Technology in Parkinson's disease: Challenges and opportunities

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 s...

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Published inMovement disorders Vol. 31; no. 9; pp. 1272 - 1282
Main Authors Espay, Alberto J., Bonato, Paolo, Nahab, Fatta B., Maetzler, Walter, Dean, John M., Klucken, Jochen, Eskofier, Bjoern M., Merola, Aristide, Horak, Fay, Lang, Anthony E., Reilmann, Ralf, Giuffrida, Joe, Nieuwboer, Alice, Horne, Malcolm, Little, Max A., Litvan, Irene, Simuni, Tanya, Dorsey, E. Ray, Burack, Michelle A., Kubota, Ken, Kamondi, Anita, Godinho, Catarina, Daneault, Jean-Francois, Mitsi, Georgia, Krinke, Lothar, Hausdorff, Jeffery M., Bloem, Bastiaan R., Papapetropoulos, Spyros
Format Journal Article
LanguageEnglish
Published United States Blackwell Publishing Ltd 01.09.2016
Wiley Subscription Services, Inc
Subjects
Biomedical Technology - standards
digital biomarkers
digital health
eHealth
Humans
Movement disorders
Parkinson Disease - diagnosis
Parkinson Disease - therapy
Parkinson's disease
precision medicine
remote monitoring
technology
wearable technology
digital biomarkers
Parkinson's disease
digital health
precision medicine
remote monitoring
technology
wearable technology
eHealth
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Abstract 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
AbstractList 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.
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
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. copyright 2016 International Parkinson and Movement Disorder Society
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
Author Mitsi, Georgia
Kubota, Ken
Nahab, Fatta B.
Godinho, Catarina
Lang, Anthony E.
Horne, Malcolm
Simuni, Tanya
Bloem, Bastiaan R.
Espay, Alberto J.
Bonato, Paolo
Maetzler, Walter
Nieuwboer, Alice
Krinke, Lothar
Litvan, Irene
Burack, Michelle A.
Little, Max A.
Papapetropoulos, Spyros
Hausdorff, Jeffery M.
Dean, John M.
Reilmann, Ralf
Dorsey, E. Ray
Eskofier, Bjoern M.
Daneault, Jean-Francois
Merola, Aristide
Giuffrida, Joe
Kamondi, Anita
Horak, Fay
Klucken, Jochen
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  organization: James J. and Joan A. Gardner Family Center for Parkinson's disease and Movement Disorders, University of Cincinnati, Ohio, Cincinnati, USA
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  organization: Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
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  givenname: Bjoern M.
  surname: Eskofier
  fullname: Eskofier, Bjoern M.
  organization: Digital Sports Group, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
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  organization: Department of Neuroscience "Rita Levi Montalcini", Città della salute e della scienza di Torino, Torino, Italy
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  surname: Horak
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  organization: Department of Neurology, Oregon Health & Science University, Portland VA Medical System, Portland, Oregon
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  givenname: Anthony E.
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  organization: Morton and Gloria Movement Disorders Clinic and the Edmond J. Safra Program in Parkinson's Disease, Toronto Western Hospital, Toronto, Canada
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  organization: Great Lakes NeuroTechnologies, Ohio, Cleveland, USA
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  surname: Horne
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  organization: Global Kinetics Corporation & Florey Institute for Neuroscience and Mental Health, University of Melbourne, Victoria, Parkville, Australia
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  givenname: Max A.
  surname: Little
  fullname: Little, Max A.
  organization: Department of Mathematics, Aston University, Birmingham, UK
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  givenname: Irene
  surname: Litvan
  fullname: Litvan, Irene
  organization: Department of Neurosciences, University of California San Diego, CA, La Jolla, USA
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  givenname: Tanya
  surname: Simuni
  fullname: Simuni, Tanya
  organization: Department of Neurology, Feinberg School of Medicine, Northwestern University, Illinois, Chicago, USA
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  givenname: E. Ray
  surname: Dorsey
  fullname: Dorsey, E. Ray
  organization: Department of Neurology, University of Rochester Medical Center, New York, Rochester, USA
– sequence: 19
  givenname: Michelle A.
