Novel methods and technologies for 21st-century clinical trials: a review

Artificial intelligence-based video monitoring of movement disorders in the elderly: a review on current and future landscapes

ABSTRACT

Due to global ageing, the burden of chronic movement and neurological disorders (Parkinson's disease and essential tremor) is rapidly increasing. Current diagnosis and monitoring of these disorders rely largely on face-to-face assessments utilising clinical rating scales, which are semi-subjective and time-consuming. To address these challenges, the utilisation of artificial intelligence (AI) has emerged. This review explores the advantages and challenges associated with using AI-driven video monitoring to care for elderly patients with movement disorders. The AI-based video monitoring systems offer improved efficiency and objectivity in remote patient monitoring, enabling real-time analysis of data, more uniform outcomes and augmented support for clinical trials. However, challenges, such as video quality, privacy compliance and noisy training labels, during development need to be addressed. Ultimately, the advancement of video monitoring for movement disorders is expected to evolve towards discreet, home-based evaluations during routine daily activities. This progression must incorporate data security, ethical considerations and adherence to regulatory standards.

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.

Narrative review of telemedicine applications in decentralized research

Telemedicine enables critical human communication and interaction between researchers and participants in decentralized research studies. There is a need to better understand the overall scope of telemedicine applications in clinical research as the basis for further research. This narrative, nonsystematic review of the literature sought to review and discuss applications of telemedicine, in the form of synchronous videoconferencing, in clinical research. We searched PubMed to identify relevant literature published between January 1, 2013, and June 30, 2023. Two independent screeners assessed titles and abstracts for inclusion, followed by single-reviewer full-text screening, and we organized the literature into core themes through consensus discussion. We screened 1044 publications for inclusion. Forty-eight publications met our inclusion and exclusion criteria. We identified six core themes to serve as the structure for the narrative review: infrastructure and training, recruitment, informed consent, assessment, monitoring, and engagement. Telemedicine applications span all stages of clinical research from initial planning and recruitment to informed consent and data collection. While the evidence base for using telemedicine in clinical research is not well-developed, existing evidence suggests that telemedicine is a potentially powerful tool in clinical research.

At-home wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis causes degeneration of motor neurons, resulting in progressive muscle weakness and impairment in motor function. Promising drug development efforts have accelerated in amyotrophic lateral sclerosis, but are constrained by a lack of objective, sensitive, and accessible outcome measures. Here we investigate the use of wearable sensors, worn on four limbs at home during natural behavior, to quantify motor function and disease progression in 376 individuals with amyotrophic lateral sclerosis. We use an analysis approach that automatically detects and characterizes submovements from passively collected accelerometer data and produces a machine-learned severity score for each limb that is independent of clinical ratings. We show that this approach produces scores that progress faster than the gold standard Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (-0.86 ± 0.70 SD/year versus -0.73 ± 0.74 SD/year), resulting in smaller clinical trial sample size estimates (N = 76 versus N = 121). This method offers an ecologically valid and scalable measure for potential use in amyotrophic lateral sclerosis trials and clinical care.

The temporal event-based model: Learning event timelines in progressive diseases

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80 % with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.

Machine Learning and the Digital Measurement of Psychological Health

Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.

Real-life ankle submovements and computer mouse use reflect patient-reported function in adult ataxias

Novel disease-modifying therapies are being evaluated in spinocerebellar ataxias and multiple system atrophy. Clinician-performed disease rating scales are relatively insensitive for measuring disease change over time, resulting in large and long clinical trials. We tested the hypothesis that sensors worn continuously at home during natural behaviour and a web-based computer mouse task performed at home could produce interpretable, meaningful and reliable motor measures for potential use in clinical trials. Thirty-four individuals with degenerative ataxias (spinocerebellar ataxia types 1, 2, 3 and 6 and multiple system atrophy of the cerebellar type) and eight age-matched controls completed the cross-sectional study. Participants wore an ankle and wrist sensor continuously at home for 1 week and completed the Hevelius computer mouse task eight times over 4 weeks. We examined properties of motor primitives called 'submovements' derived from the continuous wearable sensors and properties of computer mouse clicks and trajectories in relationship to patient-reported measures of function (Patient-Reported Outcome Measure of Ataxia) and ataxia rating scales (Scale for the Assessment and Rating of Ataxia and the Brief Ataxia Rating Scale). The test-retest reliability of digital measures and differences between ataxia and control participants were evaluated. Individuals with ataxia had smaller, slower and less powerful ankle submovements during natural behaviour at home. A composite measure based on ankle submovements strongly correlated with ataxia rating scale scores (Pearson's r = 0.82-0.88), strongly correlated with self-reported function (r = 0.81), had high test-retest reliability (intraclass correlation coefficient = 0.95) and distinguished ataxia and control participants, including preataxic individuals (n = 4) from controls. A composite measure based on computer mouse movements and clicks strongly correlated with ataxia rating scale total (r = 0.86-0.88) and arm scores (r = 0.65-0.75), correlated well with self-reported function (r = 0.72-0.73) and had high test-retest reliability (intraclass correlation coefficient = 0.99). These data indicate that interpretable, meaningful and highly reliable motor measures can be obtained from continuous measurement of natural movement, particularly at the ankle location, and from computer mouse movements during a simple point-and-click task performed at home. This study supports the use of these two inexpensive and easy-to-use technologies in longitudinal natural history studies in spinocerebellar ataxias and multiple system atrophy of the cerebellar type and shows promise as potential motor outcome measures in interventional trials.

Decentralised, patient-centric, site-less, virtual, and digital clinical trials? From confusion to consensus

There is increasing interest in clinical trials that use technologies and other innovative operational approaches to organise trial activities around trial participants instead of investigator sites. A range of terms has been introduced to refer to this operational clinical trial model, including virtual, digital, remote, and decentralised clinical trials (DCTs). However, this lack of standardised terminology can cause confusion over what a particular trial model entails and for what purposes it can be used, hampering discussions by stakeholders on its acceptability and suitability. Here, we review the different terms described in the scientific literature, advocate the consistent use of a unified term, 'decentralised clinical trial,' and provide a detailed definition of this term.

