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Technology in Parkinson’s disease: challenges and opportunities
Open accessAbstract
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
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Adams, J., Dinesh, K., Xiong, M., Tarolli, C. G., Sharma, S., Sheth, N., Aranyosi, A. J., Zhu, W. Y., Goldenthal, S. B., Biglan, K., et al. (2017) · Digital Biomarkers · doi:10.1159/000479018 · Cited by 3,190
Self-citationMultifunctional wearable devices for diagnosis and therapy of movement disorders
Son, D., Lee, J., Qiao, S., Ghaffari, R., Kim, J., Lee, J. E., Song, C., Kim, S. J., Lee, D. J., Jun, S. W., et al. (2014) · Nature Nanotechnology · doi:10.1038/nnano.2014.38 · Cited by 1,462
Unobtrusive Sensing and Wearable Devices for Health Informatics
Zheng, Y.-L., Ding, X., Poon, C. C. Y., Lo, B. P. L., Zhang, H., Zhou, X.-L., Yang, G., Zhao, N., & Zhang, Y. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2309951 · Cited by 725
Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom
Bachlin, M., Plotnik, M., Roggen, D., Maidan, I., Hausdorff, J. M., Giladi, N., & Troster, G. (2010) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2036165 · Cited by 693 · cited 1×
“The detection of such motor behaviors could trigger a device designed to deliver proprioceptive cues that could lead to a change in postural control and stepping pattern, which in turn could prevent a fall.(43,52,53)”
BackgroundSelf-citationMonitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors
Patel, S., Lorincz, K., Hughes, R., Huggins, N., Growdon, J., Standaert, D., Akay, M., Dy, J., Welsh, M., & Bonato, P. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2033471 · Cited by 623
Self-citationFereshtehnejad, S.-M., Romenets, S. R., Anang, J. B. M., Latreille, V., Gagnon, J.-F., & Postuma, R. B. (2015) · JAMA Neurology · doi:10.1001/jamaneurol.2015.0703 · Cited by 615
Motor fluctuations and dyskinesias in Parkinson's disease: Clinical manifestations
Jankovic, J. J. (2005) · Movement Disorders · doi:10.1002/mds.20458 · Cited by 549
Thenganatt, M. A., & Jankovic, J. J. (2014) · JAMA Neurology · doi:10.1001/jamaneurol.2013.6233 · Cited by 542
Bonato, P. (2010) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2010.936554 · Cited by 423
Self-citationDetecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study
Arora, S., Venkataraman, V., Zhan, A., Donohue, S., Biglan, K. M., Dorsey, E. R., & Little, M. A. (2015) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2015.02.026 · Cited by 387
Self-citationShull, P. B., Jirattigalachote, W., Hunt, M. A., Cutkosky, M. R., & Delp, S. L. (2014) · Gait & Posture · doi:10.1016/j.gaitpost.2014.03.189 · Cited by 384
Quantitative wearable sensors for objective assessment of Parkinson's disease
Maetzler, W., Domingos, J., Srulijes, K., Ferreira, J. J., & Bloem, B. R. (2013) · Movement Disorders · doi:10.1002/mds.25628 · Cited by 374
Self-citationDin, S. D., Godfrey, A., & Rochester, L. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2419317 · Cited by 363
Pasluosta, C. F., Gassner, H., Winkler, J., Klucken, J., & Eskofier, B. M. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2461555 · Cited by 323
Self-citationProgression of Parkinson's disease in the clinical phase: potential markers
Maetzler, W., Liepelt, I., & Berg, D. (2009) · The Lancet Neurology · doi:10.1016/s1474-4422(09)70291-1 · Cited by 307
Self-citationInertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients
Rampp, A., Barth, J., Schülein, S., Gaßmann, K., Klucken, J., & Eskofier, B. M. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2368211 · Cited by 300
Self-citationOn-Shoe Wearable Sensors for Gait and Turning Assessment of Patients With Parkinson's Disease
Mariani, B., Jiménez, M. C., Vingerhoets, F. J. G., & Aminian, K. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2227317 · Cited by 277
Objective biomarkers of balance and gait for Parkinson's disease using body‐worn sensors