  surname: Burack
  fullname: Burack, Michelle A.
  organization: Department of Neurology, University of Rochester Medical Center, New York, Rochester, USA
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  organization: Michael J Fox Foundation for Parkinson's Research, New York, New York City, USA
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  organization: Department of Neurology, National Institute of Clinical Neurosciences, Budapest, Hungary
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  surname: Papapetropoulos
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  organization: Massachusetts General Hospital, Massachusetts, Boston, USA
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Issue 9
Keywords digital biomarkers
Parkinson's disease
digital health
precision medicine
remote monitoring
technology
wearable technology
eHealth
Language English
License 2016 International Parkinson and Movement Disorder Society.
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The MDS Technology Task Force assembled a group of experts from the device and biopharmaceutical industry, clinical researchers, and engineers. The authors have taken every effort to minimize the influence of their home institutions and industries on the summarized outcome of the deliberations. A.J.E. has received grant support from Great Lakes Neurotechnologies. He serves as chair of the MDS Technology Task Force. P.B. is supported by the Michael J Fox Foundation, the National Institutes of Health, the National Science Foundation, and the Office of Naval Research. F.N. has received an educational grant from Medtronic Inc. W.M. has received grants from the European Union for FAIR‐PARK‐II, Moving beyond and SENSE‐PARK. J.M.D. is supported by the Davis Phinney Foundation to direct their Healthcare Strategy and Technology division. J.K. has received grant support Astrum IT and LicherMT and is chair of the task force “sensor‐based movement analysis” of the German Parkinson Society. B.M.E. has received grant support from Bosch Sensortec and Astrum IT and is co‐chair of the task force “sensor‐based movement analysis” of the German Parkinson Society. F.H. has research grants from Medtronic and has an equity/interest in APDM, a technology company. A.E.L. has served as an advisor for and received honoraria from Medtronic. R.R. is founding director and owner of the George‐Huntington‐Institute, a private research institute and QuantiMedis, a clinical research organization providing Q‐Motor (quantitative motor) services in clinical trials and research. J.G. is a full‐time employee of Great Lakes Neurotechnologies. M.H. has a financial interest in Global Kinetics Corporation, a company that manufactures and supplies the Parkinson's KinetiGraph (PKG), a wearable technology. M.A.L. has nothing to disclose. T.S. has received funding support for educational programming from GE Medical and Medtronic. E.R.D. has filed for a patent related to telemedicine and neurology and has received research funding from Great Lakes Neurotechnologies and Prana Biotechnology. K.K. directs data science and the partnership with Intel on wearable technologies and analytics enacted through the Michael J Fox Foundation for Parkinson's Research full time. G.M. is the founder and chief executive officer of Apptomics LLC. L.K. is a full‐time employee of Medtronic and serves as Board Observer at Functional Neuromodulation, Ltd. J.M.H. submitted a patent application on the use of body‐fixed sensors in Parkinson disease. The intellectual property rights for this patent application are held by the Tel Aviv Sourasky Medical Center. B.R.B. received research support from the Netherlands Organization for Scientific Research, the Michael J Fox Foundation, the Prinses Beatrix Foundation, the Stichting Parkinson Fonds, the National Parkinson Foundation, the Hersenstichting Nederland and the Parkinson Vereniging. S.P. is a full‐time employee of TEVA Pharmaceuticals. He serves as co‐chair of the MDS Technology Task Force. All other authors have no financial disclosure to report related to research covered in this article.