Remote assessment of myotonic dystrophy type 1: A feasibility study

INTRODUCTION/AIMS

Remote study visits (RSVs) are emerging as important tools for clinical research. We tested the feasibility of using RSVs to evaluate patients with myotonic dystrophy type 1 (DM1), including remote quantitative assessment of muscle function, and we assessed correlations of remote assessments with patient-reported function.

METHODS

Twenty three subjects with DM1 were consented remotely. Toolkits containing a tablet computer, grip dynamometer, and spirometer were shipped to participants. The tablets were loaded with software for video-conferencing and questionnaires about functional impairment, patient experience with technology, and willingness to participate in future remote studies. Grip strength, forced vital capacity, peak cough flow, timed-up-and-go (TUG), and grip myotonia (hand opening time) were determined during RSVs. We assessed correlations of remote assessments with patient-reported outcomes of muscle function and with CTG repeat size.

RESULTS

All 23 subjects completed RSVs. 95% of participants were able to complete all components of the remote study. All toolkit components were returned upon completion. Grip strength and TUG demonstrated moderate to strong correlations with self-reported inventories of upper and lower extremity impairment, respectively (ρ = 0.7 and ρ = -0.52). A total of 91% of subjects expressed interest in participating in future RSVs.

DISCUSSION

Results of this study support the feasibility of using portable devices and video-conferencing for remote collection of patient-reported outcomes and quantitative assessment of muscle function in DM1.

Neurodegenerative Diseases: The Value of Early Predictive End Points

Use of early predictive biomarkers of neurodegenerative disease in phase I clinical trials may improve the translation of novel drug therapies from preclinical development through late-stage studies. This article provides a categorical summary of promising biomarker approaches or clinical end points in molecular, cellular, metabolic, electrophysiological, or clinical function that can be used to predict or quantify the progression of neurodegenerative disorders and guide program support.

Wearable sensors during drawing tasks to measure the severity of essential tremor

Commonly used methods to assess the severity of essential tremor (ET) are based on clinical observation and lack objectivity. This study proposes the use of wearable accelerometer sensors for the quantitative assessment of ET. Acceleration data was recorded by inertial measurement unit (IMU) sensors during sketching of Archimedes spirals in 17 ET participants and 18 healthy controls. IMUs were placed at three points (dorsum of hand, posterior forearm, posterior upper arm) of each participant's dominant arm. Movement disorder neurologists who were blinded to clinical information scored ET patients on the Fahn-Tolosa-Marin rating scale (FTM) and conducted phenotyping according to the recent Consensus Statement on the Classification of Tremors. The ratio of power spectral density of acceleration data in 4-12 Hz to 0.5-4 Hz bands and the total duration of the action were inputs to a support vector machine that was trained to classify the ET subtype. Regression analysis was performed to determine the relationship of acceleration and temporal data with the FTM scores. The results show that the sensor located on the forearm had the best classification and regression results, with accuracy of 85.71% for binary classification of ET versus control. There was a moderate to good correlation (r² = 0.561) between FTM and a combination of power spectral density ratio and task time. However, the system could not accurately differentiate ET phenotypes according to the Consensus classification scheme. Potential applications of machine-based assessment of ET using wearable sensors include clinical trials and remote monitoring of patients.

Wearable sensors detect impaired gait and coordination in LBSL during remote assessments

Background

Leukoencephalopathy with brainstem and spinal cord involvement and lactate elevation (LBSL) is a rare leukodystrophy with motor impairment due to biallelic mutations in DARS2, which encodes mitochondrial aspartyl tRNA synthetase. Progressive ataxia is the primary feature.

Objective

The study objective is to determine the feasibility of remotely collecting quantitative gait and balance measures in LBSL.

Methods

The study design uses wearable accelerometers and the scale for the assessment and rating of ataxia (SARA) scale to assess gait and postural sway in LBSL and control participants' homes through video conferencing.

Results

Lateral step variability (LSV), which indicates stride variability, and elevation of the step at mid‐swing are increased for LBSL patients during brief walking tests. During stance with the eyes closed, LBSL participants show rapid accelerations and decelerations of body movement covering a large sway area and path. Both the LSV and sway area during stance with the feet together and eyes closed correlate strongly with the SARA.

Conclusions

Wearable accelerometers are valid and sensitive for detecting ataxia in LBSL patients during remote assessments. The finding of large increases in the sway area during stance with the eyes closed is intriguing since dorsal column dysfunction is universally seen in LBSL. This approach can be applied to related rare diseases that feature ataxia.

Participant Experiences of a COVID-19 Virtual Clinical Study Using the Current Health Remote Monitoring Platform: A Case Study and Qualitative Analysis (Preprint)

Background

During the COVID-19 pandemic, individuals with a positive viral test were enrolled in a study, within 48 hours, to remotely monitor their vital signs to characterize disease progression and recovery. A virtual trial design was adopted to reduce risks to participants and the research community in a study titled Risk Stratification and Early Alerting Regarding COVID-19 Hospitalization (RiskSEARCH). The Food and Drug Administration–cleared Current Health platform with a wearable device is a continuous remote patient monitoring technology that supports hospital-at-home care and is used as a data collection tool. Enrolled participants wore the Current Health wearable device continuously for up to 30 days and took a daily symptom survey via a tablet that was provided. A qualitative substudy was conducted in parallel to better understand virtual trial implementation, including barriers and facilitators for participants.

Objective

This study aimed to understand the barriers and facilitators of the user experience of interacting with a virtual care platform and research team, while participating in a fully virtual study using qualitative and quantitative data.

Methods

Semistructured interviews were conducted to understand participants’ experience of participating in a virtual study during a global pandemic. The schedule included their experience of enrollment and their interactions with equipment and study staff. A total of 3 RiskSEARCH participants were interviewed over telephone, and transcriptions were inductively coded and analyzed using thematic analysis. Themes were mapped onto the Theoretical Domains Framework (TDF) to identify and describe the factors that influenced study adherence. Quantitative metrics, including adherence to wearable and scheduled tasks collected as part of the RiskSEARCH main study, were paired with the interviews to present an overall picture of participation.

Results

All participants exceeded our definition of a fully adherent participant and reported that participation was feasible and had a low burden. The symptoms progressively resolved during the trial. Inductive thematic analysis identified 13 main themes from the interview data, which were deductively mapped onto 11 of the 14 TDF domains, highlighting barriers and facilitators for each.

