Evidence map›Paper›PMID 36042445›Full record

ArticleBMC musculoskeletal disorders2022

Effects of walking speeds and durations on the plantar pressure gradient and pressure gradient angle.

Chi-Wen Lung, Pu-Chun Mo, Chunmei Cao, Keying Zhang, Fu-Lien Wu, Ben-Yi Liau, Yih-Kuen Jan

Open access · goldAbstract read
In one paragraph

Article in BMC musculoskeletal disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.2field-weighted citation impact, top 10% 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

13 citing papers in PubMed, 19 citations in OpenAlex.

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  12. Analysis of Diabetic Foot Deformation and Plantar Pressure Distribution of Women at Different Walking Speeds.International journal of environmental research and public health · 2023
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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

7 authors at 3 institutions in 3 countries.

Chi-Wen LungRehabilitation Engineering Lab, Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Pu-Chun MoRehabilitation Engineering Lab, Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Chunmei CaoDivision of Sports Science and Physical Education, Tsinghua University, Beijing, China.
Keying ZhangDivision of Sports Science and Physical Education, Tsinghua University, Beijing, China.
Fu-Lien WuRehabilitation Engineering Lab, Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Ben-Yi LiauDepartment of Biomedical Engineering, Hungkuang University, Taichung, Taiwan.
Yih-Kuen JanRehabilitation Engineering Lab, Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL, USA. yjan@illinois.edu.
University of Illinois Urbana-Champaign · USTsinghua University · CNHungkuang University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWalking exercise has been demonstrated to improve health in people with diabetes. However, it is largely unknown the influences of various walking intensities such as walking speeds and durations on dynamic plantar pressure distributions in non-diabetics and diabetics. Traditional methods ignoring time-series changes of plantar pressure patterns may not fully capture the effect of walking intensities on plantar tissues. The purpose of this study was to investigate the effect of various walking intensities on the dynamic plantar pressure distributions. In this study, we introduced the peak pressure gradient (PPG) and its dynamic patterns defined as the pressure gradient angle (PGA) to quantify dynamic changes of plantar pressure distributions during walking at various intensities.

methodsTwelve healthy participants (5 males and 7 females) were recruited in this study. The demographic data were: age, 27.1 ± 5.8 years; height, 1.7 ± 0.1 m; and weight, 63.5 ± 13.5 kg (mean ± standard deviation). An insole plantar pressure measurement system was used to measure plantar pressures during walking at three walking speeds (slow walking 1.8 mph, brisk walking 3.6 mph, and slow running 5.4 mph) for two durations (10 and 20 min). The gradient at a location is defined as the unique vector field in the two-dimensional Cartesian coordinate system with a Euclidean metric. PGA was calculated by quantifying the directional variation of the instantaneous peak gradient vector during stance phase of walking. PPG and PGA were calculated in the plantar regions of the first toe, first metatarsal head, second metatarsal head, and heel at higher risk for foot ulcers. Two-way ANOVA with Fisher's post-hoc analysis was used to examine the speed and duration factors on PPG and PGA.

resultsThe results showed that the walking speeds significantly affect PPG (P < 0.05) and PGA (P < 0.05), and the walking durations does not. No interaction between the walking duration and speed was observed. PPG in the first toe region after 5.4 mph for either 10 or 20 min was significantly higher than 1.8 mph. Meanwhile, after 3.6 mph for 20 min, PPG in the heel region was significantly higher than 1.8 mph. Results also indicate that PGA in the forefoot region after 3.6 mph for 20 min was significantly narrower than 1.8 mph.

conclusionsOur findings indicate that people may walk at a slow speed at 1.8 mph for reducing PPG and preventing PGA concentrated over a small area compared to brisk walking at 3.6 mph and slow running at 5.4 mph.

Indexed as

Walking SpeedAdultFemaleFootHumansMalePressureShoesWalkingYoung AdultDiabetic foot ulcersPeak plantar pressurePeak pressure gradientPressure gradient angleWalking durationsWalking speeds

Identifiers

PMID36042445
PMCPMC9426236
OpenAlexW4293661035

What OpenQuestion 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 OpenQuestion graph.