Relevant conflicts of interests/financial disclosures
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PublicationCentury 2000
PublicationDate September 2016
PublicationDateYYYYMMDD 2016-09-01
PublicationDate_xml – month: 09
  year: 2016
  text: September 2016
PublicationDecade 2010
PublicationPlace United States
PublicationPlace_xml – name: United States
– name: Hoboken
PublicationTitle Movement disorders
PublicationTitleAlternate Mov Disord
PublicationYear 2016
Publisher Blackwell Publishing Ltd
Wiley Subscription Services, Inc
Publisher_xml – name: Blackwell Publishing Ltd
– name: Wiley Subscription Services, Inc
References Del Din S, Godfrey A, Rochester L. Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson's disease: toward clinical and at home use. IEEE J Biomed Health Inform 2015 [Epub ahead of print].
Roy SH, Cole BT, Gilmore LD, et al. High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity. Mov Disord 2013;28(8):1080-1087.
Ellis T, Cavanaugh JT, Earhart GM, Ford MP, Foreman KB, Dibble LE. Which measures of physical function and motor impairment best predict quality of life in Parkinson's disease? Parkinsonism Relat Disord 2011;17(9):693-697.
Mariani B, Jimenez MC, Vingerhoets FJ, Aminian K. On-shoe wearable sensors for gait and turning assessment of patients with Parkinson's disease. IEEE Trans Biomed Eng 2013;60(1):155-158.
Fereshtehnejad SM, Romenets SR, Anang JB, Latreille V, Gagnon JF, Postuma RB. New clinical subtypes of parkinson disease and their longitudinal progression: a prospective cohort comparison with other phenotypes. JAMA Neurol 2015;72(8):863-873.
Berganzo K, Tijero B, Gonzalez-Eizaguirre A, et al. Motor and non-motor symptoms of Parkinson's disease and their impact on quality of life and on different clinical subgroups. Neurologia 2014, pii: S0213-4853(14)00233-3, doi: 10.1016/j.nrl.2014.10.010.
Lieber B, Taylor BE, Appelboom G, McKhann G, Connolly ES Jr. Motion sensors to assess and monitor medical and surgical management of Parkinson disease. World Neurosurg 2015;84(2):561-566.
Papapetropoulos S, Mitsi G, Espay AJ. Digital health revolution: is it time for affordable remote monitoring for Parkinson's disease? Front Neurol 2015;6:34.
Bonato P. Wearable sensors and systems. From enabling technology to clinical applications. IEEE Eng Med Biol Mag 2010;29(3):25-36.
Chen BR, Patel S, Buckley T, et al. A web-based system for home monitoring of patients with Parkinson's disease using wearable sensors. IEEE Trans Biomed Eng 2011;58(3):831-836.
Arora S, Venkataraman V, Zhan A, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord 2015;21(6):650-653.
Thenganatt MA, Jankovic J. Parkinson disease subtypes. JAMA Neurol 2014;71(4):499-504.
Bachlin M, Plotnik M, Roggen D, et al. Wearable assistant for Parkinson's disease patients with the freezing of gait symptom. IEEE Trans Inf Technol Biomed 2010;14(2):436-446.
Espay AJ, Giuffrida JP, Chen R, et al. Differential response of speed, amplitude, and rhythm to dopaminergic medications in Parkinson's disease. Mov Disord 2011;26(14):2504-2508.
Mendiola MF, Kalnicki M, Lindenauer S. Valuable features in mobile health apps for patients and consumers: content analysis of apps and user ratings. JMIR Mhealth Uhealth 2015;3(2):e40.
Consumer Intelligence Series: The Wearable Future. Pricewaterhouse Coopers. http://www.pwc.com/us/en/retail-consumer/publications/assets/pwc-cis-wearable-future.pdf
Ozinga SJ, Machado AG, Miller Koop M, Rosenfeldt AB, Alberts JL. Objective assessment of postural stability in Parkinson's disease using mobile technology. Mov Disord 2015;30(9):1214-1221.
Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW. Body-worn sensors in Parkinson's disease: evaluating their acceptability to patients. Telemed J E Health 2016;22:63-69.
Horak FB, Mancini M. Objective biomarkers of balance and gait for Parkinson's disease using body-worn sensors. Mov Disord 2013;28(11):1544-1551.
Quinn EJ, Blumenfeld Z, Velisar A, et al. Beta oscillations in freely moving Parkinson's subjects are attenuated during deep brain stimulation. Mov Disord 2015;30:1750-1758.
Liu G, Ho C, Slappey N, et al. A wearable conductivity sensor for wireless real-time sweat monitoring. Sensors Actuat B Chem 2016;227:35-42.
Jankovic J. Motor fluctuations and dyskinesias in Parkinson's disease: clinical manifestations. Mov Disord 2005;20(suppl 11):S11-S16.
Shukla P, Basu I, Tuninetti D. Towards closed-loop deep brain stimulation: decision tree-based essential tremor patient's state classifier and tremor reappearance predictor. Conf Proc IEEE Eng Med Biol Soc 2014;2014:2605-2608.
McCall C, McCall WV. Comparison of actigraphy with polysomnography and sleep logs in depressed insomniacs. J Sleep Res 2012;21(1):122-127.
Hellman AM, Shah SP, Pawlowski SM, Duda JE, Morley JF. Continuous non-invasive monitoring to detect covert autonomic dysfunction in Parkinson's disease. Parkinsonism Relat Disord 2015;21(7):723-728.
Klucken J, Barth J, Kugler P, et al. Unbiased and mobile gait analysis detects motor impairment in Parkinson's disease. PLoS ONE 2013;8(2):e56956.
Son D, Lee J, Qiao S, et al. Multifunctional wearable devices for diagnosis and therapy of movement disorders. Nat Nanotechnol 2014;9(5):397-404.
Shull PB, Jirattigalachote W, Hunt MA, Cutkosky MR, Delp SL. Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture 2014;40(1):11-19.
Maetzler W, Domingos J, Srulijes K, Ferreira JJ, Bloem BR. Quantitative wearable sensors for objective assessment of Parkinson's disease. Mov Disord 2013;28(12):1628-1637.
Rahimi F, Bee C, Duval C, et al. Using ecological whole body kinematics to evaluate effects of medication adjustment in Parkinson disease. J Parkinsons Dis 2014;4(4):617-627.
Weiss A, Herman T, Giladi N, Hausdorff JM. Objective assessment of fall risk in Parkinson's disease using a body-fixed sensor worn for 3 days. PLoS ONE 2014;9(5):e96675.
Das S, Trutoiu L, Murai A, et al. Quantitative measurement of motor symptoms in Parkinson's disease: a study with full-body motion capture data. Conf Proc IEEE Eng Med Biol Soc 2011;2011:6789-6792.
Mazilu S, Calatroni A, Gazit E, Mirelman A, Hausdorff JM, Troster G. Prediction of freezing of gait in parkinson's from physiological wearables: an exploratory study. IEEE J Biomed Health Inform 2015;19(6):1843-1854.
Ferreira JJ, Godinho C, Santos AT, et al. Quantitative home-based assessment of Parkinson's symptoms: the SENSE-PARK feasibility and usability study. BMC Neurol 2015;15:89.
Moore ST, Yungher DA, Morris TR, et al. Autonomous identification of freezing of gait in Parkinson's disease from lower-body segmental accelerometry. J Neuroeng Rehabil 2013;10:19.
Guo Y, Graber A, McBurney RN, Balasubramanian R. Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms. BMC Bioinformatics 2010;11:447.
Zheng YL, Ding XR, Poon CC, et al. Unobtrusive sensing and wearable devices for health informatics. IEEE Trans Biomed Eng 2014;61(5):1538-1554.
Mera TO, Burack MA, Giuffrida JP. Quantitative assessment of levodopa-induced dyskinesia using automated motion sensing technology. Conf Proc IEEE Eng Med Biol Soc 2012;2012:154-157.
Patel S, Lorincz K, Hughes R, et al. Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors. IEEE Trans Inf Technol Biomed 2009;13(6):864-873.
