Children with Marfan (MFS) and Loeys-Dietz syndrome (LDS) report limitations in physical activities, sports, school, leisure, and work participation in daily life. This observational, cross-sectional, multicenter study explores associations between physical fitness and cardiovascular parameters, systemic manifestations, fatigue, and pain in children with MFS and LDS. Forty-two participants, aged 6–18 years (mean (SD) 11.5(3.7)), diagnosed with MFS (n = 36) or LDS (n = 6), were enrolled. Physical fitness was evaluated using the Fitkids Treadmill Test’s time to exhaustion (TTE) outcome measure. Cardiovascular parameters (e.g., echocardiographic parameters, aortic surgery, cardiovascular medication) and systemic manifestations (systemic score of the revised Ghent criteria) were collected. Pain was obtained by visual analog scale. Fatigue was evaluated by PROMIS® Fatigue-10a-Pediatric-v2.0-short-form and PROMIS® Fatigue-10a-Parent-Proxy-v2.0-short-form. Multivariate linear regression analyses explored associations between physical fitness (dependent variable) and independent variables that emerged from the univariate linear regression analyses (criterion p <.05). The total group (MFS and LDS) and the MFS subgroup scored below norms on physical fitness TTE Z-score (mean (SD) −3.1 (2.9); −3.0 (3.0), respectively). Univariate analyses showed associations between TTE Z-score aortic surgery, fatigue, and pain (criterion p <.05). Multivariate analyses showed an association between physical fitness and pediatric self-reported fatigue that explained 48%; 49%, respectively, of TTE Z-score variance (F (1,18) = 18.6, p ≤.001, r2 =.48; F (1,15) = 16,3, p =.01, r2 =.49, respectively). Conclusions: Physical fitness is low in children with MFS or LDS and associated with self-reported fatigue. Our findings emphasize the potential of standardized and tailored exercise programs to improve physical fitness and reduce fatigue, ultimately enhancing the physical activity and sports, school, leisure, and work participation of children with MFS and LDS. (Table presented.)
The purpose of the study was to assess the accuracy of estimates of step frequency from trunk acceleration data analyzed with commonly used algorithms and time window lengths, at a wide range of gait speeds. Twenty healthy young subjects performed an incremental treadmill protocol from 1 km/h up to 6 km/h, with steps of 1 km/h. Each speed condition was maintained for two minutes. A waist worn accelerometer recorded trunk accelerations, while video analysis provided the correct number of steps taken during each gait speed condition. Accuracy of two commonly used signal analysis methods was examined with several different time windows.
Objective: This exploratory study investigated to what extent gait characteristics and clinical physical therapy assessments predict falls in chronic stroke survivors. Design: Prospective study. Subjects: Chronic fall-prone and non-fall-prone stroke survivors. Methods: Steady-state gait characteristics were collected from 40 participants while walking on a treadmill with motion capture of spatio-temporal, variability, and stability measures. An accelerometer was used to collect daily-life gait characteristics during 7 days. Six physical and psychological assessments were administered. Fall events were determined using a “fall calendar” and monthly phone calls over a 6-month period. After data reduction through principal component analysis, the predictive capacity of each method was determined by logistic regression. Results: Thirty-eight percent of the participants were classified as fallers. Laboratory-based and daily-life gait characteristics predicted falls acceptably well, with an area under the curve of, 0.73 and 0.72, respectively, while fall predictions from clinical assessments were limited (0.64). Conclusion: Independent of the type of gait assessment, qualitative gait characteristics are better fall predictors than clinical assessments. Clinicians should therefore consider gait analyses as an alternative for identifying fall-prone stroke survivors.
