Longitudinal Change in Quality of Life in Neurological Disorders Measures Over 3 Years in Patients with Early Parkinson's Disease.

BACKGROUND: The Quality of Life in Neurological Disorders (Neuro-QoL) is a publicly available health-related quality-of-life measurement system. OBJECTIVE: The aim of this study was to evaluate the utility of Neuro-QoL item banks as outcome measures for clinical trials in Parkinson's disease. METHODS: An analysis of Neuro-QoL responsiveness to change and construct validity was performed in a multicenter clinical trial cohort. RESULTS: Among 310 participants over 3 years, changes in five of eight Neuro-QoL domains were significant (P < 0.05) but very modest. The largest effect sizes were seen in the cognition and mobility domains (0.35-0.39). The largest effect size for change over the year in which levodopa was initiated was -0.19 for lower extremity function-mobility. For a similarly designed clinical trial, estimated sample size required to demonstrate a 50% reduction in worsening ranged from 420 to more than 1000 participants per group. CONCLUSIONS: More sensitive tools will be required to serve as an outcome measure in early Parkinson's disease. © 2021 International Parkinson and Movement Disorder Society.

Approach to the Patient with Gait Disturbance.

The assessment of patients presenting with disorders of gait can be a daunting task for neurologists given the broad potential localization and differential diagnosis. However, gait disorders are extremely common in outpatient neurology, and all neurologists should be comfortable with the assessment, triage, and management of patients presenting with difficulty walking. Here, we aim to present a manageable framework for neurologists to approach the assessment of patients presenting with gait dysfunction. We suggest a chief complaint-based phenomenological characterization of gait, using components of the neurological history and examination to guide testing and treatment. We present the framework to mirror the outpatient visit with the patient, highlighting (1) important features of the gait history, including the most common gait-related chief complaints and common secondary (medical) causes of gait dysfunction; (2) gait physiology and a systematic approach to the gait examination allowing appropriate characterization of gait phenomenology; (3) an algorithmic approach to ancillary testing for patients with gait dysfunction based on historical and examination features; and (4) definitive and supportive therapies for the management of patients presenting with common neurological disorders of gait.

Palliative Care in Movement Disorders: an Evolving Field.

PURPOSE OF REVIEW: This review summarizes the current state of evidence for palliative care (PC) in movement disorders, describes the application of PC to clinical practice, and suggests future research directions. RECENT FINDINGS: PC needs are common in persons living with movement disorders and their families from the time of diagnosis through end-of-life and contribute to quality of life. Early advance care planning is preferred by patients, impacts outcomes and is promoted by PC frameworks. Systematic assessment of non-motor symptoms, psychosocial needs and spiritual/existential distress may address gaps in current models of care. Several complementary and emerging models of PC may be utilized to meet the needs of this population. A PC approach may identify and improve important patient and caregiver-centered outcomes. As a relatively new application of PC, there is a need for research to adapt, develop and implement approaches to meet the unique needs of this population.

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study.

BACKGROUND: Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases-fueled mostly by environmental pollution and an aging population-can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD. OBJECTIVE: In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD. METHODS: We collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, "the quick brown fox jumps over the lazy dog." We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning-based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model's output. RESULTS: We achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost-a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing "ahh") influence the model's decision the most. CONCLUSIONS: Our model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care.

Managing Neurophobia: How Can We Meet the Current and Future Needs of Our Students?

Neurophobia was defined more than two decades ago as a "fear of neural sciences and clinical neurology" among medical students. Despite recognition of the ailment and research into underlying causes, medical students and neurological educators continue to struggle with neurophobia today. At our institution, we have been successful at mitigating neurophobia. Here, we define the underlying drivers of neurophobia, based on the relevant literature. We also describe our strategies for battling neurophobia in the preclinical and clinical years by (1) establishing a continuum of neurological education; (2) incorporating active and observed learning throughout neurological education; and (3) enhancing socialization into neurology. Finally, we consider the future of neurological education, describe strategies for educators to mitigate neurophobia, and propose a call to action to further understand neurophobia. Neurophobia is not inevitable; effective curricula and dedicated faculty can engage students and ensure students better understand and enjoy their neurological education.

Digital assessment of speech in Huntington disease.

Background: Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration. Methods: We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features. Results: Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%. Conclusion: Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials.

Using Wearable Sensors and Machine Learning to Assess Upper Limb Function in Huntington's Disease.

Huntington's disease (HD), like many other neurological disorders, affects both lower and upper limb function that is typically assessed in the clinic - providing a snapshot of disease symptoms. Wearable sensors enable the collection of real-world data that can complement such clinical assessments and provide a more comprehensive insight into disease symptoms. In this context, almost all studies are focused on assessing lower limb function via monitoring of gait, physical activity and ambulation. In this study, we monitor upper limb function during activities of daily living in individuals with HD (n = 16), prodromal HD (pHD, n = 7), and controls (CTR, n = 16) using a wrist-worn wearable sensor, called PAMSys ULM, over seven days. The participants were highly compliant in wearing the sensor with an average daily compliance of 99% (100% for HD, 98% for pHD, and 99% for CTR). Goal-directed movements (GDM) of the hand were detected using a deep learning model, and kinematic features of each GDM were estimated. The collected data was used to predict disease groups (i.e., HD, pHD, and CTR) and clinical scores using a combination of statistical and machine learning-based models. Significant differences in GDM features were observed between the groups. HD participants performed fewer GDMs with long duration (> 7.5 seconds) compared to CTR (p-val = 0.021, d = -0.86). In velocity and acceleration metrics, the highest effect size feature was the entropy of the velocity zero-crossing length segments (HD vs CTR p-val <0.001, d = -1.67; HD vs pHD p-val = 0.043, d=-0.98; CTR vs pHD p-val = 0.046, d=0.96). In addition, this same variable showed a strongest correlation with clinical scores. Classification models achieved good performance in distinguishing HD, pHD and CTR individuals with a balanced accuracy of 67% and a 0.72 recall for the HD group, while regression models accurately predicted clinical scores. Notably the explained variance for the upper extremity function subdomain scale of Unified Huntington's Disease Rating Scale (UHDRS) was the highest, with the model capturing 60% of the variance. Our findings suggest the potential of wearables and machine learning for early identification of phenoconversion, remote monitoring in HD, and evaluating new treatments efficacy in clinical trials and medicine.

Curriculum Innovations: Enhancing Medical Student Neuroscience Training With a Team-Based Learning Curriculum.

Background and Problem Statement: Neurophobia, the fear of, discomfort with, and dislike of clinical neurology, is frequently due to poor experiences in preclinical neuroscience education among medical providers. We developed, implemented, and assessed a curricular innovation using clinician-educators and team-based learning (TBL) with the goals to demonstrate clinical relevance in neuropathology, enhance student engagement in neuropathology education, and promote direct application of knowledge. Methods and Curriculum Description: We identified an underperforming neuropathology curriculum within the second-year medical student neuroscience course at the University of Rochester School of Medicine and Dentistry and implemented a traditional TBL curriculum to deliver this content. In addition, we transitioned to primarily clinician-led lectures in the neuropathology curriculum. We assessed student opinions of the curricular changes though end-of-course feedback, the implementation of a novel survey, and semistructured interviews with students. We assessed outcomes on the course final examination and overall course performance, comparing student performance in the preimplementation phase (year 2020-2021) with that in the postimplementation phase (year 2021-2022) using a 2-sample t test. Results and Assessment: Student opinions of the curricular changes were positive on the end-of-course evaluation (79.4% rated TBL as good or excellent) and novel survey (89%-96% of students rated the portions of the curriculum positively). Themes identified in free text responses and through qualitative interviews included an appreciation of the streamlined course content and a sense that the various sessions within the neuropathology curriculum effectively reinforced learning. Student performance on the final examination was similar in the preimplementation vs postimplementation phases (81.2% correct vs 80.3% correct; p = 0.37). Performance on the neuropathology subsection of the final examination was also similar among the 2 cohorts (82.6% correct vs 83.9% correct; p = 0.36). Discussion and Lessons Learned: We demonstrate the feasibility and utility of a transition to primarily neurologist and neurosurgeon-led lectures and the implementation of a TBL curriculum within a neuroscience course. While we report data from implementation at a single center, these results have potential relevance to other courses, given our demonstration that TBL is a useful method to deliver neuroscience learning, nonpathologist lecturers can effectively provide neuropathology education, and a small number of educational faculty can be engaged to deliver this material.

Integrating Virtual Teaching in a New Era of Medical Education: Lessons from a Neurology Course.

The COVID-19 pandemic has led to a sudden shift toward virtual learning in neurology education, which presents challenges for educators. However, virtual learning is here to stay for three key reasons: demand among students, ease of dissemination, and potential to improve educational quality. Despite challenges, educators can teach effectively using appropriate virtual tools and methods, with innovative approaches that will ultimately lead to sustained improvements in neurology education. Here, we aim to help educators effectively incorporate virtual instruction into their "new normal" by offering practical, evidence-based tips for balancing in-person and virtual learning, selecting the appropriate tools and methods for virtual teaching, and creating a supportive virtual learning environment. Using a systematic approach, educators can identify specific, achievable goals, determine the content's scope, appropriate assessments, select appropriate teaching methods, and create the session schedule and materials. Here we described evidence-based strategies for best practices, developing virtual material, and creating the appropriate virtual learning environment.

