The Importance of Digital Health Literacy in an Evolving Parkinson's Disease Care System.

Digital health technologies are growing at a rapid pace and changing the healthcare landscape. Our current understanding of digital health literacy in Parkinson's disease (PD) is limited. In this review, we discuss the potential challenges of low digital health literacy in PD with particular attention to telehealth, deep brain stimulation, wearable sensors, and smartphone applications. We also highlight inequities in access to digital health technologies. Future research is needed to better understand digital health literacy among individuals with PD and to develop effective solutions. We must invest resources to evaluate, understand, and enhance digital health literacy for individuals with PD.

Impact of deep brain stimulation on gait in Parkinson disease: A kinematic study.

BACKGROUND: The effect of Deep Brain Stimulation (DBS) on gait in Parkinson's Disease (PD) is poorly understood. Kinematic studies utilizing quantitative gait outcomes such as speed, cadence, and stride length have shown mixed results and were done mostly before and after acute DBS discontinuation. OBJECTIVE: To examine longitudinal changes in kinematic gait outcomes before and after DBS surgery. METHOD: We retrospectively assessed changes in quantitative gait outcomes via motion capture in 22 PD patients before and after subthalamic (STN) or globus pallidus internus (GPi) DBS, in on medication state. Associations between gait outcomes and clinical variables were also assessed. RESULT: Gait speed reduced from 110.7 ± 21.3 cm/s before surgery to 93.6 ± 24.9 after surgery (7.7 ± 2.9 months post-surgery, duration between assessments was 15.0 ± 3.8 months). Cadence, step length, stride length, and single support time reduced, while total support time, and initial double support time increased. Despite this, there was overall improvement in the Movement Disorder Society-Unified Parkinson Disease Rating Scale-Part III score "on medication/on stimulation" score (from 19.8 ± 10.7-13.9 ± 8.6). Change of gait speed was not related to changes in levodopa dosage, disease duration, unilateral vs bilateral stimulation, or target nucleus. CONCLUSION: Quantitative gait outcomes in on medication state worsened after chronic DBS therapy despite improvement in other clinical outcomes. Whether these changes reflect the effects of DBS as opposed to ongoing disease progression is unknown.

Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson's Disease from Full-Body Kinematics.

Characterizing motor subtypes of Parkinson's disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of motor signs, which may indicate differences in underlying pathology and potential response to treatment. However, the conventional method for distinguishing PD motor subtypes involves resource-intensive physical examination by a movement disorders specialist. Moreover, the standardized rating scales for PD rely on subjective observation, which requires specialized training and unavoidable inter-rater variability. In this work, we propose a system that uses machine learning models to automatically and objectively identify some PD motor subtypes, specifically Tremor-Dominant (TD) and Postural Instability and Gait Difficulty (PIGD), from 3D kinematic data recorded during walking tasks for patients with PD (MDS-UPDRS-III Score, 34.7 ± 10.5, average disease duration 7.5 ± 4.5 years). This study demonstrates a machine learning model utilizing kinematic data that identifies PD motor subtypes with a 79.6% F1 score (N = 55 patients with parkinsonism). This significantly outperformed a comparison model using classification based on gait features (19.8% F1 score). Variants of our model trained to individual patients achieved a 95.4% F1 score. This analysis revealed that both temporal, spectral, and statistical features from lower body movements are helpful in distinguishing motor subtypes. Automatically assessing PD motor subtypes simply from walking may reduce the time and resources required from specialists, thereby improving patient care for PD treatments. Furthermore, this system can provide objective assessments to track the changes in PD motor subtypes over time to implement and modify appropriate treatment plans for individual patients as needed.

Patients with Cognitive Impairment in Parkinson's Disease Benefit from Deep Brain Stimulation: A Case-Control Study.

