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The aim of this study was to investigate to what extent PD affects the ability to walk, respond to balance perturbations in a single training session, and produce acute short-term effects to improve compensatory reactions and control of unperturbed walking stability. Understanding the mechanism of compensation and neuroplasticity to unexpected step perturbation training during walking and static stance can inform treatment of PD by helping to design effective training regimens that remediate fall risk. Current rehabilitation therapies are inadequate at reducing falls in people with Parkinson’s disease (PD). While pharmacologic and surgical treatments have proved largely ineffective in treating postural instability and gait dysfunction in people with PD, studies have demonstrated that therapy specifically focusing on posture, gait, and balance may significantly improve these factors and reduce falls. The primary goal of this study was to assess the effectiveness of a novel and promising intervention therapy (protective step training – i.e., PST) to improve balance and reduce falls in people with PD. A secondary goal was to understand the effects of PST on proactive and reactive feedback responses during stance and gait tasks. Multiple-baseline, repeated measures analyses were performed on the multitude of proactive and reactive performance measures to assess the effects of PST on gait and postural stability parameters. In general, the results indicate that participants with PD were able to use experiences with perturbation training to integrate and adapt feedforward and feedback behaviors to reduce falls. The ability of the participants with PD to adapt to changes in task demands suggests that individuals with PD could benefit from the protective step training to facilitate balance control during rehabilitation. 

society as a whole. Technologies that are able to detect individuals at risk of fall before occurrence could help reduce this burden by targeting those individuals for rehabilitation to reduce risk of falls. Wearable technologies especially, which can continuously monitor aspects of gait, balance, vital signs, and other aspects of health known to be related to falls, may be useful and are in need of study. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) 2009 guidelines to identify articles related to the use of wearable sensors to predict fall risk. Fifty four studies were analyzed. The majority of studies (98.0%) utilized inertial measurement units (IMUs) located at the lower back (58.0%), sternum (28.0%), and shins (28.0%). Most assessments were conducted in a structured setting (67.3%) instead of with free-living data. Fall risk was calculated based on retrospective falls history (48.9%), prospective falls reporting (36.2%), or clinical scales (19.1%). Measures of the duration spent walking and standing during free-living monitoring, linear measures such as gait speed and step length, and nonlinear measures such as entropy correlate with fall risk, and machine learning methods can distinguish between falls. However, because many studies generating machine learning models did not list the exact factors being considered, it is difficult to compare these models directly. Few studies to date have utilized results to give feedback about fall risk to the patient or to supply treatment or lifestyle suggestions to prevent fall, though these are considered important by end users. Wearable technology demonstrates considerable promise in detecting subtle changes in biomarkers of gait and balance related to an increase in fall risk. However, more large-scale studies measuring increasing fall risk before first fall are needed, and exact biomarkers and machine learning methods used need to be shared to compare results and pursue the most promising fall risk measurements. There is a great need for devices measuring fall risk also to supply patients with information about their fall risk and strategies and treatments for prevention. 

Our retrospective study of falls and resultant trauma in consecutive Parkinson disease (PD) patients seen in one year at the Muhammad Ali Parkinson Clinic found that multiple-fallers could be divided into patients who fell mainly when walking or those who fell mainly when standing. Patients who fell when walking were more likely to visit an emergency room or be admitted to a hospital. Of 455 consecutive patients who were evaluated over a one-year period, 51 were excluded because they had atypical Parkinson disorders, had multiple risk factors for falling, or were demented. Unified Parkinson Disease Rating Scales and Zeno Walkway results were compared among non-fallers, single-fallers, and multiple-fallers. Among multiple-fallers, comparisons were made between patients who fell mainly when standing and those who fell mainly when walking. Most patients (197, 49%) did not fall, 142 (35%) fell once, and 65 (16%) fell more than once. Multiple-fallers differed significantly from single-fallers and non-fallers: they had PD significantly longer (p<0.001), were more severely affected (p<0.001), and took shorter steps (p<0.001). Of 65 multiple-fallers, 26 (40%) fell mainly when standing, 28 (43%) fell mainly when walking, and 11 (17%) fell equally often when standing or walking. Falls when walking resulted in more severe injuries. Patients who fell mainly when standing did not realize they could fall when standing; engaged in inappropriate weight shifting, bending, reaching, and multitasking; and failed to use their assistive devices. Such patients would benefit from being counseled about falling when standing. Patients who fell mainly when walking were aware they could fall, despite using an assisted device, and were more likely to have freezing of gait (FOG). They were more likely to sustain a severe injury, and were more likely to be admitted to an emergency room or hospital. Such patients would benefit from reducing, if possible, FOG.