The Clinical Value of Patient Home Videos in Movement Disorders

The Growing Role of Telerehabilitation and Teleassessment in the Management of Movement Disorders in Rare Neurological Diseases: A Scoping Review

Background: People with rare neurological diseases (RNDs) often experience symptoms related to movement disorders, requiring a multidisciplinary approach, including rehabilitation. Telemedicine applied to rehabilitation and symptom monitoring may be suitable to ensure treatment consistency and personalized intervention. The objective of this scoping review aimed to emphasize the potential role of telerehabilitation and teleassessment in managing movement disorders within RNDs. By providing a systematic overview of the available literature, we sought to highlight potential interventions, outcomes, and critical issues. Methods: A literature search was conducted on PubMed, Google Scholar, IEEE, and Scopus up to March 2024. Two inclusion criteria were followed: (1) papers focusing on telerehabilitation and teleassessment and (2) papers dealing with movement disorders in RNDs. Results: Eighteen papers fulfilled the inclusion criteria. The main interventions were home-based software and training programs, exergames, wearable sensors, smartphone applications, virtual reality and digital music players for telerehabilitation; wearable sensors, mobile applications, and patient home video for teleassessment. Key findings revealed positive outcomes in gait, balance, limb disability, and in remote monitoring. Limitations include small sample sizes, short intervention durations, and the lack of standardized protocols. Conclusion: This review highlighted the potential of telerehabilitation and teleassessment in addressing movement disorders within RNDs. Data indicate that these modalities may play a major role in supporting conventional programs. Addressing limitations through multicenter studies, longer-term follow-ups, and standardized protocols is essential. These measures are essential for improving remote rehabilitation and assessment, contributing to an improved quality of life for people with RNDs.

Neurological Examination via Telemedicine: An Updated Review Focusing on Movement Disorders

Patients with movement disorders such as Parkinson's disease (PD) living in remote and underserved areas often have limited access to specialized healthcare, while the feasibility and reliability of the video-based examination remains unclear. The aim of this narrative review is to examine which parts of remote neurological assessment are feasible and reliable in movement disorders. Clinical studies have demonstrated that most parts of the video-based neurological examination are feasible, even in the absence of a third party, including stance and gait-if an assistive device is not required-bradykinesia, tremor, dystonia, some ocular mobility parts, coordination, and gross muscle power and sensation assessment. Technical issues (video quality, internet connection, camera placement) might affect bradykinesia and tremor evaluation, especially in mild cases, possibly due to their rhythmic nature. Rigidity, postural instability and deep tendon reflexes cannot be remotely performed unless a trained healthcare professional is present. A modified version of incomplete Unified Parkinson's Disease Rating Scale (UPDRS)-III and a related equation lacking rigidity and pull testing items can reliably predict total UPDRS-III. UPDRS-II, -IV, Timed "Up and Go", and non-motor and quality of life scales can be administered remotely, while the remote Movement Disorder Society (MDS)-UPDRS-III requires further investigation. In conclusion, most parts of neurological examination can be performed virtually in PD, except for rigidity and postural instability, while technical issues might affect the assessment of mild bradykinesia and tremor. The combined use of wearable devices may at least partially compensate for these challenges in the future.

The role of stand-alone videos in the diagnosis of seizures

The diagnosis of seizures and seizure mimics relies primarily on the history, but history has well-known limitations. Video recordings of events are a powerful extension of the history because they allow neurologists to view the events in question. In addition, they are readily available in situation, whereas the gold standard of EEG-video is not. That includes underserved or rural areas, and events that are too infrequent to be captured during a few days of EEG-video monitoring. Brief cellphone videos have been shown to be valuable to suggest or guide the correct diagnosis.

