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Parkinson’s disease (PD) is one of the world’s fastest growing neurological disorders. Much is unknown about PD-associated economic burdens in the United States (U.S.) and other high-income nations. This study provides a comprehensive analysis of the economic burdens of PD in the U.S. (2017) and projections for the next two decades. Multiple data sources were used to estimate the costs of PD, including public and private administrative claims data, Medicare Current Beneficiary Survey, Medical Expenditure Panel Survey, and a primary survey (n = 4,548) designed for this study. We estimated a U.S. prevalence of approximately one million individuals with diagnosed Parkinson’s disease in 2017 and a total economic burden of $51.9 billion. The total burden of PD includes direct medical costs of $25.4 billion and $26.5 billion in indirect and non-medical costs, including an indirect cost of $14.2 billion (PWP and caregiver burden combined), non-medical costs of $7.5 billion, and $4.8 billion due to disability income received by PWPs. The Medicare program bears the largest share of excess medical costs, as most PD patients are over age 65. Projected PD prevalence will be more than 1.6 million with projected total economic burden surpassing $79 billion by 2037. The economic burden of PD was previously underestimated. Our findings underscore the substantial burden of PD to society, payers, patients, and caregivers. Interventions to reduce PD incidence, delay disease progression, and alleviate symptom burden may reduce the future economic burden of PD.

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson’s disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73–100% for sensitivity and 67–100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets.

Background Parkinson’s disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD. Methods Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis. Results First, there was no effect of device type for classifying tremor symptoms ( p  > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models ( p  = 0.01). Second, model performance decreased with sampling frequency ( p  < 0.001) for tremor, but not bradykinesia ( p  > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set. Conclusions Our findings demonstrate the ability to simplify measurement characteristics from body-worn sensors while maintaining performance in PD symptom detection. Understanding the trade-off between model performance and data resolution is crucial to design efficient, accurate wearable sensing systems. This approach may improve the feasibility of long-term, continuous, and real-time monitoring of PD symptoms by reducing computational burden on wearable devices.

Several characteristics associated with increased risk for Parkinson's disease (PD) have been identified, including specific genotypes and various non-motor symptoms. Characterizing non-motor features, such as cognitive abilities, among individuals considered at-risk for PD is essential to improving prediction of future neurodegeneration. Participants belonging to the following cohorts of the Parkinson Progression Markers Initiative (PPMI) study were included: de novo PD with dopamine transporter binding deficit (n = 423), idiopathic REM sleep behavior disorder (RBD, n = 39), hyposmia (n = 26) and non-PD mutation carrier (NMC; Leucine-rich repeat kinase 2 (LRRK2) G2019S (n = 88) and glucocerebrosidase (GBA) gene (n = 38) mutations)). Inclusion criteria enriched the RBD and hyposmia cohorts, but not the NMC cohort, with individuals with dopamine transporter binding deficit. Baseline neuropsychological performance was compared, and analyses were adjusted for age, sex, education, and depression. The RBD cohort performed significantly worse than the hyposmia and NMC cohorts on Symbol Digit Modality Test (mean (SD) 32.4 (9.16) vs. 41.8 (9.98), p = 0.002 and vs. 45.2 (10.9), p<0.001) and Judgment of Line Orientation (11.3 (2.36) vs.12.9 (1.87), p = 0.004 and vs. 12.9 (1.87), p<0.001). The RBD cohort also performed worse than the hyposmia cohort on the Montreal Cognitive Assessment (25.5 (4.13) vs. 27.3 (1.71), p = 0.02). Hyposmics did not differ from PD or NMC cohorts on any cognitive test score. Among individuals across a spectrum of risk for PD, cognitive function is worse among those with the characteristic most strongly associated with future risk of PD or dementia with Lewy bodies, namely RBD.

With the recent approval of disease‐modifying treatments for mild cognitive impairment (MCI) and mild Alzheimer's disease (AD) by the United States Food and Drug Administration (FDA), Medicines and Healthcare products Regulatory Agency (MHRA), European Medicine Agency's Committee for Medicinal Products for Human Use (EMA/CHMP) entities, there is a growing sense of urgency and renewed efforts to reassess and understand what constitutes a clinically meaningful benefit in the context of new treatments for AD care, despite the discordance between regulatory entities in regulatory decision‐making. While the concept of minimal clinically important difference (MCID) was introduced many years ago, there remains an ongoing debate about how best to evaluate and define clinical benefit in the context of emerging and new therapies for dementia. In this perspective piece, we assess how MCID can be applied to common endpoints and identify areas where MCID application or generation could be useful to enable a better valuation of therapeutic innovation. We offer recommendations for greater consistency in measures used to define MCID, and encourage the prioritized use of patient‐reported measures in early AD to build fieldwide consensus for MCID estimation methods and application in AD. Highlights There is no gold standard or field‐wide consensus on what constitutes a clinically meaningful change in Alzheimer's disease (AD) progression trajectories. Anchor‐based minimal clinically important difference (MCID) may be used as a tool that can be leveraged for greater contextualization of the clinical relevance of a treatment effect. Patient‐reported outcomes (PROs) should be used to define MCID, particularly within mild cognitive impairment (MCI), prodrome/mild AD groups. Greater consistency is needed in the outcome measures used to detect cognitive and functional change to define MCID. This will enable MCID comparisons and support replications of MCID estimates across AD populations. Observational data can augment the clinical characterization and impact of treatment effect and help establish a “ground truth” MCID. MCID estimates for AD outcomes may be used in regulatory submissions to help contextualize the importance of a statistically significant treatment effect.