Background: Deep brain stimulation (DBS) for Parkinson's disease (PD) is generally contraindicated in persons with dementia but it is frequently performed in people with mild cognitive impairment or normal cognition, and current clinical guidelines are primarily based on these cohorts. Objectives: To determine if moderately cognitive impaired individuals including those with mild dementia could meaningfully benefit from DBS in terms of motor and non-motor outcomes. Methods: In this retrospective case-control study, we identified a cohort of 40 patients with PD who exhibited moderate (two or more standard deviations below normative scores) cognitive impairment (CI) during presurgical workup and compared their 1-year clinical outcomes to a cohort of 40 matched patients with normal cognition (NC). The surgery targeted subthalamus, pallidus or motor thalamus, in a unilateral, bilateral or staged approach. Results: At preoperative baseline, the CI cohort had higher Unified Parkinson's Disease Rating Scale (UPDRS) subscores, but similar levodopa responsiveness compared to the NC cohort. The NC and CI cohorts demonstrated comparable degrees of postoperative improvement in the OFF-medication motor scores, motor fluctuations, and medication reduction. There was no difference in adverse event rates between the two cohorts. Outcomes in the CI cohort did not depend on the target, surgical staging, or impaired cognitive domain. Conclusions: Moderately cognitively impaired patients with PD can experience meaningful motor benefit and medication reduction with DBS.

An Explainable Spatial-Temporal Graphical Convolutional Network to Score Freezing of Gait in Parkinsonian Patients.

Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (i.e., MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. A major innovation of our study is that it is the first study of its kind that uses the largest sample size (>30 h, N = 57) in order to apply explainable, multi-task deep learning models for quantifying FOG over the course of the medication cycle and at varying levels of parkinsonism severity. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N = 57 patients during levodopa challenge tests. The proposed model was able to explain how kinematic movements are associated with each FOG severity level that were highly consistent with the features, in which movement disorders specialists are trained to identify as characteristics of freezing. Overall, we demonstrate that deep learning models' capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.

An explainable spatial-temporal graphical convolutional network to score freezing of gait in parkinsonian patients.

Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (ie. MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N=57 patients during levodopa challenge tests. The proposed model was able to identify kinematic features associated with each FOG severity level that were highly consistent with the features that movement disorders specialists are trained to identify as characteristic of freezing. In this work, we demonstrate that deep learning models' capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.

Crossing the Virtual Chasm: Practical Considerations for Rethinking Curriculum, Competency, and Culture in the Virtual Care Era.

Virtual care, introduced previously as a replacement for in-person visits, is now being integrated into clinical care delivery models to complement in-person visits. The COVID-19 pandemic sped up this process. The rapid uptake of virtual care at the start of the pandemic prevented educators from taking deliberate steps to design the foundational elements of the related learning environment, including workflow, competencies, and assessment methods. Educators must now pursue an informed and purposeful approach to design a curriculum and implement virtual care in the learning environment. Engaging learners in virtual care offers opportunities for novel ways to teach and assess their performance and to effectively integrate technology such that it is accessible and equitable. It also offers opportunities for learners to demonstrate professionalism in a virtual environment, to obtain a patient's history incorporating interpersonal and communication skills, to interact with multiple parties during a patient encounter (patient, caregiver, translator, telepresenter, faculty member), to enhance physical examination techniques via videoconferencing, and ideally to optimize demonstrations of empathy through "webside manner." Feedback and assessment, important features of training in any setting, must be timely, specific, and actionable in the new virtual care environment. Recognizing the importance of integrating virtual care into education, leaders from across the United States convened on September 10, 2020, for a symposium titled, "Crossing the Virtual Chasm: Rethinking Curriculum, Competency, and Culture in the Virtual Care Era." In this article, the authors share recommendations that came out of this symposium for the implementation of educational tools in the evolving virtual care environment. They present core competencies, assessment tools, precepting workflows, and technology to optimize the delivery of high-quality virtual care that is safe, timely, effective, efficient, equitable, and patient-centered.

Necessity and feasibility of remote tele-programming of deep brain stimulation systems in Parkinson's disease.

