Please use this identifier to cite or link to this item: doi:10.22028/D291-39100
Title: Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle
Author(s): Wolff, Christian
Steinheimer, Patrick
Warmerdam, Elke
Dahmen, Tim
Slusallek, Philipp
Schlinkmann, Christian
Chen, Fei
Orth, Marcel
Pohlemann, Tim
Ganse, Bergita
Language: English
Title: Frontiers in Bioengineering and Biotechnology
Volume: 11
Publisher/Platform: Frontiers
Year of Publication: 2023
Free key words: gait
motion analysis
ground reaction (forces)
ageing
obesity
insoles
handgrip strengh
smart healthcare
DDC notations: 610 Medicine and health
Publikation type: Journal Article
Abstract: The analysis of gait patterns and plantar pressure distributions via insoles is increasingly used to monitor patients and treatment progress, such as recovery after surgeries. Despite the popularity of pedography, also known as baropodography, characteristic effects of anthropometric and other individual parameters on the trajectory of the stance phase curve of the gait cycle have not been previously reported. We hypothesized characteristic changes of age, body height, body weight, body mass index and handgrip strength on the plantar pressure curve trajectory during gait in healthy participants. Thirty-seven healthy women and men with an average age of 43.65 ± 17.59 years were fitted with Moticon OpenGO insoles equipped with 16 pressure sensors each. Data were recorded at a frequency of 100 Hz during walking at 4 km/h on a level treadmill for 1 minute. Data were processed via a custom-made step detection algorithm. The loading and unloading slopes as well as force extrema-based parameters were computed and characteristic correlations with the targeted parameters were identified via multiple linear regression analysis. Age showed a negative correlation with the mean loading slope. Body height correlated with Fmeanload and the loading slope. Body weight and the body mass index correlated with all analyzed parameters, except the loading slope. In addition, handgrip strength correlated with changes in the second half of the stance phase and did not affect the first half, which is likely due to stronger kick-off. However, only up to 46% of the variability can be explained by age, body weight, height, body mass index and hand grip strength. Thus, further factors must affect the trajectory of the gait cycle curve that were not considered in the present analysis. In conclusion, all analyzed measures affect the trajectory of the stance phase curve. When analyzing insole data, it might be useful to correct for the factors that were identified by using the regression coefficients presented in this paper.
DOI of the first publication: 10.3389/fbioe.2023.1110099
URL of the first publication: https://www.frontiersin.org/articles/10.3389/fbioe.2023.1110099
Link to this record: urn:nbn:de:bsz:291--ds-391002
hdl:20.500.11880/35256
http://dx.doi.org/10.22028/D291-39100
ISSN: 2296-4185
Date of registration: 21-Feb-2023
Description of the related object: Supplementary Material
Related object: https://www.frontiersin.org/articles/file/downloadfile/1110099_supplementary-materials_datasheets_1_xlsx/octet-stream/Data%20Sheet%201.XLSX/1/1110099
Faculty: M - Medizinische Fakultät
MI - Fakultät für Mathematik und Informatik
Department: M - Chirurgie
MI - Informatik
Professorship: M - Prof. Dr. med. Bergita Ganse
M - Prof. Dr. Tim Pohlemann
MI - Prof. Dr. Philipp Slusallek
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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