Balance differences in people with Parkinson disease with and without freezing of gait

Published in final edited form as: Gait Posture. 2015 September ; 42(3): 306–309. doi:10.1016/j.gaitpost.2015.06.007.BACKGROUND: Freezing of gait (FOG) is a relatively common and remarkably disabling impairment associated with Parkinson disease (PD). Laboratory-based measures indicate that individuals with FOG (PD+FOG) have greater balance deficits than those without FOG (PD-FOG). Whether such differences also can be detected using clinical balance tests has not been investigated. We sought to determine if balance and specific aspects of balance, measured using Balance Evaluation Systems Test (BESTest), differs between PD+FOG and PD-FOG. Furthermore, we aimed to determine if time-efficient clinical balance measures (i.e. Mini-BESTest, Berg Balance Scale (BBS)) could detect balance differences between PD+FOG and PD-FOG. METHODS: Balance of 78 individuals with PD, grouped as either PD+FOG (n=32) or PD-FOG (n=46), was measured using the BESTest, Mini-BESTest, and BBS. Between-groups comparisons were conducted for these measures and for the six sections of the BESTest using analysis of covariance. A PD composite score was used as a covariate. RESULTS: Controlling for motor sign severity, PD duration, and age, PD+FOG had worse balance than PD-FOG when measured using the BESTest (p=0.008, F=7.35) and Mini-BESTest (p=0.002, F=10.37), but not the BBS (p=0.27, F=1.26). BESTest section differences were noted between PD+FOG and PD-FOG for reactive postural responses (p<0.001, F=14.42) and stability in gait (p=0.003, F=9.18). CONCLUSIONS: The BESTest and Mini-BESTest, which specifically assessed reactive postural responses and stability in gait, were more likely than the BBS to detect differences in balance between PD+FOG and PD-FOG. Because it is more time efficient to administer, the Mini-BESTest may be the preferred tool for assessing balance deficits associated with FOG.This study was conducted with funding from the Davis Phinney Foundation, Parkinson's Disease Foundation, NIH R01 NS077959, NIH UL1 TR000448, Greater St. Louis American Parkinson Disease Association (APDA), APDA Center for Advanced PD Research at Washington University in St. Louis. The funding sources had no role in the study design, in the collection, analysis and interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. (Davis Phinney Foundation; Parkinson's Disease Foundation; R01 NS077959 - NIH; UL1 TR000448 - NIH; Greater St. Louis American Parkinson Disease Association (APDA); APDA Center for Advanced PD Research at Washington University in St. Louis

Artificial intelligence convolutional neural networks map giant kelp forests from satellite imagery

Climate change is producing shifts in the distribution and abundance of marine species. Such is the case of kelp forests, important marine ecosystem-structuring species whose distributional range limits have been shifting worldwide. Synthesizing long-term time series of kelp forest observations is therefore vital for understanding the drivers shaping ecosystem dynamics and for predicting responses to ongoing and future climate changes. Traditional methods of mapping kelp from satellite imagery are time-consuming and expensive, as they require high amount of human effort for image processing and algorithm optimization. Here we propose the use of mask region-based convolutional neural networks (Mask R-CNN) to automatically assimilate data from open-source satellite imagery (Landsat Thematic Mapper) and detect kelp forest canopy cover. The analyses focused on the giant kelp Macrocystis pyrifera along the shorelines of southern California and Baja California in the northeastern Pacific. Model hyper-parameterization was tuned through cross-validation procedures testing the effect of data augmentation, and different learning rates and anchor sizes. The optimal model detected kelp forests with high performance and low levels of overprediction (Jaccard's index: 0.87 +/- 0.07; Dice index: 0.93 +/- 0.04; over prediction: 0.06) and allowed reconstructing a time series of 32 years in Baja California (Mexico), a region known for its high variability in kelp owing to El Nino events. The proposed framework based on Mask R-CNN now joins the list of cost-efficient tools for long-term marine ecological monitoring, facilitating well-informed biodiversity conservation, management and decision making.LA/P/0101/2020info:eu-repo/semantics/publishedVersio

