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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A trial of buspirone for anxiety in Parkinson's disease: Safety and tolerability.

INTRODUCTION: In Parkinson's disease (PD), anxiety is common, associated with lower health-related quality of life, and undertreated. The primary objective of this study was to determine the tolerability of buspirone for the treatment of anxiety in PD. METHODS: Individuals with PD and clinically significant anxiety were randomized 4:1 to flexible dosage buspirone or placebo for 12 weeks. Treatment was initiated at 7.5 mg twice daily and titrated based on response and tolerability to an optimal dosage (maximum 30 mg twice daily). The primary outcome was the proportion of participants who failed to complete the study on study drug. Secondary outcomes included adverse events, dosage reductions, motor function, dyskinesias, and anxiety. RESULTS: A total of 21 participants enrolled, 4 were randomized to placebo and 17 to buspirone (mean (SD) age 65.5 (9.8), 76.5% male, 88% on concomitant antidepressant or anxiolytic). In the buspirone group, 7 (41%) failed to complete the study on drug, 5 due to intolerability. The median buspirone dosage was 7.5 mg twice daily. No serious adverse events occurred. A total of 9 (53%) buspirone participants experienced adverse events consistent with worsened motor function. In the buspirone group, mean (SD) improvement from baseline to week 12 in Hamilton Anxiety Rating Scale was -3.9 (3.8) and Parkinson Anxiety Scale -7.1 (6.4). CONCLUSION: Tolerability concerns do not support moving immediately forward with a large-scale efficacy trial. However, concomitant anxiolytics may have affected tolerability and a signal of efficacy was seen suggesting that future studies of buspirone monotherapy be considered.

Feasibility, Reliability, and Value of Remote Video-Based Trial Visits in Parkinson's Disease.

BACKGROUND: There is rising interest in remote clinical trial assessments, particularly in the setting of the COVID-19 pandemic. OBJECTIVE: To demonstrate the feasibility, reliability, and value of remote visits in a phase III clinical trial of individuals with Parkinson's disease. METHODS: We invited individuals with Parkinson's disease enrolled in a phase III clinical trial (STEADY-PD III) to enroll in a sub-study of remote video-based visits. Participants completed three remote visits over one year within four weeks of an in-person visit and completed assessments performed during the remote visit. We evaluated the ability to complete scheduled assessments remotely; agreement between remote and in-person outcome measures; and opinions of remote visits. RESULTS: We enrolled 40 participants (mean (SD) age 64.3 (10.4), 29% women), and 38 (95%) completed all remote visits. There was excellent correlation (ICC 0.81-0.87) between remote and in-person patient-reported outcomes, and moderate correlation (ICC 0.43-0.51) between remote and in-person motor assessments. On average, remote visits took around one quarter of the time of in-person visits (54 vs 190 minutes). Nearly all participants liked remote visits, and three-quarters said they would be more likely to participate in future trials if some visits could be conducted remotely. CONCLUSION: Remote visits are feasible and reliable in a phase III clinical trial of individuals with early, untreated Parkinson's disease. These visits are shorter, reduce participant burden, and enable safe conduct of research visits, which is especially important in the COVID-19 pandemic.

Deep Phenotyping of Parkinson's Disease.

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.