Horak, F. B., & Mancini, M. (2013) · Movement Disorders · doi:10.1002/mds.25684 · Cited by 256
Self-citationBeta oscillations in freely moving Parkinson's subjects are attenuated during deep brain stimulation
Quinn, E. J., Blumenfeld, Z., Velisar, A., Koop, M. M., Shreve, L., Trager, M. H., Hill, B. C., Kilbane, C., Henderson, J. M., & Bronte-Stewart, H. M. (2015) · Movement Disorders · doi:10.1002/mds.26376 · Cited by 227
Role of Body-Worn Movement Monitor Technology for Balance and Gait Rehabilitation
Horak, F. B., King, L. A., & Mancini, M. (2014) · Physical Therapy · doi:10.2522/ptj.20140253 · Cited by 227
Self-citationMendiola, M. F., Kalnicki, M., & Lindenauer, S. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4283 · Cited by 226
Objective Assessment of Fall Risk in Parkinson's Disease Using a Body-Fixed Sensor Worn for 3 Days
Weiss, A., Herman, T., Giladi, N., & Hausdorff, J. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096675 · Cited by 225
Self-citationUnbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease
Klucken, J., Barth, J., Kugler, P., Schlachetzki, J. C. M., Henze, T., Marxreiter, F., Kohl, Z., Steidl, R., Joachim Hornegger, A., Eskofier, B. M., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056956 · Cited by 217
Self-citationHubble, R. P., Naughton, G. A., Silburn, P. A., & Cole, M. H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123705 · Cited by 210
Moore, S. T., Yungher, D. A., Morris, T. R., Dilda, V., MacDougall, H. G., Shine, J. M., Naismith, S. L., & Lewis, S. J. (2013) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-10-19 · Cited by 204
Comparison of actigraphy with polysomnography and sleep logs in depressed insomniacs
McCall, C., & McCall, W. V. (2011) · Journal of Sleep Research · doi:10.1111/j.1365-2869.2011.00917.x · Cited by 192
Caldara, M., Colleoni, C., Guido, E., Re, V., & Rosace, G. (2015) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2015.08.073 · Cited by 181
A frequency and correlation analysis of motor deficits in Parkinson patients
Nieuwboer, A., Weerdt, W. D., Dom, R., & Lesaffre, E. (1998) · Disability and Rehabilitation · doi:10.3109/09638289809166074 · Cited by 175
Self-citationA wearable conductivity sensor for wireless real-time sweat monitoring
Liu, G.-Y., Ho, C., Slappey, N., Zhou, Z., Snelgrove, S. E., Brown, M., Grabinski, A. K., Guo, X., Chen, Y., Miller, K. C., et al. (2015) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2015.12.034 · Cited by 164
A Web-Based System for Home Monitoring of Patients With Parkinson's Disease Using Wearable Sensors
Chen, B.-R., Patel, S., Buckley, T. A., Rednic, R., McClure, D., Shih, L. C., Tarsy, D., Welsh, M., & Bonato, P. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2090044 · Cited by 162
Self-citationEllis, T. D., Cavanaugh, J. T., Earhart, G. M., Ford, M. P., Foreman, K. B., & Dibble, L. E. (2011) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2011.07.004 · Cited by 158
Espay, A. J., Giuffrida, J. P., Chen, R. E., Payne, M., Mazzella, F., Dunn, E. J., Vaughan, J. E., Duker, A. P., Sahay, A., Kim, S. J., et al. (2011) · Movement Disorders · doi:10.1002/mds.23893 · Cited by 156
Self-citationGoetz, C. G., Stebbins, G. T., WOLFF, D. A., Deleeuw, W. W., Bronte-Stewart, H. M., Elble, R. J., Hallett, M., Nutt, J. G., Ramig, L. A., Sanger, T. D., et al. (2008) · Movement Disorders · doi:10.1002/mds.22379 · Cited by 152
Self-citationPrediction of Freezing of Gait in Parkinson's From Physiological Wearables: An Exploratory Study
Mazilu, S., Calatroni, A., Gazit, E., Mirelman, A., Hausdorff, J. M., & Tröster, G. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2465134 · Cited by 146
Self-citationCapturing Ambulatory Activity Decline in Parkinson's Disease
Cavanaugh, J. T., Ellis, T. D., Earhart, G. M., Ford, M. P., Foreman, K. B., & Dibble, L. E. (2012) · Journal of Neurologic Physical Therapy · doi:10.1097/npt.0b013e318254ba7a · Cited by 141