Maetzler W, Liepelt I, Berg D. Progression of Parkinson's disease in the clinical phase: potential markers. Lancet Neurol 2009;8(12):1158-1171.
Horak F, King L, Mancini M. Role of body-worn movement monitor technology for balance and gait rehabilitation. Phys Ther 2015;95(3):461-470.
Nieuwboer A, De Weerdt W, Dom R, Lesaffre E. A frequency and correlation analysis of motor deficits in Parkinson patients. Disabil Rehabil 1998;20(4):142-150.
Cavanaugh JT, Ellis TD, Earhart GM, Ford MP, Foreman KB, Dibble LE. Capturing ambulatory activity decline in Parkinson's disease. J Neurol Phys Ther 2012;36(2):51-57.
Stamford JA, Schmidt PN, Friedl KE. What engineering technology could do for quality of life in Parkinson's disease: a review of current needs and opportunities. IEEE J Biomed Health Inform 2015;19(6):1862-1872.
Rampp A, Barth J, Schulein S, Gassmann KG, Klucken J, Eskofier BM. Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients. IEEE Trans Biomed Eng 2015;62(4):1089-1097.
Goetz CG, Stebbins GT, Wolff D, et al. Testing objective measures of motor impairment in early Parkinson's disease: feasibility study of an at-home testing device. Mov Disord 2009;24(4):551-556.
Cazalé A, Sant W, Ginot F, et al. Physiological stress monitoring using sodium ion potentiometric microsensors for sweat analysis. Sensors Actuat B Chem 2016;225:1-9.
Pasluosta CF, Gassner H, Winkler J, Klucken J, Eskofier BM. An emerging era in the management of parkinson's disease: wearable technologies and the internet of things. IEEE J Biomed Health Inform 2015;19(6):1873-1881.
Basu I, Graupe D, Tuninetti D, et al. Pathological tremor prediction using surface electromyogram and acceleration: potential use in "ON-OFF" demand driven deep brain stimulator design. J Neural Eng 2013;10(3):036019.
Brun L, Lefaucheur R, Fetter D, et al. Non-motor fluctuations in Parkinson's disease: prevalence, characteristics and management in a large cohort of parkinsonian outpatients. Clin Neurol Neurosurg 2014;127:93-96.
Valappil RA, Black JE, Broderick MJ, et al. Exploring the electrocardiogram as a potential tool to screen for premotor Parkinson's disease. Mov Disord 2010;25(14):2296-2303.
Bayestehtashk A, Asgari M, Shafran I, McNames J. Fully automated assessment of the severity of Parkinson's disease from speech. Comput Speech Lang 2015;29(1):172-185.
Hubble RP, Naughton GA, Silburn PA, Cole MH. Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: a systematic review. PLoS ONE 2015;10(4):e0123705.
Wang H, Jafari R, Zhou G, et al. Guest Editorial: Special Issue on Internet of Things for Smart and Connected Health. IEEE IoT J 2015;2(1):1-4.
Solla P, Cadeddu C, Cannas A, et al. Heart rate variability shows different cardiovascular modulation in Parkinson's disease patients with tremor dominant subtype compared to those with akinetic rigid dominant subtype. J Neural Transm 2015:1-6.
Hung SW, Adeli GM, Arenovich T, Fox SH, Lang AE. Patien
2015; 2
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2015; 95
2015; 72
2015; 30
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2014; 61
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2015; 62
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2014; 71
2012; 21
2016; 22
References_xml – reference: Ferreira JJ, Godinho C, Santos AT, et al. Quantitative home-based assessment of Parkinson's symptoms: the SENSE-PARK feasibility and usability study. BMC Neurol 2015;15:89.
– reference: Horak F, King L, Mancini M. Role of body-worn movement monitor technology for balance and gait rehabilitation. Phys Ther 2015;95(3):461-470.