Gait impairments are among the most common and disabling symptoms of Parkinson's disease. Nonetheless, gait is not routinely assessed quantitatively but is described in general terms that are not sensitive to changes ensuing with disease progression. Quantifying multiple gait features (eg, speed, variability, and asymmetry) under natural and more challenging conditions (eg, dual-tasking, turning, and daily living) enhanced sensitivity of gait quantification. Studies of neural connectivity and structural network topology have provided information on the mechanisms of gait impairment. Advances in the understanding of the multifactorial origins of gait changes in patients with Parkinson's disease promoted the development of new intervention strategies, such as neurostimulation and virtual reality, aimed at alleviating gait impairments and enhancing functional mobility. For clinical applicability, it is important to establish clear links between specific gait impairments, their underlying mechanisms, and disease progression to foster the acceptance and usability of quantitative gait measures as outcomes in future disease-modifying clinical trials.

Knowledge of the relationship between depressive symptoms and cognition in older adults has primarily come from studies of clinically depressed, functionally impaired or cognitively impaired individuals, and in predominately White samples. Limited minority representation in depression research exposes the need to examine these associations in more ethnic/racially diverse populations. We sought to examine the relationship between depressive symptoms and cognition in a sample of non-demented older African Americans recruited from surrounding U.S. cities of New York, Greensboro, Miami, and Nashville (N=944). Depressive symptoms were evaluated with the Geriatric Depression Scale (GDS). Cognition was evaluated with a comprehensive neuropsychological battery. Test scores were summarized into attention, executive function, memory, language, and processing speed composites. Controlling for age, education, reading level, and sex, African American older adults who endorsed more symptoms obtained significantly lower scores on measures of memory, language, processing speed, and executive functioning. Further investigation of the causal pathway underlying this association, as well as potential mediators of the relationship between depressive symptoms and cognitive test performance among older African Americans, such as cardiovascular and cerebrovascular disease, may offer potential avenues for intervention. (JINS, 2014, 20, 1–8)

BackgroundSleep disturbances and an increased risk of suicide are common in people with Huntington’s disease (PwHD), with rates significantly higher than those of the general population as well as other neurodegenerative diseases. However, the magnitude of these problems is still poorly understood. In this piece, we provide evidence for five European countries (EU5) using data from Huntington’s Disease Burden of Illness (HDBOI) study.MethodsThe HDBOI is a retrospective, cross-sectional study in which physicians reported information on PwHD demographic and clinical characteristics, including suicidal ideation. Moreover, PwHD completed the HDQLife tool including information on concerns about death and dying as well as sleep disturbances through an optional questionaire. PwHD were classifed as early (ES), mid (MS) or advanced stage (AS), according to the opinion of the treating physician.ResultsThe sample had 1,602 PwHD (Table 1) of which were 40% ES, 34% MS, and 26% AS. Suicidal risk increased for more advance stages: 12% of ES, 12% of MS and 14% of AS were currently displaying suicide ideation. Overall, over 5% of PwHD had attempt suicide. HDQLife results showed that 61% PwHD felt anxiety about dying and 12% presented suicidal thoughts often or always. Over 46% of PwHD (N=445) reported to have moderate or severe difficulty sleeping and the percentage increases up to 70% for AS.Abstract F64 Table 1Key demographics and main results Total ES MS AS N Mean SD N Mean SD N Mean SD N Mean SD Age (years) 1,601 47.5 13.7 632 43.3 13.0 547 48.7 13.7 422 52.4 13.0 N % N % N % N % Gender Male 952 59.4 362 57.2 308 56.3 282 66.8 Female 624 39.0 260 41.1 227 41.5 137 32.5 Prefer not to say 26 1.6 11 1.7 12 2.2 3 0.7 Suicidal ideation (Physician reported) Currently displaying suicidal ideation 198 12.4 74 11.7 64 11.7 60 14.2 Previously displayed suicidal ideation 468 29.2 134 21.2 165 30.2 169 40.0 No, the patient has never displayed suicidal ideation 838 52.3 397 62.7 291 53.2 150 35.6 Don’t know 98 6.1 28 4.4 27 4.9 43 10.2 Difficulty sleeping (PwHD reported) No difficulty sleeping 44 9.9 27 14.3 13 8.6 4 3.9 Some mild difficulty sleeping 194 43.6 103 54.5 63 41.5 28 26.9 Moderate difficulty sleeping 158 35.5 46 24.3 61 40.1 51 49.0 Severe difficulty sleeping 44 9.9 12 6.4 14 9.2 18 17.3 Hardly ever sleep at all 5 1.1 1 0.5 1 0.7 3 2.9 N Mean SD N Mean SD N Mean SD N Mean SD HDQLife* (PwHD reported) 1. I think about how I will die. 445 2.8 1.0 189 2.7 0.9 152 2.6 1.0 104 3.1 1.1 2. How often did you worry about how your family would cope with your death? 445 2.6 1.1 189 2.6 1.0 152 2.5 1.1 104 2.9 1.2 3. How often did you think about your own death? 445 2.7 1.1 189 2.6 1.0 152 2.5 1.1 104 3.1 1.2 4.How often did you think about dying? 445 2.7 1.1 189 2.6 0.9 152 2.5 1.1 104 3.2 1.2 5. How often did you feel anxiety that you would die? 445 2.7 1.1 189 2.6 1.0 152 2.6 1.1 104 3.1 1.2 6. How often did you think about ending your life? 445 2.1 1.1 189 2.0 1.0 152 2.1 1.0 104 2.5 1.1 *The HDQLIFE Concern with Death and Dying item bank examines the impact that concerns and preoccupation with death and dying has on health-related quality of life for individuals with Huntington disease. The short form consists of 6 questions with 5 scoring answers: 1 Never; 2 Rarely; 3 Sometimes; 4 Often; 5 AlwaysConclusionSleep disturbances and suicidal ideation are high among PwHD and increase as diseases progresses. Interventions aimed at ensuring the provision of adequate mental health and preventive services for PwHD are required to reduce the burden on PwHD.

