A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies

Disease-modifying therapies for Parkinson disease: lessons from multiple sclerosis

The development of disease-modifying therapies (DMTs) for neurological disorders is an important goal in modern neurology, and the associated challenges are similar in many chronic neurological conditions. Major advances have been made in the multiple sclerosis (MS) field, with a range of DMTs being approved for relapsing MS and the introduction of the first DMTs for progressive MS. By contrast, people with Parkinson disease (PD) still lack such treatment options, relying instead on decades-old therapeutic approaches that provide only symptomatic relief. To address this unmet need, an in-person symposium was held in Toronto, Canada, in November 2022 for international researchers and experts in MS and PD to discuss strategies for advancing DMT development. In this Roadmap article, we highlight discussions from the symposium, which focused on therapeutic targets and preclinical models, disease spectra and subclassifications, and clinical trial design and outcome measures. From these discussions, we propose areas for novel or deeper exploration in PD using lessons learned from therapeutic development in MS. In addition, we identify challenges common to the PD and MS fields that need to be addressed to further advance the discovery and development of effective DMTs.

Wearable Sensor-Based Assessments for Remotely Screening Early-Stage Parkinson's Disease

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.

Dysrhythmia as a prominent feature of Parkinson's disease: An app-based tapping test

INTRODUCTION

Smartphone applications (apps) are instruments that assist with objective measurements during the clinical assessment of patients with movement disorders. We aim to test the hypothesis that Parkinson's disease (PD) patients will exhibit an increase in tapping variability and a decrease in tapping speed over a one-year period, compared to healthy controls (HC).

METHODS

Data was prospectively collected from participants enrolled in our Cincinnati Cohort Biomarker Program, in 2021-2023. Participants diagnosed with PD and age-matched HC were examined over a one-year-interval with a tapping test performed with customized smartphone app. Tapping speed (taps/s), inter-tap intervals and variability (movement regularity), and sequence effect were measured.

RESULTS

We included 295 PD patients and 62 HC. At baseline, PD subjects showed higher inter-tap variability than HC (coefficient-of-variation-CV, 37 ms [22-64] vs 26 ms [8-51]) (p = 0.007). Conversely, there was no difference in inter-tap intervals (411 ms [199-593] in PD versus 478 ms [243-618] in HC) and tapping speed (3.42[2.70-4.76] taps/s in PD versus 3.21 taps/s [2.57-4.54] in HC) (p > 0.05). Only PD subjects (n = 135), at the one-year follow-up, showed a decreased tapping speed vs baseline (3.44 taps/s [2.86-4.81] versus 3.39 taps/s [2.58,4.30]) (p = 0.036), without significant changes in inter-tap variability (CV, 32 ms [18,55] baseline versus 34 ms [22,59] follow-up) (p = 0.142). No changes were found in HC at the one-year follow up (all p values>0.05).

CONCLUSIONS

Inter-tap variability (dysrhythmia) but no inter-tap intervals or tapping speed are reliably distinctive feature of an app-based bradykinesia assessment in PD.

Digital biomarkers for non-motor symptoms in Parkinson's disease: the state of the art

Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson's disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant's own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice.

Multi-Shared-Task Self-Supervised CNN-LSTM for Monitoring Free-Body Movement UPDRS-III Using Wearable Sensors

The Unified Parkinson's Disease Rating Scale (UPDRS) is used to recognize patients with Parkinson's disease (PD) and rate its severity. The rating is crucial for disease progression monitoring and treatment adjustment. This study aims to advance the capabilities of PD management by developing an innovative framework that integrates deep learning with wearable sensor technology to enhance the precision of UPDRS assessments. We introduce a series of deep learning models to estimate UPDRS Part III scores, utilizing motion data from wearable sensors. Our approach leverages a novel Multi-shared-task Self-supervised Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework that processes raw gyroscope signals and their spectrogram representations. This technique aims to refine the estimation accuracy of PD severity during naturalistic human activities. Utilizing 526 min of data from 24 PD patients engaged in everyday activities, our methodology demonstrates a strong correlation of 0.89 between estimated and clinically assessed UPDRS-III scores. This model outperforms the benchmark set by single and multichannel CNN, LSTM, and CNN-LSTM models and establishes a new standard in UPDRS-III score estimation for free-body movements compared to recent state-of-the-art methods. These results signify a substantial step forward in bioengineering applications for PD monitoring, providing a robust framework for reliable and continuous assessment of PD symptoms in daily living settings.

Interpretable video-based tracking and quantification of parkinsonism clinical motor states

Quantification of motor symptom progression in Parkinson's disease (PD) patients is crucial for assessing disease progression and for optimizing therapeutic interventions, such as dopaminergic medications and deep brain stimulation. Cumulative and heuristic clinical experience has identified various clinical signs associated with PD severity, but these are neither objectively quantifiable nor robustly validated. Video-based objective symptom quantification enabled by machine learning (ML) introduces a potential solution. However, video-based diagnostic tools often have implementation challenges due to expensive and inaccessible technology, and typical "black-box" ML implementations are not tailored to be clinically interpretable. Here, we address these needs by releasing a comprehensive kinematic dataset and developing an interpretable video-based framework that predicts high versus low PD motor symptom severity according to MDS-UPDRS Part III metrics. This data driven approach validated and robustly quantified canonical movement features and identified new clinical insights, not previously appreciated as related to clinical severity, including pinkie finger movements and lower limb and axial features of gait. Our framework is enabled by retrospective, single-view, seconds-long videos recorded on consumer-grade devices such as smartphones, tablets, and digital cameras, thereby eliminating the requirement for specialized equipment. Following interpretable ML principles, our framework enforces robustness and interpretability by integrating (1) automatic, data-driven kinematic metric evaluation guided by pre-defined digital features of movement, (2) combination of bi-domain (body and hand) kinematic features, and (3) sparsity-inducing and stability-driven ML analysis with simple-to-interpret models. These elements ensure that the proposed framework quantifies clinically meaningful motor features useful for both ML predictions and clinical analysis.

Assisted technology in Parkinson's disease gait: what's up?

BACKGROUND

 Gait disturbances are prevalent and debilitating symptoms, diminishing mobility and quality of life for Parkinson's disease (PD) individuals. While traditional treatments offer partial relief, there is a growing interest in alternative interventions to address this challenge. Recently, a remarkable surge in assisted technology (AT) development was witnessed to aid individuals with PD.

OBJECTIVE

 To explore the burgeoning landscape of AT interventions tailored to alleviate PD-related gait impairments and describe current research related to such aim.

METHODS

 In this review, we searched on PubMed for papers published in English (2018-2023). Additionally, the abstract of each study was read to ensure inclusion. Four researchers searched independently, including studies according to our inclusion and exclusion criteria.

RESULTS

 We included studies that met all inclusion criteria. We identified key trends in assistive technology of gait parameters analysis in PD. These encompass wearable sensors, gait analysis, real-time feedback and cueing techniques, virtual reality, and robotics.

CONCLUSION

 This review provides a resource for guiding future research, informing clinical decisions, and fostering collaboration among researchers, clinicians, and policymakers. By delineating this rapidly evolving field's contours, it aims to inspire further innovation, ultimately improving the lives of PD patients through more effective and personalized interventions.

