Matthew A. D. Brodie

Rivastigmine for gait stability in patients with Parkinson's disease (ReSPonD): a randomised, double-blind, placebo-controlled, phase 2 trial

BACKGROUND Falls are a frequent and serious complication of Parkinsons disease and are related partly to an underlying cholinergic deficit that contributes to gait and cognitive dysfunction in these patients. Gait dysfunction can lead to an increased variability of gait from one step to another, raising the likelihood of falls. In the ReSPonD trial we aimed to assess whether ameliorating this cholinergic deficit with the acetylcholinesterase inhibitor rivastigmine would reduce gait variability. METHODS We did this randomised, double-blind, placebo-controlled, phase 2 trial at the North Bristol NHS Trust Hospital, Bristol, UK, in patients with Parkinsons disease recruited from community and hospital settings in the UK. We included patients who had fallen at least once in the year before enrolment, were able to walk 18 m without an aid, had no previous exposure to an acetylcholinesterase inhibitor, and did not have dementia. Our clinical trials unit randomly assigned (1:1) patients to oral rivastigmine or placebo capsules (both taken twice a day) using a computer-generated randomisation sequence and web-based allocation. Rivastigmine was uptitrated from 3 mg per day to the target dose of 12 mg per day over 12 weeks. Both the trial team and patients were masked to treatment allocation. Masking was achieved with matched placebo capsules and a dummy uptitration schedule. The primary endpoint was difference in step time variability between the two groups at 32 weeks, adjusted for baseline age, cognition, step time variability, and number of falls in the previous year. We measured step time variability with a triaxial accelerometer during an 18 m walking task in three conditions: normal walking, simple dual task with phonemic verbal fluency (walking while naming words beginning with a single letter), and complex dual task switching with phonemic verbal fluency (walking while naming words, alternating between two letters of the alphabet). Analysis was by modified intention to treat; we excluded from the primary analysis patients who withdrew, died, or did not attend the 32 week assessment. This trial is registered with ISRCTN, number 19880883. FINDINGS Between Oct 4, 2012 and March 28, 2013, we enrolled 130 patients and randomly assigned 65 to the rivastigmine group and 65 to the placebo group. At week 32, compared with patients assigned to placebo (59 assessed), those assigned to rivastigmine (55 assessed) had improved step time variability for normal walking (ratio of geometric means 0.72, 95% CI 0.58-0.88; p=0.002) and the simple dual task (0.79; 0.62-0.99; p=0.045). Improvements in step time variability for the complex dual task did not differ between groups (0.81, 0.60-1.09; p=0.17). Gastrointestinal side-effects were more common in the rivastigmine group than in the placebo group (p<0.0001); 20 (31%) patients in the rivastigmine group versus three (5%) in the placebo group had nausea and 15 (17%) versus three (5%) had vomiting. INTERPRETATION Rivastigmine can improve gait stability and might reduce the frequency of falls. A phase 3 study is needed to confirm these findings and show cost-effectiveness of rivastigmine treatment. FUNDING Parkinsons UK.

A comparison of activity classification in younger and older cohorts using a smartphone.

Automatic recognition of human activity is useful as a means of estimating energy expenditure and has potential for use in fall detection and prediction. The emergence of the smartphone as a ubiquitous device presents an opportunity to utilize its embedded sensors, computational power and data connectivity as a platform for continuous health monitoring. In the study described herein, 37 older people (83.9  ±  3.4 years) performed a series of activities of daily living (ADLs) while a smartphone (containing a triaxial accelerometer, triaxial gyroscope and barometric pressure sensor) was placed in the front pocket of their trousers. These results are compared to a similar trial conducted previously in which 20 young people (21.9  ±  1.65 years) were asked to perform the same ADLs using the same smartphone (again in the front pocket of their trousers).In each trial, the participants were asked to perform several activities (standing, sitting, lying, walking on level ground, up and down staircases, and riding an elevator up and down) in a free-living environment. During each acquisition session, the internal sensor signals were recorded and subsequently used to develop activity classifiers based on a decision tree algorithm that classified ADL in epochs of ~1.25 s. When training and testing with the younger cohort, using a leave-one-out cross validation procedure, a total classification sensitivity of 80.9% ± 9.57% ([Formula: see text] = 0.75  ±  0.12) was obtained. Retraining and testing on the older cohort, again using cross validation, gives a comparable total class sensitivity of 82.0% ± 8.88% ([Formula: see text] =0.74  ±  0.12).When trained with the younger group and tested on the older group, a total class sensitivity of 69.2% ± 24.8% (95% confidence interval [69.6%, 70.6%]) and [Formula: see text] = 0.60  ±  0.27 (95% confidence interval [0.58, 0.59]) was obtained. When trained on the older group and tested on the younger group, a total class sensitivity of 80.5% ± 6.80% (95% confidence interval [79.0%, 80.6%]) and [Formula: see text] = 0.74  ±  0.08 (95% confidence interval [0.73, 0.75]) was obtained.An instance of the decision tree classifier developed was implemented on the smartphone as a software application. It was capable of performing real-time activity classification for a period of 17 h on a single battery charge, illustrating that smartphone technology provides a viable platform on which to perform long-term activity monitoring.

