Literature DB >> 27125836

Technology in Parkinson's disease: Challenges and opportunities.

Alberto J Espay1, Paolo Bonato2, Fatta B Nahab3, Walter Maetzler4,5, John M Dean6, Jochen Klucken7, Bjoern M Eskofier8, Aristide Merola9, Fay Horak10,11, Anthony E Lang12, Ralf Reilmann13,14,15, Joe Giuffrida16, Alice Nieuwboer17, Malcolm Horne18, Max A Little19,20, Irene Litvan3, Tanya Simuni21, E Ray Dorsey22, Michelle A Burack22, Ken Kubota23, Anita Kamondi24, Catarina Godinho25, Jean-Francois Daneault2, Georgia Mitsi26, Lothar Krinke27, Jeffery M Hausdorff28, Bastiaan R Bloem29, Spyros Papapetropoulos30.   

Abstract

The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD.
© 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

Entities:  

Keywords:  Parkinson's disease; digital biomarkers; digital health; eHealth; precision medicine; remote monitoring; technology; wearable technology

Mesh:

Year:  2016        PMID: 27125836      PMCID: PMC5014594          DOI: 10.1002/mds.26642

Source DB:  PubMed          Journal:  Mov Disord        ISSN: 0885-3185            Impact factor:   10.338


  55 in total

1.  Capturing ambulatory activity decline in Parkinson's disease.

Authors:  James T Cavanaugh; Terry D Ellis; Gammon M Earhart; Matthew P Ford; K Bo Foreman; Leland E Dibble
Journal:  J Neurol Phys Ther       Date:  2012-06       Impact factor: 3.649

2.  Continuous non-invasive monitoring to detect covert autonomic dysfunction in Parkinson's disease.

Authors:  Amy M Hellman; Shital P Shah; Stephanie M Pawlowski; John E Duda; James F Morley
Journal:  Parkinsonism Relat Disord       Date:  2015-04-23       Impact factor: 4.891

3.  Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study.

Authors:  S Arora; V Venkataraman; A Zhan; S Donohue; K M Biglan; E R Dorsey; M A Little
Journal:  Parkinsonism Relat Disord       Date:  2015-03-07       Impact factor: 4.891

4.  Multifunctional wearable devices for diagnosis and therapy of movement disorders.

Authors:  Donghee Son; Jongha Lee; Shutao Qiao; Roozbeh Ghaffari; Jaemin Kim; Ji Eun Lee; Changyeong Song; Seok Joo Kim; Dong Jun Lee; Samuel Woojoo Jun; Shixuan Yang; Minjoon Park; Jiho Shin; Kyungsik Do; Mincheol Lee; Kwanghun Kang; Cheol Seong Hwang; Nanshu Lu; Taeghwan Hyeon; Dae-Hyeong Kim
Journal:  Nat Nanotechnol       Date:  2014-03-30       Impact factor: 39.213

5.  Quantitative measurement of motor symptoms in Parkinson's disease: a study with full-body motion capture data.

Authors:  Samarjit Das; Laura Trutoiu; Akihiko Murai; Dunbar Alcindor; Michael Oh; Fernando De la Torre; Jessica Hodgins
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

6.  Towards closed-loop deep brain stimulation: decision tree-based essential tremor patient's state classifier and tremor reappearance predictor.

Authors:  Pitamber Shukla; Ishita Basu; Daniela Tuninetti
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2014

7.  Patient perception of dyskinesia in Parkinson's disease.

Authors:  S W Hung; G M Adeli; T Arenovich; S H Fox; A E Lang
Journal:  J Neurol Neurosurg Psychiatry       Date:  2010-07-28       Impact factor: 10.154

8.  High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity.

Authors:  Serge H Roy; Bryan T Cole; L Don Gilmore; Carlo J De Luca; Cathi A Thomas; Marie M Saint-Hilaire; S Hamid Nawab
Journal:  Mov Disord       Date:  2013-03-20       Impact factor: 10.338