Technology-Assisted Balance and Gait Training Reduces Falls in Patients With Parkinson’s Disease
Shen, X., & Mak, K. Y. (2014) · Neurorehabilitation and neural repair · doi:10.1177/1545968314537559 · Cited by 115
Fully automated assessment of the severity of Parkinson's disease from speech
Bayestehtashk, A., Asgari, M., Izhak Shafran, J., & McNames, J. (2014) · Computer Speech & Language · doi:10.1016/j.csl.2013.12.001 · Cited by 112
Berganzo, K., Tijero, B., González-Eizaguirre, A., Somme, J. H., Lezcano, E., Gabilondo, I., Fernandez, M., Zarranz, J. J., & Gómez‐Esteban, J. C. (2016) · Neurología (English Edition) · doi:10.1016/j.nrleng.2014.10.016 · Cited by 109
Berganzo, K., Tijero, B., González-Eizaguirre, A., Somme, J. H., Lezcano, E., Gabilondo, I., Fernández, M., Zarranz, J. J., & Gómez‐Esteban, J. C. (2014) · Neurología · doi:10.1016/j.nrl.2014.10.010 · Cited by 105
Guo, Y. F., Graber, A., McBurney, R. N., & Balasubramanian, R. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-447 · Cited by 102
Basu, I., Graupe, D., Tuninetti, D., Shukla, P., Slavin, K. V., Metman, L. V., & Corcos, D. M. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/3/036019 · Cited by 99
Ferreira, J. J., Godinho, C., Santos, A. T., Domingos, J., Abreu, D., Lobo, R., Gonçalves, N., Barra, M., Larsen, F., Fagerbakke, Ø., et al. (2015) · BMC Neurology · doi:10.1186/s12883-015-0343-z · Cited by 96
Self-citationExploring the electrocardiogram as a potential tool to screen for premotor Parkinson's disease
Valappil, R. A., Black, J. E., Broderick, M. J., Carrillo, O., Frenette, E., Sullivan, S. S., Goldman, S. M., Tanner, C. M., & Langston, J. W. (2010) · Movement Disorders · doi:10.1002/mds.23348 · Cited by 90
Das, S., Trutoiu, L., Murai, A., Alcindor, D. S., Oh, M. Y., la Torre, F. D., & Hodgins, J. K. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091674 · Cited by 90
Stamford, J. A., Schmidt, P. N., & Friedl, K. E. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2464354 · Cited by 88
Physiological stress monitoring using sodium ion potentiometric microsensors for sweat analysis
Cazalé, A., Sant, W., Ginot, F., Launay, J., Savourey, G., Revol-Cavalier, F., Lagarde, J., Heinry, D., Launay, J., & Temple‐Boyer, P. (2015) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2015.10.114 · Cited by 81
Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients
Fisher, J. M., Hammerla, N. Y., Rochester, L., Andras, P., & Walker, R. W. (2016) · Telemedicine and e-Health · doi:10.1089/tmj.2015.0026 · Cited by 81
Objective assessment of postural stability in Parkinson's disease using mobile technology
Ozinga, S. J., Machado, A. G., Koop, M. M., Rosenfeldt, A. B., & Alberts, J. L. (2015) · Movement Disorders · doi:10.1002/mds.26214 · Cited by 75
Dubey, H., Goldberg, J. C., Abtahi, M., Mahler, L. A., & Mankodiya, K. (2015) · Wireless Health · doi:10.1145/2811780.2811957 · Cited by 67
High‐resolution tracking of motor disorders in Parkinson's disease during unconstrained activity
Roy, S. H., Cole, B. T., Gilmore, L., Luca, C. J. D., Thomas, C. A., Saint‐Hilaire, M., & Nawab, S. H. (2013) · Movement Disorders · doi:10.1002/mds.25391 · Cited by 61
Patient perception of dyskinesia in Parkinson's disease
Hung, S., Adeli, G., Arenovich, T., Fox, S. H., & Lang, A. E. (2010) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2009.173286 · Cited by 59
Self-citationBrun, L., Lefaucheur, R., Fetter, D., Derrey, S., Borden, A., Wallon, D., Bourre, B., & Maltête, D. (2014) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2014.10.006 · Cited by 41
Motion Sensors to Assess and Monitor Medical and Surgical Management of Parkinson Disease
Lieber, B. A., Taylor, B. E. S., Appelboom, G., McKhann, G. M., & Connolly, E. S. (2015) · World Neurosurgery · doi:10.1016/j.wneu.2015.03.024 · Cited by 37
Digital Health Revolution: Is it Time for Affordable Remote Monitoring for Parkinson’s Disease?