Eight-Week Remote Monitoring Using a Freely Worn Device Reveals Unstable Gait Patterns in Older Fallers

Objectives: Develop algorithms to detect gait impairments remotely using data from freely worn devices during long-term monitoring. Identify statistical models that describe how gait performances are distributed over several weeks. Determine the data window required to reliably assess an increased propensity for falling. Methods: 1085 days of walking data were collected from eighteen independent-living older people (mean age 83 years) using a freely worn pendant sensor (housing a triaxial accelerometer and pressure sensor). Statistical distributions from several accelerometer-derived gait features (encompassing quantity, exposure, intensity, and quality) were compared for those with and without a history of falling. Results: Participants completed more short walks relative to long walks, as approximated by a power law. Walks less than 13.1 s comprised 50% of exposure to walking-related falls. Daily-life cadence was bimodal and step-time variability followed a log-normal distribution. Fallers took significantly fewer steps per walk and had relatively more exposure from short walks and greater mode of step-time variability. Conclusions: Using a freely worn device and wavelet-based analysis tools allowed long-term monitoring of walks greater than or equal to three steps. In older people, short walks constitute a large proportion of exposure to falls. To identify fallers, mode of variability may be a better measure of central tendency than mean of variability. A weeks monitoring is sufficient to reliably assess the long-term propensity for falling. Significance: Statistical distributions of gait performances provide a reference for future wearable device development and research into the complex relationships between daily-life walking patterns, morbidity, and falls.

Kinect-Based Five-Times-Sit-to-Stand Test for Clinical and In-Home Assessment of Fall Risk in Older People

Background: Accidental falls remain an important problem in older people. The five-times-sit-to-stand (5STS) test is commonly used as a functional test to assess fall risk. Recent advances in sensor technologies hold great promise for more objective and accurate assessments. Objective: The aims of this study were: (1) to examine the feasibility of a low-cost and portable Kinect-based 5STS test to discriminate between fallers and nonfallers and (2) to investigate whether this test can be used for supervised clinical, supervised and unsupervised in-home fall risk assessments. Methods: A total of 94 community-dwelling older adults were assessed by the Kinect-based 5STS test in the laboratory and 20 participants were tested in their own homes. An algorithm was developed to automatically calculate timing- and speed-related measurements from the Kinect-based sensor data to discriminate between fallers and nonfallers. The associations of these measurements with standard clinical fall risk tests and the results of supervised and unsupervised in-home assessments were examined. Results: Fallers were significantly slower than nonfallers on Kinect-based measures. The mean velocity of the sit-to-stand transitions discriminated well between the fallers and nonfallers based on 12-month retrospective fall data. The Kinect-based measures collected in the laboratory correlated strongly with those collected in the supervised (r = 0.704-0.832) and unsupervised (r = 0.775-0.931) in-home assessments. Conclusion: In summary, we found that the Kinect-based 5STS test discriminated well between the fallers and nonfallers and was feasible to administer in clinical and supervised in-home settings. This test may be useful in clinical settings for identifying high-risk fallers for further intervention or for regular in-home assessments in the future.

