Everyday walking with Parkinson's disease: understanding personal challenges and strategies

PURPOSE: This qualitative study was designed to explore the personal experience of everyday walking with Parkinson's disease (PD), the challenges and the strategies employed to compensate for difficulties, to help contextualise the scientific knowledge base. METHODS: Semi-structured interviews were undertaken with a sample of 20 people with idiopathic PD (12 male, 8 female; mean age 65 years (range 50 - 80); mean disease duration 10 years (range 2.5 - 26). Verbatim interview transcripts were analyzed thematically using NUD*IST N6 qualitative data analysis software. RESULTS: Walking was invariably performed as an integral part of a purposeful activity within a specific context, termed walking 'plus', with challenges encountered by people with PD in three main areas: Undertaking tasks; negotiating environments; and making transitions to walking. The two key strategies to compensate for difficulties experienced were monitoring through the use of concentration, and correcting through generating rhythm and size of steps. Carers supported monitoring and correcting. CONCLUSION: People with PD need to constantly assess and drive their walking performance. Attentional resources, which can themselves be compromised in PD, were used to accomplish what is normally a largely automatic activity. Personal accounts support scientific hypotheses. Rehabilitation interventions and measurements in PD need to reflect both the physical and psychosocial context of everyday walking

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Acute modulation of brain connectivity in Parkinson disease after automatic mechanical peripheral stimulation: A pilot study

The present study shows the results of a double-blind sham-controlled pilot trial to test whether measurable stimulus-specific functional connectivity changes exist after Automatic Mechanical Peripheral Stimulation (AMPS) in patients with idiopathic Parkinson Disease.Eleven patients (6 women and 5 men) with idiopathic Parkinson Disease underwent brain fMRI immediately before and after sham or effective AMPS. Resting state Functional Connectivity (RSFC) was assessed using the seed-ROI based analysis. Seed ROIs were positioned on basal ganglia, on primary sensory-motor cortices, on the supplementary motor areas and on the cerebellum. Individual differences for pre- and post-effective AMPS and pre- and post-sham condition were obtained and first entered in respective one-sample t-test analyses, to evaluate the mean effect of condition.Effective AMPS, but not sham stimulation, induced increase of RSFC of the sensory motor cortex, nucleus striatum and cerebellum. Secondly, individual differences for both conditions were entered into paired group t-test analysis to rule out sub-threshold effects of sham stimulation, which showed stronger connectivity of the striatum nucleus with the right lateral occipital cortex and the cuneal cortex (max Z score 3.12) and with the right anterior temporal lobe (max Z score 3.42) and of the cerebellum with the right lateral occipital cortex and the right cerebellar cortex (max Z score 3.79).Our results suggest that effective AMPS acutely increases RSFC of brain regions involved in visuo-spatial and sensory-motor integration.This study provides Class II evidence that automatic mechanical peripheral stimulation is effective in modulating brain functional connectivity of patients with Parkinson Disease at rest.Clinical Trials.gov NCT01815281

The Comparison of Dual-Tasking and Functional Fitness in Older Females

Context: America’s aging population is growing faster than ever, resulting in increasing challenges for healthcare providers and caregivers. Over 33% of adults aged 65 and older fall annually, and falls are the number one cause of injury-related death in this age group. Assessing fall risk is difficult due to its multifactorial nature, but functional fitness serves as a proxy measure. Women are at a particularly high risk for fall-related injury due to lower bone mineral density and higher fall frequency when compared to males. Fear of falling is also a serious contributor to fall risk, and it affects up to 89% of older adults. Objective: This study investigated the effects of functional fitness on walking speed under dual-tasking conditions. We hypothesized that women who were less functionally fit would experience greater declines in dual-task walking speed. Design: This experiment had a cross-sectional design. Setting: Tests were conducted at a retirement community in northwest Arkansas. Participants: Participants were females over the age of 65 y, with a mean age of 79.6 y. They were recruited on a volunteer basis and divided in two groups of 13 based on functional fitness levels. Interventions: Functional fitness was determined using the 8-foot up-and-go, a measure of agility and dynamic balance. For walking speed assessments, subjects walked a 10-meter distance with 3 meters extra on each end to account for acceleration and deceleration. Speed was measured with a laser timer. Dual-task assessment required subjects to count backwards by threes from a predetermined number. Four protocols with two trials each were used: single-task walking at habitual and maximal speeds, and dual-task walking at habitual and maximal speeds. Main Outcome Measures: The independent variable was functional fitness level (moderate or high). The dependent variables were dual-task walking time and Dual-Task Cost, calculated by subtracting single-task from dual-task walking time. A one-way ANOVA determined differences between dual-task decrement of the habitual and maximal walking speed trials. Statistical significance was set at α=.05. Results: Average 8-foot up-and-go time for the high functioning group was 5.74 seconds. The average time for the moderate functioning group was 8.33 seconds. Dual-task time difference between the two groups for habitual walking was statistically insignificant (p =.789). For maximal speed, dual-task time difference was statistically significant (p =.04). The moderate group exhibited smaller Dual-Task Costs than the high group for both habitual (difference of 1.3 ± 1.5 s) and maximal (difference of 0.3 ± 0.3 s) conditions. These Dual-Task Cost differences were insignificant for usual speed (p = .11) and maximal speed (p = .38) Discussion: The results did not support the hypothesis of Dual-Task Costs being related to functional fitness level. However, there was a significant difference in maximal dual-task speed between the groups. This shows that maximal dual-task walking speed is more closely linked to functional fitness than is habitual dual-task speed

Kinematic discrimination of ataxia in horses is facilitated by blindfolding

BACKGROUND: Agreement among experienced clinicians is poor when assessing the presence and severity of ataxia, especially when signs are mild. Consequently, objective gait measurements might be beneficial for assessment of horses with neurological diseases. OBJECTIVES: To assess diagnostic criteria using motion capture to measure variability in spatial gait-characteristics and swing duration derived from ataxic and non-ataxic horses, and to assess if variability increases with blindfolding. STUDY DESIGN: Cross-sectional. METHODS: A total of 21 horses underwent measurements in a gait laboratory and live neurological grading by multiple raters. In the gait laboratory, the horses were made to walk across a runway surrounded by a 12-camera motion capture system with a sample frequency of 240 Hz. They were made to walk normally and with a blindfold in at least three trials each. Displacements of reflective markers on head, fetlock, hoof, fourth lumbar vertebra, tuber coxae and sacrum derived from three to four consecutive strides were processed and descriptive statistics, receiver operator characteristics (ROC) to determine the diagnostic sensitivity, specificity and area under the curve (AUC), and correlation between median ataxia grade and gait parameters were determined. RESULTS: For horses with a median ataxia grade ≥2, coefficient of variation for the location of maximum vertical displacement of pelvic and thoracic distal limbs generated good diagnostic yield. The hoofs of the thoracic limbs yielded an AUC of 0.81 with 64% sensitivity and 90% specificity. Blindfolding exacerbated the variation for ataxic horses compared to non-ataxic horses with the hoof marker having an AUC of 0.89 with 82% sensitivity and 90% specificity. MAIN LIMITATIONS: The low number of consecutive strides per horse obtained with motion capture could decrease diagnostic utility. CONCLUSIONS: Motion capture can objectively aid the assessment of horses with ataxia. Furthermore, blindfolding increases variation in distal pelvic limb kinematics making it a useful clinical tool

Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach

Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson’s disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process