Loading characteristics data applied on osseointegrated implant by transfemoral bone-anchored prostheses fitted with state-of-the-art components during daily activities

The data in this paper are related to the research article entitled “Load applied on osseointegrated implant by transfemoral bone-anchored prostheses fitted with state-of-the-art prosthetic components” (Frossard et al. Clinical Biomechanics, 89 (2021) 105457. DOI: 10.1016/j.clinbiomech.2021.105457). This article contains the overall and individual loading characteristics applied on transfemoral press-fit osseointegrated implant generated by bone-anchored prostheses fitted with state-of-the-art components during daily activities (i.e., microprocessor-controlled Rheo Knee XC knee, energy-storing-and-returning Pro-Flex XC or LP feet (ÖSSUR, Iceland)). Confounders of the loads are presented. The load profiles are characterized by the loading patterns, loading boundaries and loading local extrema of the forces and moments applied during straight-level walking, ascending and descending ramp and stairs at self-selected comfortable pace. The confounders of the loading information as well as new insights into inter-participants variability of loading patterns, loading boundaries and loading local extrema can inform the design of subsequent cross-sectional and longitudinal studies as well as literature reviews and meta-analyzes. The loading datasets are critical to clinicians and engineers designing finite element models of osseointegrated implants (e.g., medullar and percutaneous parts) and prosthetic components, algorithms capable to recognize the loading patterns applied on a residuum during daily activities, as well as clinical trials assessing the effects of particular prosthetic care interventions. Altogether, these datasets provide promoters of prosthetic care innovations with valuable insights informing the prescription of advanced prosthetic components to the growing population of individuals suffering from lower limb loss choosing bionics solutions. Online repository contains the files: https://data.mendeley.com/datasets/gmsyv97cpc/1

Abstract licence: CC BY-NC

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