Choice stepping reaction time test using exergame technology for fall risk assessment in older people

Accidental falls remain an important problem in older people. Stepping is a common task to avoid a fall and requires good interplay between sensory functions, central processing and motor execution. Increased choice stepping reaction time has been associated with recurrent falls in older people. The aim of this study was to examine if a sensor-based Exergame Choice Stepping Reaction Time test can successfully discriminate older fallers from non-fallers. The stepping test was conducted in a cohort of 104 community-dwelling older people (mean age: 80.7 ± 7.0 years). Participants were asked to step laterally as quickly as possible after a light stimulus appeared on a TV screen. Spatial and temporal measurements of the lower and upper body were derived from a low-cost and portable 3D-depth sensor (i.e. Microsoft Kinect) and 3D-accelerometer. Fallers had a slower stepping reaction time (970 ± 228 ms vs. 858 ± 123 ms, P = 0.001) and a slower reaction of their upper body (719 ± 289 ms vs. 631 ± 166 ms, P = 0.052) compared to non-fallers. It took fallers significantly longer than non-fallers to recover their balance after initiating the step (2147 ± 800 ms vs. 1841 ± 591 ms, P = 0.029). This study demonstrated that a sensor-based, low-cost and easy to administer stepping test, with the potential to be used in clinical practice or regular unsupervised home assessments, was able to identify significant differences between performances by fallers and non-fallers.

Good lateral harmonic stability combined with adequate gait speed is required for low fall risk in older people

Background: Good lateral harmonic stability in gait may be important for minimising fall risk in older people because many falls occur during walking when the base of support is narrowest in the mediolateral (ML) direction. However, the traditional ML harmonic ratio (MLHR) may be a sub-optimal measure of gait quality because of insufficient frequency resolution. Objective: The primary objective was to investigate if a new measure of lateral harmonic stability, the 8-step MLHR, could discriminate older fallers from non-fallers while taking different walking speeds into account. Methods: Repeat walks over 20 m were completed by 96 older people (mean age 80, SD 4 years); 35 participants had a history of one or more falls in the past year. The traditional MLHR and the 8-step MLHR were obtained from an accelerometer attached to the sacrum. Results: Compared to the traditional MLHR, the 8-step MLHR demonstrated similar univariate ability to identify significant differences in fall risk based on age, walking speed and physiology (p ≤ 0.05). When differences in walking speed were taken into account, we observed that participants who walked both faster than average and had above-average lateral harmonic stability (by the 8-step MLHR) were 5.3 times less likely to be fallers than all other participants (relative risk: 0.19, 95% confidence interval: 0.06-0.57). For the traditional MLHR, however, no significant differences between the fallers and non-fallers were evident. Conclusions: The findings indicate that good lateral harmonic stability interacts with adequate gait speed and, when coincident, are associated with reduced fall risk in older people. Future research could examine whether interventions focusing on enhancing both gait speed and lateral stability can reduce fall risk and whether these combined gait measures can remotely predict deteriorating health using wearable technology.

Visuospatial tasks affect locomotor control more than nonspatial tasks in older people.

Background Previous research has shown that visuospatial processing requiring working memory is particularly important for balance control during standing and stepping, and that limited spatial encoding contributes to increased interference in postural control dual tasks. However, visuospatial involvement during locomotion has not been directly determined. This study examined the effects of a visuospatial cognitive task versus a nonspatial cognitive task on gait speed, smoothness and variability in older people, while controlling for task difficulty. Methods Thirty-six people aged ≥75 years performed three walking trials along a 20 m walkway under the following conditions: (i) an easy nonspatial task; (ii) a difficult nonspatial task; (iii) an easy visuospatial task; and (iv) a difficult visuospatial task. Gait parameters were computed from a tri-axial accelerometer attached to the sacrum. The cognitive task response times and percentage of correct answers during walking and seated trials were also computed. Results No significant differences in either cognitive task type error rates or response times were evident in the seated conditions, indicating equivalent task difficulty. In the walking trials, participants responded faster to the visuospatial tasks than the nonspatial tasks but at the cost of making significantly more cognitive task errors. Participants also walked slower, took shorter steps, had greater step time variability and less smooth pelvis accelerations when concurrently performing the visuospatial tasks compared with the nonspatial tasks and when performing the difficult compared with the easy cognitive tasks. Conclusions Compared with nonspatial cognitive tasks, visuospatial cognitive tasks led to a slower, more variable and less smooth gait pattern. These findings suggest that visuospatial processing might share common networks with locomotor control, further supporting the hypothesis that gait changes during dual task paradigms are not simply due to limited attentional resources but to competition for common networks for spatial information encoding.

Spatial variability during gait initiation while dual tasking is increased in individuals with mild cognitive impairment

BackgroundGait initiation (GI) is a complex transition phase of gait that can induce postural instability. Gait impairment has been well documented in people with Alzheimer’s disease, but it is still inconclusive in individuals with Mild Cognitive Impairment (MCI). Previous studies have usually investigated gait performance of cognitive impaired persons under steady state walking.ObjectiveThis study aimed to examine spatiotemporal variability during GI under single- and dual-task conditions in people with and without MCI.MethodsSpatiotemporal stepping characteristics and variability under single- and dual-task conditions (counting backwards by 3s) were assessed in 30 older adults with MCI and 30 cognitively intact controls. Mean and coefficients of variation (COV) of swing time, step time, step length and step width were compared between the two groups.ResultsMixed-model repeated measures ANOVA revealed a significant Group x Walking condition interaction for COV of step length and step width (P<0.05). Post-hoc analysis revealed that variability for these measures were significantly larger in the MCI group compared with the control group under the dual-task condition (P<0.05).ConclusionsStep length and step width variability is increased in people with MCI during GI, particularly in a condition involving a secondary cognitive task. These findings suggest that individuals with MCI have reduced balance control when undertaking a challenging walking task such as gait initiation, and this is exacerbated with an added cognitive task. Future studies should prospectively investigate the relationship between GI variability and fall risk in this population.

New Methods to Monitor Stair Ascents Using a Wearable Pendant Device Reveal How Behavior, Fear, and Frailty Influence Falls in Octogenarians

Goals: To investigate if the stair negotiation by older people during activities of daily life (ADL) can be accurately identified using a freely worn pendant device. To investigate how usual stair-ascent performances during ADL relate to clinical assessments and prospective falls. Methods: ADL were recorded for 30 min by 52 community-dwelling older people (83 ± 4 years) using a small pendant device. Classification accuracy was assessed using annotated video and four-fold cross validation. Correlations between sensor-derived stair-ascent features (comprising intensity, variability, and stability) and a battery of clinical tests (comprising physiological, psychological, health, and follow-up falls) were investigated. Results: Accurate identification of stair events (99.8%, Kappa 0.92) was possible in both “frail” and “athletic” participants by scaling the barometer threshold to stair cadences. Cautious double-stepping strategy could be identified remotely. Stair-ascent performance was correlated with ascent strategy (r = -0.67), age (r = -0.44), concern about falling (r = -0.43), fall-risk scores (r = -0.41), processing speed (r = -0.38), and contrast sensitivity (r = 0.32). Follow-up falls were correlated with ascent stability (r = -0.35). Conclusion: Remote analysis of stair ascents is feasible. In our healthy older people, outcomes appeared more related to mental rather than physiological factors. The ascent strategies we observed in some older people may have reflected an appropriate behavioral response to increased concerns about falling. Significance: Given acceptance of wearable devices is increasing; reduced functional performance and altered strategies for undertaking ADL could soon be routinely tracked to augment health care.

Inertial measurements of free-living activities: Assessing mobility to predict falls

An exploratory analysis was conducted into how simple features, from acceleration at the lower back and ankle during simulated free-living walking, stair ascent and descent, correlate with age, the overall fall risk from a clinically validated Physiological Profile Assessment (PPA), and its sub-components. Inertial data were captured from 92 older adults aged 78-95 (42 female, mean age 84.1, standard deviation 3.9 years). The dominant frequency, peak width from Welchs power spectral density estimate, and signal variance along each axis, from each sensor location and for each activity were calculated. Several correlations were found between these features and the physiological risk factors. The strongest correlations were from the dominant frequency at the ankle along the mediolateral direction during stair ascent (Spearmans correlation coefficient p = - 0.45) with anterioposterior sway, and signal variance of the anterioposterior acceleration at the lower back during stair descent (p = - 0.45) with age. These findings should aid future attempts to classify activities and predict falls in older adults, based on true free-living data from a range of activities.