Modern Phenomenology in Movement Disorders: Introduction to a New Special Issue

The field of movement disorders advanced tremendously between the mid‐1970s and 1980s, thanks to the characterization by Stanley Fahn, David Marsden and many other pioneers of the field, of the different phenomena comprised within the hypo‐ and hyperkinetic subgroups. This led to the foundation of the Movement Disorders Society in 1985, followed a year later by the first issue of the Movement Disorders journal. This represented a true revolution in clinical neuroscience, which fostered huge advances in the exploration of basic mechanisms of these phenomena, their early diagnosis and advances in different therapeutic strategies to control them. A healthy scientific field is one which keeps questioning its principles and postulates by discussing its caveats and offering potential solutions to overcome existing limitations. This type of discussion, pertaining to movement disorders phenomenology, is currently ongoing, ¹ and the active work of international societal task forces in preparing or updating classification systems of different movement disorders (eg, dystonia, ² tremor, ³ myoclonus, ⁴ tics and others) is a testament of this vitality. The relentless improvement and dissemination of digital tools that we are presently witnessing defines what would not be an exaggeration to label a new revolution in our field, which might lead to substantial changes in the way we characterize physiological and pathological motor phenomena, as well as in the way we manage them routinely. The potential advantages brought on by digital measurements go well beyond conceiving these as ancillary tools and are the object of extensive research in the disciplines of biomedical engineering, computational science, clinimetrics and therapeutics, more broadly.

In March 2025, the Asian and Oceanian Parkinson's Disease and Movement Disorders Congress hosted the 4th Movement Disorders Clinical Practice Conference, entitled “Modern Phenomenology in Movement Disorders.” Its focus was to review the existing knowledge of phenomenological assessment of movement disorders, with special attention to what digital technology has offered—and is predicted to offer—to enhance or even redefine our skills and abilities in assessing parkinsonism, tremor, dystonia, chorea, myoclonus, ataxia and other pathological motor phenomena. The outstanding presentations and insightful discussion that took place during the conference planted the seeds for this Special Issue, which aims to summarize the state‐of‐the‐art of digital technology applied to movement disorders and trace future developments.

Oyama's viewpoint ⁵ tackles the complexity of parkinsonism and the need to converge traditional clinical skills with digital and artificial intelligence (AI) tools. The latter should offer the possibility to collect continuous data from both appendicular and axial body segments to cover the entire spectrum of the parkinsonian syndrome. Important considerations are also offered to the need to integrate the new biological definition constructs with digital biomarkers, as well as to the need of refining our ability to monitor treatment response (a highly changing clinical dimension of parkinsonism regardless of the treatment approach) in real time.

Following on this, Merello's viewpoint ⁶ focuses on the existing efforts towards a digital characterization of bradykinesia, a phenomenon that has challenged movement researchers for decades. The viewpoint analyzes the many caveats of the application of digital tools to measure bradykinesia, the challenges brought on by its high phenomenological variability, and the need to resort to Big Data analysis platforms to mitigate potential confounding factors.

As a motor phenomenon characterized by relatively easier direct “measurability,” tremor is likely to benefit significantly from the digital revolution of movement disorders. In their viewpoint, Panyakaew and Carandang ⁷ discuss thoroughly the limitations of current tremor recording techniques and present the potential of a digitization of the existing task‐based assessments of tremor. Machine learning algorithmic approaches have already shown their value in defining new indices and metrics of tremor, which will help advance its digital phenotyping and overcome some of the nosological challenges highlighted by the recent classification system.

In their viewpoint, Saranza and Lin ⁸ discuss advantages and limitations of both the traditional and digital evaluation of chorea across the most common choreic syndromes. Although still only applied to date in research and academic settings, digital tools could offer real‐time monitoring of chorea across different environmental settings but are also still limited by sensor data quality and the need to refine algorithms that could discriminate between voluntary movements and chorea.

Eguchi et al ⁹ put in context advantages and challenges of digital assessments over traditional clinical evaluations of the complex phenomenology construct of ataxia. Compared to other motor phenomena, ataxia is multifaceted and requires different tasks to be assessed comprehensively, which calls for very articulated digital assessment protocols that include sophisticated three‐dimensional motion capture systems and computer vision techniques such as pose‐estimation algorithms. This viewpoint contextualizes these technologies with respect to their potential application, in order to reduce the heterogeneity of assessment protocols in clinical trials and allow collection of long‐term datasets of patients with ataxia.

In their viewpoint on dystonia, Jeyakumar and Kumar ¹⁰ introduce this topic by synthesizing current concepts of classification and genetic testing of this disorder, to subsequently discuss the still heavily under‐explored potential of wearable devices and artificial intelligence/machine learning methodologies. The spectrum of dystonia is also phenomenologically complex, for both motor and non‐motor features, and future, multi‐centric efforts should focus on collection of multimodal kinematic data analyzed by deep learning methods that can generate diagnostic algorithms and pattern clustering. These potentially transformative approaches could also inform a refinement of the pathophysiology of dystonia.

Myoclonus is, by definition, the movement disorder that relies the most on instrumental testing for its diagnosis and monitoring. Hamada's viewpoint ¹¹ offers sharp insight on the problem of misclassification and differential diagnosis, particularly with tremor, which demands a step further in clinical routine's computational set‐up, as recent research has highlighted.

We feel confident that this collation of viewpoints will serve as a reference for clinicians and educators when liaising with patients, families, trainees and younger colleagues, as well as for future studies that aim to accelerate progress throughout this new era of movement disorders neurology.

Disclosures

Ethical Compliance Statement: Institutional review board approval was not necessary for this study. Informed patient consent was not necessary for this work. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines.

Funding Sources and Conflict of Interest: No specific funding was received for this work. The author declares that there are no conflicts of interest relevant to this work.

Financial Disclosures for the previous 12 months: DM has received consultancies fees from Roche, and honoraria as speaker from Abbvie, Merz Pharmaceuticals, the Dystonia Medical Research Foundation Canada, the International Parkinson's Movement Disorders Society, and Canadian Movement Disorders Society. He receives honoraria as scientific advisor for the Ministry of University and Research of Italy. He receives royalties from Springer‐Verlag and Oxford University Press. He has received research grants from the Weston Family Foundation, the New Frontiers for Research Fund, the Neuroscience, Rehabilitation and Vision Strategic Clinical Network of the Alberta Health Services, the National Institutes of Health (Dystonia Coalition), the Canadian Institutes of Health Research, the Parkinson Foundation, the Azrieli Foundation and the Calgary Parkinson's Research Initiative (CaPRI). He is a site PI for a clinical trial for UCB and for an observational study supported by MRM Health NV and Nimble Science. DM is chair of the Research Committee of the Board of the charity Parkinson's Association of Alberta.