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Understanding what matters to people with Parkinson’s and their family is essential to derive relevant clinical outcome measures and guide clinical care. The purpose of this study was to explore what is important to people with Parkinson’s disease vs. family over time. A qualitative content-analysis of online survey data collected by Parkinson’s UK was conducted to identify types and frequencies of important symptoms and impacts of Parkinson’s for people with the disease vs. family of people with Parkinson’s. Independent T-tests were used to identify significance of between group differences for patients vs. family at < 2, 2–5, 6–10, 11–20, > 20-year durations. ANOVA was used to assess for within group differences by disease duration. We found that symptom priority changed significantly over time with longer disease duration. Tremor was reported less often later on, whereas mobility, dyskinesias, gait and speech/communication symptoms gained priority. In general, patients identified movement-related symptoms (e.g., walking, bradykinesia) as the most bothersome at all durations while family more strongly prioritized the physical and psychosocial impacts of disease (e.g., mobility, safety, interpersonal interactions, independence, and family impact). We conclude that important differences exist between family and patient perspectives of what matters and change over time with longer duration of disease.

The Huntington’s Disease Cognitive Assessment Battery (HD-CAB) is frequently used in clinical trials; however, it is unclear whether the HD-CAB captures meaningful symptoms from patients’ perspectives. We aimed to explore whether HD-CAB tests are considered meaningful to people with Huntington’s disease (HD) by mapping them to relevant symptoms and impacts. Eighteen people with HD before and after clinical motor diagnosis completed a semi-structured interview with symptom mapping to hierarchically rank the importance and bothersomeness of cognitive symptoms and discussed the relevance of each test to meaningful symptoms. Reflexive Thematic Analysis (RTA) was used to explore participant perceptions of the relevance of HD-CAB tests for assessing disease progression. Content coding was used to identify the frequency of important symptoms and impacts, and we used binomial tests to determine whether there was statistically significant consensus (agreement > 50%). Our findings showed that all HD-CAB tests were viewed as meaningful by a significant majority of participants and all subtests were agreed to assess important or bothersome symptoms ( p  < .05). Participants identified two factors contributing to the importance of the HD-CAB tests for measuring HD changes: (1) The tests of the HD-CAB provide participants with knowledge about their abilities and disease; and (2) The tests of the HD-CAB capture changes that are important for everyday life. Overall, the HD-CAB was a meaningful cognitive assessment tool from the perspective of people with HD.

Prevalence estimates of Parkinson’s disease (PD)—the fastest-growing neurodegenerative disease—are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.

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.

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.

Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors. We demonstrate how this framework can be applied to data collected with different types of DHTs deployed in the WATCH-PD clinical study of Parkinson’s disease. This framework provides a means to pre-specify and therefore standardize aspects of the use of DHTs, promoting comparability of DHTs across future studies.

Background Digital health technologies show promise for improving the measurement of Parkinson’s disease in clinical research and trials. However, it is not clear whether digital measures demonstrate enhanced sensitivity to disease progression compared to traditional measurement approaches. Methods To this end, we develop a wearable sensor-based digital algorithm for deriving features of upper and lower-body bradykinesia and evaluate the sensitivity of digital measures to 1-year longitudinal progression using data from the WATCH-PD study, a multicenter, observational digital assessment study in participants with early, untreated Parkinson’s disease. In total, 82 early, untreated Parkinson’s disease participants and 50 age-matched controls were recruited and took part in a variety of motor tasks over the course of a 12-month period while wearing body-worn inertial sensors. We establish clinical validity of sensor-based digital measures by investigating convergent validity with appropriate clinical constructs, known groups validity by distinguishing patients from healthy volunteers, and test-retest reliability by comparing measurements between visits. Results We demonstrate clinical validity of the digital measures, and importantly, superior sensitivity of digital measures for distinguishing 1-year longitudinal change in early-stage PD relative to corresponding clinical constructs. Conclusions Our results demonstrate the potential of digital health technologies to enhance sensitivity to disease progression relative to existing measurement standards and may constitute the basis for use as drug development tools in clinical research. Plain language summary Parkinson’s disease can impact a person’s ability to move, which can result in slow or rigid movements. Wearable sensors can be used to measure these symptoms and could be particularly useful to detect changes early in the course of the disease when symptoms may be subtle. We developed a wearable sensor-based method to measure movement in people with early Parkinson’s disease that uses wrist and foot-worn sensors. Our results demonstrate that our sensor-based measurements can accurately quantify progressive changes in movement function. Such measurements may allow researchers to more accurately evaluate how well treatments designed to slow the course of Parkinson’s disease are working in the future. Czech et al. develop and clinically validate a sensor-based approach to measure upper and lower body bradykinesia in an early Parkinson’s disease population. Results demonstrate enhanced sensitivity of sensor-based digital measurements to disease progression over one year relative to current clinical measurement standards.

