Palliative Care in Huntington Disease: Personal Reflections and a Review of the Literature

Background Huntington disease is a fatal, autosomal dominant, neurodegenerative disorder manifest by the triad of a movement disorder, behavioral disturbances, and dementia. At present, no curative or disease modifying therapies exist for the condition and current treatments are symptomatic. Palliative care is an approach to care that focuses on symptom relief, patient and caregiver support, and end of life care. There is increasing evidence of the benefit of palliative care throughout the course of neurodegenerative conditions including Parkinson disease and amyotrophic lateral sclerosis. However, beyond its application at the end of life, little is known about the role of palliative care in Huntington disease.Methods In this article, we discuss what is known about palliative care in Huntington disease, specifically related to early disease burden, caregiver burnout, advance care planning, and end of life care.Results We provide a review of the current literature and discuss our own care practices.Discussion We conclude by discussing questions that remain unanswered and positing ideas for future work in the field.</p

A real-world study of wearable sensors in Parkinson\u27s disease

Most wearable sensor studies in Parkinson\u27s 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\u27s disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson\u27s 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\u27s, 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\u27s in real-world settings

Passive Monitoring at Home: A Pilot Study in Parkinson Disease

We conducted a pilot study using a passive radio-wave-based home monitor in individuals with Parkinson disease (PD) with a focus on gait, home activity, and time in bed. We enrolled 7 ambulatory individuals to have the device installed in the bedroom of their homes over 8 weeks and performed standard PD assessments at baseline. We evaluated the ability of the device to objectively measure gait and time in bed and to generate novel visualizations of home activity. We captured 353 days of monitoring. Mean gait speed (0.39-0.78 m/s), time in bed per day (4.4-12.1 h), and number (1.4-5.9) and duration (15.0-49.8 min) of nightly awakenings varied substantially across and within individuals. Derived gait speed correlated well with the Movement Disorder Society-Unified Parkinson's Disease Rating Scale total (r = -0.88, p = 0.009) and motor sub-score (r = -0.95, p = 0.001). Six of the seven participants agreed that their activity was typical and indicated a willingness to continue monitoring. This technology provided promising new insights into the home activities of those with PD and may be broadly applicable to other chronic conditions.National Institute of Neurological Disorders and Stroke (Grants P20 NS092529-02, P50 NS108676-01)

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

BackgroundThe Quality of Life in Neurological Disorders (Neuro- QoL) is a publicly available health- related quality- of- life measurement system.ObjectiveThe aim of this study was to evaluate the utility of Neuro- QoL item banks as outcome measures for clinical trials in Parkinson’s disease.MethodsAn analysis of Neuro- QoL responsiveness to change and construct validity was performed in a multicenter clinical trial cohort.ResultsAmong 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.ConclusionsMore sensitive tools will be required to serve as an outcome measure in early Parkinson’s disease. © 2021 International Parkinson and Movement Disorder SocietyPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/169296/1/mds28641.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/169296/2/mds28641_am.pd

Data_Sheet_1_Digital assessment of speech in Huntington disease.doc

BackgroundSpeech 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.MethodsWe 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.ResultsSignificant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p ConclusionSpeech 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.</p

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

BackgroundThe Quality of Life in Neurological Disorders (Neuro- QoL) is a publicly available health- related quality- of- life measurement system.ObjectiveThe aim of this study was to evaluate the utility of Neuro- QoL item banks as outcome measures for clinical trials in Parkinson’s disease.MethodsAn analysis of Neuro- QoL responsiveness to change and construct validity was performed in a multicenter clinical trial cohort.ResultsAmong 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.ConclusionsMore sensitive tools will be required to serve as an outcome measure in early Parkinson’s disease. © 2021 International Parkinson and Movement Disorder SocietyPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/169296/1/mds28641.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/169296/2/mds28641_am.pd

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

IntroductionParkinson'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.MethodsIndividuals 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.ResultsA 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&nbsp;=&nbsp;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.ConclusionAmong the Fox Insight cohort, self-reported diagnosis is accurate and interest in virtual research studies is high

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

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 pheno-typing 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