Effects of automatic mechanical peripheral stimulation on gait biomechanics in older adults with Parkinson's disease: a randomized crossover clinical trial.

BACKGROUND: Automated mechanical peripheral stimulation (AMPS) is a rehabilitation technique suggested to correct gait abnormalities on Parkinson's disease. Although previous studies have suggested increments in functional performance and gait speed after AMPS intervention, little is known about its effect on gait biomechanics. OBJECTIVE: To analyze the effect of an AMPS session on functional performance and gait biomechanics in subjects with Parkinson's disease. METHODS: Twenty-eight subjects aged 67±3 years old participated in this study. Kinematics and muscle activation were recorded during walking at a preferred gait speed before and after AMPS and sham interventions. Footswitches sensors were used to record the kinematic parameters. Electromyographic (EMG) signals of tibialis anterior (TA) and gastrocnemius lateralis (GL) were recorded. Timed up and go (TUG) test and Short Physical Performance Battery (SPPB) were performed to assess functional performance. RESULTS: GL activation increased after AMPS intervention before and after heel strike (p = 0.04; p < 0.01) and before and after toe-off (p = 0.013; p = 0.038). Also, after AMPS intervention, TA activation increased after heel strike (p = 0.007); and after sham intervention, TA activation reduced before and after heel strike (p = 0.038; and p = 0.007) and before toe-off (p = 0.013). The time of TUG test was shorter after AMPS intervention (p = 0.015). CONCLUSION: AMPS intervention changed the EMG activation of ankle muscles during walking and functional performance. However, AMPS intervention did not change gait kinematics.

Cortical activity and gait parameter characteristics in people with multiple sclerosis during unobstructed gait and obstacle avoidance.

BACKGROUND: People with Multiple Sclerosis (PwMS) present higher cortical activity during walking. However, the cortical activity during gait while avoiding an obstacle is still not clear. OBJECTIVE: To investigate cortical activity and gait spatial-temporal parameters in PwMS during two different gait tasks (i.e., unobstructed and obstacle avoidance). METHOD: Fifteen PwMS and 15 healthy controls (CG) were recruited. Participants performed ten trials in each gait condition, wearing a 64-electrode cap electroencephalogram (EEG) at 1024 Hz. Kinematic data were obtained through 10 Vicon® cameras at 200 Hz. EEG was analyzed through four cortical areas (frontal, motor, parietal, and occipital cortex areas) and five frequency bands (delta, theta, alpha, beta, and gamma) obtained through the power spectral density. In addition, spatial-temporal gait parameters (e.g., step length and velocity) were measured. Two-way ANOVA (group x gait condition) and MANOVA (group x gait condition) were used to compare gait and EEG parameters, respectively. One-way ANOVA was used to compare groups in the crossing phase of the obstacle avoidance condition. RESULTS: PwMS presented lower step length and velocity, and higher cortical activity in frontal (beta and gamma) and parietal (gamma) cortical areas in both gait conditions compared to CG. Moreover, PwMS presented increased cortical activation (frontal and parietal) and decreased step length and velocity in obstacle avoidance compared with unobstructed gait. In addition, PwMS required more cortical resources (frontal and parietal) than CG to accomplish both gait conditions. During the obstacle avoidance task, it was further observed that PwMS positioned their feet closer to the obstacle, before and after the task, compared to CG. CONCLUSION: PwMS demand higher cortical resources to accomplish gait tasks, mainly when it is necessary to negotiate an obstacle in the pathway. This higher cortical activity may be a compensatory mechanism to deal with damage in subcortical structures caused by multiple sclerosis.

Is it possible to predict falls in older adults using gait kinematics?

BACKGROUND: Gait kinematic parameters have been reported as an important clinical tool to assess the risk of falls in older adults. However, the ability of these parameters to predict falls in the older population is still unclear. OBJECTIVE: To identify the ability that gait kinematic parameters present to predict fall in older adults. METHODS: Data from 102 older adults, who live in a community setting, were considered for this study. For data collection, older subjects had to walk on a 14 meter-walkway in their preferred gait speed. The incidence of falls was recorded at baseline together with gait kinematics and then every three months during the period of the study. The ability of gait kinematic parameters to predict falls was tested using the ROC curve. RESULTS: Stance time variability, swing time, and stride length presented a sensitivity to predict falls in older adults higher than 70%. CONCLUSION: Gait kinematic parameters, such as stance variability, swing time, and stride length may predict future falls in older adults.