Background: The ongoing COVID-19 pandemic has many consequences for people with Parkinson’s disease (PD). Social distancing measures complicate regular care and result in lifestyle changes, which may indirectly cause psychological stress and worsening of PD symptoms. Objective: To assess whether the COVID-19 pandemic was associated with increased psychological distress and decreased physical activity in PD, how these changes related to PD motor and non-motor symptom severity, and what frequency and burden of COVID-related stressors were. Methods: We sent an online survey to the Personalized Parkinson Project (PPP) cohort (n = 498 PD patients) in the Netherlands. In the survey, we distinguished between COVID-related stressor load, psychological distress, PD symptom severity, and physical activity. We related inter-individual differences to personality factors and clinical factors collected before the pandemic occurred. Results: 358 PD patients completed the survey between April 21 and May 25, 2020 (response rate 71.9%). Patients with higher COVID-related stressor load experienced more PD symptoms, and this effect was mediated by the degree of psychological distress. 46.6% of PD patients were less physically active since the COVID-19 pandemic, and reduced physical activity correlated with worse PD symptoms. Symptoms that worsened most were rigidity, fatigue, tremor, pain and concentration. Presence of neuropsychiatric symptoms (anxiety, depression) before the pandemic, as well as cognitive dysfunction and several personality traits predicted increased psychological distress during the COVID-19 pandemic. Conclusion: Our findings show how an external stressor (the COVID-19 pandemic) leads to a worsening of PD symptoms by evoking psychological distress as well as lifestyle changes (reduced physical activity).

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

BackgroundAn important challenge in Parkinson's disease research is how to measure disease progression, ideally at the individual patient level. The MDS‐UPDRS, a clinical assessment of motor and nonmotor impairments, is widely used in longitudinal studies. However, its ability to assess within‐subject changes is not well known. The objective of this study was to estimate the reliability of the MDS‐UPDRS when used to measure within‐subject changes in disease progression under real‐world conditions.MethodsData were obtained from the Parkinson's Progression Markers Initiative cohort and included repeated MDS‐UPDRS measurements from 423 de novo Parkinson's disease patients (median follow‐up: 54 months). Subtotals were calculated for parts I, II, and III (in on and off states). In addition, factor scores were extracted from each part. A linear Gaussian state space model was used to differentiate variance introduced by long‐lasting changes from variance introduced by measurement error and short‐term fluctuations. Based on this, we determined the within‐subject reliability of 1‐year change scores.ResultsOverall, the within‐subject reliability ranged from 0.13 to 0.62. Of the subscales, parts II and III (OFF) demonstrated the highest within‐subject reliability (both 0.50). Of the factor scores, the scores related to gait/posture (0.62), mobility (0.45), and rest tremor (0.43) showed the most consistent behavior.ConclusionsOur results highlight that MDS‐UPDRS change scores contain a substantial amount of error variance, underscoring the need for more reliable instruments to forward our understanding of the heterogeneity in PD progression. Focusing on gait and rest tremor may be a promising approach for an early Parkinson's disease population. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Background Our understanding of the etiology, pathophysiology, phenotypic diversity, and progression of Parkinson’s disease has stagnated. Consequently, patients do not receive the best care, leading to unnecessary disability, and to mounting costs for society. The Personalized Parkinson Project (PPP) proposes an unbiased approach to biomarker development with multiple biomarkers measured longitudinally. Our main aims are: (a) to perform a set of hypothesis-driven analyses on the comprehensive dataset, correlating established and novel biomarkers to the rate of disease progression and to treatment response; and (b) to create a widely accessible dataset for discovery of novel biomarkers and new targets for therapeutic interventions in Parkinson’s disease. Methods/design This is a prospective, longitudinal, single-center cohort study. The cohort will comprise 650 persons with Parkinson’s disease. The inclusion criteria are purposely broad: age ≥ 18 years; and disease duration ≤5 years. Participants are followed for 2 years, with three annual assessments at the study center. Outcomes include a clinical assessment (including motor and neuro-psychological tests), collection of biospecimens (stool, whole blood, and cerebrospinal fluid), magnetic resonance imaging (both structural and functional), and ECG recordings (both 12-lead and Holter). Additionally, collection of physiological and environmental data in daily life over 2 years will be enabled through the Verily Study Watch. All data are stored with polymorphic encryptions and pseudonyms, to guarantee the participants’ privacy on the one hand, and to enable data sharing on the other. The data and biospecimens will become available for scientists to address Parkinson’s disease-related research questions. Discussion The PPP has several distinguishing elements: all assessments are done in a single center; inclusion of “real life” subjects; deep and repeated multi-dimensional phenotyping; and continuous monitoring with a wearable device for 2 years. Also, the PPP is powered by privacy and security by design, allowing for data sharing with scientists worldwide respecting participants’ privacy. The data are expected to open the way for important new insights, including identification of biomarkers to predict differences in prognosis and treatment response between patients. Our long-term aim is to improve existing treatments, develop new therapeutic approaches, and offer Parkinson’s disease patients a more personalized disease management approach. Trial registration Clinical Trials NCT03364894 . Registered December 6, 2017 (retrospectively registered).

Background Parkinson’s disease (PD) is a chronic and neurodegenerative disease associated with a wide variety of symptoms. The risk of complications increases with progression of the disease. These complications have a tremendous impact on the quality of life of people with PD. The aim of this study was to examine health care professionals’ experiences of potential barriers and facilitators in providing palliative care for people with PD in the Netherlands. Methods This was a qualitative descriptive study. The data were collected from 10 individual in-depth interviews and three focus groups ( n = 29) with health care professionals. Health care professionals were selected based on a positive answer to the question: “In the past 2 years, did you treat or support a person with PD who subsequently died?” The data were analyzed by thematic text analysis. Results Health care professionals supported the development of a palliative care system for PD but needed to better understand the essence of palliative care. In daily practice, they struggled to identify persons’ needs due to interfering PD-specific symptoms such as cognitive decline and communication deficits. Timely addressing the personal preferences for providing palliative care was identified as an important facilitator. Health care professionals acknowledged being aware of their lack of knowledge and of their little competence in managing complex PD. Findings indicate a perceived lack of care continuity, fragmentation of services, time pressure and information discontinuity. Conclusions Health care professionals experienced several facilitators and barriers to the provision of palliative care to people with PD. There is a need to improve the knowledge on complex PD and the continuity of information, as well as optimize coordination and deliver care based on a persons’ preferences. Additional training can help to become more knowledgeable and confident. Electronic supplementary material The online version of this article (10.1186/s12904-019-0441-6) contains supplementary material, which is available to authorized users.

Introduction Falling is among the most serious clinical problems in Parkinson's disease (PD). We used body‐worn sensors (falls detector worn as a necklace) to quantify the hazard ratio of falls in PD patients in real life. Methods We matched all 2063 elderly individuals with self‐reported PD to 2063 elderly individuals without PD based on age, gender, comorbidity, and living conditions. We analyzed fall events collected at home via a wearable sensor. Fall events were collected either automatically using the wearable falls detector or were registered by a button push on the same device. We extracted fall events from a 2.5‐year window, with an average follow‐up of 1.1 years. All falls included were confirmed immediately by a subsequent telephone call. The outcomes evaluated were (1) incidence rate of any fall, (2) incidence rate of a new fall after enrollment (ie, hazard ratio), and (3) 1‐year cumulative incidence of falling. Results The incidence rate of any fall was higher among self‐reported PD patients than controls (2.1 vs. 0.7 falls/person, respectively; P < .0001). The incidence rate of a new fall after enrollment (ie, hazard ratio) was 1.8 times higher for self‐reported PD patients than controls (95% confidence interval, 1.6–2.0). Conclusion Having PD nearly doubles the incidence of falling in real life. These findings highlight PD as a prime “falling disease.” The results also point to the feasibility of using body‐worn sensors to monitor falls in daily life. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Declaration of interest:The CLaSP study is funded by the European Commission (Joint Programme -Neurodegenerative Disease Research "European research projects for the evaluation of health care policies, strategies and interventions for Neurodegenerative Diseases").

Clinical decision making for Parkinson's disease patients is supported by a combination of three distinct information resources: best available scientific evidence, professional expertise, and the personal needs and preferences of patients. All three sources have clear value but also share several important limitations, mainly regarding subjectivity, generalizability and variability. For example, current scientific evidence, especially from controlled clinical trials, is often based on selected study populations, making it difficult to translate the outcome to the care for individual patients in everyday clinical practice. Big data, including data from real-life unselected Parkinson populations, can help to bridge this information gap. Finegrained patient profiles created from big data have the potential to aid in identifying therapeutic approaches that will be most effective given each patient's individual characteristics, which is particularly important for a disorder characterized by such tremendous interindividual variability as Parkinson's disease. In this viewpoint, we argue that big data approaches should be acknowledged and harnessed, not to replace existing information resources, but rather as a fourth and complimentary source of information in clinical decision making, helping to represent the full complexity of individual patients. We introduce the 'quadruple decision making' model and illustrate its mode of action by showing how this can be used to pursue precision medicine for persons living with Parkinson's disease.