External validation of a simple clinical tool used to predict falls in people with Parkinson disease

Published in final edited form as: Parkinsonism Relat Disord. 2015 August ; 21(8): 960–963. doi:10.1016/j.parkreldis.2015.05.008.BACKGROUND: Assessment of fall risk in an individual with Parkinson disease (PD) is a critical yet often time consuming component of patient care. Recently a simple clinical prediction tool based only on fall history in the previous year, freezing of gait in the past month, and gait velocity <1.1 m/s was developed and accurately predicted future falls in a sample of individuals with PD. METHODS: We sought to externally validate the utility of the tool by administering it to a different cohort of 171 individuals with PD. Falls were monitored prospectively for 6 months following predictor assessment. RESULTS: The tool accurately discriminated future fallers from non-fallers (area under the curve [AUC] = 0.83; 95% CI 0.76–0.89), comparable to the developmental study. CONCLUSION: The results validated the utility of the tool for allowing clinicians to quickly and accurately identify an individual's risk of an impending fall.Davis Phinney Foundation, Parkinson Disease Foundation, NIH, APDA. (Davis Phinney Foundation; Parkinson Disease Foundation; NIH; APDA

Are the average gait speeds during the 10 meter and 6 minute walk tests redundant in Parkinson disease?

Published in final edited form as: Gait Posture. 2017 February ; 52: 178–182. doi:10.1016/j.gaitpost.2016.11.033.We investigated the relationships between average gait speed collected with the 10Meter Walk Test (Comfortable and Fast) and 6Minute Walk Test (6MWT) in 346 people with Parkinson disease (PD) and how the relationships change with increasing disease severity. Pearson correlation and linear regression analyses determined relationships between 10Meter Walk Test and 6MWT gait speed values for the entire sample and for sub-samples stratified by Hoehn & Yahr (H&Y) stage I (n=53), II (n=141), III (n=135) and IV (n=17). We hypothesized that redundant tests would be highly and significantly correlated (i.e. r>0.70, p<0.05) and would have a linear regression model slope of 1 and intercept of 0. For the entire sample, 6MWT gait speed was significantly (p<0.001) related to the Comfortable 10 Meter Walk Test (r=0.75) and Fast 10Meter Walk Test (r=0.79) gait speed, with 56% and 62% of the variance in 6MWT gait speed explained, respectively. The regression model of 6MWT gait speed predicted by Comfortable 10 Meter Walk gait speed produced slope and intercept values near 1 and 0, respectively, especially for participants in H&Y stages II-IV. In contrast, slope and intercept values were further from 1 and 0, respectively, for the Fast 10Meter Walk Test. Comfortable 10 Meter Walk Test and 6MWT gait speeds appeared to be redundant in people with moderate to severe PD, suggesting the Comfortable 10 Meter Walk Test can be used to estimate 6MWT distance in this population.This study was funded by the Davis Phinney Foundation, the Parkinson's Disease Foundation, and the National Institutes of Health (R01 NS077959, K12 HD055931, UL1 TR000448). The funding sources had no input related to study design, data collection, or decision to submit for publication. (Davis Phinney Foundation; Parkinson's Disease Foundation; R01 NS077959 - National Institutes of Health; K12 HD055931 - National Institutes of Health; UL1 TR000448 - National Institutes of Health

Diabetes Numeracy: An overlooked factor in understanding racial disparities in glycemic control