INTRODUCTION: Outcomes after deep brain stimulation (DBS) therapy are dependent on good surgical placement in the target nucleus and optimized stimulation parameters through multiple programming sessions. This often requires frequent travel to a specialized DBS center, which presents a challenge for those with limited access. Recently, the FDA approved a remote tele-programming solution for DBS. To determine if remote tele-programming of DBS systems is beneficial and useful for Parkinson's Disease (PD) patients, Parkinson's Foundation hosted a survey in collaboration with Abbott Labs. METHODS: The survey was conducted to assess the need for telemedicine among PD patients with DBS and the usability of the telehealth interface for DBS teleprogramming. The survey included two validated instruments: The Effective Accessibility and Accommodation survey (EAA) and the Telehealth Usability Questionnaire (TUQ). RESULTS: 47 patients completed the EAA and 41 completed the TUQ. Results from the EAA revealed more than a third of PD patients cannot easily get to a clinic for various reasons, and more than a quarter reported difficulty contacting their clinic for advice. Results from the TUQ revealed overall satisfaction with the DBS remote programming telehealth interface and care provided. The majority of respondents reported that remote tele-programming visits are similar in quality to in-person visits. CONCLUSION: This study provides support for the use of telehealth and tele-programming for DBS management in PD patients. The ability to use remote technologies for care will increase access to DBS and mitigate the disparities that currently prevent access to care.

Temporal profile of improvement of tardive dystonia after globus pallidus deep brain stimulation.

BACKGROUND: Several case reports and small series have indicated that tardive dystonia is responsive to globus pallidus deep brain stimulation. Whether different subtypes or distributions of tardive dystonia are associated with different outcomes remains unknown. METHODS: We assessed the outcomes and temporal profile of improvement of eight tardive dystonia patients who underwent globus pallidus deep brain stimulation over the past six years through record review. Due to the retrospective nature of this study, it was not blinded or placebo controlled. RESULTS: Consistent with previous studies, deep brain stimulation improved the overall the Burke-Fahn-Marsden motor scores by 85.1 ± 13.5%. The distributions with best responses in descending order were upper face, lower face, larynx/pharynx, limbs, trunk, and neck. Patients with prominent cervical dystonia demonstrated improvement in the Toronto Western Spasmodic Torticollis Rating Scale but improvements took several months. In four patients the effects of deep brain stimulation on improvement in Burke Fahn Marsden score was rapid, while in four cases there was partial rapid response of neck and trunk dystonia followed by was gradual resolution of residual symptoms over 48 months. CONCLUSION: Our retrospective analysis shows excellent resolution of tardive dystonia after globus pallidus deep brain stimulation. We found instantaneous response, except with neck and trunk dystonia where partial recovery was followed by further resolution at slower rate. Such outcome is encouraging for using deep brain stimulation in treatment of tardive dystonia.

Lingual protrusion dystonia: frequency, etiology and botulinum toxin therapy.

The purpose of this study was to examine lingual protrusion dystonia (LPD); its frequency, etiology and response to botulinum toxin therapy. Previous literature suggests that LPD is more frequently the result of heredodegenerative disease and that the use of botulinum toxin therapy in LPD is associated with significant adverse effects. This is a retrospective database and record review from a movement disorder clinic. Of 421 dystonia patients, we identified 17 with LPD (4%). Of these cases, the diagnoses were: primary cranial dystonia (5), primary generalized dystonia (2), tardive dystonia (7), heredodegenerative disease (1), multifactorial (1) and post-infectious (1). All primary cases had concomitant oromandibular dystonia. In some secondary cases the LPD was the only cranial feature. Nine received botulinum toxin injections and 55.6% sustained moderate or marked improvement. Of 89 total botulinum toxin sessions, 66.3% had an excellent response, and 92.1% had some response. 97.8% of the sessions resulted in no significant adverse effects. On one occasion one patient developed severe dysphagia requiring placement of a percutaneous gastrostomy (PEG) tube. We conclude that LPD is rare, most commonly the result of tardive and primary dystonia. Botulinum toxin therapy may be very effective but needs to be utilized with care because of the possibility for the development of dysphagia.

