Digital Technology in Movement Disorders: Updates, Applications, and Challenges.

PURPOSE OF REVIEW: Digital technology affords the opportunity to provide objective, frequent, and sensitive assessment of disease outside of the clinic environment. This article reviews recent literature on the application of digital technology in movement disorders, with a focus on Parkinson's disease (PD) and Huntington's disease. RECENT FINDINGS: Recent research has demonstrated the ability for digital technology to discriminate between individuals with and without PD, identify those at high risk for PD, quantify specific motor features, predict clinical events in PD, inform clinical management, and generate novel insights. Digital technology has enormous potential to transform clinical research and care in movement disorders. However, more work is needed to better validate existing digital measures, including in new populations, and to develop new more holistic digital measures that move beyond motor features.

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.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study over 12 months.

Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.

Relative Meaningfulness and Impacts of Symptoms in People with Early-Stage Parkinson's Disease.

BACKGROUND: Patient perspectives on meaningful symptoms and impacts in early Parkinson's disease (PD) are lacking and are urgently needed to clarify priority areas for monitoring, management, and new therapies. OBJECTIVE: To examine experiences of people with early-stage PD, systematically describe meaningful symptoms and impacts, and determine which are most bothersome or important. METHODS: Forty adults with early PD who participated in a study evaluating smartwatch and smartphone digital measures (WATCH-PD study) completed online interviews with symptom mapping to hierarchically delineate symptoms and impacts of disease from "Most bothersome" to "Not present," and to identify which of these were viewed as most important and why. Individual symptom maps were coded for types, frequencies, and bothersomeness of symptoms and their impacts, with thematic analysis of narratives to explore perceptions. RESULTS: The three most bothersome and important symptoms were tremor, fine motor difficulties, and slow movements. Symptoms had the greatest impact on sleep, job functioning, exercise, communication, relationships, and self-concept- commonly expressed as a sense of being limited by PD. Thematically, most bothersome symptoms were those that were personally limiting with broadest negative impact on well-being and activities. However, symptoms could be important to patients even when not present or limiting (e.g., speech, cognition). CONCLUSION: Meaningful symptoms of early PD can include symptoms that are present or anticipated future symptoms that are important to the individual. Systematic assessment of meaningful symptoms should aim to assess the extent to which symptoms are personally important, present, bothersome, and limiting.

Mapping Relevance of Digital Measures to Meaningful Symptoms and Impacts in Early Parkinson's Disease.

BACKGROUND: Adoption of new digital measures for clinical trials and practice has been hindered by lack of actionable qualitative data demonstrating relevance of these metrics to people with Parkinson's disease. OBJECTIVE: This study evaluated of relevance of WATCH-PD digital measures to monitoring meaningful symptoms and impacts of early Parkinson's disease from the patient perspective. METHODS: Participants with early Parkinson's disease (N = 40) completed surveys and 1:1 online-interviews. Interviews combined: 1) symptom mapping to delineate meaningful symptoms/impacts of disease, 2) cognitive interviewing to assess content validity of digital measures, and 3) mapping of digital measures back to personal symptoms to assess relevance from the patient perspective. Content analysis and descriptive techniques were used to analyze data. RESULTS: Participants perceived mapping as deeply engaging, with 39/40 reporting improved ability to communicate important symptoms and relevance of measures. Most measures (9/10) were rated relevant by both cognitive interviewing (70-92.5%) and mapping (80-100%). Two measures related to actively bothersome symptoms for more than 80% of participants (Tremor, Shape rotation). Tasks were generally deemed relevant if they met three participant context criteria: 1) understanding what the task measured, 2) believing it targeted an important symptom of PD (past, present, or future), and 3) believing the task was a good test of that important symptom. Participants did not require that a task relate to active symptoms or "real" life to be relevant. CONCLUSION: Digital measures of tremor and hand dexterity were rated most relevant in early PD. Use of mapping enabled precise quantification of qualitative data for more rigorous evaluation of new measures.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study.

Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.

Recruitment for Remote Decentralized Studies in Parkinson's Disease.

