With life there is motion. Activity biomarkers signal important health and performance outcomes.

ABSTRACT Background:  Telemedicine develops from technology that offers opportunities for knowledge transfer and information sharing and allows the provision of health services at a distance. Objective:  To evaluate the number of publications on teleneurology in the last two decades in PubMed and the available evidence on the use of this technology in neurological clinical conditions. Methods:  A quantitative assessment of publications related to telemedicine and neurology in the last two decades. A search was performed on the PubMed database for the descriptors ("Telemedicine"[Mesh]) AND "Neurology"[Mesh]). A review of the articles retrieved on the topic was carried out to evaluate the innovation processes used and applications in various clinical conditions involving teleneurology. Results:  The search performed on March 14th 2022 resulted in 229 publications involving the topic of telemedicine and neurology between 1999 and 2022. Since 2000, there has been an increase in publications related to this topic, with a peak of 71 articles published in 2020, the year in which the World Health Organization defined the COVID-19 pandemic status. Conclusion:  In the last two decades, teleneurology has been developing through the expansion of technological resources and the COVID-19 pandemic intensified this process. Different modalities of teleneurology are studied in several neurology subfields and include teleconsultation (between healthcare professionals or between healthcare professionals and patients), telerehabilitation, telemonitoring and tele-education. The advances achieved by teleneurology in this period encouraged technological innovations and health processes that developed opportunities to improve the care provided in a mechanism of constant evolution.

https://www.scielo.br/j/anp/a/pnZMQJKPqCNTvLXLBPp9gNq/?format=pdf&lang=en

Telemedicine in neurology: advances and possibilities

Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson’s disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient’s physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.

/pdf/mobile-app-interventions-for-parkinsons-disease-multiple-4taw0ejo.pdf

Mobile App Interventions for Parkinson’s Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review

Digital biomarkers, including wearable devices, smartphones and other digital technologies, have the potential to revolutionize the diagnosis and management of movement disorders such as Parkinson's and Huntington's disease. In addition, digital biomarkers can be used to monitor the effectiveness of treatment in real-time, allowing for more precise and individualized treatment management. This can include adjusting medication dosages, tracking symptoms and side effects, but also identifying patterns of disease progression and, as such digital biomarkers could have the potential to be used as outcomes in prevention trials. This is an overview of current research on the use of digital biomarkers in movement disorders, including the potential benefits and limitations, as well as future directions for research in this field. 

Digital biomarkers in movement disorders

Parkinson’s Disease (PD) is a neurodegenerative disorder with highly heterogeneous phenotypes. Accordingly, it has been challenging to robustly identify genetic factors associated with disease risk, prognosis and therapy response via genome-wide association studies (GWAS). In this review we first provide an overview of existing statistical methods to detect associations between genetic variants and the disease phenotypes in existing PD GWAS. Secondly, we discuss the potential of machine learning approaches to better quantify disease phenotypes and to move beyond disease understanding towards a better-personalized treatment of the disease.

Genetics in parkinson’s disease: From better disease understanding to machine learning based precision medicine

Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.

/pdf/leveraging-the-potential-of-digital-technology-for-better-ebavcm5t.pdf

Noémi Bontridder

Papers

Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease