Evidence map›Paper›PMID 39231175›Full record

ArticlePloS one2024

In-shoe plantar shear stress sensor design, calibration and evaluation for the diabetic foot.

Athia H Haron, Lutong Li, Jiawei Shuang, Chaofan Lin, Helen Dawes, Maedeh Mansoubi, Damian Crosby, Garry Massey, Neil Reeves, Frank Bowling and 2 more

Abstract read
In one paragraph

Article in PloS one, 2024. 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
–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

4 citing papers in PubMed.

  1. Article
  2. Development and evaluation of a novel 3D in-shoe plantar strain measurement system: STAMPS3D.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2025
    Article
  3. Article
  4. Article
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

12 authors.

Athia H HaronDepartment of Mechanical, Aerospace and Civil Engineering (MACE), University of Manchester, Manchester, United Kingdom.ORCID 0000-0002-9170-6409
Lutong LiDepartment of Mechanical, Aerospace and Civil Engineering (MACE), University of Manchester, Manchester, United Kingdom.ORCID 0000-0003-0110-0452
Jiawei ShuangDepartment of Mechanical, Aerospace and Civil Engineering (MACE), University of Manchester, Manchester, United Kingdom.
Chaofan LinDepartment of Mechanical, Aerospace and Civil Engineering (MACE), University of Manchester, Manchester, United Kingdom.ORCID 0009-0000-4938-1180
Helen DawesMedical School, NIHR Exeter BRC, University of Exeter, Exeter, United Kingdom.
Maedeh MansoubiMedical School, NIHR Exeter BRC, University of Exeter, Exeter, United Kingdom.
Damian CrosbyDepartment of Mechanical, Aerospace and Civil Engineering (MACE), University of Manchester, Manchester, United Kingdom.ORCID 0000-0002-6879-3460
Garry MasseyMedical School, NIHR Exeter BRC, University of Exeter, Exeter, United Kingdom.
Neil ReevesMusculoskeletal Biomechanics and Research in Science and Engineering faculty of Manchester Metropolitan University, Manchester, United Kingdom.
Frank BowlingManchester University NHS Foundation Trust within the Departments of Diabetes and Vascular Surgery, Manchester, United Kingdom.
Glen CooperDepartment of Mechanical, Aerospace and Civil Engineering (MACE), University of Manchester, Manchester, United Kingdom.
Andrew WeightmanDepartment of Mechanical, Aerospace and Civil Engineering (MACE), University of Manchester, Manchester, United Kingdom.ORCID 0000-0001-7232-4942

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plantar shear stress may have an important role in the formation of a Diabetic Foot Ulcer, but its measurement is regarded as challenging and has limited research. This paper highlights the importance of anatomical specific shear sensor calibration and presents a feasibility study of a novel shear sensing system which has measured in-shoe shear stress from gait activity on both healthy and diabetic subjects. The sensing insole was based on a strain gauge array embedded in a silicone insole backed with a commercial normal pressure sensor. Sensor calibration factors were investigated using a custom mechanical test rig with indenter to exert both normal and shear forces. Indenter size and location were varied to investigate the importance of both loading area and position on measurement accuracy. The sensing insole, coupled with the calibration procedure, was tested one participant with diabetes and one healthy participant during two sessions of 15 minutes of treadmill walking. Calibration with different indenter areas (from 78.5 mm2 to 707 mm2) and different positions (up to 40 mm from sensor centre) showed variation in measurements of up to 80% and 90% respectively. Shear sensing results demonstrated high repeatability (>97%) and good accuracy (mean absolute error < ±18 kPa) in bench top mechanical tests and less than 21% variability within walking of 15-minutes duration. The results indicate the importance of mechanical coupling between embedded shear sensors and insole materials. It also highlights the importance of using an appropriate calibration method to ensure accurate shear stress measurement. The novel shear stress measurement system presented in this paper, demonstrates a viable method to measure accurate and repeatable in-shoe shear stress using the calibration procedure described. The validation and calibration methods outlined in this paper could be utilised as a standardised approach for the research community to develop and validate similar measurement technologies.

Indexed as

Diabetic FootShoesStress, MechanicalAdultBiomechanical PhenomenaCalibrationFemaleFootGaitHumansMaleMiddle AgedPressureWalking

Identifiers

PMID39231175
PMCPMC11373826

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.