Technology-based assessment of motor and nonmotor phenomena in Parkinson disease

Wearable Sensors and Motion Analysis for Neurological Patient Support

This work discusses the state of the art and challenges in using wearable sensors for the monitoring of neurological patients. The authors share their experience from their participation in numerous projects, ranging from drug trials to rehabilitation intervention assessment, and identify the obstacles in the way of the integrated adoption of wearable sensors in clinical and rehabilitation practices for neurological patients. Several highly promising developments are outlined and analyzed. It is considered that intelligent textiles are an attractive option, as they offer an esthetic outlook to and positive interaction with their users.

Identifying Individuals in the Prodromal Phase of Parkinson's Disease: A Prospective Cohort Study

OBJECTIVE

We prospectively evaluated how well combinations of signs and symptoms can identify individuals in the prodromal phase of Parkinson's disease (PD).

METHODS

The study comprised 6,108 men who underwent repeated assessments of key prodromal features and were prospectively followed for the development of PD. Two composite measures of prodromal PD were evaluated: (i) the co-occurrence of constipation, probable rapid eye movement (REM) sleep behavior disorder (pRBD), and hyposmia, and (ii) the probability of prodromal PD based on the Movement Disorders Society (MDS) research criteria. We also examined the progression and heterogeneity of the prodromal PD phase.

RESULTS

One hundred three individuals were newly diagnosed with PD over an average follow-up of 3.4 years. Men with constipation, pRBD, and hyposmia had a 23-fold higher risk of receiving a PD diagnosis in the subsequent 3 years, compared with men without these features (risk ratio [RR] = 23.35, 95% confidence interval [CI] = 10.62-51.33). The risk of PD was 21-fold higher in men with a probability of prodromal PD ≥ 0.8 compared with those with a probability < 0.2 (RR = 21.96, 95% CI = 11.17-43.17). Both the co-occurrence of the 3 non-motor features and an MDS-based probability ≥ 0.8 had comparable predictive values, and both were stronger predictors of PD than any of the features individually. We identified 2 prodromal PD subtypes where RBD and visual color impairment were key discriminators.

INTERPRETATION

Our study demonstrates that combinations of key signs and symptoms strongly predict future clinically manifest PD. These measures may be integrated into screening strategies to identify individuals who could be targeted for enrollment into PD prevention trials. ANN NEUROL 2024.

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.

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 ).

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.

May Bradykinesia Features Aid in Distinguishing Parkinson's Disease, Essential Tremor, And Healthy Elderly Individuals?

BACKGROUND

Bradykinesia is the hallmark feature of Parkinson's disease (PD); however, it can manifest in other conditions, including essential tremor (ET), and in healthy elderly individuals.

OBJECTIVE

Here we assessed whether bradykinesia features aid in distinguishing PD, ET, and healthy elderly individuals.

METHODS

We conducted simultaneous video and kinematic recordings of finger tapping in 44 PD patients, 69 ET patients, and 77 healthy elderly individuals. Videos were evaluated blindly by expert neurologists. Kinematic recordings were blindly analyzed. We calculated the inter-raters agreement and compared data among groups. Density plots assessed the overlapping in the distribution of kinematic data. Regression analyses and receiver operating characteristic curves determined how the kinematics influenced the likelihood of belonging to a clinical score category and diagnostic group.

RESULTS

The inter-rater agreement was fair (Fleiss K = 0.32). Rater found the highest clinical scores in PD, and higher scores in ET than healthy elderly individuals (p < 0.001). In regard to kinematic analysis, the groups showed variations in movement velocity, with PD presenting the slowest values and ET displaying less velocity than healthy elderly individuals (all ps < 0.001). Additionally, PD patients showed irregular rhythm and sequence effect. However, kinematic data significantly overlapped. Regression analyses showed that kinematic analysis had high specificity in differentiating between PD and healthy elderly individuals. Nonetheless, accuracy decreased when evaluating subjects with intermediate kinematic values, i.e., ET patients.

CONCLUSION

Despite a considerable degree of overlap, bradykinesia features vary to some extent in PD, ET, and healthy elderly individuals. Our findings have implications for defining bradykinesia and categorizing patients.

