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Digital Health Technologies in Parkinson’s Disease and Related Disorders
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Digital Health Technologies in Parkinson’s Disease and Related Disorders

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Clinical Neurology".

Deadline for manuscript submissions: 25 October 2025 | Viewed by 3914

Special Issue Editors

Dr. Ruth B. Schneider

E-Mail Website
Guest Editor
Department of Neurology, University of Rochester Medical Center, Rochester, NY 14618, USA
Interests: movement disorders; Parkinson’s disease; Huntington’s disease; essential tremor; telehealth; digital health technologies
Dr. Christine Doss Esper

E-Mail Website
Guest Editor
Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
Interests: movement disorders; Parkinson’s disease; essential tremor; deep brain stimulation; 3D kinematics; telehealth

Special Issue Information

Dear colleagues,

Digital health technologies, such as smartphones, wearable sensors, and ambient sensors, are increasingly being studied in Parkinson’s disease. The potential uses of digital health technologies in Parkinson’s disease are broad. Digital health technologies have the potential to aid in the diagnosis of Parkinson’s disease, enable symptom tracking, objectively assess disease and progression, distinguish different phenotypes, generate novel insights into functional ability, detect pre-clinical disease, and improve the prediction of clinical outcomes. Such technologies stand to improve the clinical care of individuals with Parkinson’s disease and provide more sensitive measures of disease progression that may speed up the development of novel therapeutics. However, barriers to the widespread use of digital health technologies in Parkinson’s disease remain, and less is known about the use of digital health technologies in related disorders and for assessing non-motor symptoms.

This Special Issue aims to advance understanding of the use of digital health technologies in Parkinson’s disease and related disorders. We encourage submissions that examine the clinical care and research applications of digital health technologies across all stages of Parkinson’s disease and related disorders.  In this Special Issue, original research articles and reviews are welcome.

We look forward to receiving your contributions.

Dr. Ruth B. Schneider
Dr. Christine Doss Esper
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital health technologies
  • telehealth
  • wearables
  • smartphones
  • Parkinson’s disease
  • atypical parkinsonism

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Published Papers (4 papers)