Background: Clinician rating scales and patient-reported outcomes are the principal means of assessing motor symptoms in Parkinson disease and Huntington disease. However, these assessments are subjective and generally limited to episodic in-person visits. Wearable sensors can objectively and continuously measure motor features and could be valuable in clinical research and care. Methods: We recruited participants with Parkinson disease, Huntington disease, and prodromal Huntington disease (individuals who carry the genetic marker but do not yet exhibit symptoms of the disease), and controls to wear 5 accelerometer-based sensors on their chest and limbs for standardized in-clinic assessments and for 2 days at home. The study’s aims were to assess the feasibility of use of wearable sensors, to determine the activity (lying, sitting, standing, walking) of participants, and to survey participants on their experience. Results: Fifty-six individuals (16 with Parkinson disease, 15 with Huntington disease, 5 with prodromal Huntington disease, and 20 controls) were enrolled in the study. Data were successfully obtained from 99.3% (278/280) of sensors dispatched. On average, individuals with Huntington disease spent over 50% of the total time lying down, substantially more than individuals with prodromal Huntington disease (33%, p = 0.003), Parkinson disease (38%, p = 0.01), and controls (34%; p < 0.001). Most (86%) participants were “willing” or “very willing” to wear the sensors again. Conclusions: Among individuals with movement disorders, the use of wearable sensors in clinic and at home was feasible and well-received. These sensors can identify statistically significant differences in activity profiles between individuals with movement disorders and those without. In addition, continuous, objective monitoring can reveal disease characteristics not observed in clinic.

Background Huntington’s disease, a neurodegenerative disorder, impairs both upper and lower limb function, typically assessed in clinical settings. However, wearable sensors offer the opportunity to monitor real-world data that complements clinical assessments, providing a more comprehensive understanding of disease symptoms. Methods In this study, we monitor upper limb function in individuals with Huntington’s disease (HD, n  = 16), prodromal HD (pHD, n  = 7), and controls (CTR, n  = 16) using a wrist-worn wearable sensor over a 7-day period. Goal-directed hand movements are detected through a deep learning model, and kinematic features of each movement are analyzed. The collected data is used to predict disease groups and clinical scores using statistical and machine learning models. Results Here we show that significant differences in goal-directed movement features exist between the groups. Additionally, several of these features strongly correlate with clinical scores. Classification models accurately distinguish between HD, pHD, and CTR individuals, achieving a balanced accuracy of 67% and a recall of 0.72 for the HD group. Regression models effectively predict clinical scores. Conclusions This study demonstrates the potential of wearable sensors and machine learning to monitor upper limb function in Huntington’s disease, offering a tool for early detection, remote monitoring, and assessing treatment efficacy in clinical trials. Plain language summary People with Huntington’s disease can have difficulty moving, experiencing involuntary movement of the limbs. This study aimed to better understand how Huntington’s disease affects hand movements and whether wearable devices can be used to monitor this. Individuals with Huntington’s disease, those at risk of it, and healthy participants wore a small wearable device on their wrist for a week to track their hand movements during daily activities. We used advanced computer models to analyze data and predict disease severity. The main finding was that the device could clearly distinguish between people with Huntington’s disease, those at risk, and healthy people, helping to predict symptoms. This research shows that wearable technology could be used to monitor disease and the effect of treatments in the future. Nunes et al. predict Huntington’s disease score by applying deep learning to readings from a wrist-worn sensor obtained over a 7 day period. Differences are seen in goal-directed movement that correlate with clinical score.

A comprehensive, patient-centered conceptual model of early Parkinson’s is lacking and is greatly needed. A systematic review and meta-synthesis of qualitative and quantitative research was conducted by a multi-stakeholder taskforce using JBI Mixed Methods Review criteria and GRADE-CERQual standards for assessment of evidence. Over 340 symptoms and impacts were identified across ten symptom domains (Movement, Cognitive, Psychiatric, Sleep, Sensory, Speech, Digestive, Urinary, Sexual, Autonomic) and two impact domains (Physical and Psychosocial functioning). A wide range of motor and non-motor symptoms were present in early disease, with strongest support for tremor, dexterity, gait, stiffness, slow movements, cognitive, mood, and sleep alterations, urinary dysfunction, constipation, pain, and fatigue. These affected mobility, self-concept, coping, effort of living, interactions and important activities, with evidence of many understudied concepts. This model offers the most comprehensive catalogue of symptoms and impacts in Parkinson’s to date and will support clinical practice and endpoint selection for therapeutic trials.