Telerehabilitation by Videoconferencing for Balance and Gait in People with Parkinson's Disease: A Scoping Review

Although supervised and real-time telerehabilitation by videoconferencing is now becoming common for people with Parkinson's disease (PD), its efficacy for balance and gait is still unclear. This paper uses a scoping approach to review the current evidence on the effects of telerehabilitation by videoconferencing on balance and gait for patients with PD. We also explored whether studies have used wearable technology during telerehabilitation to assess and treat balance and gait via videoconferencing. Literature searches were conducted using PubMed, ISI's Web of Knowledge, Cochrane's Library, and Embase. The data were extracted for study design, treatment, and outcomes. Fourteen studies were included in this review. Of these, seven studies investigated the effects of telerehabilitation (e.g., tele-yoga and adapted physiotherapy exercises) on balance and gait measures (e.g., self-reported balance, balance scale, walking speed, mobility, and motor symptoms) using videoconferencing in both assessment and treatment. The telerehabilitation programs by videoconferencing were feasible and safe for people with PD; however, the efficacy still needs to be determined, as only four studies had a parallel group. In addition, no study used wearable technology. Robust evidence of the effects of telerehabilitation by videoconferencing on balance and gait for patients with PD was not found, suggesting that future powered, prospective, and robust clinical trials are needed.

Personalized medicine in old age psychiatry and Alzheimer's disease

Elderly patients show us unfolded lives with unique individual characteristics. An increasing life span is associated with increasing physical and mental disease burden. Alzheimer's disease (AD) is an increasing challenge in old age. AD cannot be cured but it can be treated. The complexity of old age and AD offer targets for personalized medicine (PM). Targets for stratification of patients, detection of patients at risk for AD or for future targeted therapy are plentiful and can be found in several omic-levels.

Construction of the Digital Health Equity-Focused Implementation Research Conceptual Model - Bridging the Divide Between Equity-focused Digital Health and Implementation Research

Digital health implementations and investments continue to expand. As the reliance on digital health increases, it is imperative to implement technologies with inclusive and accessible approaches. A conceptual model can be used to guide equity-focused digital health implementations to improve suitability and uptake in diverse populations. The objective of this study is expand an implementation model with recommendations on the equitable implementation of new digital health technologies. The Digital Health Equity-Focused Implementation Research (DH-EquIR) conceptual model was developed based on a rigorous review of digital health implementation and health equity literature. The Equity-Focused Implementation Research for Health Programs (EquIR) model was used as a starting point and merged with digital equity and digital health implementation models. Existing theoretical frameworks and models were appraised as well as individual equity-sensitive implementation studies. Patient and program-related concepts related to digital equity, digital health implementation, and assessment of social/digital determinants of health were included. Sixty-two articles were analyzed to inform the adaption of the EquIR model for digital health. These articles included digital health equity models and frameworks, digital health implementation models and frameworks, research articles, guidelines, and concept analyses. Concepts were organized into EquIR conceptual groupings, including population health status, planning the program, designing the program, implementing the program, and equity-focused implementation outcomes. The adapted DH-EquIR conceptual model diagram was created as well as detailed tables displaying related equity concepts, evidence gaps in source articles, and analysis of existing equity-related models and tools. The DH-EquIR model serves to guide digital health developers and implementation specialists to promote the inclusion of health-equity planning in every phase of implementation. In addition, it can assist researchers and product developers to avoid repeating the mistakes that have led to inequities in the implementation of digital health across populations.

Experiences from the implementation of physical therapy via telehealth for individuals with Parkinson disease during the COVID-19 pandemic

PURPOSE

To (1) determine the characteristics and participation rate of adults with Parkinson disease (PD) in physical therapy (PT) delivered via telehealth, (2) identify the outcome measures and interventions implemented, (3) determine the safety of and (4) patient and therapist satisfaction with PT via telehealth in a clinic specializing in the care of people with PD during the coronavirus pandemic.

MATERIALS & METHODS

A retrospective analysis of PT services via telehealth was conducted. Participating patients completed a satisfaction survey. Physical therapists (PTs) who delivered this care were interviewed. Three coders conducted thematic analysis of interviews. Descriptive statistics described the participation rate, demographics, outcome measures, interventions, and safety.

RESULTS

There was a 71.4% participation rate. Participants (n = 55) were white (96%), non-Hispanic (100%), older adult (mean = 69.5 years (8.3)) males (65.5%). Non-participants (n = 22) had similar demographics. Therapists selected patient-reported measures more often than performance-based measures. Therapeutic exercise was the most common intervention. All patients (80% response rate) reported satisfaction with their experience. PTs reported the home enhanced specificity of training but impeded evaluation. Therapists endorsed a hybrid model for future practice.

CONCLUSIONS

Patients reported satisfaction with PT via telehealth during the pandemic. A hybrid model may support optimal delivery of PT.IMPLICATIONS FOR REHABILITATIONPhysical therapy via telehealth for patients with Parkinson disease was acceptable to patients and physical therapists in our study.Physical therapy via telehealth was safe for people with Parkinson disease in our study, although availability and benefits may not be reaching all populations equitably.Both physical therapists and patients endorse a hybrid model of care (a combination of in-person and remote assessment and treatment) to profit from the strengths of in-person and virtual formats while minimizing barriers to access.

Smartphone applications for Movement Disorders: Towards collaboration and re-use

BACKGROUND

Numerous smartphone and tablet applications (apps) are available to monitor movement disorders, but an overview of their purpose and stage of development is missing.

OBJECTIVES

To systematically review published literature and classify smartphone and tablet apps with objective measurement capabilities for the diagnosis, monitoring, assessment, or treatment of movement disorders.

METHODS

We systematically searched for publications covering smartphone or tablet apps to monitor movement disorders until November 22nd, 2023. We reviewed the target population, measured domains, purpose, and technology readiness level (TRL) of the proposed app and checked their availability in common app stores.

RESULTS

We identified 113 apps. Most apps were developed for Parkinson's disease specifically (n = 82; 73%) or for movement disorders in general (n = 17; 15%). Apps were either designed to momentarily assess symptoms (n = 65; 58%), support treatment (n = 22; 19%), aid in diagnosis (n = 16; 14%), or passively track symptoms (n = 11; 10%). Commonly assessed domains across movement disorders included fine motor skills (n = 34; 30%), gait (n = 36; 32%), and tremor (n = 32; 28%) for the motor domain and cognition (n = 16; 14%) for the non-motor domain. Twenty-six (23%) apps were proof-of-concepts (TRL 1-3), while most apps were tested in a controlled setting (TRL 4-6; n = 63; 56%). Twenty-four apps were tested in their target setting (TRL 7-9) of which 10 were accessible in common app stores or as Android Package.

CONCLUSIONS

The development of apps strongly gravitates towards Parkinson's disease and a selection of motor symptoms. Collaboration, re-use and further development of existing apps is encouraged to avoid reinventions of the wheel.

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Neurodegenerative diseases (NDs) such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores "digital biomarkers" (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.

Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease

BACKGROUND

Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression.

OBJECTIVES

The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale.

METHODS

Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time.

RESULTS

Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]).

CONCLUSIONS

Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Coproduction and Usability of a Smartphone App for Falls Reporting in Parkinson Disease

OBJECTIVE

The purpose of this study was to coproduce a smart-phone application for digital falls reporting in people with Parkinson disease (PD) and to determine usability using an explanatory mixed-methods approach.

METHODS

This study was undertaken in 3 phases. Phase 1 was the development phase, in which people with PD were recruited as co-researchers to the project. The researchers, alongside a project advisory group, coproduced the app over 6 months. Phase 2 was the implementation phase, in which 15 people with PD were invited to test the usability of the app. Phase 3 was the evaluation phase, in which usability was assessed using the systems usability scale by 2 focus groups with 10 people with PD from phase 2.

RESULTS

A prototype was successfully developed by researchers and the project advisory group. The usability of the app was determined as good (75.8%) by people with PD when rating using the systems usability scale. Two focus groups (n = 5 per group) identified themes of 1) usability, 2) enhancing and understanding management of falls, and 3) recommendations and future developments.

CONCLUSIONS

A successful prototype of the iFall app was developed and deemed easy to use by people with PD. The iFall app has potential use as a self-management tool for people with PD alongside integration into clinical care and research studies.

IMPACT

This is the first digital outcome tool to offer reporting of falls and near-miss fall events. The app may benefit people with PD by supporting self-management, aiding clinical decisions in practice, and providing an accurate and reliable outcome measure for future research.

LAY SUMMARY

A smartphone application designed in collaboration with people who have PD to record their falls was acceptable and easy to use by people with PD.

Ten Years of Experience With a Telemedicine Platform Dedicated to Health Care Personnel: Implementation Report

BACKGROUND

Telemedicine, a term that encompasses several applications and tasks, generally involves the remote management and treatment of patients by physicians. It is known as transversal telemedicine when practiced among health care professionals (HCPs).

OBJECTIVE

We describe the experience of implementing our telemedicine Eumeda platform for HCPs over the last 10 years.

METHODS

A web-based informatics platform was developed that had continuously updated hypertext created using advanced technology and the following features: security, data insertion, dedicated software for image analysis, and the ability to export data for statistical surveys. Customizable files called "modules" were designed and built for different fields of medicine, mainly in the ophthalmology subspecialty. Each module was used by HCPs with different authorization profiles.

IMPLEMENTATION (RESULTS)

Twelve representative modules for different projects are presented in this manuscript. These modules evolved over time, with varying degrees of interconnectivity, including the participation of a number of centers in 19 cities across Italy. The number of HCP operators involved in each single module ranged from 6 to 114 (average 21.8, SD 28.5). Data related to 2574 participants were inserted across all the modules. The average percentage of completed text/image fields in the 12 modules was 65.7%. All modules were evaluated in terms of access, acceptability, and medical efficacy. In their final evaluation, the participants judged the modules to be useful and efficient for clinical use.

CONCLUSIONS

Our results demonstrate the usefulness of the telemedicine platform for HCPs in terms of improved knowledge in medicine, patient care, scientific research, teaching, and the choice of therapies. It would be useful to start similar projects across various health care fields, considering that in the near future medicine as we know it will completely change.

Home-based monitoring of persons with advanced Parkinson's disease using smartwatch-smartphone technology

Movement deterioration is the hallmark of Parkinson's disease (PD), characterized by levodopa-induced motor-fluctuations (i.e., symptoms' variability related to the medication cycle) in advanced stages. However, motor symptoms are typically too sporadically and/or subjectively assessed, ultimately preventing the effective monitoring of their progression, and thus leading to suboptimal treatment/therapeutic choices. Smartwatches (SW) enable a quantitative-oriented approach to motor-symptoms evaluation, namely home-based monitoring (HBM) using an embedded inertial measurement unit. Studies validated such approach against in-clinic evaluations. In this work, we aimed at delineating personalized motor-fluctuations' profiles, thus capturing individual differences. 21 advanced PD patients with motor fluctuations were monitored for 2 weeks using a SW and a smartphone-dedicated app (Intel Pharma Analytics Platform). The SW continuously collected passive data (tremor, dyskinesia, level of activity using dedicated algorithms) and active data, i.e., time-up-and-go, finger tapping, hand tremor and hand rotation carried out daily, once in OFF and once in ON levodopa periods. We observed overall high compliance with the protocol. Furthermore, we observed striking differences among the individual patterns of symptoms' levodopa-related variations across the HBM, allowing to divide our participants among four data-driven, motor-fluctuations' profiles. This highlights the potential of HBM using SW technology for revolutionizing clinical practices.

Exploring how the design and provision of digital self-management technology can improve the uptake by older adults with chronic kidney disease, diabetes and dementia: A modified e-Delphi study

Objectives: As development and introduction of digital self-management technologies continues to increase, the gap between those who can benefit, and those who cannot correspondingly widens. This research aimed to explore the use of digital self-management technology by older adults with three highly-prevalent long-term conditions (chronic kidney disease, diabetes and dementia), and build expert consensus across the conditions on changes needed to improve effective usage. Method: This qualitative research involved a modified e-Delphi Study. The Delphi panel was comprised of experts with personal, academic or clinical expertise related to one of the long-term conditions and/or the development and use of digital self-management technology. The e-Delphi involved a round of online semi-structured interviews followed by two rounds of a structured online survey. Results: Fourteen experts participated in the study, with eleven of the fourteen completing all three rounds. Analysis of the interviews (round 1 of the Delphi) led to 7 main themes and 29 sub-themes. These were translated into 26 statements that formed the basis of the online survey questions. In the first administration of the survey (round 2) 19 statements reached consensus. After the second administration a further 6 statements reach consensus. Conclusion: The findings reflect expert consensus on barriers to the use of digital self-management by older adults with 3 different, but inter-related conditions, and identify ways in which the design and provision of such technologies could be improved to facilitate more effective use. It is concluded that both the design and the provision of technologies should consider a combination of individual, condition-specific and age-related requirements. By building a consensus on issues and potential strategies common across the three conditions, we aim to inform future research and practice and facilitate effective self-management by older adults.

Identifying Subtle Motor Deficits Before Parkinson's Disease is Diagnosed: What to Look for?

Motor deficits typical of Parkinson's disease (PD), such as gait and balance disturbances, tremor, reduced arm swing and finger movement, and voice and breathing changes, are believed to manifest several years prior to clinical diagnosis. Here we describe the evidence for the presence and progression of motor deficits in this pre-diagnostic phase in order to provide suggestions for the design of future observational studies for an effective, quantitatively oriented investigation. On the one hand, these future studies must detect these motor deficits in as large (potentially, population-based) cohorts as possible with high sensitivity and specificity. On the other hand, they must describe the progression of these motor deficits in the pre-diagnostic phase as accurately as possible, to support the testing of the effect of pharmacological and non-pharmacological interventions. Digital technologies and artificial intelligence can substantially accelerate this process.

