BackgroundExercise-induced fatigue is a common consequence of physical activities. Particularly in older adults, it can affect gait performance. Due to a wide variety in fatiguing protocols and gait parameters used in experimental settings, pooled effects are not yet clear. Furthermore, specific elements of fatiguing protocols (i.e., intensity, duration, and type of activity) might lead to different changes in gait parameters. We aimed to systematically quantify to what extent exercise-induced fatigue alters gait in community-dwelling older adults, and whether specific elements of fatiguing protocols could be identified.MethodsThis systematic review and meta-analysis was conducted in accordance with the PRISMA guidelines. In April 2023, PubMed, Web of Science, Scopus, Cochrane and CINAHL databases were searched. Two independent researchers screened and assessed articles using ASReview, Rayyan, and ROBINS-I. The extracted data related to spatio-temporal, stability, and variability gait parameters of healthy older adults (55 +) before and after a fatiguing protocol or prolonged physical exercise. Random-effects meta-analyses were performed on both absolute and non-absolute effect sizes in RStudio. Moderator analyses were performed on six clusters of gait parameters (Dynamic Balance, Lower Limb Kinematics, Regularity, Spatio-temporal Parameters, Symmetry, Velocity).ResultsWe included 573 effect sizes on gait parameters from 31 studies. The included studies reflected a total population of 761 older adults (57% female), with a mean age of 71 (SD 3) years. Meta-analysis indicated that exercise-induced fatigue affected gait with a standardized mean change of 0.31 (p < .001). Further analyses showed no statistical differences between the different clusters, and within clusters, the effects were non-uniform, resulting in an (indistinguishable from) zero overall effect within all clusters. Elements of fatiguing protocols like duration, (perceived) intensity, or type of activity did not moderate effects.DiscussionDue to the (mainly) low GRADE certainty ratings as a result of the heterogeneity between studies, and possible different strategies to cope with fatigue between participants, the only conclusion that can be drawn is that older adults, therapist, and researchers should be aware of the small to moderate changes in gait parameters as a result of exercise-induced fatigue.
BackgroundIn adolescents with non-pathological and pathological joint hypermobility, gait deviations have been associated with pain and fatigue. It remains unclear what distinguishes the non-pathological form of joint hypermobility (JH) from pathological forms (i.e. hypermobile Ehlers-Danlos syndrome (hEDS) or hypermobility spectrum disorders (HSD). Our objective was to identify discriminative clinical characteristics and biomechanical gait features between adolescents with hEDS/HSD, JH, and healthy controls (HC).MethodsThirty-two adolescents were classified into three subgroups (hEDS/HSD=12, JH=5, HC=15). Clinical characteristics (e.g. pain intensity and surface, fatigue, functional disability) were inventoried.The gait pattern was assessed using a three-dimensional, eight-camera VICON MX1.3 motion capture system, operating at a sample rate of 100 Hz (VICON, Oxford, UK). Spatiotemporal parameters, joint angles (sagittal plane), joint work, joint impulse, ground reaction force and gait variability expressed as percentage using Principal Component Analysis (PCA) were assessed and analysed using multivariate analysis. Multivariate analysis data is expressed in mean differences(MD), standard error(SE) and P-values.ResultsThe hEDS/HSD-group had significantly higher fatigue score (+51.5 points, p = <0.001) and functional disability (+1.6, p < .001) than the HC-group. Pain intensity was significantly higher in the hEDS/HSD-group than the other subgroups (JH; +37 mm p = .004, HC; +38 mm, p = .001). The hEDS/HSD-group showed significantly more gait variability (JH; +7.2(2.0)% p = .003, HC; + 7.8(1.4)%, p = <0.001) and lower joint work (JH; −0.07(0.03)J/kg, p = .007, HC; − 0.06(0.03)J/kg, p = .013) than the other subgroups. The JH-group showed significantly increased ankle dorsiflexion during terminal stance (+5.0(1.5)degree, p = .001) compared to hEDS/HSD-group and knee flexion during loading response compared to HC-group (+5.7(1.8) degree, p = .011).SignificanceA distinctive difference in gait pattern between adolescents with non-pathological and pathological joint hypermobility is found in gait variability, rather than in the biomechanical features of gait. This suggests that a specific gait variability metric is more appropriate than biomechanical individual joint patterns for assessing gait in adolescents with hEDS/HSD.
Background The gait modification strategies Trunk Lean and Medial Thrust have been shown to reduce the external knee adduction moment (EKAM) in patients with knee osteoarthritis which could contribute to reduced progression of the disease. Which strategy is most optimal differs between individuals, but the underlying mechanism that causes this remains unknown. Research question Which gait parameters determine the optimal gait modification strategy for individual patients with knee osteoarthritis? Methods Forty-seven participants with symptomatic medial knee osteoarthritis underwent 3-dimensional motion analysis during comfortable gait and with two gait modification strategies: Medial Thrust and Trunk Lean. Kinematic and kinetic variables were calculated. Participants were then categorized into one of the two subgroups, based on the modification strategy that reduced the EKAM the most for them. Multiple logistic regression analysis with backward elimination was used to investigate the predictive nature of dynamic parameters obtained during comfortable walking on the optimal modification gait strategy. Results For 68.1 % of the participants, Trunk Lean was the optimal strategy in reducing the EKAM. Baseline characteristics, kinematics and kinetics did not differ significantly between subgroups during comfortable walking. Changes to frontal trunk and tibia angles correlated significantly with EKAM reduction during the Trunk Lean and Medial Thrust strategies, respectively. Regression analysis showed that MT is likely optimal when the frontal tibia angle range of motion and peak knee flexion angle in early stance during comfortable walking are high (R2Nagelkerke = 0.12). Significance Our regression model based solely on kinematic parameters from comfortable walking contained characteristics of the frontal tibia angle and knee flexion angle. As the model explains only 12.3 % of variance, clinical application does not seem feasible. Direct assessment of kinetics seems to be the most optimal strategy for selecting the most optimal gait modification strategy for individual patients with knee osteoarthritis.
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