Conclusions

Participants in the RiskSEARCH substudy showed high levels of adherence and engagement throughout participation. Although participants experienced some challenges in setting up and maintaining the Current Health kit (eg, charging devices), they reported feeling that the requirements of participation were both reasonable and realistic. We demonstrated that the TDF can be used for inductive thematic analysis. We anticipate expanding this work in future virtual studies and trials to identify barriers and enabling factors for implementation.

Digital Phenotyping in Clinical Neurology

Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.

Diagnostic Testing for Sepsis: A Systematic Review of Economic Evaluations

Introduction: Sepsis is a serious and expensive healthcare problem, when caused by a multidrug-resistant (MDR) bacteria mortality and costs increase. A reduction in the time until the start of treatment improves clinical results. The objective is to perform a systematic review of economic evaluations to analyze the cost-effectiveness of diagnostic methods in sepsis and to draw lessons on the methods used to incorporate antimicrobial resistance (AMR) in these studies. Material and Methods: the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the Consolidated Health Economic Evaluation Reporting standards (CHEERS) checklist was used to extract the information from the texts. Results: A total of 16 articles were found. A decision model was performed in 14. We found two ways to handle resistance while modelling: the test could identify infections caused by a resistant pathogen or resistance-related inputs, or outcomes were included (the incidence of AMR in sepsis patients, antibiotic use, and infection caused by resistant bacterial pathogens). Conclusion: Using a diagnostic technique to detect sepsis early on is more cost-effective than standard care. Setting a direct relationship between the implementation of a testing strategy and the reduction of AMR cases, we made several assumptions about the efficacy of antibiotics and the length-of-stay of patients.

A Systematic Review of Methods used to Conduct Decentralised Clinical Trials

Aims

To evaluate, using quantitative and qualitative approaches, published data on the design and conduct of decentralised clinical trials (DCTs).

Methods

We searched MEDLINE, EMBASE, CENTRAL, PsycINFO, ProQuest Dissertations and Theses, ClinicalTrials.gov, OpenGrey and Google Scholar for publications reporting, discussing, or evaluating decentralised clinical research methods. Reports of randomised clinical trials using decentralised methods were included in a focused quantitative analysis with a primary outcome of number of randomised participants. All publications discussing or evaluating DCTs were included in a wider qualitative analysis to identify advantages, disadvantages, facilitators, barriers and stakeholder opinions of decentralised clinical trials. Quantitative data were summarised using descriptive statistics, and qualitative data analysed using a thematic approach.

Results

Initial searches identified 19 704 articles. After removal of duplicates, 18 553 were screened, resulting in 237 eligible for full‐text assessment. Forty‐five trials were included in the quantitative analysis; 117 documents were included in the qualitative analysis. Trials were widely heterogeneous in design and reporting, precluding meta‐analysis of the effect of DCT methods on the primary recruitment outcome. Qualitative analysis formulated 4 broad themes: value, burden, safety and equity. Participant and stakeholder experiences of DCTs were incompletely represented.

Conclusion

DCTs are developing rapidly. However, there is insufficient evidence to confirm which methods are most effective in trial recruitment, retention, or overall cost. The identified advantages, disadvantages, facilitators and barriers should inform the development of DCT methods. We recommend further research on how DCTs are experienced and perceived by participants and stakeholders to maximise potential benefits.

Simplification or simulation: Power calculation in clinical trials

BACKGROUND AND OBJECTIVES

A justifiable sample size is essential at trial design stage. Generally this task is completed by forming the main research question into a statistical procedure and then implementing the published formulae or software packages. When these standard statistical formulae/software packages become unavailable for studies with complex statistical procedures, some statisticians choose to fill this gap by assuming an alternative simplified sample size calculation. Monte Carlo simulations can also be deployed, particularly for complex trials. However, it is still unclear on how to determine the appropriate approach under certain practical scenarios.

METHODS

We adopted real clinical trials as examples and investigated on simplification and simulation-based sample size calculation approaches.

RESULTS

Compared to simplified sample size calculation, the simulation approach can better address the non-ignorable impact of baseline/follow-up outcome correlation on study power. For studies with multiple endpoints and multiple co-primary endpoints, the sample sizes calculated by simplification approach should be scrutinized.

CONCLUSIONS

Directly using the simplification approach for sample size calculation should be restricted. We recommend to utilize the simulation approach, particularly for complex trials, at least as a sensitivity checking and a useful triangulation to the simplification approach outlined.

Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption

The assessment of health and disease requires a set of criteria to define health status and progression. These health measures are referred to as "endpoints." A "digital endpoint" is defined by its use of sensor-generated data often collected outside of a clinical setting such as in a patient's free-living environment. Applicable sensors exist in an array of devices and can be applied in a diverse set of contexts. For example, a smartphone's microphone might be used to diagnose or predict mild cognitive impairment due to Alzheimer's disease or a wrist-worn activity monitor (such as those found in smartwatches) may be used to measure a drug's effect on the nocturnal activity of patients with sickle cell disease. Digital endpoints are generating considerable excitement because they permit a more authentic assessment of the patient's experience, reveal formerly untold realities of disease burden, and can cut drug discovery costs in half. However, before these benefits can be realized, effort must be applied not only to the technical creation of digital endpoints but also to the environment that allows for their development and application. The future of digital endpoints rests on meaningful interdisciplinary collaboration, sufficient evidence that digital endpoints can realize their promise, and the development of an ecosystem in which the vast quantities of data that digital endpoints generate can be analyzed. The fundamental nature of health care is changing. With coronavirus disease 2019 serving as a catalyst, there has been a rapid expansion of home care models, telehealth, and remote patient monitoring. The increasing adoption of these health-care innovations will expedite the requirement for a digital characterization of clinical status as current assessment tools often rely upon direct interaction with patients and thus are not fit for purpose to be administered remotely. With the ubiquity of relatively inexpensive sensors, digital endpoints are positioned to drive this consequential change. It is therefore not surprising that regulators, physicians, researchers, and consultants have each offered their assessment of these novel tools. However, as we further describe later, the broad adoption of digital endpoints will require a cooperative effort. In this article, we present an analysis of the current state of digital endpoints. We also attempt to unify the perspectives of the parties involved in the development and deployment of these tools. We conclude with an interdependent list of challenges that must be collaboratively addressed before these endpoints are widely adopted.

Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson's Disease?

The use of artificial intelligence (AI) to help diagnose and manage disease is of increasing interest to researchers and clinicians. Volumes of health data are generated from smartphones and ubiquitous inexpensive sensors. By using these data, AI can offer otherwise unobtainable insights about disease burden and patient status in a free-living environment. Moreover, from clinical datasets AI can improve patient symptom monitoring and global epidemiologic efforts. While these applications are exciting, it is necessary to examine both the utility and limitations of these novel analytic methods. The most promising uses of AI remain aspirational. For example, defining the molecular subtypes of Parkinson's disease will be assisted by future applications of AI to relevant datasets. This will allow clinicians to match patients to molecular therapies and will thus help launch precision medicine. Until AI proves its potential in pushing the frontier of precision medicine, its utility will primarily remain in individualized monitoring, complementing but not replacing movement disorders specialists.

GEORGE®: A Pilot Study of a Smartphone Application for Huntington's Disease

BACKGROUND

Current Huntington's disease (HD) measures are limited to subjective, episodic assessments conducted in clinic. Smartphones can enable the collection of objective, real-world data but their use has not been extensively evaluated in HD.

OBJECTIVE

Develop and evaluate a smartphone application to assess feasibility of use and key features of HD in clinic and at home.

METHODS

We developed GEORGE®, an Android smartphone application for HD which assesses voice, chorea, balance, gait, and finger tapping speed. We then conducted an observational pilot study of individuals with manifest HD, prodromal HD, and without a movement disorder. In clinic, participants performed standard clinical assessments and a battery of active tasks in GEORGE. At home, participants were instructed to complete the activities thrice daily for one month. Sensor data were used to measure chorea, tap rate, and step count. Audio data was not analyzed.

RESULTS

Twenty-three participants (8 manifest HD, 5 prodromal HD, 10 controls) enrolled, and all but one completed the study. On average, participants used the application 2.1 times daily. We observed a significant difference in chorea score (HD: 19.5; prodromal HD: 4.5, p = 0.007; controls: 4.3, p = 0.001) and tap rate (HD: 2.5 taps/s; prodromal HD: 8.9 taps/s, p = 0.001; controls: 8.1 taps/s, p = 0.001) between individuals with and without manifest HD. Tap rate correlated strongly with the traditional UHDRS finger tapping score (left hand: r = -0.82, p = 0.022; right hand: r = -0.79, p = 0.03).

CONCLUSION

GEORGE is an acceptable and effective tool to differentiate individuals with and without manifest HD and measure key disease features. Refinement of the application's interface and activities will improve its usability and sensitivity and, ideally, make it useful for clinical care and research.

Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks

Levodopa-induced dyskinesias are abnormal involuntary movements experienced by the majority of persons with Parkinson's disease (PwP) at some point over the course of the disease. Choreiform as the most common phenomenology of levodopa-induced dyskinesias can be managed by adjusting the dose/frequency of PD medication(s) based on a PwP's motor fluctuations over a typical day. We developed a sensor-based assessment system to provide such information. We used movement data collected from the upper and lower extremities of 15 PwPs along with a deep recurrent model to estimate dyskinesia severity as they perform different activities of daily living (ADL). Subjects performed a variety of ADLs during a 4-h period while their dyskinesia severity was rated by the movement disorder experts. The estimated dyskinesia severity scores from our model correlated highly with the expert-rated scores (r = 0.87 (p < 0.001)), which was higher than the performance of linear regression that is commonly used for dyskinesia estimation (r = 0.81 (p < 0.001)). Our model provided consistent performance at different ADLs with minimum r = 0.70 (during walking) to maximum r = 0.84 (drinking) in comparison to linear regression with r = 0.00 (walking) to r = 0.76 (cutting food). These findings suggest that when our model is applied to at-home sensor data, it can provide an accurate picture of changes of dyskinesia severity facilitating effective medication adjustments.

Neurologists' perceptions of utilising tele-neurology to practice remotely during the COVID-19 pandemic

OBJECTIVES

The COVID-19 pandemic enforced an almost complete switch from face-to-face clinical work to tele-neurology. This study explores neurologists' perceptions of telephone and videophone remote consultations.

METHODS

Semi-structured interviews were conducted with neurologists and a GP with a specialist interest (n = 22). Interviews were conducted remotely via Zoom®, audio-recorded, transcribed verbatim and analysed using the principles of thematic analysis.

RESULTS

Four main themes emerged: 'unknown unknowns (risks/uncertainties)', 'better service', 'challenges', and 'beyond the pandemic'. Thematic saturation was achieved by interview 19. Participants highlighted a number of benefits of remote consultations but over 80% also complained of a reduction in work satisfaction.

CONCLUSION

The sudden introduction of tele-neurology is unlikely to be fully reversed when pandemic-related restrictions have been lifted. However, this study confirms tele-neurology cannot completely replace face-to-face consultations. Some patient groups and consultation types require direct contact. Moreover, significant administrative and infrastructural investment will be required to develop the full potential of tele-neurology.

PRACTICE IMPLICATIONS

Tele-medicine is capable of improving access and efficiency of specialist neurology services, but limited by lack of non-verbal communication and technical problems. It could enhance service provision with sufficient infrastructural and administrative investment, but may reduce neurologists' job statisfaction.

Design of a virtual longitudinal observational study in Parkinson's disease (AT-HOME PD)

OBJECTIVE

The expanding power and accessibility of personal technology provide an opportunity to reduce burdens and costs of traditional clinical site-centric therapeutic trials in Parkinson's disease and generate novel insights. The value of this approach has never been more evident than during the current COVID-19 pandemic. We sought to (1) establish and implement the infrastructure for longitudinal, virtual follow-up of clinical trial participants, (2) compare changes in smartphone-based assessments, online patient-reported outcomes, and remote expert assessments, and (3) explore novel digital markers of Parkinson's disease disability and progression.

METHODS

Participants from two recently completed phase III clinical trials of inosine and isradipine enrolled in Assessing Tele-Health Outcomes in Multiyear Extensions of Parkinson's Disease trials (AT-HOME PD), a two-year virtual cohort study. After providing electronic informed consent, individuals complete annual video visits with a movement disorder specialist, smartphone-based assessments of motor function and socialization, and patient-reported outcomes online.

