Moving Forward from the COVID-19 Pandemic: Needed Changes in Movement Disorders Care and Research

An International Multi-Stakeholder Delphi Survey Study on the Design of Disease Modifying Parkinson's Disease Trials

BACKGROUND

Design of disease modification (DM) trials for Parkinson's disease (PD) is challenging. Successful delivery requires a shared understanding of priorities and practicalities.

OBJECTIVE

To seek stakeholder consensus on phase 3 trials' overall goals and structure, inclusion criteria, outcome measures, and trial delivery and understand where perspectives differ.

METHODS

An international expert panel comprising people with Parkinson's (PwP), care partners (CP), clinical scientists, representatives from industry, funders and regulators participated in a survey-based Delphi study. Survey items were informed by a scoping review of DM trials and PwP input. Respondents scored item agreement over 3 rounds. Scores and reasoning were summarized by participant group each round until consensus, defined as≥70% of at least 3 participant groups falling within the same 3-point region of a 9-point Likert scale.

RESULTS

92/121 individuals from 13 countries (46/69 PwP, 13/18 CP, 20/20 clinical scientists, representatives from 8/8 companies, 4/5 funders, and 1/1 regulator) completed the study. Consensus was reached on 14/31 survey items: 5/8 overall goals and structure, 1/8 Eligibility criteria, 7/13 outcome measures, and 1/2 trial delivery items. Extent of stakeholder endorsement for 428 reasons for scores was collated across items.

CONCLUSIONS

This is the first systematic multi-stakeholder consultation generating a unique repository of perspectives on pivotal aspects of DM trial design including those of PwP and CP. The panel endorsed outcomes that holistically measure PD and the importance of inclusive trials with hybrid delivery models. Areas of disagreement will inform mitigating strategies of researchers to ensure successful delivery of future trials.

Telemedicine in the Management of Parkinson's Disease: Achievements, Challenges, and Future Perspectives

BACKGROUND

As the global population grows, there is an increasing demand for neurologic consultation that prompts new ways to reach more patients. Telemedicine can provide an accessible, cost-effective, and high-quality healthcare services.

OBJECTIVES

In this article, we highlight recent developments, achievements, and challenges regarding outcomes, clinical care, tele-education, teletreatment, teleresearch, and cybersecurity for telemedicine applied to Parkinson´s disease (PD) and other neurological conditions.

RESULTS

A growing body of evidence supports the feasibility and effectiveness of telemedicine tools for PD and other movement disorders. Outcome variables regarding satisfaction and efficacy in clinical care and specific issues about education, research, and treatment are reviewed. Additionally, a specific legal framework for teleconsultation has been developed in some centers worldwide. Yet, the implementation of telemedicine is conditioned by the limitations inherent to remote neurological examination, the variable computer usage literacy among patients, and the availability of a reliable internet connection. At present, telemedicine can be considered an additional tool in the clinical management of PD patients.

CONCLUSIONS

There is an increasing use of remote clinical practice regarding the management of PD and other neurological conditions. Telemedicine is a new and promising tool aimed at special settings and subpopulations.

Handwriting Declines With Human Aging: A Machine Learning Study

Background

Handwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders.

Materials and Methods

One-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm.

Results

Stroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83).

Discussion

Handwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms.