[How reliable are data from 3d-gait analysis]

Kinematic Gait Analysis After Primary Total Hip Replacement: A Systematic Review: Gait After Total Hip Replacement: A Systematic Review

Total hip replacement (THR) is a surgical procedure indicated for patients affected by severe hip osteoarthritis. Although this technique has proved to be effective in relieving pain and restoring function, gait limitations may persist following this procedure. The objective of this systematic review was to evaluate gait kinematics after THR and compare the results with those of the pre-operative state and with healthy control individuals. PubMed/MEDLINE, Embase, Web of Science, CENTRAL and Scopus databases were searched until December 2019. Methodological quality and internal validity score of each study were assessed using the PEDro and the Newcastle-Ottawa scales. In all, ten studies met our inclusion criteria. Following THR, statistically significant improvements were seen in dynamic hip and knee range of motion of both the affected and the contralateral limb, single-limb support time symmetry, step length, stride length, walking speed and gait pattern. However, deficits were observed in all the previous parameters, as well as in hip adduction angle in comparison with healthy subjects. In conclusion, gait patterns improve after THR in comparison with the pre-operative state, although there are deficits relative to healthy individuals.

Managing variability in the summary and comparison of gait data

Variability in quantitative gait data arises from many potential sources, including natural temporal dynamics of neuromotor control, pathologies of the neurological or musculoskeletal systems, the effects of aging, as well as variations in the external environment, assistive devices, instrumentation or data collection methodologies. In light of this variability, unidimensional, cycle-based gait variables such as stride period should be viewed as random variables and prototypical single-cycle kinematic or kinetic curves ought to be considered as random functions of time. Within this framework, we exemplify some practical solutions to a number of commonly encountered analytical challenges in dealing with gait variability. On the topic of univariate gait variables, robust estimation is proposed as a means of coping with contaminated gait data, and the summary of non-normally distributed gait data is demonstrated by way of empirical examples. On the summary of gait curves, we discuss methods to manage undesirable phase variation and non-robust spread estimates. To overcome the limitations of conventional comparisons among curve landmarks or parameters, we propose as a viable alternative, the combination of curve registration, robust estimation, and formal statistical testing of curves as coherent units. On the basis of these discussions, we provide heuristic guidelines for the summary of gait variables and the comparison of gait curves.