Postural control of prolonged standing in people with Parkinson's disease.

People with Parkinson's disease (pwPD) have reduced adaptability to postural control during prolonged standing compared to neurologically healthy individuals (control). Objective. The study aimed to characterize postural changes during prolonged standing and their effect on postural control in pwPD compared to control. We recorded the body sway of the second lumbar vertebra of 23 pwPD and 23 control while they performed prolonged standing (15 min). The number and amplitude of the body sway patterns (shifts, fidgets, and drifts), the root mean square, velocity, and frequency of the body sway were analyzed. The number of shifts in the anterior-posterior (AP) and medial-lateral (ML) directions was greater for the pwPD than the control. In addition, the amplitudes of shifts in the AP direction and fidgets in the AP and ML directions were greater for the pwPD than the control. Our results show that: (1) A larger number of shifts of body sway suggest references positions are frequently changing; (2) Fidgets is a pumping mechanism and can be sensory-demand action to restore mechanoreceptors activity on the foot sole; and (3) No drift changes may suggest there is no slow migration of reference position. We conclude that pwPD exhibits different behavior than healthy ones during prolonged standing, suggesting that prolonged standing could distinguish individuals with Parkinson's disease.

Machine learning models for Parkinson's disease detection and stage classification based on spatial-temporal gait parameters.

BACKGROUND: Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up. RESEARCH QUESTION: This article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry. METHODS: Gait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments. RESULTS: In the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability. SIGNIFICANCE: The results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.

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.

Effect of the combination of automated peripheral mechanical stimulation and physical exercise on aerobic functional capacity and cardiac autonomic control in patients with Parkinson's disease: a randomized clinical trial protocol.

BACKGROUND: Automated peripheral mechanical stimulation (AMPS) has been proposed as a new complementary therapy with potential for improving motor and cardiovascular abnormalities in Parkinson's disease (PD). However, AMPS long-term effects and its combination with physical exercise are unknown. Thus, this study aims to compare the effects of a program of 12 weeks of physical exercise with a 12-week intervention program combining physical exercise and AMPS on the aerobic capacity, cardiac autonomic control, and gait parameters in patients with PD. METHODS: A randomized, controlled clinical trial will be conducted. Older volunteers with PD will be randomly assigned to one of the two groups studied: (1) exercise or (2) AMPS + exercise. Both groups will undergo an exercise program of 24 sessions, for 12 weeks, performed twice a week. Before exercise sessions, the group AMPS + exercise will receive a session of active AMPS, while the group exercise will receive an AMPS sham intervention. Shapiro-Wilk's and Levene's tests will be used to check for data normality and homogeneity, respectively. In case parametric assumptions are fulfilled, per-protocol and intention-to-treat analyses will be performed using a mixed model analysis of variance to check for group*time interaction. Significance level will be set at 5%. DISCUSSION: Several non-pharmacological treatment modalities have been proposed for PD, focusing primarily on the reduction of motor and musculoskeletal disorders. Regular exercise and motor training have been shown to be effective in improving quality of life. However, treatment options in general remain limited given the high prevalence and adverse impact of these disorders. So, developing new strategies that can potentiate the improvement of motor disabilities and also improve non-motor symptoms in PD is relevant. It is expected that the participants from both groups will improve their quality of life, gait parameters, and their cardiac autonomic control, with greater improvements being observed in the group combining active AMPS and physical exercise. TRIAL REGISTRATION: ClinicalTrials.gov NCT04251728 . Registered on February 05, 2020.

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.