– reference: Ozinga SJ, Machado AG, Miller Koop M, Rosenfeldt AB, Alberts JL. Objective assessment of postural stability in Parkinson's disease using mobile technology. Mov Disord 2015;30(9):1214-1221.
– reference: Pasluosta CF, Gassner H, Winkler J, Klucken J, Eskofier BM. An emerging era in the management of parkinson's disease: wearable technologies and the internet of things. IEEE J Biomed Health Inform 2015;19(6):1873-1881.
– reference: Weiss A, Herman T, Giladi N, Hausdorff JM. Objective assessment of fall risk in Parkinson's disease using a body-fixed sensor worn for 3 days. PLoS ONE 2014;9(5):e96675.
– reference: Zheng YL, Ding XR, Poon CC, et al. Unobtrusive sensing and wearable devices for health informatics. IEEE Trans Biomed Eng 2014;61(5):1538-1554.
– reference: Wang H, Jafari R, Zhou G, et al. Guest Editorial: Special Issue on Internet of Things for Smart and Connected Health. IEEE IoT J 2015;2(1):1-4.
– reference: Ledger D, McCaffrey D. Inside wearables: how the science of human behavior change offers the secret to long-term engagement. In: LLC EP, Dan Ledger and Daniel McCarey, eds. Endeavour Partners LLC; Cambridge, MA, 2014;1-17.
– reference: Maetzler W, Domingos J, Srulijes K, Ferreira JJ, Bloem BR. Quantitative wearable sensors for objective assessment of Parkinson's disease. Mov Disord 2013;28(12):1628-1637.
– reference: Horak FB, Mancini M. Objective biomarkers of balance and gait for Parkinson's disease using body-worn sensors. Mov Disord 2013;28(11):1544-1551.
– reference: Maetzler W, Liepelt I, Berg D. Progression of Parkinson's disease in the clinical phase: potential markers. Lancet Neurol 2009;8(12):1158-1171.
– reference: Hubble RP, Naughton GA, Silburn PA, Cole MH. Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: a systematic review. PLoS ONE 2015;10(4):e0123705.
– reference: Moore ST, Yungher DA, Morris TR, et al. Autonomous identification of freezing of gait in Parkinson's disease from lower-body segmental accelerometry. J Neuroeng Rehabil 2013;10:19.
– reference: Mazilu S, Calatroni A, Gazit E, Mirelman A, Hausdorff JM, Troster G. Prediction of freezing of gait in parkinson's from physiological wearables: an exploratory study. IEEE J Biomed Health Inform 2015;19(6):1843-1854.
– reference: Rampp A, Barth J, Schulein S, Gassmann KG, Klucken J, Eskofier BM. Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients. IEEE Trans Biomed Eng 2015;62(4):1089-1097.
– reference: Shull PB, Jirattigalachote W, Hunt MA, Cutkosky MR, Delp SL. Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture 2014;40(1):11-19.
– reference: Hellman AM, Shah SP, Pawlowski SM, Duda JE, Morley JF. Continuous non-invasive monitoring to detect covert autonomic dysfunction in Parkinson's disease. Parkinsonism Relat Disord 2015;21(7):723-728.
– reference: Valappil RA, Black JE, Broderick MJ, et al. Exploring the electrocardiogram as a potential tool to screen for premotor Parkinson's disease. Mov Disord 2010;25(14):2296-2303.
– reference: Patel S, Lorincz K, Hughes R, et al. Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors. IEEE Trans Inf Technol Biomed 2009;13(6):864-873.
– reference: Lieber B, Taylor BE, Appelboom G, McKhann G, Connolly ES Jr. Motion sensors to assess and monitor medical and surgical management of Parkinson disease. World Neurosurg 2015;84(2):561-566.
– reference: Liu G, Ho C, Slappey N, et al. A wearable conductivity sensor for wireless real-time sweat monitoring. Sensors Actuat B Chem 2016;227:35-42.