Wrist Rigidity Evaluation in Parkinson's Disease: A Scoping Review

(1) Background: One of the main cardinal signs of Parkinson's disease (PD) is rigidity, whose assessment is important for monitoring the patient's recovery. The wrist is one of the joints most affected by this symptom, which has a great impact on activities of daily living and consequently on quality of life. The assessment of rigidity is traditionally made by clinical scales, which have limitations due to their subjectivity and low intra- and inter-examiner reliability. (2) Objectives: To compile the main methods used to assess wrist rigidity in PD and to study their validity and reliability, a scope review was conducted. (3) Methods: PubMed, IEEE/IET Electronic Library, Web of Science, Scopus, Cochrane, Bireme, Google Scholar and Science Direct databases were used. (4) Results: Twenty-eight studies were included. The studies presented several methods for quantitative assessment of rigidity using instruments such as force and inertial sensors. (5) Conclusions: Such methods present good correlation with clinical scales and are useful for detecting and monitoring rigidity. However, the development of a standard quantitative method for assessing rigidity in clinical practice remains a challenge.

Ethical and Legal Aspects of Technology-Assisted Care in Neurodegenerative Disease

Technological solutions are increasingly seen as a way to respond to the demands of managing complex chronic conditions, especially neurodegenerative diseases such as Parkinson’s Disease. All of these new possibilities provide a variety of chances to improve the lives of affected persons and their families, friends, and caregivers. However, there are also a number of challenges that should be considered in order to safeguard the interests of affected persons. In this article, we discuss the ethical and legal considerations associated with the use of technology-assisted care in the context of neurodegenerative conditions.

Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint

The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.

Clinical outcome measures in dementia with Lewy bodies trials: critique and recommendations

The selection of appropriate outcome measures is fundamental to the design of any successful clinical trial. Although dementia with Lewy bodies (DLB) is one of the most common neurodegenerative conditions, assessment of therapeutic benefit in clinical trials often relies on tools developed for other conditions, such as Alzheimer's or Parkinson's disease. These may not be sufficiently valid or sensitive to treatment changes in DLB, decreasing their utility. In this review, we discuss the limitations and strengths of selected available tools used to measure DLB-associated outcomes in clinical trials and highlight the potential roles for more specific objective measures. We emphasize that the existing outcome measures require validation in the DLB population and that DLB-specific outcomes need to be developed. Finally, we highlight how the selection of outcome measures may vary between symptomatic and disease-modifying therapy trials.

Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson's Disease

Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson's disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient's sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole-Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.

Video-Based Automated Assessment of Movement Parameters Consistent with MDS-UPDRS III in Parkinson's Disease

BACKGROUND

Among motor symptoms of Parkinson's disease (PD), including rigidity and resting tremor, bradykinesia is a mandatory feature to define the parkinsonian syndrome. MDS-UPDRS III is the worldwide reference scale to evaluate the parkinsonian motor impairment, especially bradykinesia. However, MDS-UPDRS III is an agent-based score making reproducible measurements and follow-up challenging.

OBJECTIVE

Using a deep learning approach, we developed a tool to compute an objective score of bradykinesia based on the guidelines of the gold-standard MDS-UPDRS III.

METHODS

We adapted and applied two deep learning algorithms to detect a two-dimensional (2D) skeleton of the hand composed of 21 predefined points, and transposed it into a three-dimensional (3D) skeleton for a large database of videos of parkinsonian patients performing MDS-UPDRS III protocols acquired in the Movement Disorder unit of Avicenne University Hospital.

RESULTS

We developed a 2D and 3D automated analysis tool to study the evolution of several key parameters during the protocol repetitions of the MDS-UPDRS III. Scores from 2D automated analysis showed a significant correlation with gold-standard ratings of MDS-UPDRS III, measured with coefficients of determination for the tapping (0.609) and hand movements (0.701) protocols using decision tree algorithms. The individual correlations of the different parameters measured with MDS-UPDRS III scores carry meaningful information and are consistent with MDS-UPDRS III guidelines.

CONCLUSION

We developed a deep learning-based tool to precisely analyze movement parameters allowing to reliably score bradykinesia for parkinsonian patients in a MDS-UPDRS manner.