RESULTS

From the two clinical trials, 226 individuals from 42 states in the United States and Canada enrolled. Of these, 181 (80%) have successfully downloaded the study's smartphone application and 161 (71%) have completed patient-reported outcomes on the online platform.

INTERPRETATION

It is feasible to conduct a large-scale, international virtual observational study following the completion of participation in brick-and-mortar clinical trials in Parkinson's disease. This study, which brings research to participants, will compare established clinical endpoints with novel digital biomarkers and thereby inform the longitudinal follow-up of clinical trial participants and design of future clinical trials.

Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks

Resting tremor in Parkinson's disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients' quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients' daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients' daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.

An Interactive Text Message Survey as a Novel Assessment for Bedtime Routines in Public Health Research: Observational Study

BACKGROUND

Traditional research approaches, especially questionnaires and paper-based assessments, limit in-depth understanding of the fluid dynamic processes associated with child well-being and development. This includes bedtime routine activities such as toothbrushing and reading a book before bed. The increase in innovative digital technologies alongside greater use and familiarity among the public creates unique opportunities to use these technical developments in research.

OBJECTIVE

This study aimed to (1) examine the best way of assessing bedtime routines in families and develop an automated, interactive, text message survey assessment delivered directly to participants' mobile phones and (2) test the assessment within a predominately deprived sociodemographic sample to explore retention, uptake, feedback, and effectiveness.

METHODS

A public and patient involvement project showed clear preference for interactive text surveys regarding bedtime routines. The developed interactive text survey included questions on bedtime routine activities and was delivered for seven consecutive nights to participating parents' mobile phones. A total of 200 parents participated. Apart from the completion of the text survey, feedback was provided by participants, and data on response, completion, and retention rates were captured.

RESULTS

There was a high retention rate (185/200, 92.5%), and the response rate was high (160/185, 86.5%). In total, 114 participants provided anonymized feedback. Only a small percentage (5/114, 4.4%) of participants reported problems associated with completing the assessment. The majority (99/114, 86.8%) of participants enjoyed their participation in the study, with an average satisfaction score of 4.6 out of 5.

CONCLUSIONS

This study demonstrated the potential of deploying SMS text message-based surveys to capture and quantify real-time information on recurrent dynamic processes in public health research. Changes and adaptations based on recommendations are crucial next steps in further exploring the diagnostic and potential intervention properties of text survey and text messaging approaches.

Feasibility, Reliability, and Value of Remote Video-Based Trial Visits in Parkinson's Disease

BACKGROUND

There is rising interest in remote clinical trial assessments, particularly in the setting of the COVID-19 pandemic.

OBJECTIVE

To demonstrate the feasibility, reliability, and value of remote visits in a phase III clinical trial of individuals with Parkinson's disease.

METHODS

We invited individuals with Parkinson's disease enrolled in a phase III clinical trial (STEADY-PD III) to enroll in a sub-study of remote video-based visits. Participants completed three remote visits over one year within four weeks of an in-person visit and completed assessments performed during the remote visit. We evaluated the ability to complete scheduled assessments remotely; agreement between remote and in-person outcome measures; and opinions of remote visits.

RESULTS

We enrolled 40 participants (mean (SD) age 64.3 (10.4), 29% women), and 38 (95%) completed all remote visits. There was excellent correlation (ICC 0.81-0.87) between remote and in-person patient-reported outcomes, and moderate correlation (ICC 0.43-0.51) between remote and in-person motor assessments. On average, remote visits took around one quarter of the time of in-person visits (54 vs 190 minutes). Nearly all participants liked remote visits, and three-quarters said they would be more likely to participate in future trials if some visits could be conducted remotely.

CONCLUSION

Remote visits are feasible and reliable in a phase III clinical trial of individuals with early, untreated Parkinson's disease. These visits are shorter, reduce participant burden, and enable safe conduct of research visits, which is especially important in the COVID-19 pandemic.

Dyskinesia Severity Estimation in Patients with Parkinson's Disease Using Wearable Sensors and A Deep LSTM Network

Dyskinesias are abnormal involuntary movements that patients with mid-stage and advanced Parkinson's disease (PD) may suffer from. These troublesome motor impairments are reduced by adjusting the dose or frequency of medication levodopa. However, to make a successful adjustment, the treating physician needs information about the severity rating of dyskinesia as patients experience in their natural living environment. In this work, we used movement data collected from the upper and lower extremities of PD patients along with a deep model based on Long Short-Term Memory to estimate the severity of dyskinesia. We trained and validated our model on a dataset of 14 PD subjects with dyskinesia. The subjects performed a variety of daily living activities while their dyskinesia severity was rated by a neurologist. The estimated dyskinesia severity ratings from our developed model highly correlated with the neurologist-rated dyskinesia scores (r=0.86 (p<0.001) and 1.77 MAE (6%)) indicating the potential of the developed the approach in providing the information required for effective medication adjustments for dyskinesia management.

Care, Convenience, Comfort, Confidentiality, and Contagion: The 5 C's that Will Shape the Future of Telemedicine

The COVID-19 pandemic has driven rapid, widespread adoption of telemedicine. The distribution of clinicians, long travel distances, and disability all limit access to care, especially for persons with Parkinson's disease. Telemedicine is not a panacea for all of these challenges but does offer advantages. These advantages can be summarized as the 5 C's: accessible care, increased convenience, enhanced comfort, greater confidentiality to patients and families, and now reduced risk of contagion. Telemedicine also has its limitations, including the inability to perform parts of the physical examination and inequitable access to the Internet and related technologies. Future models will deliver care to patients from a diverse set of specialties. These will include mental health specialists, physiotherapists, neurosurgeons, speech-language therapists, dieticians, social workers, and exercise coaches. Along with these new care models, digital therapeutics, defined as treatments delivered through software programs, are emerging. Telemedicine is now being introduced as a bridge to restart clinical trials and will increasingly become a normal part of future research studies. From this pandemic will be a wealth of new telemedicine approaches which will fundamentally change and improve care as well as research for individuals with Parkinson's disease.

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

Background:

The rise of direct-to-consumer genetic testing has enabled many to learn of their possible increased risk for rare diseases, some of which may be suitable for gene-targeted therapies. However, recruiting a large and representative population for rare diseases or genetically defined sub-populations of common diseases is slow, difficult, and expensive.