– reference: Solla P, Cadeddu C, Cannas A, et al. Heart rate variability shows different cardiovascular modulation in Parkinson's disease patients with tremor dominant subtype compared to those with akinetic rigid dominant subtype. J Neural Transm 2015:1-6.
– reference: Klucken J, Barth J, Kugler P, et al. Unbiased and mobile gait analysis detects motor impairment in Parkinson's disease. PLoS ONE 2013;8(2):e56956.
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– reference: Roy SH, Cole BT, Gilmore LD, et al. High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity. Mov Disord 2013;28(8):1080-1087.
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– reference: Berganzo K, Tijero B, Gonzalez-Eizaguirre A, et al. Motor and non-motor symptoms of Parkinson's disease and their impact on quality of life and on different clinical subgroups. Neurologia 2014, pii: S0213-4853(14)00233-3, doi: 10.1016/j.nrl.2014.10.010.
– reference: Espay AJ, Giuffrida JP, Chen R, et al. Differential response of speed, amplitude, and rhythm to dopaminergic medications in Parkinson's disease. Mov Disord 2011;26(14):2504-2508.
– reference: Nieuwboer A, De Weerdt W, Dom R, Lesaffre E. A frequency and correlation analysis of motor deficits in Parkinson patients. Disabil Rehabil 1998;20(4):142-150.
– reference: Hung SW, Adeli GM, Arenovich T, Fox SH, Lang AE. Patient perception of dyskinesia in Parkinson's disease. J Neurol Neurosurg Psychiatry 2010;81(10):1112-1115.
– reference: Ellis T, Cavanaugh JT, Earhart GM, Ford MP, Foreman KB, Dibble LE. Which measures of physical function and motor impairment best predict quality of life in Parkinson's disease? Parkinsonism Relat Disord 2011;17(9):693-697.
– reference: Quinn EJ, Blumenfeld Z, Velisar A, et al. Beta oscillations in freely moving Parkinson's subjects are attenuated during deep brain stimulation. Mov Disord 2015;30:1750-1758.
– reference: Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW. Body-worn sensors in Parkinson's disease: evaluating their acceptability to patients. Telemed J E Health 2016;22:63-69.
– reference: Fereshtehnejad SM, Romenets SR, Anang JB, Latreille V, Gagnon JF, Postuma RB. New clinical subtypes of parkinson disease and their longitudinal progression: a prospective cohort comparison with other phenotypes. JAMA Neurol 2015;72(8):863-873.
– reference: Del Din S, Godfrey A, Rochester L. Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson's disease: toward clinical and at home use. IEEE J Biomed Health Inform 2015 [Epub ahead of print].
– reference: Brun L, Lefaucheur R, Fetter D, et al. Non-motor fluctuations in Parkinson's disease: prevalence, characteristics and management in a large cohort of parkinsonian outpatients. Clin Neurol Neurosurg 2014;127:93-96.
– reference: Mera TO, Burack MA, Giuffrida JP. Quantitative assessment of levodopa-induced dyskinesia using automated motion sensing technology. Conf Proc IEEE Eng Med Biol Soc 2012;2012:154-157.
– reference: McCall C, McCall WV. Comparison of actigraphy with polysomnography and sleep logs in depressed insomniacs. J Sleep Res 2012;21(1):122-127.
– reference: Papapetropoulos S, Mitsi G, Espay AJ. Digital health revolution: is it time for affordable remote monitoring for Parkinson's disease? Front Neurol 2015;6:34.
– reference: Cazalé A, Sant W, Ginot F, et al. Physiological stress monitoring using sodium ion potentiometric microsensors for sweat analysis. Sensors Actuat B Chem 2016;225:1-9.
– reference: Cavanaugh JT, Ellis TD, Earhart GM, Ford MP, Foreman KB, Dibble LE. Capturing ambulatory activity decline in Parkinson's disease. J Neurol Phys Ther 2012;36(2):51-57.