Effects of Continuous Dopaminergic Stimulation on Parkinson's Disease Gait: A Longitudinal Prospective Study with Levodopa Intestinal Gel Infusion

Background

Gait issues, including reduced speed, stride length and freezing of gait (FoG), are disabling in advanced phases of Parkinson's disease (PD), and their treatment is challenging. Levodopa/carbidopa intestinal gel (LCIG) can improve these symptoms in PD patients with suboptimal control of motor fluctuations, but it is unclear if continuous dopaminergic stimulation can further improve gait issues, independently from reducing Off-time.

Objective

To analyze before (T0) and after 3 (T1) and 6 (T2) months of LCIG initiation: a) the objective improvement of gait and balance; b) the improvement of FoG severity; c) the improvement of motor complications and their correlation with changes in gait parameters and FoG severity.

Methods

This prospective, longitudinal 6-months study analyzed quantitative gait parameters using wearable inertial sensors, FoG with the New Freezing of Gait Questionnaire (NFoG-Q), and motor complications, as per the MDS-UPDRS part IV scores.

Results

Gait speed and stride length increased and duration of Timed up and Go and of sit-to-stand transition was significantly reduced comparing T0 with T2, but not between T0-T1. NFoG-Q score decreased significantly from 19.3±4.6 (T0) to 11.8±7.9 (T1) and 8.4±7.6 (T2) (T1-T0 p = 0.018; T2-T0 p < 0.001). Improvement of MDS-UPDRS-IV (T0-T2, p = 0.002, T0-T1 p = 0.024) was not correlated with improvement of gait parameters and NFoG-Q from T0 to T2. LEDD did not change significantly after LCIG initiation.

Conclusion

Continuous dopaminergic stimulation provided by LCIG infusion progressively ameliorates gait and alleviates FoG in PD patients over time, independently from improvement of motor fluctuations and without increase of daily dosage of dopaminergic therapy.

A multimodal dataset of real world mobility activities in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterised by motor symptoms such as gait dysfunction and postural instability. Technological tools to continuously monitor outcomes could capture the hour-by-hour symptom fluctuations of PD. Development of such tools is hampered by the lack of labelled datasets from home settings. To this end, we propose REMAP (REal-world Mobility Activities in Parkinson's disease), a human rater-labelled dataset collected in a home-like setting. It includes people with and without PD doing sit-to-stand transitions and turns in gait. These discrete activities are captured from periods of free-living (unobserved, unstructured) and during clinical assessments. The PD participants withheld their dopaminergic medications for a time (causing increased symptoms), so their activities are labelled as being "on" or "off" medications. Accelerometry from wrist-worn wearables and skeleton pose video data is included. We present an open dataset, where the data is coarsened to reduce re-identifiability, and a controlled dataset available on application which contains more refined data. A use-case for the data to estimate sit-to-stand speed and duration is illustrated.

Monipar: movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones

Introduction

Parkinson's disease (PD) is a neurodegenerative disorder commonly characterized by motor impairments. The development of mobile health (m-health) technologies, such as wearable and smart devices, presents an opportunity for the implementation of clinical tools that can support tasks such as early diagnosis and objective quantification of symptoms.

Objective

This study evaluates a framework to monitor motor symptoms of PD patients based on the performance of standardized exercises such as those performed during clinic evaluation. To implement this framework, an m-health tool named Monipar was developed that uses off-the-shelf smart devices.

Methods

An experimental protocol was conducted with the participation of 21 early-stage PD patients and 7 healthy controls who used Monipar installed in off-the-shelf smartwatches and smartphones. Movement data collected using the built-in acceleration sensors were used to extract relevant digital indicators (features). These indicators were then compared with clinical evaluations performed using the MDS-UPDRS scale.

Results

The results showed moderate to strong (significant) correlations between the clinical evaluations (MDS-UPDRS scale) and features extracted from the movement data used to assess resting tremor (i.e., the standard deviation of the time series: r = 0.772, p < 0.001) and data from the pronation and supination movements (i.e., power in the band of 1-4 Hz: r = -0.662, p < 0.001).

Conclusion

These results suggest that the proposed framework could be used as a complementary tool for the evaluation of motor symptoms in early-stage PD patients, providing a feasible and cost-effective solution for remote and ambulatory monitoring of specific motor symptoms such as resting tremor or bradykinesia.

Overview on wearable sensors for the management of Parkinson's disease

Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.

Everyday technology use among people with Parkinson's disease

OBJECTIVES

To explore the relevance of and ability to use everyday technology (ET) among people with Parkinson's Disease (PD) and to explore associations between ET use and global cognition and motor ability.

MATERIALS AND METHODS

Cross-sectional data was collected from 34 people with PD using the Short Everyday Technology Use Questionnaire+ (S-ETUQ+), the Movement Disorder Society-Unified Parkinson's Disease Rating Scale and the Montreal Cognitive Assessment (MoCA).

RESULTS

Out of 41 ETs in the S-ETUQ+, the mean number perceived as relevant was 27.5 (min-max 19-35, SD 3.6). A good ability to use ET was reported where many ETs had a challenge measure below participants' ability to use them. A strong positive correlation between the ability to use ET and global cognition (MoCA) (r = .676, p = <0.01) was shown.

CONCLUSIONS

ET use has become integrated into everyday life and is important for participation. This study showed a high relevance of and good ability to use ET and a correlation between ET use and global cognition among people with mild-moderate PD. Evaluation and support to use ET in PD are important for maintaining independence and participation, especially among those with cognitive decline.

Application of single wrist-wearable accelerometry for objective motor diary assessment in fluctuating Parkinson's disease

Advanced Parkinson's disease (PD) is characterized by motor fluctuations including unpredictable oscillations remarkably impairing quality of life. Effective management and development of novel therapies for these response fluctuations largely depend on clinical rating instruments such as the widely-used PD home diary, which are associated with biases and errors. Recent advancements in digital health technologies provide user-friendly wearables that can be tailored for continuous monitoring of motor fluctuations. Their criterion validity under real-world conditions using clinical examination as the gold standard remains to be determined. We prospectively examined this validity of a wearable accelerometer-based digital Parkinson's Motor Diary (adPMD) using the Parkinson's Kinetigraph (PKG®) in an alternative application by converting its continuous data into one of the three motor categories of the PD home diary (Off, On and Dyskinetic state). Sixty-three out of 91 eligible participants with fluctuating PD (46% men, average age 66) had predefined sufficient adPMD datasets (>70% of half-hour periods) from 2 consecutive days. 92% of per-protocol assessments were completed. adPMD monitoring of daily times in motor states showed moderate validity for Off and Dyskinetic state (ICC = 0.43-0.51), while inter-rating methods agreements on half-hour-level can be characterized as poor (median Cohen's κ = 0.13-0.21). Individualization of adPMD thresholds for transferring accelerometer data into diary categories improved temporal agreements up to moderate level for Dyskinetic state detection (median Cohen's κ = 0.25-0.41). Here we report that adPMD real-world-monitoring captures daily times in Off and Dyskinetic state in advanced PD with moderate validities, while temporal agreement of adPMD and clinical observer diary data is limited.