OBJECTIVE: Understanding the reasons and eliminating the pervasive health disparities in diabetes is a major research, clinical, and health policy goal. We examined whether health literacy, general numeracy, and diabetes-related numeracy explain the association between African American race and poor glycemic control (A1C) in patients with diabetes. RESEARCH DESIGN AND METHODS: Adults with type 2 diabetes (n = 383) were enrolled in a cross-sectional study at primary care and diabetes clinics at three medical centers. Data collected included the following: self-reported race, health literacy, general numeracy, diabetes-related numeracy, A1C, and sociodemographic factors. A series of structural equation models were estimated to explore the interrelations between variables and test for mediation. RESULTS: In model 1, younger age (r = -0.21, P < 0.001), insulin use (r = 0.27, P < 0.001), greater years with diabetes (r = 0.16, P < 0.01), and African American race (r = 0.12, P < 0.01) were all associated with poorer glycemic control. In model 2, diabetes-related numeracy emerged as a strong predictor of A1C (r = -0.46, P < 0.001), reducing the association between African American and poor glycemic control to nonsignificance (r = 0.10, NS). In model 3, African American race and older age were associated with lower diabetes-related numeracy; younger age, insulin use, more years with diabetes, and lower diabetes-related numeracy were associated with poor glycemic control. CONCLUSIONS: Diabetes-related numeracy reduced the explanatory power of African American race, such that low diabetes-related numeracy, not African American race, was significantly related to poor glycemic control. Interventions that address numeracy could help to reduce racial disparities in diabetes

Comparative utility of the BESTest, mini-BESTest, and brief-BESTest for predicting falls in individuals with Parkinson disease: A cohort study

BACKGROUND: The newly developed Brief–Balance Evaluation System Test (Brief-BESTest) may be useful for measuring balance and predicting falls in individuals with Parkinson disease (PD). OBJECTIVES: The purposes of this study were: (1) to describe the balance performance of those with PD using the Brief-BESTest, (2) to determine the relationships among the scores derived from the 3 versions of the BESTest (ie, full BESTest, Mini-BESTest, and Brief-BESTest), and (3) to compare the accuracy of the Brief-BESTest with that of the Mini-BESTest and BESTest in identifying recurrent fallers among people with PD. DESIGN: This was a prospective cohort study. METHODS: Eighty participants with PD completed a baseline balance assessment. All participants reported a fall history during the previous 6 months. Fall history was again collected 6 months (n=51) and 12 months (n=40) later. RESULTS: At baseline, participants had varying levels of balance impairment, and Brief-BESTest scores were significantly correlated with Mini-BESTest (r=.94, P<.001) and BESTest (r=.95, P<.001) scores. Six-month retrospective fall prediction accuracy of the Brief-BESTest was moderately high (area under the curve [AUC]=0.82, sensitivity=0.76, and specificity=0.84). Prospective fall prediction accuracy over 6 months was similarly accurate (AUC=0.88, sensitivity=0.71, and specificity=0.87), but was less sensitive over 12 months (AUC=0.76, sensitivity=0.53, and specificity=0.93). LIMITATIONS: The sample included primarily individuals with mild to moderate PD. Also, there was a moderate dropout rate at 6 and 12 months. CONCLUSIONS: All versions of the BESTest were reasonably accurate in identifying future recurrent fallers, especially during the 6 months following assessment. Clinicians can reasonably rely on the Brief-BESTest for predicting falls, particularly when time and equipment constraints are of concern

Accuracy of Fall Prediction in Parkinson Disease: Six-Month and 12-Month Prospective Analyses

Introduction. We analyzed the ability of four balance assessments to predict falls in people with Parkinson Disease (PD) prospectively over six and 12 months. Materials and Methods. The BESTest, Mini-BESTest, Functional Gait Assessment (FGA), and Berg Balance Scale (BBS) were administered to 80 participants with idiopathic PD at baseline. Falls were then tracked for 12 months. Ability of each test to predict falls at six and 12 months was assessed using ROC curves and likelihood ratios (LR). Results. Twenty-seven percent of the sample had fallen at six months, and 32% of the sample had fallen at 12 months. At six months, areas under the ROC curve (AUC) for the tests ranged from 0.8 (FGA) to 0.89 (BESTest) with LR+ of 3.4 (FGA) to 5.8 (BESTest). At 12 months, AUCs ranged from 0.68 (BESTest, BBS) to 0.77 (Mini-BESTest) with LR+ of 1.8 (BESTest) to 2.4 (BBS, FGA). Discussion. The various balance tests were effective in predicting falls at six months. All tests were relatively ineffective at 12 months. Conclusion. This pilot study suggests that people with PD should be assessed biannually for fall risk

