Fall Prediction Based on Instrumented Measures of Gait and Turning in Daily Life in People with Multiple Sclerosis

This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced \u3e1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p \u3c 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk

Inertial Sensor Algorithm to Estimate Walk Distance

The “total distance walked” obtained during a standardized walking test is an integral component of physical fitness and health status tracking in a range of consumer and clinical applications. Wearable inertial sensors offer the advantages of providing accurate, objective, and reliable measures of gait while streamlining walk test administration. The aim of this study was to develop an inertial sensor-based algorithm to estimate the total distance walked using older subjects with impaired fasting glucose (Study I), and to test the generalizability of the proposed algorithm in patients with Multiple Sclerosis (Study II). All subjects wore two inertial sensors (Opals by Clario-APDM Wearable Technologies) on their feet. The walking distance algorithm was developed based on 108 older adults in Study I performing a 400 m walk test along a 20 m straight walkway. The validity of the algorithm was tested using a 6-minute walk test (6MWT) in two sub-studies of Study II with different lengths of a walkway, 15 m (Study II-A, n = 24) and 20 m (Study II-B, n = 22), respectively. The start and turn around points were marked with lines on the floor while smaller horizontal lines placed every 1 m served to calculate the manual distance walked (ground truth). The proposed algorithm calculates the forward distance traveled during each step as the change in the horizontal position from each foot-flat period to the subsequent foot-flat period. The total distance walked is then computed as the sum of walk distances for each stride, including turns. The proposed algorithm achieved an average absolute error rate of 1.92% with respect to a fixed 400 m distance for Study I. The same algorithm achieved an absolute error rate of 4.17% and 3.21% with respect to an averaged manual distance for 6MWT in Study II-A and Study II-B, respectively. These results demonstrate the potential of an inertial sensor-based algorithm to estimate a total distance walked with good accuracy with respect to the manual, clinical standard. Further work is needed to test the generalizability of the proposed algorithm with different administrators and populations, as well as larger diverse cohorts

Fall Prediction Based on Instrumented Measures of Gait and Turning in Daily Life in People with Multiple Sclerosis

This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk

Evaluating Genetic Modifiers of Duchenne Muscular Dystrophy Disease Progression Using Modeling and MRI.

BACKGROUND AND OBJECTIVES: Duchenne muscular dystrophy (DMD) is a progressive muscle degenerative disorder with a well-characterized disease phenotype but considerable interindividual heterogeneity that is not well understood. The aim of this study was to evaluate the effects of dystrophin variations and genetic modifiers of DMD on rate and age of muscle replacement by fat. METHODS: One hundred seventy-five corticosteroid treated participants from the ImagingDMD natural history study underwent repeated magnetic resonance spectroscopy (MRS) of the vastus lateralis (VL) and soleus (SOL) to determine muscle fat fraction (FF). MRS was performed annually in most instances; however, some individuals had additional visits at 3 or 6 monthss intervals. FF changes over time were modeled using nonlinear mixed effects to estimate disease trajectories based on the age that the VL or SOL reached half-maximum change in FF (mu) and the time required for FF change (sigma). Computed mu and sigma values were evaluated for dystrophin variations that have demonstrated the ability to lead to a mild phenotype as well as compared between different genetic polymorphism groups. RESULTS: Participants with dystrophin gene deletions amenable to exon 8 skipping (n = 4) had minimal increases in SOL FF and had an increase in VL mu value by 4.4 years compared with a reference cohort (p = 0.039). Participants with nonsense variations within exons that may produce milder phenotypes (n = 11) also had minimal increases in SOL and VL FFs. No differences in estimated FF trajectories were seen for individuals amenable to exon 44 skipping (n = 10). Modeling of the SPP1, LTBP4, and thrombospondin-1 (THBS1) genetic modifiers did not result in significant differences in muscle FF trajectories between genotype groups (p > 0.05); however, trends were noted for the polymorphisms associated with long-range regulation of LTBP4 and THBS1 that deserve further follow-up. DISCUSSION: The results of this study link the historically mild phenotypes seen in individuals amenable to exon 8 skipping and with certain nonsense variations with alterations in trajectories of lower extremity muscle replacement by fat