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BackgroundWearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments. ObjectiveThis paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD. MethodsThe Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation. ResultsFrom 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor. ConclusionsWe present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.

Motor fluctuations in Parkinson's disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson's patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson's patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

Background: Both patients and physicians may choose to delay initiation of dopamine replacement therapy in Parkinson's disease (PD) for various reasons. We used observational data to estimate the effect of earlier treatment in PD. Observational data offer a valuable source of evidence, complementary to controlled trials. Method: We studied the Parkinson's Progression Markers Initiative cohort of patients with de novo PD to estimate the effects of duration of PD treatment during the first 2 years of follow‐up, exploiting natural interindividual variation in the time to start first treatment. We estimated the Movement Disorder Society–Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) Part III (primary outcome) and several functionally relevant outcomes at 2, 3, and 4 years after baseline. To adjust for time‐varying confounding, we used marginal structural models with inverse probability of treatment weighting and the parametric g‐formula. Results: We included 302 patients from the Parkinson's Progression Markers Initiative cohort. There was a small improvement in MDS‐UPDRS Part III scores after 2 years of follow‐up for patients who started treatment earlier, and similar, but nonstatistically significant, differences in subsequent years. We found no statistically significant differences in most secondary outcomes, including the presence of motor fluctuations, nonmotor symptoms, MDS‐UPDRS Part II scores, and the Schwab and England Activities of Daily Living Scale. Conclusion: Earlier treatment initiation does not lead to worse MDS‐UPDRS motor scores and may offer small improvements. These findings, based on observational data, are in line with earlier findings from clinical trials. Observational data, when combined with appropriate causal methods, are a valuable source of additional evidence to support real‐world clinical decisions. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: YG serves as scientific advisor for Eli Lilly and Company and Merck & Co.; serves as scientific advisor and receives grants from Merck KGaA. YH has grant funding through BBJ from Sanofi S.A. and UCB for unrelated projects. BBJ has grant funding from Sanofi S.A. and UCB for unrelated projects. AJ is funded by MJ Fox Foundation for data curation. All other authors report no competing interests., (Copyright: © 2023 Sieberts et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Both patients and physicians may choose to delay initiation of dopamine replacement therapy in Parkinson's disease (PD) for various reasons. We used observational data to estimate the effect of earlier treatment in PD. Observational data offer a valuable source of evidence, complementary to controlled trials., Method: We studied the Parkinson's Progression Markers Initiative cohort of patients with de novo PD to estimate the effects of duration of PD treatment during the first 2 years of follow-up, exploiting natural interindividual variation in the time to start first treatment. We estimated the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III (primary outcome) and several functionally relevant outcomes at 2, 3, and 4 years after baseline. To adjust for time-varying confounding, we used marginal structural models with inverse probability of treatment weighting and the parametric g-formula., Results: We included 302 patients from the Parkinson's Progression Markers Initiative cohort. There was a small improvement in MDS-UPDRS Part III scores after 2 years of follow-up for patients who started treatment earlier, and similar, but nonstatistically significant, differences in subsequent years. We found no statistically significant differences in most secondary outcomes, including the presence of motor fluctuations, nonmotor symptoms, MDS-UPDRS Part II scores, and the Schwab and England Activities of Daily Living Scale., Conclusion: Earlier treatment initiation does not lead to worse MDS-UPDRS motor scores and may offer small improvements. These findings, based on observational data, are in line with earlier findings from clinical trials. Observational data, when combined with appropriate causal methods, are a valuable source of additional evidence to support real-world clinical decisions. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson's Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies., Competing Interests: Critical Path Institute staff (D. Stephenson, V. Aggarawal, R. Bhatnagar, K. Romero, J. Burton, S. Sadar, and M. Minchick) and R. Badawy, L. Bain, J.M. Cedarbaum, D.T. Dexter, M. Frasier, D. Hill Michael, and P. Lawton have no conflict of interests to declare. R. Alexander, N. Zach, and R. Rubens are full-time employees of Takeda. A.V. Dowling is an employee of Takeda Pharmaceuticals and also serves on the Strategic Advisory Board of the DiMe Society. B.R. Bloem currently serves as associate editor for the Journal of Parkinson Disease, serves on the editorial board of Practical Neurology and Digital Biomarkers, has received honoraria from serving on the scientific advisory board for AbbVie, Biogen, UCB, and Walk with Path, has received fees for speaking at conferences from AbbVie, Zambon, Roche, GE Healthcare, and Bial, and has received research support from The Netherlands Organisation for Scientific Research, the Michael J. Fox Foundation, UCB, AbbVie, the Stichting Parkinson-Fonds, the Hersenstichting Nederland, the Parkinson Foundation, Verily Life Sciences, Horizon 2020, Topsector Life Sciences and Health, and the Parkinson Vereniging. B. Boroojerdi and E. Sengoku are employed by UCB. J. Cosman was a full-time employee of Biogen, Inc., and is currently a full-time employee of AbbVie, Inc. M. Dockendorf, S.J. Lee, and N. Mehrotra are employees of Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc. (Kenilworth, NJ, USA), and may own stock/stock options in Merck & Co., Inc. E.R. Dorsey has received honoraria for speaking at American Academy of Neurology courses, the American Neurological Association, and the University of Michigan. E.R. Dorsey received compensation for consulting services from: 23andMe; Abbott; AbbVie; American Well; Biogen; BrainNeuroBio; Clintrex; Curasen Therapeutics; DeciBio; Denali Therapeutics; GlaxoSmithKline; Grand Rounds; Karger; Lundbeck; MC10; MedAvante; Medical-Legal Services; Mednick Associates; the National Institute of Neurological Disorders and Stroke; the Olson Research Group; Optio; Origent Data Sciences, Inc.; Otsuka, Prilenia; Putnam Associates; Roche; Sanofi; Shire; Spark; Sunovion Pharma; Teva; Theravance; UCB; and Voyager Therapeutics. E.R. Dorsey also received research support from AbbVie, Acadia Pharmaceuticals, AMC Health, Biosensics, the Burroughs Wellcome Fund, the Davis Phinney Foundation, Duke University, the FDA, GlaxoSmithKline, the Greater Rochester Health Foundation, the Huntington Study Group, the Michael J. Fox Foundation, the National Institutes of Health/National Institute of Neurological Disorders and Stroke, the National Science Foundation, Nuredis Pharmaceuticals, the Patient-Centered Outcomes Research Institute, Pfizer, Prana Biotechnology, Raptor Pharmaceuticals, Roche, Safra Foundation, Teva Pharmaceuticals, and the University of California, Irvine; has provided editorial services for Karger Publications; and has ownership interests with Blackfynn (data integration company) and Grand Rounds (second opinion service). L.J.W. Evers has received research support from the Michael J. Fox Foundation, UCB, the Stichting Parkinson Fonds, The Netherlands Organisation for Scientific Research, and Topsector Life Sciences and Health. L. Garcia-Gancedo is a full-time employee of GSK. J.C. Goldsack is a part-time employee of HealthMode, Inc. J. Hitchcock is an employee of Hitchcock Regulatory Consulting, Inc., and has consulted for: Acumen Pharmaceuticals; Axon Advisors LLC; the Critical Path Institute; the Gerson Lehrman Group; H. Lundbeck A/S; the High Lantern Group LLC; Regenera Pharma LTD; UCB Biopharma SPRL; Vaccinex, Inc.; and Washington University. She is retired from Eli Lilly and Company and holds Lilly shares. M.T. Hu serves as a consultant for Biogen and Roche Advisory Boards and for CuraSen Therapeutics, Inc. Prof. Hu has received funding and support from Parkinson's UK, Oxford NIHR BRC, the University of Oxford, NIHR, the Michael J Fox Foundation, H2020 European Union, GE Healthcare, and the PSP Association. M. Lindemann is a consultant for F. Hoffmann-La Roche through Inovigate. M.J. Meinders has received research support from the Michael J. Fox Foundation, UCB, the Stichting Parkinson Fonds, Horizon 2020, and Topsector Life Sciences and Health. L. Oliva and K. Fisher are employees of Biogen. G. Roussos acknowledges funding from the Michael J. Fox Foundation and is a joint patent holder for UK patent GB2433856 (which is not related to healthcare). J. Scheeren was formerly employed by Bayer AG-Pharmaceuticals and as of 2019 is the president and CEO of C-Path. Dr. J. Scheeren is also currently an adjunct professor of regulatory sciences at Peking University in Beijing, China, the chair of the fellows for the Drug Information Association (DIA), a member of the advisory board for the Centre of Regulatory Excellence (CoRE), a special advisor to the scientific advisory council of the Centre for Innovation in Regulatory Science (CIRS), a lecturer at Yale University, and a foreign corresponding member of the French Académie Nationale de Pharmacie. T. Simuni, MD, has served as a consultant for Acadia, AbbVie, Accorda, Adamas, Allergan, Amneal, Aptinyx, Denali, General Electric (GE), Kyowa, Neuroderm, Neurocrine, Sanofi, Sinopia, Sunovion, Roche, Takeda, Voyager, US World Meds, the Parkinson's Foundation, and the Michael J. Fox Foundation for Parkinson research. Dr. T. Simuni has served as a speaker and received an honorarium from Acadia and Adamas, is on the scientific advisory board for Neuroderm and Sanofi, and has received research funding from the NINDS, the Parkinson's Foundation, MJFF, Biogen, Roche, Neuroderm, Sanofi, Sun Pharma, AbbVie, IMPAX, and Prevail. G. Stebbins is an employee of Rush University. Dr. G. Stebbins has consulting and advisory board membership with honoraria for: Acadia Pharmaceuticals; Adamas Pharmaceuticals, Inc.; Biogen, Inc.; Ceregene, Inc.; CHDI Management, Inc.; the Cleveland Clinic Foundation; Ingenix Pharmaceutical Services (i3 Research); MedGenesis Therapeutix, Inc.; Neurocrine Biosciences, Inc.; Pfizer, Inc.; Tools-4-Patients; Ultragenyx, Inc.; and the Sunshine Care Foundation. He has received grants from and done research for: the National Institutes of Health, the Department of Defense, the Michael J. Fox Foundation for Parkinson's Research, the Dystonia Coalition, CHDI, the Cleveland Clinic Foundation, the International Parkinson and Movement Disorder Society, and CBD Solutions, and has received honoraria from: the International Parkinson and Movement Disorder Society, the American Academy of Neurology, the Michael J. Fox Foundation for Parkinson's Research, the FDA, the National Institutes of Health, and the Alzheimer's Association. K.I. Taylor is a full-time employee and shareholder of F. Hoffmann-La Roche Ltd. B. Yang is a full-time employee at Lundbeck A/S., (Copyright © 2020 by S. Karger AG, Basel.)

The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors. These behaviors often violate the assumptions of clinimetric testing protocols, where these protocols are designed to probe for specific symptoms. Such violations are frequent outside the lab and affect the accuracy of the subsequent data analysis and scientific conclusions. To address these problems, we report on a unified algorithmic framework for automated sensor data quality control, which can identify those parts of the sensor data that are sufficiently reliable for further analysis. Combining both parametric and nonparametric signal processing and machine learning techniques, we demonstrate that across 100 subjects and 300 clinimetric tests from three different types of behavioral clinimetric protocols, the system shows an average segmentation accuracy of around 90%. By extracting reliable sensor data, it is possible to strip the data of confounding factors in the environment that may threaten reproducibility and replicability., Competing Interests: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.