BACKGROUND: Traditional in-person Parkinson's disease (PD) research studies are often slow to recruit and place unnecessary burden on participants. The ongoing COVID-19 pandemic has added new impetus to the development of new research models. OBJECTIVE: To compare recruitment processes and outcomes of three remote decentralized observational PD studies with video visits. METHODS: We examined the number of participants recruited, speed of recruitment, geographic distribution of participants, and strategies used to enhance recruitment in FIVE, a cross-sectional study of Fox Insight participants with and without PD (n = 203); VALOR-PD, a longitudinal study of 23andMe, Inc. research participants carrying the LRRK2 G2019S variant with and without PD (n = 277); and AT-HOME PD, a longitudinal study of former phase III clinical trial participants with PD (n = 226). RESULTS: Across the three studies, 706 participants from 45 U.S. states and Canada enrolled at a mean per study rate of 4.9 participants per week over an average of 51 weeks. The cohorts were demographically homogenous with regard to race (over 95%white) and level of education (over 90%with more than a high school education). The number of participants living in primary care Health Professional Shortage Areas in each study ranged from 30.3-42.9%. Participants reported interest in future observational (98.5-99.6%) and interventional (76.1-87.6%) research studies with remote video visits. CONCLUSION: Recruitment of large, geographically dispersed remote cohorts from a single location is feasible. Interest in participation in future remote decentralized PD studies is high. More work is needed to identify best practices for recruitment, particularly of diverse participants.

A Remote Longitudinal Observational Study of Individuals at Genetic Risk for Parkinson Disease: Baseline Results.

Background and Objectives: To recruit and characterize a national cohort of individuals who have a genetic variant (LRRK2 G2019S) that increases risk of Parkinson disease (PD), assess participant satisfaction with a decentralized, remote research model, and evaluate interest in future clinical trials. Methods: In partnership with 23andMe, Inc., a personal genetics company, LRRK2 G2019S carriers with and without PD were recruited to participate in an ongoing 36-month decentralized, remote natural history study. We examined concordance between self-reported and clinician-determined PD diagnosis. We applied the Movement Disorder Society Prodromal Parkinson's Disease Criteria and asked investigators to identify concern for parkinsonism to distinguish participants with probable prodromal PD. We compared baseline characteristics of LRRK2 G2019S carriers with PD, with prodromal PD, and without PD. Results: Over 15 months, we enrolled 277 LRRK2 G2019S carriers from 34 states. At baseline, 60 had self-reported PD (mean [SD] age 67.8 years [8.4], 98% White, 52% female, 80% Ashkenazi Jewish, and 67% with a family history of PD), and 217 did not (mean [SD] age 53.7 years [15.1], 95% White, 59% female, 73% Ashkenazi Jewish, and 57% with a family history of PD). Agreement between self-reported and clinician-determined PD status was excellent (κ = 0.94, 95% confidence interval 0.89-0.99). Twenty-four participants had prodromal PD; 9 met criteria for probable prodromal PD and investigators identified concern for parkinsonism in 20 cases. Compared with those without prodromal PD, participants with prodromal PD were older (63.9 years [9.0] vs 51.9 years [15.1], p < 0.001), had higher modified Movement Disorders Society-Unified Parkinson's Disease Rating Scale motor scores (5.7 [4.3] vs 0.8 [2.1], p < 0.001), and had higher Scale for Outcomes in PD for Autonomic Symptoms scores (11.5 [6.2] vs 6.9 [5.7], p = 0.002). Two-thirds of participants enrolled were new to research, 97% were satisfied with the overall study, and 94% of those without PD would participate in future preventive clinical trials. Discussion: An entirely remote national cohort of LRRK2 G2019S carriers was recruited from a single site. This study will prospectively characterize a large LRRK2 G2019S cohort, refine a new model of clinical research, and engage new research participants willing to participate in future therapeutic trials.

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.

A Virtual Cohort Study of Individuals at Genetic Risk for Parkinson's Disease: Study Protocol and Design.

BACKGROUND: The rise of direct-to-consumer genetic testing has enabled many to learn of their possible increased risk for rare diseases, some of which may be suitable for gene-targeted therapies. However, recruiting a large and representative population for rare diseases or genetically defined sub-populations of common diseases is slow, difficult, and expensive. OBJECTIVE: To assess the feasibility of recruiting and retaining a cohort of individuals who carry a genetic mutation linked to Parkinson's disease (G2019S variant of LRRK2); to characterize this cohort relative to the characteristics of traditional, in-person studies; and to evaluate this model's ability to create an engaged study cohort interested in future clinical trials of gene-directed therapies. METHODS: This single-site,3-year national longitudinal observational study will recruit between 250 to 350 LRRK2 carriers without Parkinson's disease and approximately 50 with the condition. Participants must have undergone genetic testing by the personal genetics company, 23andMe, Inc., have knowledge of their carrier status, and consent to be contacted for research studies. All participants undergo standardized assessments, including video-based cognitive and motor examination, and complete patient-reported outcomes on an annual basis. RESULTS: 263 individuals living in 33 states have enrolled. The cohort has a mean (SD) age of 56.0 (15.9) years, 59% are female, and 76% are of Ashkenazi Jewish descent. 233 have completed the baseline visit: 47 with self-reported Parkinson's disease and 186 without. CONCLUSIONS: This study establishes a promising model for developing a geographically dispersed and well-characterized cohort ready for participation in future clinical trials of gene-directed therapies.

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.