Objective:

To assess the feasibility of recruiting and retaining a cohort of individuals who carry a genetic mutation linked to Parkinson’s disease (G2019S variant of LRRK2); to characterize this cohort relative to the characteristics of traditional, in-person studies; and to evaluate this model’s ability to create an engaged study cohort interested in future clinical trials of gene-directed therapies.

Methods:

This single-site,3-year national longitudinal observational study will recruit between 250 to 350 LRRK2 carriers without Parkinson’s disease and approximately 50 with the condition. Participants must have undergone genetic testing by the personal genetics company, 23andMe, Inc., have knowledge of their carrier status, and consent to be contacted for research studies. All participants undergo standardized assessments, including video-based cognitive and motor examination, and complete patient-reported outcomes on an annual basis.

Results:

263 individuals living in 33 states have enrolled. The cohort has a mean (SD) age of 56.0 (15.9) years, 59% are female, and 76% are of Ashkenazi Jewish descent. 233 have completed the baseline visit: 47 with self-reported Parkinson’s disease and 186 without.

Conclusions:

This study establishes a promising model for developing a geographically dispersed and well-characterized cohort ready for participation in future clinical trials of gene-directed therapies.

Clinical trials in REM sleep behavioural disorder: challenges and opportunities

The rapid eye movement sleep behavioural disorder (RBD) population is an ideal study population for testing disease-modifying treatments for synucleinopathies, since RBD represents an early prodromal stage of synucleinopathy when neuropathology may be more responsive to treatment. While clonazepam and melatonin are most commonly used as symptomatic treatments for RBD, clinical trials of symptomatic treatments are also needed to identify evidence-based treatments. A comprehensive framework for both disease-modifying and symptomatic treatment trials in RBD is described, including potential treatments in the pipeline, cost-effective participant recruitment and selection, study design, outcomes and dissemination of results. For disease-modifying treatment clinical trials, the recommended primary outcome is phenoconversion to an overt synucleinopathy, and stratification features should be used to select a study population at high risk of phenoconversion, to enable more rapid clinical trials. For symptomatic treatment clinical trials, objective polysomnogram-based measurement of RBD-related movements and vocalisations should be the primary outcome measure, rather than subjective scales or diaries. Mobile technology to enable objective measurement of RBD episodes in the ambulatory setting, and advances in imaging, biofluid, tissue, and neurophysiological biomarkers of synucleinopathies, will enable more efficient clinical trials but are still in development. Increasing awareness of RBD among the general public and medical community coupled with timely diagnosis of these diseases will facilitate progress in the development of therapeutics for RBD and associated neurodegenerative disorders.

Short Keynote Paper: Mainstreaming Personalized Healthcare-Transforming Healthcare Through New Era of Artificial Intelligence

Medicine has entered the digital era, driven by data from new modalities, especially genomics and imaging, as well as new sources such as wearables and Internet of Things. As we gain a deeper understanding of the disease biology and how diseases affect an individual, we are developing targeted therapies to personalize treatments. There is a need for technologies like Artificial Intelligence (AI) to be able to support predictions for personalized treatments. In order to mainstream AI in healthcare we will need to address issues such as explainability, liability and privacy. Developing explainable algorithms and including AI training in medical education are many of the solutions that can help alleviate these concerns.

A methodological framework for drug development in rare diseases. The CRESim program: Epilogue and perspectives

Based on the 'European Child-Rare-Euro-Simulation' (CRESim) project, this article proposes a generalizable strategy utilizing datasets analysis in combination with modeling and simulation, in order to optimize the clinical drug development applied in the field of rare diseases. The global process includes: (i) the simulation of a realistic virtual population of patients (modeled from a real dataset of patients), (ii) the modeling of disease pathophysiological components and of pharmacokinetic-pharmacodynamic relations of the drug(s) of interest, (iii) the modeling of several randomized controlled clinical trials (RCTs) designs and (iv) the analysis of the results (multi-dimensional approach for RCTs durations and precision of the estimation of the treatment effect). However, whereas modeling and numerical simulation may provide supplementary tools for drug development, they cannot be considered as a substitute for RCTs performed in 'real' patients.

Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device

Objective assessment of Parkinson's disease symptoms during daily life can help improve disease management and accelerate the development of new therapies. However, many current approaches require the use of multiple devices, or performance of prescribed motor activities, which makes them ill-suited for free-living conditions. Furthermore, there is a lack of open methods that have demonstrated both criterion and discriminative validity for continuous objective assessment of motor symptoms in this population. Hence, there is a need for systems that can reduce patient burden by using a minimal sensor setup while continuously capturing clinically meaningful measures of motor symptom severity under free-living conditions. We propose a method that sequentially processes epochs of raw sensor data from a single wrist-worn accelerometer by using heuristic and machine learning models in a hierarchical framework to provide continuous monitoring of tremor and bradykinesia. Results show that sensor derived continuous measures of resting tremor and bradykinesia achieve good to strong agreement with clinical assessment of symptom severity and are able to discriminate between treatment-related changes in motor states.

New directions in clinical trials for frontotemporal lobar degeneration: Methods and outcome measures

INTRODUCTION

Frontotemporal lobar degeneration (FTLD) is the most common form of dementia for those under 60 years of age. Increasing numbers of therapeutics targeting FTLD syndromes are being developed.

METHODS

In March 2018, the Association for Frontotemporal Degeneration convened the Frontotemporal Degeneration Study Group meeting in Washington, DC, to discuss advances in the clinical science of FTLD.

RESULTS

Challenges exist for conducting clinical trials in FTLD. Two of the greatest challenges are (1) the heterogeneity of FTLD syndromes leading to difficulties in efficiently measuring treatment effects and (2) the rarity of FTLD disorders leading to recruitment challenges.

DISCUSSION

New personalized endpoints that are clinically meaningful to individuals and their families should be developed. Personalized approaches to analyzing MRI data, development of new fluid biomarkers and wearable technologies will help to improve the power to detect treatment effects in FTLD clinical trials and enable new, clinical trial designs, possibly leveraged from the experience of oncology trials. A computational visualization and analysis platform that can support novel analyses of combined clinical, genetic, imaging, biomarker data with other novel modalities will be critical to the success of these endeavors.