Balance telerehabilitation and wearable technology for people with Parkinson's disease (TelePD trial)

BACKGROUND

Balance impairments, that lead to falls, are one of the main symptoms of Parkinson's disease (PD). Telerehabilitation is becoming more common for people with PD; however, balance is particularly challenging to assess and treat virtually. The feasibility and efficacy of virtual assessment and virtual treatment of balance in people with PD are unknown. The present study protocol has three aims: I) to determine if a virtual balance and gait assessment (instrumented L-shape mobility test) with wearable sensors can predict a gold-standard, in-person clinical assessment of balance, the Mini Balance Evaluation Systems Test (Mini-BESTest); II) to explore the effects of 12 sessions of balance telerehabilitation and unsupervised home exercises on balance, gait, executive function, and clinical scales; and III) to explore if improvements after balance telerehabilitation transfer to daily-life mobility, as measured by instrumented socks with inertial sensors worn for 7 days.

METHODS

The TelePD Trial is a prospective, single-center, parallel-group, single-blind, pilot, randomized, controlled trial. This trial will enroll 80 eligible people with PD. Participants will be randomized at a 1:1 ratio into receiving home-based balance exercises in either: 1) balance telerehabilitation (experimental group, n = 40) or 2) unsupervised exercises (control group, n = 40). Both groups will perform 12 sessions of exercise at home that are 60 min long. The primary outcome will be Mini-BESTest. The secondary outcomes will be upper and lower body gait metrics from a prescribed task (instrumented L-shape mobility test); daily-life mobility measures over 7 days with wearable sensors in socks, instrumented executive function tests, and clinical scales. Baseline testing and 7 days of daily-life mobility measurement will occur before and after the intervention period.

CONCLUSION

The TelePD Trial will be the first to explore the usefulness of using wearable sensor-based measures of balance and gait remotely to assess balance, the feasibility and efficacy of balance telerehabilitation in people with PD, and the translation of balance improvements after telerehabilitation to daily-life mobility. These results will help to develop a more effective home-based balance telerehabilitation and virtual assessment that can be used remotely in people with balance impairments.

TRIAL REGISTRATION

This trial was prospectively registered on ClinicalTrials.gov (NCT05680597).

Design of the STEPS trial: a phase II randomized controlled trial evaluating eHealth-supported motor-cognitive home training for Parkinson's disease

BACKGROUND

Electronic health (eHealth) technology offers the potential to support and motivate physical activity for symptom management in Parkinson's disease (PD). It is also recommended that motor exercise in PD be complemented with cognitive training aimed at attentional or executive functions. This paper describes the protocol for a double-blind randomized controlled trial to evaluate the effects of motor-cognitive training in the home environment, supported by eHealth.

METHODS/DESIGN

The Support for home Training using Ehealth in Parkinsons diseaSe (STEPS) is a double-blind single center randomized controlled trial. Two parallel groups will include in total 120 participants with mild to moderate PD who will receive either (i) the intervention (a progressive 10-week individualized motor-cognitive eHealth training with cognitive behavioral elements to increase physical activity levels) or (ii) an active control group (an individualized 10-week paper-based home exercise program). The active control group will not receive motor-cognitive exercises or cognitive behavioral approaches to increase physical activity level. The primary outcome is walking capacity assessed by the six-minute walk test (6MWT). Secondary outcomes will include gait performance during single and dual task conditions, gait speed, functional mobility and lower limb strength, balance, physical activity behavior and a range of patient reported outcome measures relevant in PD.

DISCUSSION

The STEPS trial will answer the question whether 10 weeks of eHealth supported motor-cognitive exercise in the home environment can improve walking capacity in PD when compared to a standard paper exercise program. Findings from this study will also strengthen the evidence concerning the efficacy of PD-specific eHealth interventions with a view meeting future health care demands by addressing issues of inaccessibility to specialized neurological rehabilitation in PD.

TRIAL REGISTRATION

ClinicalTrials.gov August 2022, NCT05510739.

Treatment Detection and Movement Disorder Society-Unified Parkinson's Disease Rating Scale, Part III Estimation Using Finger Tapping Tasks

The validation of objective and easy-to-implement biomarkers that can monitor the effects of fast-acting drugs among Parkinson's disease (PD) patients would benefit antiparkinsonian drug development. We developed composite biomarkers to detect levodopa/carbidopa effects and to estimate PD symptom severity. For this development, we trained machine learning algorithms to select the optimal combination of finger tapping task features to predict treatment effects and disease severity. Data were collected during a placebo-controlled, crossover study with 20 PD patients. The alternate index and middle finger tapping (IMFT), alternative index finger tapping (IFT), and thumb-index finger tapping (TIFT) tasks and the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III were performed during treatment. We trained classification algorithms to select features consisting of the MDS-UPDRS III item scores; the individual IMFT, IFT, and TIFT; and all three tapping tasks collectively to classify treatment effects. Furthermore, we trained regression algorithms to estimate the MDS-UPDRS III total score using the tapping task features individually and collectively. The IFT composite biomarker had the best classification performance (83.50% accuracy, 93.95% precision) and outperformed the MDS-UPDRS III composite biomarker (75.75% accuracy, 73.93% precision). It also achieved the best performance when the MDS-UPDRS III total score was estimated (mean absolute error: 7.87, Pearson's correlation: 0.69). We demonstrated that the IFT composite biomarker outperformed the combined tapping tasks and the MDS-UPDRS III composite biomarkers in detecting treatment effects. This provides evidence for adopting the IFT composite biomarker for detecting antiparkinsonian treatment effect in clinical trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Impact of new technologies on neurology in Spain. Review by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology

INTRODUCTION

New technologies are increasingly widespread in biomedicine. Using the consensus definition of new technologies established by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology (SEN), we evaluated the impact of these technologies on Spanish neurology, based on communications presented at Annual Meetings of the SEN.

MATERIAL AND METHODS

We defined the concept of new technology in neurology as a novel technology or novel application of an existing technology, characterised by a certain degree of coherence persisting over time, with the potential to have an impact on the present and/or future of neurology. We conducted a descriptive study of scientific communications presented at the SEN's annual meetings from 2012 to 2018, analysing the type of technology, the field of neurology, and the geographical provenance of the studies.

RESULTS

We identified 299 communications related with new technologies from a total of 8139 (3.7%), including 120 posters and 179 oral communications, ranging from 1.6% of all communications in 2012 to 6.8% in 2018. The technologies most commonly addressed were advanced neuroimaging (24.7%), biosensors (17.1%), electrophysiology and neurostimulation (14.7%), and telemedicine (13.7%). The neurological fields where new technologies were most widely employed were movement disorders (18.4%), cerebrovascular diseases (15.7%), and dementia (13.4%). Madrid was the region presenting the highest number of communications related to new technologies (32.8%), followed by Catalonia (26.8%) and Andalusia (9.0%).

CONCLUSIONS

The number of communications addressing new technologies follows an upward trend. The number of technologies used in neurology has increased in parallel with their availability. We found scientific communications in all neurological subspecialties, with a heterogeneous geographical distribution.