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16 pages, 1790 KiB  
Article
Validation of the Comprehensive Augmented Reality Testing Platform to Quantify Parkinson’s Disease Fine Motor Performance
by Andrew Bazyk, Ryan D. Kaya, Colin Waltz, Eric Zimmerman, Joshua D. Johnston, Kathryn Scelina, Benjamin L. Walter, Junaid Siddiqui, Anson B. Rosenfeldt, Mandy Miller Koop and Jay L. Alberts
J. Clin. Med. 2025, 14(11), 3966; https://doi.org/10.3390/jcm14113966 - 4 Jun 2025
Viewed by 10
Abstract
Background/Objectives: Technological approaches for the objective, quantitative assessment of motor functions have the potential to improve the medical management of people with Parkinson’s disease (PwPD), offering more precise, data-driven insights to enhance diagnosis, monitoring, and treatment. Markerless motion capture (MMC) is a [...] Read more.
Background/Objectives: Technological approaches for the objective, quantitative assessment of motor functions have the potential to improve the medical management of people with Parkinson’s disease (PwPD), offering more precise, data-driven insights to enhance diagnosis, monitoring, and treatment. Markerless motion capture (MMC) is a promising approach for the integration of biomechanical analysis into clinical practice. The aims of this project were to evaluate a commercially available MMC system, develop and validate a custom MMC data processing algorithm, and evaluate the effectiveness of the algorithm in discriminating fine motor performance between PwPD and healthy controls (HCs). Methods: A total of 58 PwPD and 25 HCs completed finger-tapping assessments, administered and recorded by a self-worn augmented reality headset. Fine motor performance was evaluated using the headset’s built-in hand tracking software (Native-MMC) and a custom algorithm (CART-MMC). Outcomes from each were compared against a gold-standard motion capture system (Traditional-MC) to determine the equivalence. Known-group validity was evaluated using CART-MMC. Results: A total of 82 trials were analyzed for equivalence against the Traditional-MC, and 152 trials were analyzed for known-group validity. The CART-MMC outcomes were statistically equivalent to Traditional-MC (within 5%) for tap count, frequency, amplitude, and opening velocity metrics. The Native-MMC did not meet equivalence with the Traditional-MC, deviating by an average of 24% across all outcomes. The CART-MMC captured significant differences between PwPD and HCs for tapping amplitude, amplitude variability, frequency variability, finger opening and closing velocities, and their respective variabilities, and normalized path length. Conclusions: The biomechanical data gathered using a commercially available augmented reality device and analyzed via a custom algorithm accurately characterize fine motor performance in PwPD. Full article
(This article belongs to the Special Issue Digital Health Technologies in Parkinson’s Disease and Related Disorders)
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16 pages, 2003 KiB  
Article
Feasibility of an App-Assisted and Home-Based Video Version of the Timed Up and Go Test for Patients with Parkinson Disease: vTUG
by Marcus Grobe-Einsler, Anna Gerdes, Tim Feige, Vivian Maas, Clare Matthews, Alejandro Mendoza García, Laia Comas Fages, Elin Haf Davies, Thomas Klockgether and Björn H. Falkenburger
J. Clin. Med. 2025, 14(11), 3769; https://doi.org/10.3390/jcm14113769 - 28 May 2025
Viewed by 61
Abstract
Background: Parkinson Disease (PD) is a progressive neurodegenerative disorder. Current therapeutic trials investigate treatments that can potentially modify the disease course. Testing their efficiency requires outcome assessments that are relevant to patients’ daily lives, which include gait and balance. Home-based examinations may [...] Read more.
Background: Parkinson Disease (PD) is a progressive neurodegenerative disorder. Current therapeutic trials investigate treatments that can potentially modify the disease course. Testing their efficiency requires outcome assessments that are relevant to patients’ daily lives, which include gait and balance. Home-based examinations may enhance patient compliance and, in addition, produce more reliable results by assessing patients more regularly in their familiar surroundings. Objective: The objective of this pilot study was to assess the feasibility of a home-based outcome assessment designed to video record the Timed up and Go (vTUG) test via a study-specific smartphone app for patients with PD. Methods: 28 patients were recruited and asked to perform at home each week a set of three consecutive vTUG tests, over a period of 12 weeks using an app. The videos were subjected to a manual review to ascertain the durations of the individual vTUG phases, as well as to identify any errors or deviations in the setup that might have influenced the result. To evaluate the usability and user-friendliness of the vTUG and app, the System Usability Scale (SUS) and User Experience Questionnaire (UEQ) were administered to patients at the study end. Results: 19 patients completed the 12-week study, 17 of which recorded 10 videos or more. A total of 706 vTUGs with complete timings were recorded. Random Forest Regression yielded “time to walk up” as the most important segment of the vTUG for predicting the total time. Variance of vTUG total time was significantly higher between weeks than it was between the three consecutive vTUGs at one time point [F(254,23) = 6.50, p < 0.001]. The correlation between vTUG total time and UPDRS III total score was weak (r = 0.24). The correlation between vTUG and a derived gait subscore (UPDRS III items 9–13) was moderate (r = 0.59). A linear mixed-effects model revealed a significant effect of patient-reported motion status on vTUG total time. Including additional variables such as UPDRS III gait subscore, footwear and chairs used further improved the model fit. Conclusions: Assessment of gait and balance by home-based vTUG is feasible. Factors influencing the read-out were identified and could be better controlled for future use and longitudinal trials. Full article
