The use of wearables for the diagnosis and treatment of Parkinson's disease.
Digital medicine
Parkinson’s disease
Sensors
Telemedicine
Wearables
Journal
Journal of neural transmission (Vienna, Austria : 1996)
ISSN: 1435-1463
Titre abrégé: J Neural Transm (Vienna)
Pays: Austria
ID NLM: 9702341
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
received:
17
11
2022
accepted:
13
12
2022
medline:
22
5
2023
pubmed:
8
1
2023
entrez:
7
1
2023
Statut:
ppublish
Résumé
Parkinson's disease (PD) is the second most common neurodegenerative disorder, with increasing numbers of affected patients. Many patients lack adequate care due to insufficient specialist neurologists/geriatricians, and older patients experience difficulties traveling far distances to reach their treating physicians. A new option for these obstacles would be telemedicine and wearables. During the last decade, the development of wearable sensors has allowed for the continuous monitoring of bradykinesia and dyskinesia. Meanwhile, other systems can also detect tremors, freezing of gait, and gait problems. The most recently developed systems cover both sides of the body and include smartphone apps where the patients have to register their medication intake and well-being. In turn, the physicians receive advice on changing the patient's medication and recommendations for additional supportive therapies such as physiotherapy. The use of smartphone apps may also be adapted to detect PD symptoms such as bradykinesia, tremor, voice abnormalities, or changes in facial expression. Such tools can be used for the general population to detect PD early or for known PD patients to detect deterioration. It is noteworthy that most PD patients can use these digital tools. In modern times, wearable sensors and telemedicine open a new window of opportunity for patients with PD that are easy to use and accessible to most of the population.
Identifiants
pubmed: 36609737
doi: 10.1007/s00702-022-02575-5
pii: 10.1007/s00702-022-02575-5
pmc: PMC10199831
doi:
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
783-791Informations de copyright
© 2023. The Author(s).
Références
Ancona S, Faraci FD, Khatab E, Fiorillo L, Gnarra O, Nef T, Bassetti CLA, Bargiotas P (2022) Wearables in the home-based assessment of abnormal movements in Parkinson’s disease: a systematic review of the literature. J Neurol 269:100–110
doi: 10.1007/s00415-020-10350-3
pubmed: 33409603
Bendig J, Wolf AS, Mark T, Frank A, Mathiebe J, Scheibe M, Müller G, Stahr M, Schmitt J, Reichmann H, Loewenbrueck KF, Falkenburger BH (2022) Feasibility of a multimodal telemedical intervention for patients with Parkinson’s disease- A pilot study. J Clin Med 11:1074. https://doi.org/10.3390/jcm11041074
doi: 10.3390/jcm11041074
pubmed: 35207351
pmcid: 8875136
Channa A, Popescu N, Ciobanu V (2020) Wearable solutions for patients with Parkinson’s disease and neurocognitive disorder: a systematic review. Sensors 20:2713
doi: 10.3390/s20092713
pubmed: 32397516
pmcid: 7249148
Chen J, Ho SL, Lee TMC, Chang RSK, Pang SYY, Li L (2016) Visuomotor control in patients with Parkinson’s disease. Neuropsychologia 80:102–114
doi: 10.1016/j.neuropsychologia.2015.10.036
pubmed: 26529488
Fagerberg P, Klingelhoefer L et al (2020) Lower energy intake among advanced vs. Early Parkinson’s Disease patients and healthy controls in a clinical lunch setting: a cross-sectional study. Nutrients 12(7):2109
doi: 10.3390/nu12072109
pubmed: 32708668
pmcid: 7400863
Fagerberg P, Klingelhoefer L, et al. 2019 Advanced Parkinson`s disease patients eat less food in comparison to early Parkinson`s patients and healthy controls in a controlled lunch setting. in Nutrients. Barcelona: Sciforum.net.
Farzanehfar P, Woodrow H, Horne M (2022) Sensor measurements can charaterise fluctuations and wearing off in Parkinson’s disease and guide therapy to improve motor, non-motor and quality of life scores. Front Aging Neurosci. 14:852992. https://doi.org/10.3389/fnagi.2022.852992
doi: 10.3389/fnagi.2022.852992
pubmed: 35401155
pmcid: 8984604
Goetz CG et al (1997) Efficacy of a patient-training videotape on motor fluctuations for on-off diaries in Parkinson’s disease. Mov Disord 12(6):1039–1041
doi: 10.1002/mds.870120631
pubmed: 9399233
Griffiths RI, Kotschet K, Arfon S, Xu ZM, Johnson W, Drago J, Evans A, Kempster P, Raghav S, Horne MK (2012) Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease. J Parkinsons Dis 2:47–55
doi: 10.3233/JPD-2012-11071
pubmed: 23939408
Hadjidimitriou S, et al. 2016 Active and healthy ageing for Parkinson’s Disease patients support: A user’s perspective within the i-PROGNOSIS framework. In: 1st International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW). 1–8.
