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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Movement Disorders
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1534205

RT-ring: a small wearable device for tremulous Parkinson's disease diagnosis in primary care

Provisionally accepted
Jolanda Buonocore Jolanda Buonocore 1,2Basilio Vescio Basilio Vescio 3Marida De Maria Marida De Maria 1Marianna Crasà Marianna Crasà 1Rita Nistico Rita Nistico 1Pier Paolo Arcuri Pier Paolo Arcuri 4Giuseppe Lucio Cascini Giuseppe Lucio Cascini 1,5,6Anna Latorre Anna Latorre 7Aldo Quattrone Aldo Quattrone 1Andrea Quattrone Andrea Quattrone 1,2*
  • 1 Neuroscientific Research Center, Department of Medical and Surgical Science, University of Magna Graecia, Catanzaro, Calabria, Italy
  • 2 Institute of Neurology, Department of Medical and Surgical Sciences, University of Magna Graecia, Catanzaro, Calabria, Italy
  • 3 Biotecnomed, Catanzaro, Calabria, Italy
  • 4 Institute of Radiology, University Hospital Renato Dulbecco, Catanzaro, Italy
  • 5 Nuclear Medicine Unit, University Hospital Renato Dulbecco, Catanzaro, Italy
  • 6 Department of Clinical and Experimental Medicine, Magna Græcia University of Catanzaro, Catanzaro, Calabria, Italy
  • 7 Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, London, United Kingdom

The final, formatted version of the article will be published soon.

    Introduction: Differential diagnosis of rest tremor (RT) disorders is challenging, often requiring 123I-ioflupane single-photon-emission-computed tomography (DaTscan), an expensive technique not available worldwide. In the current study, we investigated the performance of a new wearable mobile device termed "RT-ring" in predicting DaTscan result in patients presenting with RT based on rest tremor inertial features. Methods: Consecutive RT patients underwent RT-ring tremor analysis, surface electromyography (sEMG), and DaTscan. The RT-ring is a miniaturized mobile device that uses machine learning based on inertial tremor data to estimate the RT pattern. This electrophysiologic tremor feature has proven to accurately predict DaTscan result. The primary outcome was the RT-ring's performance in distinguishing patients with and without striatal dopaminergic deficit. Results: Sixty-seven RT patients were enrolled, including 42 patients with striatal dopaminergic deficit and 25 with normal DaTscan. The RT-ring showed 85.0% sensitivity, 90.9% specificity, and 87.9% balanced accuracy in predicting DaTscan result, and demonstrated 96.8% agreement with sEMG in RT pattern classification. Conclusions: The RT-ring is a promising, non-invasive, user-friendly, wearable mobile device for supporting the diagnosis of tremulous Parkinson’s disease in primary care settings, especially in low-income countries with limited access to dopamine imaging.

    Keywords: Rest tremor, Tremor Pattern, Wearable Device, RT-ring, DaTSCAN, Parkinson's disease, essential tremor plus, machine learning

    Received: 25 Nov 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Buonocore, Vescio, De Maria, Crasà, Nistico, Arcuri, Cascini, Latorre, Quattrone and Quattrone. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Andrea Quattrone, Neuroscientific Research Center, Department of Medical and Surgical Science, University of Magna Graecia, Catanzaro, Calabria, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.