Palliative care in Parkinson disease and related disorders.

Although neuropalliative care is a relatively new field, there is increasing evidence for its use among the degenerative parkinsonian syndromes, including idiopathic Parkinson disease, progressive supranuclear palsy, multiple system atrophy, dementia with Lewy bodies, and corticobasal syndrome. This chapter outlines the current state of evidence for palliative care among individuals with the degenerative parkinsonian syndromes with discussion surrounding: (1) disease burden and needs across the conditions; (2) utility, timing, and methods for advance care planning; (3) novel care models for the provision of palliative care; and 4) end-of-life care issues. We also discuss currently unmet needs and unanswered questions in the field, proposing priorities for research and the assessment of implemented care models.

Change in the Parkinson Anxiety Scale correlates with change in other clinical measures of anxiety over time.

The Parkinson's disease (PD)-specific Parkinson Anxiety Scale (PAS) is an anxiety rating scale that has been validated in cross-sectional studies. In a study of buspirone for anxiety in PD, it appears that the PAS may be sensitive to change in anxiety demonstrating moderate-to-high correlation with participant-reported and clinician-administered scales.

Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.

There are currently no effective biomarkers for diagnosing Parkinson's disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson's Disease Rating Scale (R = 0.94, P = 3.6 × 10-25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person's body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis.

Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response.

Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.

Remote smartphone monitoring of Parkinson's disease and individual response to therapy.

Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity (r = 0.71; P < 1.8 × 10-6) when compared with motor Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretation of RWD, our results support the use of smartphones and wearables in objective and personalized disease assessments.

Practicing in a Pandemic: A Clinician's Guide to Remote Neurologic Care.

Neurologists around the country and the world are rapidly transitioning from traditional in-person visits to remote neurologic care because of the coronavirus disease 2019 pandemic. Given calls and mandates for social distancing, most clinics have shuttered or are only conducting urgent and emergent visits. As a result, many neurologists are turning to teleneurology with real-time remote video-based visits with patients to provide ongoing care. Although telemedicine utilization and comfort has grown for many acute and ambulatory neurologic conditions in the past decade, remote visits and workflows remain foreign to many patients and neurologists. Here, we provide a practical framework for clinicians to orient themselves to the remote neurologic assessment, offering suggestions for clinician and patient preparation before the visit; recommendations to manage common challenges with remote neurologic care; modifications to the neurologic examination for remote performance, including subspecialty-specific considerations for a variety of neurologic conditions; and a discussion of the key limitations of remote visits. These recommendations are intended to serve as a guide for immediate implementation as neurologists transition to remote care. These will be relevant not only for practice today but also for the likely sustained expansion of teleneurology following the pandemic.

A real-world study of wearable sensors in Parkinson's disease.

Most wearable sensor studies in Parkinson's disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson's disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson's walked significantly less (median [inter-quartile range]: 4980 [2835-7163] steps/day) than controls (7367 [5106-8928] steps/day; P = 0.04). Tremor was present for 1.6 [0.4-5.9] hours (median [range]) per day in most-affected hands (MDS-UPDRS 3.17a or 3.17b = 1-4) of individuals with Parkinson's, which was significantly higher than the 0.5 [0.3-2.3] hours per day in less-affected hands (MDS-UPDRS 3.17a or 3.17b = 0). These results, which require replication in larger cohorts, advance our understanding of the manifestations of Parkinson's in real-world settings.

Neurophysiological biomarkers to optimize deep brain stimulation in movement disorders.

Intraoperative neurophysiological information could increase accuracy of surgical deep brain stimulation (DBS) lead placement. Subsequently, DBS therapy could be optimized by specifically targeting pathological activity. In Parkinson's disease, local field potentials (LFPs) excessively synchronized in the beta band (13-35 Hz) correlate with akinetic-rigid symptoms and their response to DBS therapy, particularly low beta band suppression (13-20 Hz) and high frequency gamma facilitation (35-250 Hz). In dystonia, LFPs abnormally synchronize in the theta/alpha (4-13 Hz), beta and gamma (60-90 Hz) bands. Phasic dystonic symptoms and their response to DBS correlate with changes in theta/alpha synchronization. In essential tremor, LFPs excessively synchronize in the theta/alpha and beta bands. Adaptive DBS systems will individualize pathological characteristics of neurophysiological signals to automatically deliver therapeutic DBS pulses of specific spatial and temporal parameters.

GEORGE®: A Pilot Study of a Smartphone Application for Huntington's Disease.