Excessive daytime sleepiness in cervical dystonia.

Few investigations have examined sleep in cervical dystonia (CD) patients. We assessed 43 consecutive CD patients, 19 patients with other focal movement disorders, and 49 healthy, age- and gender-matched controls for the presence of excessive daytime sleepiness (as measured by the Epworth Sleepiness Scale, ESS). All patients were receiving botulinum toxin. We found that a higher percentage of CD patients had abnormal ESS scores of >11 than did either of the two control groups (21% vs. 0% vs. 4%, p < 0.05 for each pairwise comparison with the CD group). Use of anticholinergic medications accounted for some but not all of this increase in sleepiness in the CD group. Age, gender, race, scores on the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) or its subscores (severity, disability, and pain), and other common medication use (benzodiazepines, antidepressants) were not associated with increased ESS scores. This preliminary finding of excessive daytime sleepiness in a substantial portion of CD patients suggests that further investigation into disordered sleep in CD is warranted.

Failure of recognition of drug-induced parkinsonism in the elderly.

Our objective was to evaluate the ability of neurologists to recognize and diagnose drug-induced Parkinsonism (DIP) in the elderly. DIP is a diagnostic challenge because it can be indistinguishable from Parkinson's disease, especially in the elderly. It is frequently under-recognized by psychiatrists and primary care physicians. Atypical antipsychotics (AA) are advertised for their low propensity to cause DIP. This may add to problems with recognition. We performed a retrospective record review of consecutive new parkinsonian patients seen over 2 years in a movement disorders clinic to examine the frequency, causative agents, and diagnostic accuracy of DIP by physicians, particularly neurologists. Of 354 Parkinsonian patients evaluated, 24 (6.8%) had DIP, 46% of these were due to AA and 29% were caused by metoclopramide. Of the 24 patients with DIP, only one was previously diagnosed accurately according to records. Nineteen patients (79%) were previously evaluated by a neurologist, and none of them was diagnosed with DIP. The primary reason for failure to recognize DIP relates to under-recognition of AA as possible cause. A majority remained on the inciting agents while dopaminergic drugs were prescribed. DIP was reversible when the inciting drug was stopped. DIP is a common form of parkinsonism and is under-recognized, even by neurologists. AA and metoclopramide do not appear to be well-known to cause DIP. Cessation of the offending agent results in improvement of symptoms and would eliminate the need for dopaminergic agents, which are known to commonly cause side effects in the elderly.

Current and future treatments in multiple system atrophy.

Multiple system atrophy (MSA) is an atypical parkinsonian disorder characterized clinically by the development of parkinsonism, autonomic dysfunction, corticospinal degeneration, and cerebellar abnormalities. The cause of MSA is unknown, and progression is usually faster than idiopathic Parkinson's disease. No treatment is currently available to prevent or slow the progression of this neurodegenerative disorder. Available treatments are primarily symptomatic. This article reviews the current treatment options available for MSA in addition to emerging therapies in neuroprotection and neurotransplantation. Research into the neuropathology of MSA is being conducted and could lead to new more effective treatments within the next decade. The management for this disorder currently includes symptomatic and palliative strategies, as well as family education and support; the ultimate goal is to improve the quality of life for patients and their caregivers.

Progressive supranuclear palsy.

Since progressive supranuclear palsy (PSP) was first reported as a separate clinicopathological entity in 1964, hundreds of other cases have been recorded, and PSP is now one of the most common atypical Parkinson-plus disorders. Diagnostic criteria have been developed by the National Institute of Neurological Disorders and Stroke and the Society for PSP, Inc. Because there is no biological marker for PSP, definitive diagnosis depends on neuropathological examination. Characteristics of PSP include gait disturbances, supranuclear ophthalmoplegia, axial limb rigidity, and frontal lobe dysfunction. Although there are no treatments that alter the natural history of disease in PSP and no drugs that provide significant symptomatic benefits, several supportive measures are available.