Activity Monitoring in Parkinson Disease: A Qualitative Study of Implementation Determinants

BACKGROUND AND PURPOSE

There is interest in incorporating digital health technology in routine practice. We integrate multiple stakeholder perspectives to describe implementation determinants (barriers and facilitators) regarding digital health technology use to facilitate exercise behavior change for people with Parkinson disease in outpatient physical therapy.

METHODS

The purposeful sample included people with Parkinson disease (n = 13), outpatient physical therapists (n = 12), and advanced technology stakeholders including researchers and reimbursement specialists (n = 13). Semistructured interviews were used to elicit implementation determinants related to using digital health technology for activity monitoring and exercise behavior change. Deductive codes based on the Consolidated Framework for Implementation Research were used to describe implementation determinants.

RESULTS

Key implementation determinants were similar across stakeholder groups. Essential characteristics of digital health technology included design quality and packaging, adaptability, complexity, and cost. Implementation of digital health technology by physical therapists and people with Parkinson disease was influenced by their knowledge, attitudes, and varied confidence levels in using digital health technology. Inner setting organizational determinants included available resources and access to knowledge/information. Process determinants included device interoperability with medical record systems and workflow integration. Outer setting barriers included lack of external policies, regulations, and collaboration with device companies.

DISCUSSION AND CONCLUSIONS

Future implementation interventions should address key determinants, including required processes for how and when physical therapists instruct people with Parkinson disease on digital health technology, organizational readiness, workflow integration, and characteristics of physical therapists and people with Parkinson disease who may have ingrained beliefs regarding their ability and willingness to use digital health technology. Although site-specific barriers should be addressed, digital health technology knowledge translation tools tailored to individuals with varied confidence levels may be generalizable across clinics.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content available at: http://links.lww.com/JNPT/A436 ).

Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies

Parkinson disease (PD) is a complex neurodegenerative disorder that afflicts over 10 million people worldwide, resulting in debilitating motor and cognitive impairment. In the United States alone (with approximately 1 million cases), the economic burden for treating and caring for persons with PD exceeds US $50 billion and myriad therapeutic approaches are under development, including both symptomatic- and disease-modifying agents. The challenges presented in addressing PD are compounded by observations that numerous, statistically distinct patient phenotypes present with a wide variety of motor and nonmotor symptomatic profiles, varying responses to current standard-of-care symptom-alleviating medications (L-DOPA and dopaminergic agonists), and different disease trajectories. The existence of these differing phenotypes highlights the opportunities in personalized approaches to symptom management and disease control. The prodromal period of PD can span across several decades, allowing the potential to leverage the unique array of composite symptoms presented to trigger early interventions. This may be especially beneficial as disease progression in PD (alongside Alzheimer disease and Huntington disease) may be influenced by biological processes such as oxidative stress, offering the potential for individual lifestyle factors to be tailored to delay disease onset. In this viewpoint, we offer potential scenarios where emerging diagnostic and monitoring strategies might be tailored to the individual patient under the tenets of P4 medicine (predict, prevent, personalize, and participate). These approaches may be especially relevant as the causative factors and biochemical pathways responsible for the observed neurodegeneration in patients with PD remain areas of fluid debate. The numerous observational patient cohorts established globally offer an excellent opportunity to test and refine approaches to detect, characterize, control, modify the course, and ultimately stop progression of this debilitating disease. Such approaches may also help development of parallel interventive strategies in other diseases such as Alzheimer disease and Huntington disease, which share common traits and etiologies with PD. In this overview, we highlight near-term opportunities to apply P4 medicine principles for patients with PD and introduce the concept of composite orthogonal patient monitoring.

A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings

Walking activity and gait parameters are considered among the most relevant mobility-related parameters. Currently, gait assessments have been mainly analyzed in laboratory or hospital settings, which only partially reflect usual performance (i.e., real world behavior). In this study, we aim to validate a robust walking detection algorithm using a single foot-worn inertial measurement unit (IMU) in real-life settings. We used a challenging dataset including 18 individuals performing free-living activities. A multi-sensor wearable system including pressure insoles, multiple IMUs, and infrared distance sensors (INDIP) was used as reference. Accurate walking detection was obtained, with sensitivity and specificity of 98 and 91% respectively. As robust walking detection is needed for ambulatory monitoring to complete the processing pipeline from raw recorded data to walking/mobility outcomes, a validated algorithm would pave the way for assessing patient performance and gait quality in real-world conditions.

The Facilitators and Barriers to Digital Health for Managing Nutrition in People With Parkinson's Disease and Their Caregivers: A Formative, Qualitative Study

OBJECTIVE

Identify techniques to assist in designing digital health platforms for nutrition services for people with Parkinson's disease and caregivers to improve their quality of life.

DESIGN

Semistructured, dyadic interviews with 20 dyads (20 people with Parkinson's disease and 20 caregivers).

SETTING

Home visits were conducted in the northeast US.

PARTICIPANTS

People with Parkinson's disease and their caregivers were recruited via email, flyers, news articles and announcements at support groups.

PHENOMENON OF INTEREST

Identification of facilitators and barriers to using digital health platforms to inform future digital nutrition services.

ANALYSIS

Interviews were recorded, transcribed and double-coded using a framework analysis method.

RESULTS

Reported digital health platforms utilization facilitators were: knowledge acquisition, convenience, intention to use, socializing, enjoyment, and forced adoption. Barriers included: negative feelings toward technology, lack of access or knowledge, disinterest, product design, frustration and functional reliability, and applying health information.

CONCLUSIONS AND IMPLICATIONS

Although dyads often lack knowledge on both how to use technology and nutrition, they are willing to use digital health platforms to increase their nutrition knowledge if platforms are convenient. Based on the identified facilitators and barriers, the added benefits of access and training nutrition digital health platforms must be clearly communicated to end-users to improve their quality of life.

Personalized Integrated Care Promoting Quality of Life for Older People: Protocol for a Multicenter Randomized Controlled Trial

BACKGROUND

Alzheimer disease (AD) and Parkinson disease (PD) are the 2 most common neurodegenerative diseases affecting millions of people worldwide. The Personalized Integrated Care Promoting Quality of Life for Older People (PC4L) project proposes an integrated, scalable, and interactive care ecosystem that can be easily adapted to the needs of several neurodegenerative and chronic diseases, care institutions, and end user requirements.

OBJECTIVE

The study protocol developed within the framework of the PC4L project aims to iteratively test the integrated platform and its modules, and focuses primarily on assessing the impact of the proposed solution (ie, the PC4L platform) on patients' quality of life, as well as its usability and feasibility on a large-scale sample size in 3 different scenarios (home, neurorehabilitation, and day care centers).

METHODS

A prospective multicenter clinical study is conducted in 5 European countries (Germany, Italy, Portugal, Romania, and Spain) at 6 different pilot centers, for 3 months, in patients with PD, Parkinsonism, AD, and other dementias (ODs). Patients were randomized in a ratio of 1:1 to the intervention group (use of the PC4L system) or the control group (no intervention). The PC4L system consists mainly of a wristband for monitoring parameters such as steps and levels of physical activity, and the PC4L app, which includes different engaging functionalities. Both groups are assessed through baseline and end-of-study clinical evaluations, including assessment of quality of life through the EQ-5D-3L scale.