Papapetropoulos, S., Mitsi, G., & Espay, A. J. (2015) · Frontiers in Neurology · doi:10.3389/fneur.2015.00034 · Cited by 26
Self-citationSolla, P., Dessalvi, C. C., Cannas, A., Deidda, M., Mura, N., Mercuro, G. G., & Marrosu, F. (2015) · Journal of Neural Transmission · doi:10.1007/s00702-015-1393-5 · Cited by 25
Rahimi, F., the EMAP Group, Bee, C., Duval, C., Boissy, P., Edwards, R., & Jog, M. (2014) · Journal of Parkinson s Disease · doi:10.3233/jpd-140370 · Cited by 24
Quantitative assessment of levodopa-induced dyskinesia using automated motion sensing technology
Mera, T. O., Burack, M. A., & Giuffrida, J. P. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6345894 · Cited by 20
Self-citationGuest Editorial : Special Issue on Internet of Things for Smart and Connected Health
Wang, H., Jafari, R., Zhou, G., Venkatasubramanian, K. K., Sun, J., Bonato, P., & Wu, D. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2394696 · Cited by 19
Self-citationDiminished event‐related cortical arousals and altered heart rate response in Parkinson's disease
Sommerauer, M., Imbach, L., Jarallah, M., Baumann, C. R., & Valko, P. O. (2015) · Movement Disorders · doi:10.1002/mds.26165 · Cited by 18
Continuous non-invasive monitoring to detect covert autonomic dysfunction in Parkinson's disease
Hellman, A. M., Shah, S. P., Pawlowski, S. M., Duda, J. E., & Morley, J. F. (2015) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2015.04.016 · Cited by 18
A novel method for assessing the severity of levodopa-induced dyskinesia using wearable sensors
Lee, S. I., Daneault, J., Golabchi, F. N., Patel, S., Paganoni, S., Shih, L. C., & Bonato, P. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7320270 · Cited by 18
Self-citationShukla, P., Basu, I., & Tuninetti, D. (2014) · doi:10.1109/embc.2014.6944156 · Cited by 17
Research works printed but not linked: 2
- Ledger D, 2014, Inside wearables: how the science of human behavior change offers the secret to long‐term engagement, 1
- Consumer Intelligence Series: The Wearable Future
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Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare
Farahani, B., Firouzi, F., Chang, V. I. C., Badaroglu, M., Constant, N. P., & Mankodiya, K. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.04.036 · Cited by 991
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Metadata
- Authors
- Espay, A. J., Bonato, P., Nahab, F. B., Maetzler, W., Dean, J. M., Klucken, J., Eskofier, B. M., Merola, A., Horak, F., Lang, A. E., Reilmann, R., Giuffrida, J., Nieuwboer, A., Horne, M., Little, M. A., Litvan, I., Simuni, T., Dorsey, E. R., Burack, M. A., Kubota, K., Kamondi, A., Godinho, C., Daneault, J., Mitsi, G., Krinke, L., Hausdorff, J. M., Bloem, B. R., Papapetropoulos, S., & on behalf of the Movement Disorders Society Task Force on Technology
- Affiliations
- University of Cincinnati (US)
- Harvard University (US)
- University of California San Diego (US)
- German Center for Neurodegenerative Diseases (DE)
- Davis Phinney Foundation (US)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Azienda Ospedaliera Citta' della Salute e della Scienza di Torino (IT)
- Oregon Health & Science University (US)
- Toronto Western Hospital (CA)
- University of Münster (DE)
- Great Lakes NeuroTechnologies (United States) (US)
- KU Leuven (BE)
- The University of Melbourne (AU)
- Aston University (GB)
- Northwestern University (US)
- University of Rochester Medicine (US)
- Michael J. Fox Foundation (US)
- Országos Idegsebészeti Tudományos Intézet (HU)
- Escola Superior de Saúde Egas Moniz (PT)
- Department of Physical Medicine and Rehabilitation Harvard Medical School Boston Massachusetts USA
- Apptomics LLC Wellesley Massachusetts USA
- Medtronic (United States) (US)
- Sackler School of Medicine, Tel Aviv University and Center for the Study of Movement Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center Tel Aviv Israel
- Radboud University Nijmegen (NL)
- Massachusetts General Hospital Boston Massachusetts USA
- Corresponding author
- Espay, Alberto J.
- ORCID
- Venue
- Movement Disorders
- Publisher
- Wiley
- Published
- 2016-04-29
- Volume
- 31
- Issue
- 9
- Pages
- 1272-1282
- ISSN
- 0885-3185 (ISSN-L)
- 1531-8257
- PMC
- PMC5014594
- OpenAlex
- W2343290709
- Cited by
- 654
- References
- 62
- Citations by year
- 2026: 50 · 2025: 66 · 2024: 71 · 2023: 69 · 2022: 70 · 2021: 72 · 2020: 72 · 2019: 70 · 2018: 57 · 2017: 44 · 2016: 13
- FWCI
- 8.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Movement Disorders (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Aston Publications Explorer (Aston University) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Lirias (KU Leuven) (submittedVersion)
- DZNE Pub (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- Parkinson's UK (J-1403, J-0901)
- National Institutes of Health (UL1TR000460)
- Canadian Institutes of Health Research
- National Institute on Aging (R01AG006457, P50AG005131)
- National Heart, Lung, and Blood Institute (T35HL007491)
- National Institute of Mental Health (K23MH092735)
- National Institute of Neurological Disorders and Stroke (U01NS086659, R01NS073683, U54NS092089, P20NS092529)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Clinical Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Medicine › Clinical Neurology › Deep Brain Stimulation and Movement Disorders
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Keywords
- Parkinson's disease, wearable sensors, closed-loop therapy, open-source hardware, open hardware platform, objective biomarkers, phenotyping, disease-modifying therapy, personalized treatment, patient adherence
- MeSH terms
- Humans, Parkinson Disease, Biomedical Technology
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