IMPORTANCE Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.OBJECTIVES To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy. DESIGN, SETTING, AND PARTICIPANTSThis observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months. MAIN OUTCOMES AND MEASURESAbility of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication. RESULTSThe mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy. CONCLUSIONS AND RELEVANCEUsing a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.

PurposeWhile there is strong evidence supporting the importance of telemedicine in stroke, its role in other areas of neurology is not as clear. The goal of this review is to provide an overview of evidence-based data on the role of teleneurology in the care of patients with neurologic disorders other than stroke.Recent findingsStudies across multiple specialties report noninferiority of evaluations by telemedicine compared with traditional, in-person evaluations in terms of patient and caregiver satisfaction. Evidence reports benefits in expediting care, increasing access, reducing cost, and improving diagnostic accuracy and health outcomes. However, many studies are limited, and gaps in knowledge remain.SummaryTelemedicine use is expanding across the vast array of neurologic disorders. More studies are needed to validate and support its use.

Remote health assessments that gather real-world data (RWD) outside of 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 six months of study commencement, 960 participants had enrolled and performed at least 5 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 (AUC=0.8) and correlated with in-clinic evaluation of disease severity (r =0.71; p<1.8×10 -6 ) when compared with motor 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.RWD offers the opportunity to improve our understanding and management of health and disease outside of the clinical setting. 1 An increasingly popular method for collecting RWD is the use of remote digital assessments that allow frequent sampling and have been used to aid in the diagnosis, treatment, and monitoring of multiple conditions, including atrial fibrillation and diabetes. 2,3 When used in population-based studies, remote assessment can increase understanding of heterogeneity in disease manifestation, the Bas Bloem currently serves as Editor-in-Chief for the Journal of Parkinson's disease, serves on the editorial board of Practical Neurology and Digital Biomarkers, has received honoraria from serving on the scientific advisory board for Zambon, Biogen, UCB and Walk with Path, has

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.

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.