RESULTS

The study protocol is part of a project approved and funded by the European Commission Horizon 2020 (grant agreement number 875221). The ethics committees of all involved centers reviewed and approved the study protocol. The study began with the recruitment phase in September 2022, and enrollment ended in February 2023. Recruitment is now closed (April 2023). The results of this study are expected to be published in summer 2023. A total of 558 patients, 279 per study group, were recruited. The results will allow to clarify the impact of PC4L on quality of life, will assess the empowerment of patients and the medical resources use, as well as the usability of the final version of the PC4L system. It will also provide information on the support of the system as a tool to facilitate the decision-making process.

CONCLUSIONS

The PC4L project intends to test a technology-based, integrated, scalable, and interactive care platform on patients with neurodegenerative diseases and proposes a good coordinated care model between all involved actors. Future developments of the PC4L solution may involve caregivers and socio-health professionals in the decision-making process in order to facilitate efficient communication between all stakeholders and ensure reliable and protected access to data within Europe.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05538455; https://clinicaltrials.gov/study/NCT05538455.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/47916.

In vivo effects of the alpha-synuclein misfolding inhibitor minzasolmin supports clinical development in Parkinson's disease

Direct targeting of alpha-synuclein (ASYN) has emerged as a disease-modifying strategy for Parkinson's disease and other synucleinopathies which is being approached using both small molecule compounds and ASYN-targeted biologics. Minzasolmin (UCB0599) is an orally bioavailable and brain-penetrant small molecule ASYN misfolding inhibitor in clinical development as a disease-modifying therapeutic for Parkinson's disease. Herein the results of preclinical evaluations of minzasolmin that formed the basis for subsequent clinical development are described. Pharmacokinetic evaluations of intraperitoneal 1 and 5 mg/kg minzasolmin in wildtype mice revealed parallel and dose-proportional exposures in brain and plasma. Three-month administration studies in the Line 61 transgenic mouse model of PD were conducted to measure ASYN pathology and other PD-relevant endpoints including markers of CNS inflammation, striatal DAT labeling and gait. Reductions in ASYN pathology were correlated with improved aspects of gait and balance, reductions in CNS inflammation marker abundance, and normalized striatal DAT levels. These findings provide support for human dose determinations and have informed the translational strategy for clinical trial design and biomarker selection for the ongoing clinical studies of minzasolmin in patients living with early-stage Parkinson's disease (ClinicalTrials.gov ID: NCT04658186; EudraCT Number 2020-003265).

Machine Learning in Clinical Trials: A Primer with Applications to Neurology

We reviewed foundational concepts in artificial intelligence (AI) and machine learning (ML) and discussed ways in which these methodologies may be employed to enhance progress in clinical trials and research, with particular attention to applications in the design, conduct, and interpretation of clinical trials for neurologic diseases. We discussed ways in which ML may help to accelerate the pace of subject recruitment, provide realistic simulation of medical interventions, and enhance remote trial administration via novel digital biomarkers and therapeutics. Lastly, we provide a brief overview of the technical, administrative, and regulatory challenges that must be addressed as ML achieves greater integration into clinical trial workflows.

Objective measurement versus clinician-based assessment for Parkinson's disease

INTRODUCTION

Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients.

AREAS COVERED

The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management.

EXPERT OPINION

In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.

Balance and gait in progressive supranuclear palsy: a narrative review of objective metrics and exercise interventions

Background

The use of objective gait and balance metrics is rapidly expanding for evaluation of atypical parkinsonism, and these measures add to clinical observations. Evidence for rehabilitation interventions to improve objective measures of balance and gait in atypical parkinsonism is needed.

Aim

Our aim is to review, with a narrative approach, current evidence on objective metrics for gait and balance and exercise interventions in progressive supranuclear palsy (PSP).

Methods

Literature searches were conducted in four computerized databases from the earliest record up to April 2023: PubMed, ISI's Web of Knowledge, Cochrane's Library, and Embase. Data were extracted for study type (cross-sectional, longitudinal, and rehabilitation interventions), study design (e.g., experimental design and case series), sample characteristics, and gait and balance measurements.

Results

Eighteen gait and balance (16 cross-sectional and 4 longitudinal) and 14 rehabilitation intervention studies were included. Cross-sectional studies showed that people with PSP have impairments in gait initiation and steady-state gait using wearable sensors, and in static and dynamic balance assessed by posturography when compared to Parkinson's disease (PD) and healthy controls. Two longitudinal studies observed that wearable sensors can serve as objective measures of PSP progression, using relevant variables of change in turn velocity, stride length variability, toe off angle, cadence, and cycle duration. Rehabilitation studies investigated the effect of different interventions (e.g., balance training, body-weight supported treadmill gait, sensorimotor training, and cerebellar transcranial magnetic stimulation) on gait, clinical balance, and static and dynamic balance assessed by posturography measurements. No rehabilitation study in PSP used wearable sensors to evaluate gait and balance impairments. Although clinical balance was assessed in 6 rehabilitation studies, 3 of these studies used a quasi-experimental design, 2 used a case series, only 1 study used an experimental design, and sample sizes were relatively small.

Conclusion

Wearable sensors to quantify balance and gait impairments are emerging as a means of documenting progression of PSP. Robust evidence for improving balance and gait in PSP was not found for rehabilitation studies. Future powered, prospective and robust clinical trials are needed to investigate the effects of rehabilitation interventions on objective gait and balance outcomes in people with PSP.

Shared Decision-Making to Improve Health-Related Outcomes for Adults with Stroke Disease

Stroke is one of the leading causes of disability and death worldwide, a severe medical condition for which new solutions for prevention, monitoring, and adequate treatment are needed. This paper proposes a SDM framework for the development of innovative and effective solutions based on artificial intelligence in the rehabilitation of stroke patients by empowering patients to make decisions about the use of devices and applications developed in the European project ALAMEDA. To develop a predictive tool for improving disability in stroke patients, key aspects of stroke patient data collection journeys, monitored health parameters, and specific variables covering motor, physical, emotional, cognitive, and sleep status are presented. The proposed SDM model involved the training and consultation of patients, medical staff, carers, and representatives under the name of the Local Community Group. Consultation with LCG members, consists of 11 representative people, physicians, nurses, patients and caregivers, which led to the definition of a methodological framework to investigate the key aspects of monitoring the patient data collection journey for the stroke pilot, and a specific questionnaire to collect stroke patient requirements and preferences. A set of general and specific guidelines specifying the principles by which patients decide to use wearable sensing devices and specific applications resulted from the analysis of the data collected using the questionnaire. The preferences and recommendations collected from LCG members have already been implemented in this stage of ALAMEDA system design and development.

Parkinson's disease therapy: what lies ahead?

