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: Ambulatory children with Spina Bifida (SB) often show a decline in physical activity leading to deconditioning and functional decline. Therefore, assessment and promotion of physical activity is important. Because energy expenditure during activities is higher in these children, the use of existing pediatric equations to predict physical activity energy expenditure (PAEE) may not be valid. AIMS: (1) To evaluate criterion validity of existing predictions converting accelerocounts into PAEE in ambulatory children with SB and (2) to establish new disease-specific equations for PAEE. METHODS: Simultaneous measurements using the Actical, the Actiheart, and indirect calorimetry took place to determine PAEE in 26 ambulatory children with SB. DATA ANALYSIS: Paired T-tests, Intra-class correlations limits of agreement (LoA), and explained variance (R2) were used to analyze validity of the prediction equations using true PAEE as criterion. New equations were derived using regression techniques. RESULTS: While T-tests showed no significant differences for some models, the predictions developed in healthy children showed moderate ICC’s and large LoA with true PAEE. The best regression models to predict PAEE were: PAEE = 174.049 + 3.861 × HRAR – 60.285 × ambulatory status (R2 = 0.720) and PAEE = 220.484 + 0.67 × Actical counts – 60.717 × ambulatory status (R2 = 0.681). CONCLUSIONS: Existing equations to predict PAEE are not valid for use in children with SB for the individual evaluation of PAEE. The best regression model was based on HRAR in combination with ambulatory status, followed by a new model for the Actical monitor. A benefit of HRAR is that it does not require the use of expensive accelerometry equipment. Further cross-validation of these models is still needed.
Literature highlights the need for research on changes in lumbar movement patterns, as potential mechanisms underlying the persistence of low-back pain. Variability and local dynamic stability are frequently used to characterize movement patterns. In view of a lack of information on reliability of these measures, we determined their within- and between-session reliability in repeated seated reaching. Thirty-six participants (21 healthy, 15 LBP) executed three trials of repeated seated reaching on two days. An optical motion capture system recorded positions of cluster markers, located on the spinous processes of S1 and T8. Movement patterns were characterized by the spatial variability (meanSD) of the lumbar Euler angles: flexion–extension, lateral bending, axial rotation, temporal variability (CyclSD) and local dynamic stability (LDE). Reliability was evaluated using intraclass correlation coefficients (ICC), coefficients of variation (CV) and Bland-Altman plots. Sufficient reliability was defined as an ICC ≥ 0.5 and a CV < 20%. To determine the effect of number of repetitions on reliability, analyses were performed for the first 10, 20, 30, and 40 repetitions of each time series. MeanSD, CyclSD, and the LDE had moderate within-session reliability; meanSD: ICC = 0.60–0.73 (CV = 14–17%); CyclSD: ICC = 0.68 (CV = 17%); LDE: ICC = 0.62 (CV = 5%). Between-session reliability was somewhat lower; meanSD: ICC = 0.44–0.73 (CV = 17–19%); CyclSD: ICC = 0.45–0.56 (CV = 19–22%); LDE: ICC = 0.25–0.54 (CV = 5–6%). MeanSD, CyclSD and the LDE are sufficiently reliable to assess lumbar movement patterns in single-session experiments, and at best sufficiently reliable in multi-session experiments. Within-session, a plateau in reliability appears to be reached at 40 repetitions for meanSD (flexion–extension), meanSD (axial-rotation) and CyclSD.
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