Advancing the Use of Mobile Technologies in Clinical Trials: Recommendations from the Clinical Trials Transformation Initiative

Mobile technologies offer the potential to reduce the costs of conducting clinical trials by collecting high-quality information on health outcomes in real-world settings that are relevant to patients and clinicians. However, widespread use of mobile technologies in clinical trials has been impeded by their perceived challenges. To advance solutions to these challenges, the Clinical Trials Transformation Initiative (CTTI) has issued best practices and realistic approaches that clinical trial sponsors can now use. These include CTTI recommendations on technology selection; data collection, analysis, and interpretation; data management; protocol design and execution; and US Food and Drug Administration submission and inspection. The scientific principles underpinning the clinical trials enterprise continue to apply to studies using mobile technologies. These recommendations provide a framework for including mobile technologies in clinical trials that can lead to more efficient assessment of new therapies for patients.

Medicines discovery for auditory disorders: Challenges for industry

Currently, no approved medicines are available for the prevention or treatment of hearing loss. Pharmaceutical industry productivity across all therapeutic indications has historically been disappointing, with a 90% chance of failure in delivering a marketed drug after entering clinical evaluation. To address these failings, initiatives have been applied in the three cornerstones of medicine discovery: target selection, clinical candidate selection, and clinical studies. These changes aimed to enable data-informed decisions on the translation of preclinical observations into a safe, clinically effective medicine by ensuring the best biological target is selected, the most appropriate chemical entity is advanced, and that the clinical studies enroll the correct patients. The specific underlying pathologies need to be known to allow appropriate patient selection, so improved diagnostics are required, as are methodologies for measuring in the inner ear target engagement, drug delivery and pharmacokinetics. The different therapeutic strategies of protecting hearing or preventing hearing loss versus restoring hearing are reviewed along with potential treatments for tinnitus. Examples of current investigational drugs are discussed to highlight key challenges in drug discovery and the learnings being applied to improve the probability of success of launching a marketed medicine.

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score

Importance

Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.

Objectives

To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy.

Design, Setting, and Participants

This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months.

Main Outcomes and Measures

Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication.

Results

The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy.

Conclusions and Relevance

Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.

Information and Communication Technologies, a Promising Way to Support Pharmacotherapy for the Behavioral and Psychological Symptoms of Dementia

Health care systems face an expansion in the number of older individuals with a high prevalence of neurodegenerative diseases and related behavioral and psychological symptoms of dementia (BPSDs). Health care providers are expected to develop innovative solutions to manage and follow up patients over time in the community. To date, we are unable to continuously and accurately monitor the nature, frequency, severity, impact, progression, and response to treatment of BPSDs after the initial assessment. Technology could address this need and provide more sensitive, less biased, and more ecologically valid measures. This could provide an opportunity to reevaluate therapeutic strategies more quickly and, in some cases, to treat earlier, when symptoms are still amenable to therapeutic solutions or even prevention. Several studies confirm the relationship between sensor-based data and cognition, mood, and behavior. Most scientific work on mental health and technologies supports digital biomarkers, not so much as diagnostic tools but rather as monitoring tools, an area where unmet needs are significant. In addition to the implications for clinical care, these real-time measurements could lead to the discovery of new early biomarkers in mental health. Many also consider digital biomarkers as a way to better understand disease processes and that they may contribute to more effective pharmaceutical research by (i) targeting the earliest stage, (ii) reducing sample size required, (iii) providing more objective measures of behaviors, (iv) allowing better monitoring of noncompliance, (v) and providing a better understanding of failures. Finally, communication technologies provide us with the opportunity to support and renew our clinical and research practices.

Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements

Tremor is one of the main symptoms of Parkinson's Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does not fully represent the patients' tremor experience in their day-to-day life. Our objective in this paper was to develop algorithms that, combined with wearable sensors, can estimate total Parkinsonian tremor as the patients performed a variety of free body movements. We developed two methods: an ensemble model based on gradient tree boosting and a deep learning model based on long short-term memory (LSTM) networks. The developed methods were assessed on gyroscope sensor data from 24 PD subjects. Our analysis demonstrated that the method based on gradient tree boosting provided a high correlation (r = 0.96 using held-out testing and r = 0.93 using subject-based, leave-one-out cross-validation) between the estimated and clinically assessed tremor subscores in comparison to the LSTM-based method with a moderate correlation (r = 0.84 using held-out testing and r = 0.77 using subject-based, leave-one-out cross-validation). These results indicate that our approach holds great promise in providing a full spectrum of the patients' tremor from continuous monitoring of the subjects' movement in their natural environment.

Video research visits for atypical parkinsonian syndromes among Fox Trial Finder participants

Background

Use of video research visits in neurologic conditions is rising, but their utility has not been assessed in atypical parkinsonian syndromes. We sought to evaluate the diagnostic concordance between video-based vs self-reported diagnoses of multiple system atrophy, progressive supranuclear palsy, dementia with Lewy bodies, and corticobasal syndrome. We also assessed patient satisfaction with video-based visits.

Methods

We conducted a study of video-based research visits in individuals with an atypical parkinsonian syndrome enrolled in The Michael J. Fox Foundation's Fox Trial Finder. Participants completed a recorded real-time video visit with a remote evaluator who was blinded to the participant's self-reported diagnosis. The investigator conducted a structured interview and performed standard assessments of motor function. Following the visit, the investigator selected the most likely diagnosis. The recorded visit was reviewed by a second blinded investigator who also selected the most likely diagnosis. We evaluated diagnostic concordance between the 2 independent investigators and assessed concordance between investigator consensus diagnosis and self-reported diagnosis using Cohen's kappa. We assessed participant satisfaction with a survey.

Results

We enrolled 45 individuals with atypical parkinsonian syndromes, and 44 completed the investigator-performed video assessment. We demonstrated excellent concordance in diagnosis between the investigators (κ = 0.83) and good reliability of self-reported diagnosis (κ = 0.73). More than 90% of participants were satisfied or very satisfied with the convenience, comfort, and overall visit.

Conclusions

Video research visits are feasible and reliable in those with an atypical parkinsonian syndrome. These visits represent a promising option for reducing burden and extending the reach of clinical research to individuals with these rare and disabling conditions.

