlation between the three-dimensional (3D) features of the foot and the measurement results of footprint and to predict the evaluation results of flat feet from the footprint based on the 3D features. Finally, the three-dimensional characteristics of flat feet, which cannot be revealed by footprint, were determined.

methodsA total of 403 individuals (40-89 years) participated in this study. The proposed system was developed to identify seven skeletal features that were expected to be associated with flat feet. The loads on the soles of the feet were measured in a static standing position and with a digital footprint device. Specifically, two footprint indices were calculated: the Chippaux-Smirak index (CSI) and the Staheli index (SI). In the analysis, comparisons between male and female measurement variables were performed using the Student's t test. The relationships between the 3D foot features and footprint index parameters were determined by employing the Pearson correlation coefficient. Multiple linear regression was utilized to identify 3D foot features that were strongly associated with the CSI and SI. Foot features identified as significant in the multivariate regression analysis were compared based on a one-way analysis of variance (ANOVA) with Tukey's post hoc test.

resultsThe CSI and SI were highly correlated with the instep height (IH) and navicular height (NH) of the 3D foot scanning system and were also derived from multiple regression analysis. In addition to the NH and IH, the indicators of the forefoot, transverse arch width, and transverse arch height were considered. In the flat foot group with CSI values above 62.7%, NH was 13.5% (p < 0.001) for males and 14.9% (p = 0.01) for females, and the axis of the bone distance was 5.3% (p = 0.05) for males and 4.9% (p = 0.10) for females. In particular, for CSI values above 62.7% and NH values below 13%, the axis of the bone distance was large and the foot skeleton was deformed.

conclusionsDecreased navicular bone height could be evaluated with the 3D foot scanning system even when flat feet were not detected from the footprint. The results indicate that the use of quantitative indices for 3D foot measurements is important when evaluating the flattening of the foot. Trial registration number UMIN000037694. Name of the registry: University Hospital Medical Information Network Registry. Date of registration: August 15, 2019.

Indexed as

FlatfootAnthropometryBody HeightFemaleFootHumansLinear ModelsMale3D foot scanningAxis of bone distanceDigital footprintFlat feetNavicular height

Identifiers

PMID35945533
PMCPMC9361570
OpenAlexW4291020211

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.