Real-World Evidence for a Smartwatch-Based Parkinson's Motor Assessment App for Patients Undergoing Therapy Changes

Introduction

Parkinson's disease (PD) is poorly quantified by patients outside the clinic, and paper diaries have problems with subjective descriptions and bias. Wearable sensor platforms; however, can accurately quantify symptoms such as tremor, dyskinesia, and bradykinesia. Commercially available smartwatches are equipped with accelerometers and gyroscopes that can measure motion for objective evaluation. We sought to evaluate the clinical utility of a prescription smartwatch-based monitoring system for PD utilizing periodic task-based motor assessment.

Methods

Sixteen patients with PD used a smartphone- and smartwatch-based monitoring system to objectively assess motor symptoms for 1 week prior to instituting a doctor recommended change in therapy and for 4 weeks after the change. After 5 weeks the participants returned to the clinic to discuss their results with their doctor, who made therapy recommendations based on the reports and his clinical judgment. Symptom scores were synchronized with the medication diary and the temporal effects of therapy on weekly and hourly timescales were calculated.

Results

Thirteen participants successfully completed the study and averaged 4.9 assessments per day for 3 days per week during the study. The doctor instructed 8 participants to continue their new regimens and 5 to revert to their previous regimens. The smartwatch-based assessments successfully captured intraday fluctuations and short- and long-term responses to therapies, including detecting significant improvements (p < 0.05) in at least one symptom in 7 participants.

Conclusions

The smartwatch-based app successfully captured temporal trends in symptom scores following application of new therapy on hourly, daily, and weekly timescales. These results suggest that validated smartwatch-based PD monitoring can provide clinically relevant information and may reduce the need for traditional office visits for therapy adjustment.

Prodromal Parkinson disease subtypes - key to understanding heterogeneity

In Parkinson disease (PD), pathological processes and neurodegeneration begin long before the cardinal motor symptoms develop and enable clinical diagnosis. In this prodromal phase, risk and prodromal markers can be used to identify individuals who are likely to develop PD, as in the recently updated International Parkinson and Movement Disorders Society research criteria for prodromal PD. However, increasing evidence suggests that clinical and prodromal PD are heterogeneous, and can be classified into subtypes with different clinical manifestations, pathomechanisms and patterns of spatial and temporal progression in the CNS and PNS. Genetic, pathological and imaging markers, as well as motor and non-motor symptoms, might define prodromal subtypes of PD. Moreover, concomitant pathology or other factors, including amyloid-β and tau pathology, age and environmental factors, can cause variability in prodromal PD. Patients with REM sleep behaviour disorder (RBD) exhibit distinct patterns of α-synuclein pathology propagation and might indicate a body-first subtype rather than a brain-first subtype. Identification of prodromal PD subtypes and a full understanding of variability at this stage of the disease is crucial for early and accurate diagnosis and for targeting of neuroprotective interventions to ensure efficacy.

Wearable Health Technology to Quantify the Functional Impact of Peripheral Neuropathy on Mobility in Parkinson's Disease: A Systematic Review

The occurrence of peripheral neuropathy (PNP) is often observed in Parkinson’s disease (PD) patients with a prevalence up to 55%, leading to more prominent functional deficits. Motor assessment with mobile health technologies allows high sensitivity and accuracy and is widely adopted in PD, but scarcely used for PNP assessments. This review provides a comprehensive overview of the methodologies and the most relevant features to investigate PNP and PD motor deficits with wearables. Because of the lack of studies investigating motor impairments in this specific subset of PNP-PD patients, Pubmed, Scopus, and Web of Science electronic databases were used to summarize the state of the art on PNP motor assessment with wearable technology and compare it with the existing evidence on PD. A total of 24 papers on PNP and 13 on PD were selected for data extraction: The main characteristics were described, highlighting major findings, clinical applications, and the most relevant features. The information from both groups (PNP and PD) was merged for defining future directions for the assessment of PNP-PD patients with wearable technology. We established suggestions on the assessment protocol aiming at accurate patient monitoring, targeting personalized treatments and strategies to prevent falls and to investigate PD and PNP motor characteristics.