BackgroundHuntington’s Disease (HD) progresses over time, impacting mental health, work productivity and inter-personal relationships of the person with HD (PwHD) and their caregivers. We explore the impact of HD on caregiver’s quality of life (QoL) using the Huntington’s Disease Quality of Life Battery for Carers (HDQoL-Cs) tool.MethodsThe short version of the HDQoL-Cs tool was part of the caregiver questionnaire of the HDBOI study. It has 23 items divided into two components: ‘satisfaction with life’ and ‘feelings about living with HD’. Each item ranges from 0-10 (higher scores reflecting better QoL), and component scores result from the mean score of corresponding items. Caregivers were categorized into three groups according to the disease stage of the PwHD, categorization was based on the opinion of the treating physician. Differences in QoL were explored descriptively using ANOVA tests.ResultsThe sample has 434 caregivers (Table 1), of which 36% were caregivers of early (ES), 36% of mid (MS) and 27% of advance (AS) stage. Mean score of ‘satisfaction with life’ decreased for more advanced stages: 6.00, 5.74 and 5.38 for ES, MS and AS respectively [p< 0.05]. Satisfaction with treatment and social environment were the key items driving the satisfaction score. Similar results observed for ‘feelings about living with HD’ domain: 5.94, 5.31, and 5.12 [p< 0.05]. Stress and exhaustion were the most reported feelings experienced by caregivers.Abstract H49 Table 1HDQoL-Cs main results by HD disease stageConclusionOur results quantify the substantial humanistic burden associated with caregiving duties and highlight that the healthcare and psychosocial support needs of PwHD and their families remain largely unmet.

BackgroundThe prevalence of Huntington’s Disease (HD) has increased over time, augmenting the associated economic and humanistic burden. The HDBOI study aims to provide an up-to-date assessment of the burden of HD from a multinational perspective.MethodsThe HDBOI is a retrospective, cross-sectional dataset that captures demographic, clinical, and health resource utilization (HRU) of a cohort of HD patients, reported by treating physicians in multiple centres across the USA, Germany, Spain, Italy, France and the UK. Patients and caregivers reported information on health-related quality of life (HRQoL), non-medical and indirect costs associated with HD through optional questionnaires. The study has been governed by an Expert Review Group (ERG) that provided recommendations on the study design. Data was collected between September 2020 and May 2021.Patients and caregivers reported their HRQoL at the time of questionnaire completion, whereas physicians reported patient’s HRU for the 12-month period between March 2019 and March 2020, to avoid months with limited access to healthcare due to COVID-19. Other strategies were taken to mitigate the effect of COVID-19 on the fieldwork process: online questionnaires, extending the time in fieldwork, questions monitoring the effect of the pandemic on patients HRQoL and HRU.ResultsThe HDBOI sample has 2,094 HD patients, of which 40% were early stage, 34% mid stage and 26% advanced stage, as assessed by the treating physician. Patient representation across countries was similar. For a subsample (N=718) the Shoulson and Fahn stages were determined by the treating physician: stage I (14.6%), II (24.4%), III (31.6%), IV (28.1%) and V (1.1%). The last figure was expected by the ERG, as advanced stage patients usually live in care homes and do not attend regular consultations.ConclusionThe HDBOI study is a representative sample of the HD population across disease stages and studied countries, as confirmed by the ERG.