Approximate entropy detects the effect of a secondary cognitive task on postural control in healthy young adults: a methodological report

<p>Abstract</p> <p>Background</p> <p>Biomechanical measures of postural stability, while generally useful in neuroscience and physical rehabilitation research, may be limited in their ability to detect more subtle influences of attention on postural control. Approximate entropy (ApEn), a regularity statistic from nonlinear dynamics, recently has demonstrated relatively good measurement precision and shown promise for detecting subtle change in postural control after cerebral concussion. Our purpose was to further explore the responsiveness of ApEn by using it to evaluate the immediate, short-term effect of secondary cognitive task performance on postural control in healthy, young adults.</p> <p>Methods</p> <p>Thirty healthy, young adults performed a modified version of the Sensory Organization Test featuring single (posture only) and dual (posture plus cognitive) task trials. ApEn values, root mean square (RMS) displacement, and equilibrium scores (ES) were calculated from anterior-posterior (AP) and medial-lateral (ML) center of pressure (COP) component time series. For each sensory condition, we compared the ability of the postural control parameters to detect an effect of cognitive task performance.</p> <p>Results</p> <p>COP AP time series generally became more random (higher ApEn value) during dual task performance, resulting in a main effect of cognitive task (p = 0.004). In contrast, there was no significant effect of cognitive task for ApEn values of COP ML time series, RMS displacement (AP or ML) or ES.</p> <p>Conclusion</p> <p>During dual task performance, ApEn revealed a change in the randomness of COP oscillations that occurred in a variety of sensory conditions, independent of changes in the amplitude of COP oscillations. The finding expands current support for the potential of ApEn to detect subtle changes in postural control. Implications for future studies of attention in neuroscience and physical rehabilitation are discussed.</p

In vitro models of soft tissue damage by implant-associated frictional shear stresses

Silicone elastomer medical implants are ubiquitous in medicine, particularly for breast augmentation. However, when these devices are placed within the body, disruption of the natural biological interfaces occurs, which significantly changes the native energy-dissipation mechanisms of living systems. These new interfaces can introduce non-physiological contact pressures and tribological conditions that provoke inflammation and soft tissue damage. Despite their significance, the biotribological properties of implant-tissue and implant-extracellular matrix (ECM) interfaces remain poorly understood. Here, we developed an in vitro model of soft tissue damage using a custom-built in situ biotribometer mounted onto a confocal microscope. Sections of commercially-available silicone breast implants with distinct and clinically relevant surface roughness (Ra = 0.2 ± 0.03 μm, 2.7 ± 0.6 μm, and 32 ± 7.0 μm) were mounted to spherically-capped hydrogel probes and slid against collagen-coated hydrogel surfaces as well as healthy breast epithelial (MCF10A) cell monolayers to model implant-ECM and implant-tissue interfaces. In contrast to the “smooth” silicone implants (Ra &lt; 10 μm), we demonstrate that the “microtextured” silicone implant (10 &lt; Ra &lt; 50 μm) induced higher frictional shear stress (τ &gt; 100 Pa), which led to greater collagen removal and cell rupture/delamination. Our studies may provide insights into post-implantation tribological interactions between silicone breast implants and soft tissues.</p

Run 2 Upgrades to the CMS Level-1 Calorimeter Trigger

The CMS Level-1 calorimeter trigger is being upgraded in two stages to maintain performance as the LHC increases pile-up and instantaneous luminosity in its second run. In the first stage, improved algorithms including event-by-event pile-up corrections are used. New algorithms for heavy ion running have also been developed. In the second stage, higher granularity inputs and a time-multiplexed approach allow for improved position and energy resolution. Data processing in both stages of the upgrade is performed with new, Xilinx Virtex-7 based AMC cards.Comment: 10 pages, 7 figure