– reference: Mendiola MF, Kalnicki M, Lindenauer S. Valuable features in mobile health apps for patients and consumers: content analysis of apps and user ratings. JMIR Mhealth Uhealth 2015;3(2):e40.
– reference: Shukla P, Basu I, Tuninetti D. Towards closed-loop deep brain stimulation: decision tree-based essential tremor patient's state classifier and tremor reappearance predictor. Conf Proc IEEE Eng Med Biol Soc 2014;2014:2605-2608.
– reference: Goetz CG, Stebbins GT, Wolff D, et al. Testing objective measures of motor impairment in early Parkinson's disease: feasibility study of an at-home testing device. Mov Disord 2009;24(4):551-556.
– reference: Bonato P. Wearable sensors and systems. From enabling technology to clinical applications. IEEE Eng Med Biol Mag 2010;29(3):25-36.
– reference: Jankovic J. Motor fluctuations and dyskinesias in Parkinson's disease: clinical manifestations. Mov Disord 2005;20(suppl 11):S11-S16.
– reference: Caldara M, Colleoni C, Guido E, Re V, Rosace G. Optical monitoring of sweat pH by a textile fabric wearable sensor based on covalently bonded litmus-3-glycidoxypropyltrimethoxysilane coating. Sensors Actuat B Chem 2016;222:213-220.
– reference: Mariani B, Jimenez MC, Vingerhoets FJ, Aminian K. On-shoe wearable sensors for gait and turning assessment of patients with Parkinson's disease. IEEE Trans Biomed Eng 2013;60(1):155-158.
– reference: Arora S, Venkataraman V, Zhan A, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord 2015;21(6):650-653.
– reference: Bayestehtashk A, Asgari M, Shafran I, McNames J. Fully automated assessment of the severity of Parkinson's disease from speech. Comput Speech Lang 2015;29(1):172-185.
– reference: Thenganatt MA, Jankovic J. Parkinson disease subtypes. JAMA Neurol 2014;71(4):499-504.
– reference: Bachlin M, Plotnik M, Roggen D, et al. Wearable assistant for Parkinson's disease patients with the freezing of gait symptom. IEEE Trans Inf Technol Biomed 2010;14(2):436-446.
– reference: Basu I, Graupe D, Tuninetti D, et al. Pathological tremor prediction using surface electromyogram and acceleration: potential use in "ON-OFF" demand driven deep brain stimulator design. J Neural Eng 2013;10(3):036019.
– reference: Guo Y, Graber A, McBurney RN, Balasubramanian R. Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms. BMC Bioinformatics 2010;11:447.
– reference: Chen BR, Patel S, Buckley T, et al. A web-based system for home monitoring of patients with Parkinson's disease using wearable sensors. IEEE Trans Biomed Eng 2011;58(3):831-836.
– reference: Consumer Intelligence Series: The Wearable Future. Pricewaterhouse Coopers. http://www.pwc.com/us/en/retail-consumer/publications/assets/pwc-cis-wearable-future.pdf
– reference: Das S, Trutoiu L, Murai A, et al. Quantitative measurement of motor symptoms in Parkinson's disease: a study with full-body motion capture data. Conf Proc IEEE Eng Med Biol Soc 2011;2011:6789-6792.
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Snippet ABSTRACT The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible...
The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in...
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SubjectTerms Biomedical Technology - standards
digital biomarkers
digital health
eHealth
Humans
Movement disorders
Parkinson Disease - diagnosis
Parkinson Disease - therapy
Parkinson's disease
precision medicine
remote monitoring
technology
wearable technology
Title Technology in Parkinson's disease: Challenges and opportunities
URI https://api.istex.fr/ark:/67375/WNG-8PQWHS9T-B/fulltext.pdf
https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fmds.26642
https://www.ncbi.nlm.nih.gov/pubmed/27125836
https://www.proquest.com/docview/1817048803
https://www.proquest.com/docview/1817853406
https://www.proquest.com/docview/1827903876
Volume 31
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