Implementation of Mobile Health Technologies in Clinical Trials of Movement Disorders: Underutilized Potential

Mobile health technologies (mHealth) are patient-worn or portable devices aimed at increasing the granularity and relevance of clinical measurements. The implementation of mHealth has the potential to decrease sample size, duration, and cost of clinical trials. We performed a review of the ClinicalTrials.gov database using a standardized approach to identify adoption in and usefulness of mHealth in movement disorders interventional clinical trials. Trial phase, geographical area, availability of data captured, constructs of interest, and outcome priority were collected. Eligible trials underwent quality appraisal using an ad hoc 5-point checklist to assess mHealth feasibility, acceptability, correlation with patient-centered outcome measures, and clinical meaningfulness. A total of 29% (n = 54/184) registered trials were using mHealth, mainly in Parkinson's disease and essential tremor (59.3% and 27.8%). In most cases, mHealth were used in phase 2 trials (83.3%) as secondary outcome measures (59.3%). Only five phase 3 trials, representing 9.3% of the total, used mHealth (1 as primary outcome measure, 3 as secondary, and 1 as tertiary). Only 3.7% (n = 2/54) of all trials used mHealth for measuring both motor and non-motor symptoms, and 23.1% (n = 12/52) used mHealth for unsupervised, ecologic outcomes. Our findings suggest that mHealth remain underutilized and largely relegated to phase 2 trials for secondary or tertiary outcome measures. Efforts toward greater alignment of mHealth with patient-centered outcomes and development of a universal, common-language platform to synchronize data from one or more devices will assist future efforts toward the integration of mHealth into clinical trials.

Evaluation of Wearable Sensor Devices in Parkinson's Disease: A Review of Current Status and Future Prospects

Parkinson's disease (PD) decreases the quality of life of the affected individuals. The incidence of PD is expected to increase given the growing aging population. Motor symptoms associated with PD render the patients unable to self-care and function properly. Given that several drugs have been developed to control motor symptoms, highly sensitive scales for clinical evaluation of drug efficacy are needed. Among such scales, the objective and continuous evaluation of wearable devices is increasingly utilized by clinicians and patients. Several electronic technologies have revolutionized the clinical monitoring of PD development, especially its motor symptoms. Here, we review and discuss the recent advances in the development of wearable devices for bradykinesia, tremor, gait, and myotonia. Our aim is to capture the experiences of patients and clinicians, as well as expand our understanding on the application of wearable technology. In so-doing, we lay the foundation for further research into the use of wearable technology in the management of PD.

The Use of Social Media and Digital Devices Among Italian Neurologists

Background: Digital devices and online social networks are changing clinical practice. In this study, we explored attitudes, awareness, opinions, and experiences of neurologists toward social media and digital devices.

Methods: Each member of the Italian Society of Neurology (SIN) participated in an online survey (January to May 2018) to collect information on their attitude toward digital health.

Results: Four hundred and five neurologists participated in the study. At work, 95% of responders use the personal computer, 87% the smartphone, and 43.5% the tablet. These devices are used to obtain health information (91%), maintain contact with colleagues (71%), provide clinical information (59%), and receive updates (67%). Most participants (56%) use social media to communicate with patients, although 65% are against a friendship with them on social media. Most participants interact with patients on social media outside working hours (65.2%) and think that social media have improved (38.0%) or greatly improved (25.4%) the relationship with patients. Most responders (66.7%) have no wearable devices available in clinical practice.

Conclusion: Italian neurologists have different practices and views regarding the doctor–patient relationship in social media. The availability of digital devices in daily practice is limited. The use of social networks and digital devices will increasingly permeate into everyday life, bringing a new dimension to health care. The danger is that advancement will not go hand in hand with a legal and cultural adaptation, thus creating ambiguity and risks for clinicians and patients. Neurologists will need to be able to face the opportunities and challenges of this new scenario.

A multiple motion sensors index for motor state quantification in Parkinson's disease

AIM

To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks.

METHOD

Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients' videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS.

RESULTS

The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89.

CONCLUSION

Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.

