Literature DB >> 30269610

Technology-based assessment of motor and nonmotor phenomena in Parkinson disease.

Aristide Merola1, Andrea Sturchio1, Stephanie Hacker1, Santiago Serna1, Joaquin A Vizcarra1, Luca Marsili1, Alfonso Fasano2, Alberto J Espay1.   

Abstract

INTRODUCTION: The increasing development and availability of portable and wearable technologies is rapidly expanding the field of technology-based objective measures (TOMs) in neurological disorders, including Parkinson disease (PD). Substantial challenges remain in the recognition of disease phenomena relevant to patients and clinicians, as well as in the identification of the most appropriate devices to carry out these measurements. Areas covered: The authors systematically reviewed PubMed for studies employing technology as outcome measures in the assessment of PD-associated motor and nonmotor abnormalities. Expert commentary: TOMs minimize intra- and inter-rater variability in clinical assessments of motor and nonmotor phenomena in PD, improving the accuracy of clinical endpoints. Critical unmet needs for the integration of TOMs into clinical and research practice are the identification and validation of relevant endpoints for individual patients, the capture of motor and nonmotor activities from an ecologically valid environment, the integration of various sensor data into an open-access, common-language platforms, and the definition of a regulatory pathway for approval of TOMs. The current lack of multidomain, multisensor, smart technologies to measure in real time a wide scope of relevant changes remain a significant limitation for the integration of technology into the assessment of PD motor and nonmotor functional disability.

Entities:  

Keywords:  Biomarker; Parkinson; motor symptoms; nonmotor symptoms; technology; trial

Year:  2018        PMID: 30269610     DOI: 10.1080/14737175.2018.1530593

Source DB:  PubMed          Journal:  Expert Rev Neurother        ISSN: 1473-7175            Impact factor:   4.618


  13 in total

1.  A novel single-sensor-based method for the detection of gait-cycle breakdown and freezing of gait in Parkinson's disease.

Authors:  Taylor Chomiak; Wenbiao Xian; Zhong Pei; Bin Hu
Journal:  J Neural Transm (Vienna)       Date:  2019-06-01       Impact factor: 3.575

2.  Patient-Reported Outcome Measures in Registry-Based Studies of Type 2 Diabetes Mellitus: a Systematic Review.

Authors:  Yu Ting Chen; Yan Zhi Tan; Mcvin Cheen; Hwee-Lin Wee
Journal:  Curr Diab Rep       Date:  2019-11-20       Impact factor: 4.810

Review 3.  A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies.

Authors:  Alberto J Espay; Jeffrey M Hausdorff; Álvaro Sánchez-Ferro; Jochen Klucken; Aristide Merola; Paolo Bonato; Serene S Paul; Fay B Horak; Joaquin A Vizcarra; Tiago A Mestre; Ralf Reilmann; Alice Nieuwboer; E Ray Dorsey; Lynn Rochester; Bastiaan R Bloem; Walter Maetzler
Journal:  Mov Disord       Date:  2019-03-22       Impact factor: 10.338

4.  Real-World Evidence for a Smartwatch-Based Parkinson's Motor Assessment App for Patients Undergoing Therapy Changes.

Authors:  Aaron J Hadley; David E Riley; Dustin A Heldman
Journal:  Digit Biomark       Date:  2021-09-08

Review 5.  Evolving concepts on bradykinesia.

Authors:  Matteo Bologna; Giulia Paparella; Alfonso Fasano; Mark Hallett; Alfredo Berardelli
Journal:  Brain       Date:  2020-03-01       Impact factor: 13.501

Review 6.  Prodromal Parkinson disease subtypes - key to understanding heterogeneity.

Authors:  Daniela Berg; Per Borghammer; Seyed-Mohammad Fereshtehnejad; Sebastian Heinzel; Jacob Horsager; Eva Schaeffer; Ronald B Postuma
Journal:  Nat Rev Neurol       Date:  2021-04-20       Impact factor: 42.937

7.  The Use of Social Media and Digital Devices Among Italian Neurologists.

Authors:  Luigi Lavorgna; Francesco Brigo; Gianmarco Abbadessa; Sebastiano Bucello; Marinella Clerico; Eleonora Cocco; Rosa Iodice; Roberta Lanzillo; Letizia Leocani; Alberto Lerario; Marcello Moccia; Alessandro Padovani; Luca Prosperini; Anna Repice; Maria Stromillo; Francesca Trojsi; Gianluigi Mancardi; Gioacchino Tedeschi; Simona Bonavita
Journal:  Front Neurol       Date:  2020-06-16       Impact factor: 4.003

8.  Wearable Health Technology to Quantify the Functional Impact of Peripheral Neuropathy on Mobility in Parkinson's Disease: A Systematic Review.

Authors:  Marta Francisca Corrà; Elke Warmerdam; Nuno Vila-Chã; Walter Maetzler; Luís Maia
Journal:  Sensors (Basel)       Date:  2020-11-19       Impact factor: 3.576

9.  Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson's Disease.

Authors:  Yi-Feng Ko; Pei-Hsin Kuo; Ching-Fu Wang; Yu-Jen Chen; Pei-Chi Chuang; Shih-Zhang Li; Bo-Wei Chen; Fu-Chi Yang; Yu-Chun Lo; Yi Yang; Shuan-Chu Vina Ro; Fu-Shan Jaw; Sheng-Huang Lin; You-Yin Chen
Journal:  Biosensors (Basel)       Date:  2022-01-27

10.  Integrated robotics platform with haptic control differentiates subjects with Parkinson's disease from controls and quantifies the motor effects of levodopa.

Authors:  Pauline Gaprielian; Stephen H Scott; Catherine Lowrey; Stuart Reid; Giovanna Pari; Ron Levy
Journal:  J Neuroeng Rehabil       Date:  2019-10-26       Impact factor: 4.262

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