The worldwide prevalence of Parkinson's disease (PD) has been constantly increasing in the last decades. With rising life expectancy, a longer disease duration in PD patients is observed, further increasing the need and socioeconomic importance of adequate PD treatment. Today, PD is exclusively treated symptomatically, mainly by dopaminergic stimulation, while efforts to modify disease progression could not yet be translated to the clinics. New formulations of approved drugs and treatment options of motor fluctuations in advanced stages accompanied by telehealth monitoring have improved PD patients care. In addition, continuous improvement in the understanding of PD disease mechanisms resulted in the identification of new pharmacological targets. Applying novel trial designs, targeting of pre-symptomatic disease stages, and the acknowledgment of PD heterogeneity raise hopes to overcome past failures in the development of drugs for disease modification. In this review, we address these recent developments and venture a glimpse into the future of PD therapy in the years to come.

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 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.

Early detection of Parkinson's disease through enriching the electronic health record using a biomedical knowledge graph

Introduction

Early diagnosis of Parkinson's disease (PD) is important to identify treatments to slow neurodegeneration. People who develop PD often have symptoms before the disease manifests and may be coded as diagnoses in the electronic health record (EHR).

Methods

To predict PD diagnosis, we embedded EHR data of patients onto a biomedical knowledge graph called Scalable Precision medicine Open Knowledge Engine (SPOKE) and created patient embedding vectors. We trained and validated a classifier using these vectors from 3,004 PD patients, restricting records to 1, 3, and 5 years before diagnosis, and 457,197 non-PD group.

Results

The classifier predicted PD diagnosis with moderate accuracy (AUC = 0.77 ± 0.06, 0.74 ± 0.05, 0.72 ± 0.05 at 1, 3, and 5 years) and performed better than other benchmark methods. Nodes in the SPOKE graph, among cases, revealed novel associations, while SPOKE patient vectors revealed the basis for individual risk classification.

Discussion

The proposed method was able to explain the clinical predictions using the knowledge graph, thereby making the predictions clinically interpretable. Through enriching EHR data with biomedical associations, SPOKE may be a cost-efficient and personalized way to predict PD diagnosis years before its occurrence.

Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases

Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Role of novel endpoints and evaluations of response in Parkinson disease

With progress in our understanding of Parkinson disease (PD) and other neurodegenerative disorders, from clinical features to imaging, genetic, and molecular characterization comes the opportunity to refine and revise how we measure these diseases and what outcome measures are used as endpoints in clinical trials. While several rater-, patient-, and milestone-based outcomes for PD exist that may serve as clinical trial endpoints, there remains an unmet need for endpoints that are clinically meaningful, patient centric while also being more objective and quantitative, less susceptible to effects of symptomatic therapy (for disease-modification trials), and that can be measured over a short period and yet accurately represent longer-term outcomes. Several novel outcomes that may be used as endpoints in PD clinical trials are in development, including digital measures of signs and symptoms, as well a growing array of imaging and biospecimen biomarkers. This chapter provides an overview of the state of PD outcome measures as of 2022, including considerations for selection of clinical trial endpoints in PD, advantages and limitations of existing measures, and emerging potential novel endpoints.

Ethical, Legal, Organisational and Social Issues of Teleneurology: A Scoping Review

BACKGROUND

Neurological disorders are the leading cause of disability and the second leading cause of death worldwide. Teleneurology (TN) allows neurology to be applied when the doctor and patient are not present in the same place, and sometimes not at the same time. In February 2021, the Spanish Ministry of Health requested a health technology assessment report on the implementation of TN as a complement to face-to-face neurological care.

METHODS

A scoping review was conducted to answer the question on the ethical, legal, social, organisational, patient (ELSI) and environmental impact of TN. The assessment of these aspects was carried out by adapting the EUnetHTA Core Model 3.0 framework, the criteria established by the Spanish Network of Health Technology Assessment Agencies and the analysis criteria of the European Validate (VALues In Doing Assessments of healthcare TEchnologies) project. Key stakeholders were invited to discuss their concerns about TN in an online meeting. Subsequently, the following electronic databases were consulted from 2016 to 10 June 2021: MEDLINE and EMBASE.

RESULTS

79 studies met the inclusion criteria. This scoping review includes 37 studies related to acceptability and equity, 15 studies developed during COVID and 1 study on environmental aspects. Overall, the reported results reaffirm the necessary complementarity of TN with the usual face-to-face care.

CONCLUSIONS

This need for complementarity relates to factors such as acceptability, feasibility, risk of dehumanisation and aspects related to privacy and the confidentiality of sensitive data.

How should future clinical trials be designed in the search for disease-modifying therapies for Parkinson's disease?

INTRODUCTION

Although there has been substantial progress in research and innovations in symptomatic treatments, similar success has not been achieved in disease-modifying therapy (DMT) for Parkinson's disease (PD). Considering the enormous motor, psychosocial and financial burden associated with PD, safe and effective DMT is of paramount importance.

AREAS COVERED

One of the reasons for the lack of progress in DMT for PD is poor or inappropriate design of clinical trials. In the first part of the article, the authors focus on the plausible reasons why the previous trials have failed and in the latter part, they provide their perspectives on future DMT trials.

EXPERT OPINION

There are several potential reasons why previous trials have failed, including broad clinical and etiopathogenic heterogeneity of PD, poor definition and documentation of target engagement, lack of appropriate biomarkers and outcome measures, and short duration of follow-up. To address these deficiencies, future trials may consider- (i) a more customized approach to select the most suitable participants and therapeutic approaches, (ii) explore combination therapies that would target multiple pathogenetic mechanisms, and (iii) moving beyond targeting only motor symptoms to also assessing non-motor features of PD in well-designed longitudinal studies.

Accuracy and repeatability of the Microsoft Azure Kinect for clinical measurement of motor function

Quantitative assessment of motor function is increasingly applied in fall risk stratification, diagnosis, and disease monitoring of neuro-geriatric disorders of balance and gait. Its broad application, however, demands for low-cost and easy to use solutions that facilitate high-quality assessment outside laboratory settings. In this study, we validated in 30 healthy adults (12 female, age: 32.5 [22 - 62] years) the performance and accuracy of the latest generation of the Microsoft RGB-D camera, i.e., Azure Kinect (AK), in tracking body motion and providing estimates of clinical measures that characterise static posture, postural transitions, and locomotor function. The accuracy and repeatability of AK recordings was validated with a clinical reference standard multi-camera motion capture system (Qualisys) and compared to its predecessor Kinect version 2 (K2). Motion signal quality was evaluated by Pearson's correlation and signal-to-noise ratios while the accuracy of estimated clinical parameters was described by absolute and relative agreement based on intraclass correlation coefficients. The accuracy of AK-based body motion signals was moderate to excellent (RMSE 89 to 20 mm) and depended on the dimension of motion (highest for anterior-posterior dimension), the body region (highest for wrists and elbows, lowest for ankles and feet), and the specific motor task (highest for stand up and sit down, lowest for quiet standing). Most derived clinical parameters showed good to excellent accuracy (r .84 to .99) and repeatability (ICC(1,1) .55 to .94). The overall performance and limitations of body tracking by AK were comparable to its predecessor K2 in a cohort of young healthy adults. The observed accuracy and repeatability of AK-based evaluation of motor function indicate the potential for a broad application of high-quality and long-term monitoring of balance and gait in different non-specialised environments such as medical practices, nursing homes or community centres.