(This article belongs to the Special Issue Digital Health Technologies in Parkinson’s Disease and Related Disorders)
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19 pages, 6689 KiB  
Article
In Silico Decoding of Parkinson’s: Speech & Writing Analysis
by Robert Radu Ileșan, Sebastian-Aurelian Ștefănigă, Radu Fleșar, Michel Beyer, Elena Ginghină, Ana Sorina Peștean, Martin C. Hirsch, Lăcrămioara Perju-Dumbravă and Paul Faragó
J. Clin. Med. 2024, 13(18), 5573; https://doi.org/10.3390/jcm13185573 - 20 Sep 2024
Viewed by 1504
Abstract
Background: Parkinson’s disease (PD) has transitioned from a rare condition in 1817 to the fastest-growing neurological disorder globally. The significant increase in cases from 2.5 million in 1990 to 6.1 million in 2016, coupled with predictions of a further doubling by 2040, [...] Read more.
Background: Parkinson’s disease (PD) has transitioned from a rare condition in 1817 to the fastest-growing neurological disorder globally. The significant increase in cases from 2.5 million in 1990 to 6.1 million in 2016, coupled with predictions of a further doubling by 2040, underscores an impending healthcare challenge. This escalation aligns with global demographic shifts, including rising life expectancy and a growing global population. The economic impact, notably in the U.S., reached $51.9 billion in 2017, with projections suggesting a 46% increase by 2037, emphasizing the substantial socio-economic implications for both patients and caregivers. Coupled with a worldwide demand for health workers that is expected to rise to 80 million by 2030, we have fertile ground for a pandemic. Methods: Our transdisciplinary research focused on early PD detection through running speech and continuous handwriting analysis, incorporating medical, biomedical engineering, AI, and linguistic expertise. The cohort comprised 30 participants, including 20 PD patients at stages 1–4 on the Hoehn and Yahr scale and 10 healthy controls. We employed advanced AI techniques to analyze correlation plots generated from speech and handwriting features, aiming to identify prodromal PD biomarkers. Results: The study revealed distinct speech and handwriting patterns in PD patients compared to controls. Our ParkinsonNet model demonstrated high predictive accuracy, with F1 scores of 95.74% for speech and 96.72% for handwriting analyses. These findings highlight the potential of speech and handwriting as effective early biomarkers for PD. Conclusions: The integration of AI as a decision support system in analyzing speech and handwriting presents a promising approach for early PD detection. This methodology not only offers a novel diagnostic tool but also contributes to the broader understanding of PD’s early manifestations. Further research is required to validate these findings in larger, diverse cohorts and to integrate these tools into clinical practice for timely PD pre-diagnosis and management. Full article
(This article belongs to the Special Issue Digital Health Technologies in Parkinson’s Disease and Related Disorders)
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15 pages, 625 KiB  
Systematic Review
Artificial Intelligence in the Diagnosis and Quantitative Phenotyping of Hyperkinetic Movement Disorders: A Systematic Review
by Joaquin A. Vizcarra, Sushuma Yarlagadda, Kevin Xie, Colin A. Ellis, Meredith Spindler and Lauren H. Hammer
J. Clin. Med. 2024, 13(23), 7009; https://doi.org/10.3390/jcm13237009 - 21 Nov 2024
Cited by 1 | Viewed by 1493
Abstract
Background: Hyperkinetic movement disorders involve excessive, involuntary movements such as ataxia, chorea, dystonia, myoclonus, tics, and tremor. Recent advances in artificial intelligence (AI) allow investigators to integrate multimodal instrumented movement measurements and imaging techniques and to analyze these data together at scale. [...] Read more.
Background: Hyperkinetic movement disorders involve excessive, involuntary movements such as ataxia, chorea, dystonia, myoclonus, tics, and tremor. Recent advances in artificial intelligence (AI) allow investigators to integrate multimodal instrumented movement measurements and imaging techniques and to analyze these data together at scale. In this systematic review, we aim to characterize AI’s performance in diagnosing and quantitatively phenotyping these disorders. Methods: We searched PubMed and Embase using a semi-automated article-screening pipeline. Results: Fifty-five studies met the inclusion criteria (n = 11,946 subjects). Thirty-five studies used machine learning, sixteen used deep learning, and four used both. Thirty-eight studies reported disease diagnosis, twenty-three reported quantitative phenotyping, and six reported both. Diagnostic accuracy was reported in 36 of 38 and correlation coefficients in 10 of 23 studies. Kinematics (e.g., accelerometers and inertial measurement units) were the most used dataset. Diagnostic accuracy was reported in 36 studies and ranged from 56 to 100% compared to clinical diagnoses to differentiate them from healthy controls. The correlation coefficient was reported in 10 studies and ranged from 0.54 to 0.99 compared to clinical ratings for quantitative phenotyping. Five studies had an overall judgment of “low risk of bias” and three had external validation. Conclusion: There is a need to adopt AI-based research guidelines to minimize reporting heterogeneity and bolster clinical interpretability. Full article
(This article belongs to the Special Issue Digital Health Technologies in Parkinson’s Disease and Related Disorders)
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