BACKGROUND: Current Huntington's disease (HD) measures are limited to subjective, episodic assessments conducted in clinic. Smartphones can enable the collection of objective, real-world data but their use has not been extensively evaluated in HD. OBJECTIVE: Develop and evaluate a smartphone application to assess feasibility of use and key features of HD in clinic and at home. METHODS: We developed GEORGE®, an Android smartphone application for HD which assesses voice, chorea, balance, gait, and finger tapping speed. We then conducted an observational pilot study of individuals with manifest HD, prodromal HD, and without a movement disorder. In clinic, participants performed standard clinical assessments and a battery of active tasks in GEORGE. At home, participants were instructed to complete the activities thrice daily for one month. Sensor data were used to measure chorea, tap rate, and step count. Audio data was not analyzed. RESULTS: Twenty-three participants (8 manifest HD, 5 prodromal HD, 10 controls) enrolled, and all but one completed the study. On average, participants used the application 2.1 times daily. We observed a significant difference in chorea score (HD: 19.5; prodromal HD: 4.5, p = 0.007; controls: 4.3, p = 0.001) and tap rate (HD: 2.5 taps/s; prodromal HD: 8.9 taps/s, p = 0.001; controls: 8.1 taps/s, p = 0.001) between individuals with and without manifest HD. Tap rate correlated strongly with the traditional UHDRS finger tapping score (left hand: r = -0.82, p = 0.022; right hand: r = -0.79, p = 0.03). CONCLUSION: GEORGE is an acceptable and effective tool to differentiate individuals with and without manifest HD and measure key disease features. Refinement of the application's interface and activities will improve its usability and sensitivity and, ideally, make it useful for clinical care and research.

Video-based Parkinson's disease assessments in a nationwide cohort of Fox Insight participants.

INTRODUCTION: Parkinson's disease (PD) research is hampered by slow, inefficient recruitment and burdensome in-person assessments that may be challenging to conduct in a world affected by COVID-19. Fox Insight is an ongoing prospective clinical research study that enables individuals to participate in clinical research from their own homes by completing online questionnaires. To date, over 45,000 participants with and without PD have enrolled. We sought to validate self-reported PD diagnosis in the Fox Insight cohort, assess the validity of other self-reported health information, and evaluate the willingness of participants to participate in video-based research studies. METHODS: Individuals with and without self-reported PD enrolled in Fox Insight were invited to participate in this virtual research study. Participants completed online questionnaires and two virtual visits, during which we conducted standard cognitive and motor assessments. A movement disorder expert determined the most likely diagnosis, which was compared to self-reported diagnosis. RESULTS: A total of 203 participants from 40 U.S. states, 159 with remote clinician-determined PD and 44 without, completed the study (59% male, mean (SD) age 65.7 (9.8)). Level of agreement between self-reported PD diagnosis in Fox Insight and clinician-determined diagnosis was very good ((kappa = 0.85, 95% CI 0.76-0.94). Overall, 97.9% of participants were satisfied with the study, 98.5% were willing to participate in a future observational study with virtual visits, and 76.1% were willing to participate in an interventional trial with virtual visits. CONCLUSION: Among the Fox Insight cohort, self-reported diagnosis is accurate and interest in virtual research studies is high.

Design of a virtual longitudinal observational study in Parkinson's disease (AT-HOME PD).

OBJECTIVE: The expanding power and accessibility of personal technology provide an opportunity to reduce burdens and costs of traditional clinical site-centric therapeutic trials in Parkinson's disease and generate novel insights. The value of this approach has never been more evident than during the current COVID-19 pandemic. We sought to (1) establish and implement the infrastructure for longitudinal, virtual follow-up of clinical trial participants, (2) compare changes in smartphone-based assessments, online patient-reported outcomes, and remote expert assessments, and (3) explore novel digital markers of Parkinson's disease disability and progression. METHODS: Participants from two recently completed phase III clinical trials of inosine and isradipine enrolled in Assessing Tele-Health Outcomes in Multiyear Extensions of Parkinson's Disease trials (AT-HOME PD), a two-year virtual cohort study. After providing electronic informed consent, individuals complete annual video visits with a movement disorder specialist, smartphone-based assessments of motor function and socialization, and patient-reported outcomes online. RESULTS: From the two clinical trials, 226 individuals from 42 states in the United States and Canada enrolled. Of these, 181 (80%) have successfully downloaded the study's smartphone application and 161 (71%) have completed patient-reported outcomes on the online platform. INTERPRETATION: It is feasible to conduct a large-scale, international virtual observational study following the completion of participation in brick-and-mortar clinical trials in Parkinson's disease. This study, which brings research to participants, will compare established clinical endpoints with novel digital biomarkers and thereby inform the longitudinal follow-up of clinical trial participants and design of future clinical trials.