Video research visits for atypical parkinsonian syndromes among Fox Trial Finder participants

Background

Use of video research visits in neurologic conditions is rising, but their utility has not been assessed in atypical parkinsonian syndromes. We sought to evaluate the diagnostic concordance between video-based vs self-reported diagnoses of multiple system atrophy, progressive supranuclear palsy, dementia with Lewy bodies, and corticobasal syndrome. We also assessed patient satisfaction with video-based visits.

Methods

We conducted a study of video-based research visits in individuals with an atypical parkinsonian syndrome enrolled in The Michael J. Fox Foundation's Fox Trial Finder. Participants completed a recorded real-time video visit with a remote evaluator who was blinded to the participant's self-reported diagnosis. The investigator conducted a structured interview and performed standard assessments of motor function. Following the visit, the investigator selected the most likely diagnosis. The recorded visit was reviewed by a second blinded investigator who also selected the most likely diagnosis. We evaluated diagnostic concordance between the 2 independent investigators and assessed concordance between investigator consensus diagnosis and self-reported diagnosis using Cohen's kappa. We assessed participant satisfaction with a survey.

Results

We enrolled 45 individuals with atypical parkinsonian syndromes, and 44 completed the investigator-performed video assessment. We demonstrated excellent concordance in diagnosis between the investigators (κ = 0.83) and good reliability of self-reported diagnosis (κ = 0.73). More than 90% of participants were satisfied or very satisfied with the convenience, comfort, and overall visit.

Conclusions

Video research visits are feasible and reliable in those with an atypical parkinsonian syndrome. These visits represent a promising option for reducing burden and extending the reach of clinical research to individuals with these rare and disabling conditions.

Biomechanical System Versus Observational Rating Scale for Parkinson's Disease Tremor Assessment

There is a lack of evidence that either conventional observational rating scale or biomechanical system is a better tremor assessment tool. This work focuses on comparing a biomechanical system and the Movement Disorder Society–sponsored revision of the Unified Parkinson’s Disease Rating Scale in terms of test-retest reliability. The Parkinson’s disease tremors were quantified by biomechanical system in joint angular displacement and predicted rating, as well as assessed by three raters using observational ratings. Qualitative comparisons of the validity and function are made also. The observational rating captures the overall severity of body parts, whereas the biomechanical system provides motion- and joint-specific tremor severity. The tremor readings of the biomechanical system were previously validated against encoders’ readings and doctors’ ratings; the observational ratings were validated with previous ratings on assessing the disease and combined motor symptoms rather than on tremor specifically. Analyses show that the predicted rating is significantly more reliable than the average clinical ratings by three raters. The comparison work removes some of the inconsistent impressions of the tools and serves as guideline for selecting a tool that can improve tremor assessment. Nevertheless, further work is required to consider more variabilities that influence the overall judgement.

Sample enrichment for clinical trials to show delay of onset in huntington disease

BACKGROUND

Disease-modifying clinical trials in persons without symptoms are often limited in methods to assess the impact associated with experimental therapeutics. This study suggests sample enrichment approaches to facilitate preventive trials to delay disease onset in individuals with the dominant gene for Huntington disease.

METHODS

Using published onset prediction indexes, we conducted the receiver operating curve analysis for diagnosis within a 3-year clinical trial time frame. We determined optimal cut points on the indexes for participant recruitment and then conducted sample size and power calculations to detect varying effect sizes for treatment efficacy in reducing 3-year rates of disease onset (or diagnosis).

RESULTS

Area under the curve for 3 onset prediction indexes all demonstrated excellent value in sample enrichment methodology, with the best-performing index being the multivariate risk score (MRS).

CONCLUSIONS

This study showed that conducting an intervention trial in premanifest and prodromal individuals with the gene expansion for Huntington disease is highly feasible using sample enrichment recruitment methods. Ongoing natural history studies are highly likely to indicate additional markers of disease prior to diagnosis. Statistical modeling of identified markers can facilitate participant enrichment to increase the likelihood of detecting a difference between treatment arms in a cost-effective and efficient manner. Such variations may expedite translation of emerging therapies to persons in an earlier phase of the disease.

TRIAL REGISTRATION

PREDICT-HD is registered with www.clinicaltrials.gov, number NCT00051324. © 2019 International Parkinson and Movement Disorder Society.

Wearable Sensors to Monitor, Enable Feedback, and Measure Outcomes of Activity and Practice

PURPOSE OF REVIEW

Measurements obtained during real-world activity by wearable motion sensors may contribute more naturalistic accounts of clinically meaningful changes in impairment, activity, and participation during neurologic rehabilitation, but obstacles persist. Here we review the basics of wearable sensors, the use of existing systems for neurological and rehabilitation applications and their limitations, and strategies for future use.

RECENT FINDINGS

Commercial activity-recognition software and wearable motion sensors for community monitoring primarily calculate steps and sedentary time. Accuracy declines as walking speed slows below 0.8 m/s, less so if worn on the foot or ankle. Upper-extremity sensing is mostly limited to simple inertial activity counts. Research software and activity-recognition algorithms are beginning to provide ground truth about gait cycle variables and reveal purposeful arm actions. Increasingly, clinicians can incorporate inertial and other motion signals to monitor exercise, activities of daily living, and the practice of specific skills, as well as provide tailored feedback to encourage self-management of rehabilitation. Efforts are growing to create a compatible collection of clinically relevant sensor applications that capture the type, quantity, and quality of everyday activity and practice in known contexts. Such data would offer more ecologically sound measurement tools, while enabling clinicians to monitor and support remote physical therapies and behavioral modification when combined with telemedicine outreach.

Therapeutic approaches to Huntington disease: from the bench to the clinic

The 25 years since the identification of the gene responsible for Huntington disease (HD) have stood witness to profound discoveries about the nature of the disease and its pathogenesis. Despite this progress, however, the development of disease-modifying therapies has thus far been slow. Preclinical validation of the therapeutic potential of disrupted pathways in HD has led to the advancement of pharmacological agents, both novel and repurposed, for clinical evaluation. The most promising therapeutic approaches include huntingtin (HTT) lowering and modification as well as modulation of neuroinflammation and synaptic transmission. With clinical trials for many of these approaches imminent or currently ongoing, the coming years are promising not only for HD but also for more prevalent neurodegenerative disorders, such as Alzheimer and Parkinson disease, in which many of these pathways have been similarly implicated.