Evolving concepts on bradykinesia

Bradykinesia is one of the cardinal motor symptoms of Parkinson's disease and other parkinsonisms. The various clinical aspects related to bradykinesia and the pathophysiological mechanisms underlying bradykinesia are, however, still unclear. In this article, we review clinical and experimental studies on bradykinesia performed in patients with Parkinson's disease and atypical parkinsonism. We also review studies on animal experiments dealing with pathophysiological aspects of the parkinsonian state. In Parkinson's disease, bradykinesia is characterized by slowness, the reduced amplitude of movement, and sequence effect. These features are also present in atypical parkinsonisms, but the sequence effect is not common. Levodopa therapy improves bradykinesia, but treatment variably affects the bradykinesia features and does not significantly modify the sequence effect. Findings from animal and patients demonstrate the role of the basal ganglia and other interconnected structures, such as the primary motor cortex and cerebellum, as well as the contribution of abnormal sensorimotor processing. Bradykinesia should be interpreted as arising from network dysfunction. A better understanding of bradykinesia pathophysiology will serve as the new starting point for clinical and experimental purposes.

Clinical Rating Scales and Quantitative Assessments of Movement Disorders

This article reviews scales that have been developed for, validated in, and/or frequently used across multiple movement disorders with a focus on assessment of motor and nonmotor symptoms of Parkinson disease. Rating scales used in other disease states include those for essential tremor, dystonia (generalized dystonia, cervical dystonia, and blepharospasm), Tourette syndrome, Huntington disease, tardive dyskinesia, Wilson disease, ataxia, and functional movement disorders. Key features of each scale as well as cited criticisms and limitations of each scale are also discussed. Lastly, the article briefly discusses the emerging role of digital assessment tools (both wearable devices and digital interface applications).

Patient-Reported Outcome Measures in Registry-Based Studies of Type 2 Diabetes Mellitus: a Systematic Review

PURPOSE OF REVIEW

Patient-reported outcome measures (PROMs) and patient registries both play important roles in assessing patient outcomes. However, no study has examined the use of PROMs among registries involving patients with type 2 diabetes mellitus (T2DM). Our objective is twofold: first, to review the range of PROMs used in registry-based studies of patients with T2DM; second, to describe associations between these PROMs, T2DM and its complications.

RECENT FINDINGS

The International Consortium for Health Outcomes Measurement (ICHOM) Diabetes Standard Set recommended routine usage of PROMs to assess psychological well-being, diabetes distress, and depression among patients with T2DM. A wide variety of PROMs were used among the 15 studies included in this review. Quality of life, depressive symptoms and treatment adherence were the most common aspects of T2DM that utilised PROMs for assessment. Adoption of PROMs among registries of patients with T2DM remains uncommon, non-routine and with few that are validated before use.

Integrated robotics platform with haptic control differentiates subjects with Parkinson's disease from controls and quantifies the motor effects of levodopa

Background

The use of integrated robotic technology to quantify the spectrum of motor symptoms of Parkinson’s Disease (PD) has the potential to facilitate objective assessment that is independent of clinical ratings. The purpose of this study is to use the KINARM exoskeleton robot to (1) differentiate subjects with PD from controls and (2) quantify the motor effects of dopamine replacement therapies (DRTs).

Methods

Twenty-six subjects (Hoehn and Yahr mean 2.2; disease duration 0.5 to 15 years) were evaluated OFF (after > 12 h of their last dose) and ON their DRTs with the Unified Parkinson’s Disease Rating Scale (UPDRS) and the KINARM exoskeleton robot. Bilateral upper extremity bradykinesia, rigidity, and postural stability were quantified using a repetitive movement task to hit moving targets, a passive stretch task, and a torque unloading task, respectively. Performance was compared against healthy age-matched controls.

Results

Mean hand speed was 41% slower and 25% fewer targets were hit in subjects with PD OFF medication than in controls. Receiver operating characteristic (ROC) area for hand speed was 0.94. The torque required to stop elbow movement during the passive stretch task was 34% lower in PD subjects versus controls and resulted in an ROC area of 0.91. The torque unloading task showed a maximum displacement that was 29% shorter than controls and had an ROC area of 0.71. Laterality indices for speed and end total torque were correlated to the most affected side. Hand speed laterality index had an ROC area of 0.80 against healthy controls. DRT administration resulted in a significant reduction in a cumulative score of parameter Z-scores (a measure of global performance compared to healthy controls) in subjects with clinically effective levodopa doses. The cumulative score was also correlated to UPDRS scores for the effect of DRT.

Conclusions

Robotic assessment is able to objectively quantify parkinsonian symptoms of bradykinesia, rigidity and postural stability similar to the UPDRS. This integrated testing platform has the potential to aid clinicians in the management of PD and help assess the effects of novel therapies.