Towards automated video-based assessment of dystonia in dyskinetic cerebral palsy: A novel approach using markerless motion tracking and machine learning

Introduction: Video-based clinical rating plays an important role in assessing dystonia and monitoring the effect of treatment in dyskinetic cerebral palsy (CP). However, evaluation by clinicians is time-consuming, and the quality of rating is dependent on experience. The aim of the current study is to provide a proof-of-concept for a machine learning approach to automatically assess scoring of dystonia using 2D stick figures extracted from videos. Model performance was compared to human performance. Methods: A total of 187 video sequences of 34 individuals with dyskinetic CP (8-23 years, all non-ambulatory) were filmed at rest during lying and supported sitting. Videos were scored by three raters according to the Dyskinesia Impairment Scale (DIS) for arm and leg dystonia (normalized scores ranging from 0-1). Coordinates in pixels of the left and right wrist, elbow, shoulder, hip, knee and ankle were extracted using DeepLabCut, an open source toolbox that builds on a pose estimation algorithm. Within a subset, tracking accuracy was assessed for a pretrained human model and for models trained with an increasing number of manually labeled frames. The mean absolute error (MAE) between DeepLabCut's prediction of the position of body points and manual labels was calculated. Subsequently, movement and position features were calculated from extracted body point coordinates. These features were fed into a Random Forest Regressor to train a model to predict the clinical scores. The model performance trained with data from one rater evaluated by MAEs (model-rater) was compared to inter-rater accuracy. Results: A tracking accuracy of 4.5 pixels (approximately 1.5 cm) could be achieved by adding 15-20 manually labeled frames per video. The MAEs for the trained models ranged from 0.21 ± 0.15 for arm dystonia to 0.14 ± 0.10 for leg dystonia (normalized DIS scores). The inter-rater MAEs were 0.21 ± 0.22 and 0.16 ± 0.20, respectively. Conclusion: This proof-of-concept study shows the potential of using stick figures extracted from common videos in a machine learning approach to automatically assess dystonia. Sufficient tracking accuracy can be reached by manually adding labels within 15-20 frames per video. With a relatively small data set, it is possible to train a model that can automatically assess dystonia with a performance comparable to human scoring.

A picture is worth a thousand words: A proposal to incorporate video into the evaluation of adults with intellectual or developmental disability living outside the home

Adults with intellectual or developmental disability (IDD) comprise 1-2% of the population worldwide. IDD is a significant risk factor for premature morbidity or mortality. This is likely due in part to preventable health conditions, which are modifiable with the intervention of direct care providers in areas including nutrition, promotion of an active lifestyle and effective identification of health or functional deterioration. Adults with IDD are also at increased risk for neglect or mistreatment, a finding that has been documented across multiple countries and in a variety of care settings. Contributing factors include resource availability, lack of person-centered care, management culture and care worker training. Practical and economical interventions may address the known disparities and challenges facing the large community of adults with IDD. To promote person-centered care, improve record-keeping/documentation, and aid in protecting the health and safety of this vulnerable population, we propose incorporation of a video into the evaluation of adults with IDD living outside the home.

Parkinson's disease and Covid-19: The effect and use of telemedicine

As a result of the Coronavirus Disease 2019 (Covid-19) pandemic the use of telemedicine and remote assessments for patients has increased exponentially, enabling healthcare professionals to reduce the need for in-person clinical visits and, consequently, reduce the exposure to the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This development has been aided by increased guidance on digital health technologies and cybersecurity measures, as well as reimbursement options within healthcare systems. Having been able to continue to connect with people with Parkinson's Disease (PwP, PD) has been crucial, since many saw their symptoms worsen over the pandemic. Inspite of the success of telemedicine, sometimes even enabling delivery of treatment and research, further validation and a unified framework are necessary to measure the true benefit to both clinical outcomes and health economics. Moreover, the use of telemedicine seems to have been biased towards people from a white background, those with higher education, and reliable internet connections. As such, efforts should be pursued by being inclusive of all PwP, regardless of geographical area and ethnic background. In this chapter, we describe the effect he Covid-19 pandemic has had on the use of telemedicine for care and research in people with PD, the limiting factors for further rollout, and how telemedicine might develop further.