9.  Using ecological whole body kinematics to evaluate effects of medication adjustment in Parkinson disease.

Authors:  Fariborz Rahimi; Carina Bee; Christian Duval; Patrick Boissy; Roderick Edwards; Mandar Jog
Journal:  J Parkinsons Dis       Date:  2014       Impact factor: 5.568

10.  Body-Worn Sensors in Parkinson's Disease: Evaluating Their Acceptability to Patients.

Authors:  James M Fisher; Nils Y Hammerla; Lynn Rochester; Peter Andras; Richard W Walker
Journal:  Telemed J E Health       Date:  2015-07-17       Impact factor: 3.536

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  145 in total

1.  A novel device for continuous monitoring of tremor and other motor symptoms.

Authors:  Luigi Battista; Antonietta Romaniello
Journal:  Neurol Sci       Date:  2018-05-07       Impact factor: 3.307

2.  Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device.

Authors:  Nikhil Mahadevan; Charmaine Demanuele; Hao Zhang; Dmitri Volfson; Bryan Ho; Michael Kelley Erb; Shyamal Patel
Journal:  NPJ Digit Med       Date:  2020-01-15

3.  mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson's disease.

Authors:  M Kelley Erb; Daniel R Karlin; Bryan K Ho; Kevin C Thomas; Federico Parisi; Gloria P Vergara-Diaz; Jean-Francois Daneault; Paul W Wacnik; Hao Zhang; Tairmae Kangarloo; Charmaine Demanuele; Chris R Brooks; Craig N Detheridge; Nina Shaafi Kabiri; Jaspreet S Bhangu; Paolo Bonato
Journal:  NPJ Digit Med       Date:  2020-01-17

Review 4.  Digital phenotyping approaches and mobile devices enhance CNS biopharmaceutical research and development.

Authors:  Daniel G Smith
Journal:  Neuropsychopharmacology       Date:  2018-09-18       Impact factor: 7.853

5.  Sensor-based gait analysis of individualized improvement during apomorphine titration in Parkinson's disease.

Authors:  Franz Marxreiter; Heiko Gaßner; Olga Borozdina; Jens Barth; Zacharias Kohl; Johannes C M Schlachetzki; Caroline Thun-Hohenstein; Dieter Volc; Bjoern M Eskofier; Jürgen Winkler; Jochen Klucken
Journal:  J Neurol       Date:  2018-09-08       Impact factor: 4.849

6.  Clinical Trials for Neurogenic Orthostatic Hypotension: A Comprehensive Review of Endpoints, Pitfalls, and Challenges.

Authors:  Jose-Alberto Palma; Horacio Kaufmann
Journal:  Semin Neurol       Date:  2020-09-09       Impact factor: 3.420

7.  Maladaptive avoidance patterns in Parkinson's disease are exacerbated by symptoms of depression.

Authors:  Jony Sheynin; Irina Baetu; Lyndsey E Collins-Praino; Catherine E Myers; Robyn Winwood-Smith; Ahmed A Moustafa
Journal:  Behav Brain Res       Date:  2020-01-11       Impact factor: 3.332

Review 8.  How Machine Learning Will Transform Biomedicine.

Authors:  Jeremy Goecks; Vahid Jalili; Laura M Heiser; Joe W Gray
Journal:  Cell       Date:  2020-04-02       Impact factor: 41.582

Review 9.  The First Frontier: Digital Biomarkers for Neurodegenerative Disorders.

Authors:  E Ray Dorsey; Spyros Papapetropoulos; Mulin Xiong; Karl Kieburtz
Journal:  Digit Biomark       Date:  2017-07-04

10.  Quantification of Motor Function in Huntington Disease Patients Using Wearable Sensor Devices.

Authors:  Mark Forrest Gordon; Igor D Grachev; Itzik Mazeh; Yonatan Dolan; Ralf Reilmann; Pippa S Loupe; Shai Fine; Leehee Navon-Perry; Nicholas Gross; Spyros Papapetropoulos; Juha-Matti Savola; Michael R Hayden
Journal:  Digit Biomark       Date:  2019-09-06
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