A novel single-sensor-based method for the detection of gait-cycle breakdown and freezing of gait in Parkinson's disease

Objective measurement of walking speed and gait deficits are an important clinical tool in chronic illness management. We previously reported in Parkinson's disease that different types of gait tests can now be implemented and administered in the clinic or at home using Ambulosono smartphone-sensor technology, whereby movement sensing protocols can be standardized under voice instruction. However, a common challenge that remains for such wearable sensor systems is how meaningful data can be extracted from seemingly "noisy" raw sensor data, and do so with a high level of accuracy and efficiency. Here, we describe a novel pattern recognition algorithm for the automated detection of gait-cycle breakdown and freezing episodes. Ambulosono-gait-cycle-breakdown-and-freezing-detection (Free-D) integrates a nonlinear m-dimensional phase-space data extraction method with machine learning and Monte Carlo analysis for model building and pattern generalization. We first trained Free-D using a small number of data samples obtained from thirty participants during freezing of gait tests. We then tested the accuracy of Free-D via Monte Carlo cross-validation. We found Free-D to be remarkably effective at detecting gait-cycle breakdown, with mode error rates of 0% and mean error rates < 5%. We also demonstrate the utility of Free-D by applying it to continuous holdout traces not used for either training or testing, and found it was able to identify gait-cycle breakdown and freezing events of varying duration. These results suggest that advanced artificial intelligence and automation tools can be developed to enhance the quality, efficiency, and the expansion of wearable sensor data processing capabilities to meet market and industry demand.

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

Obtaining reliable longitudinal information about everyday functioning from individuals with Parkinson's disease (PD) in natural environments is critical for clinical care and research. Despite advances in mobile health technologies, the implementation of digital outcome measures is hindered by a lack of consensus on the type and scope of measures, the most appropriate approach for data capture (eg, in clinic or at home), and the extraction of timely information that meets the needs of patients, clinicians, caregivers, and health care regulators. The Movement Disorder Society Task Force on Technology proposes the following objectives to facilitate the adoption of mobile health technologies: (1) identification of patient-centered and clinically relevant digital outcomes; (2) selection criteria for device combinations that offer an acceptable benefit-to-burden ratio to patients and that deliver reliable, clinically relevant insights; (3) development of an accessible, scalable, and secure platform for data integration and data analytics; and (4) agreement on a pathway for approval by regulators, adoption into e-health systems and implementation by health care organizations. We have developed a tentative roadmap that addresses these needs by providing the following deliverables: (1) results and interpretation of an online survey to define patient-relevant endpoints, (2) agreement on the selection criteria for use of device combinations, (3) an example of an open-source platform for integrating mobile health technology output, and (4) recommendations for assessing readiness for deployment of promising devices and algorithms suitable for regulatory approval. This concrete implementation guidance, harmonizing the collaborative endeavor among stakeholders, can improve assessments of individuals with PD, tailor symptomatic therapy, and enhance health care outcomes. © 2019 International Parkinson and Movement Disorder Society.

Development and Validation of Ambulosono: A Wearable Sensor for Bio-Feedback Rehabilitation Training

Wearable technology-based measurement systems hold potential for the therapeutic and rehabilitation management of patients with various chronic diseases. The purpose of this study was to assess the accuracy and test–retest reliability of a new-generation wearable sensor-based system, dubbed Ambulosono, for bio-feedback training. The Ambulosono sensor system was cross-validated by comparing its functionality with the iPod touch (4th generation) sensor system. Fifteen participants underwent a gait test to measure various gait parameters while wearing both the iPod-based and Ambulosono sensors simultaneously. The physically measured values (i.e., the true values) of step length, distance traveled, velocity, and cadence were then compared to those obtained via the two-sensor systems using the same calculation algorithms. While the mean percentage error was <10% for all measured parameters, and the intra-class correlation coefficient revealed a high level of agreement between trials for both sensor systems, it was found that the Ambulosono sensor system outperformed the iPod-based system in some respects. The Ambulosono sensor system possessed both reliability and accuracy in obtaining gait parameter measurements, which suggests it can serve as an economical alternative to the iPod-based system that is currently used in various clinical rehabilitation programs.