Hansen C, Sanchez-Ferro A, Maetzler W (2018) How mobile health technology and electronic health records will change care of patients with Parkinson’s disease. J Parkinson’s Dis 8:S41-45
doi: 10.3233/JPD-181498
Iakovakis D et al (2018) Motor impairment estimates via touchscreen typing dynamics toward Parkinson’s Disease detection from data harvested in-the-wild. Front. ICT. 5:28
doi: 10.3389/fict.2018.00028
Iakovakis D, Chaudhuri KR, Klingelhoefer L, Bostanjopoulou S, Katsarou Z, Trivedi D, Reichmann H, Hadjidimitriou S, Charisis V, Hadjileontiadis LJ (2020) Screening of parkinsonian fine-motor impairment from touchscreen typing via deep learning. Sci Rep 10:12623
doi: 10.1038/s41598-020-69369-1
pubmed: 32724210
pmcid: 7387517
Iakovakis D, Mastoras RE, Hadjidimitriou S, Charisis V, Bostanjopoulou S, Katsarou Z, Klingelhoefer L, Reichmann H, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ. 2020 Smartwatch-based activity analysis during sleep for early Parkinson’s detection. Annu Int Conf IEEE Eng Med Biol Soc. 4326–4329.
Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, Charisis V, Bostanjopoulou S, Katsarou Z, Klingelhoefer L, Mayer S, Reichmann H, Dias SB. 2019 Early Parkinson’s disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. 3535–3538.
Klingelhoefer L, Rizos A, Saurbier A, McGregor S, Martinez-Martin P, Reichmann H, Horne M, Chaudhuri KR (2016) Night-time sleep in Parkinson’s disease- the potential use of Parkinson’s KintiGraph: a prospective comparative study. Eur J Neurol 23:1275–1288
doi: 10.1111/ene.13015
pubmed: 27160044
Klingelhoefer L et al (2019) Medical evaluation as gold standard to control iPrognosis application derived data for early Parkinson’s disease detection. Mov Disord 34(SupplS2):S913
Klingelhoefer L, et al. 2017 iPrognosis—towards an early detection of Parkinson's disease via a smartphone application. In: 90. Kongress der Deutschen Gesellschaft für Neurologie. DGN. Leipzig, Germany
Klingelhoefer L, et al. 2019 iPrognosis—early detection of Parkinso's disease via a smartphone application—proof of concept (iPrognosis—frühe Erkennung der Parkinson-erkrankung mittels Smartphone App—es ist möglich). In: Deutscher Kongress für Parkinson und Bewegungsstörungen. Düsseldorf, Germany.
Kotschet K et al (2014) Daytime sleep in Parkinson’s disease measured by episodes of immobility. Parkinsonism Relat Disord 20(6):578–583
doi: 10.1016/j.parkreldis.2014.02.011
pubmed: 24674770
Kyritsis K, Fagerberg P, Ioakimidis I, Chaudhuri KR, Reichmann H, Klingelhoefer L, Delopoulos A (2021) Assessment of real-life eating difficulties in Parkinson’s Disease patients by measuring plate-to-mouth movement elongation with inertial sensors. Sci Rep 11(1):1632. https://doi.org/10.1038/s41598-020-80394-y
doi: 10.1038/s41598-020-80394-y
pubmed: 33452324
pmcid: 7810687
Kyritsis K, Etter F, Ioannis I, Lisa K, Heinz R, Anastasios D, et al. 2020 Using IMU Sensors to Assess Motor Degradation of PD Patients by Modeling In-meal Plate-to-Mouth Movement Elongation. In 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE: Montréal. Québec, Canada.
Laganas C, Iakovakis D, Hadjidimitriou SK, Charisis V, Dias SB, Bostanjopoulou S, Katsarou Z, Klingelhoefer L, Reichmann H, Trivedi D, Chaudhuri R, Hadjileontiadis LJ (2021) Parkinson’s disease detection based on running speech data from phone calls. IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.2021.3116935
doi: 10.1109/TBME.2021.3116935
Little MA (2021) Smartphones for remote symptom monitoring of Parkinson’s disease. J Park Dis 11:S49–S53
Maetzler W, Liepelt I, Berg D (2009) Progression of Parkinson’s disease in the clinical phase: potential markers. Lancet Neurol 8(12):1158–1171
doi: 10.1016/S1474-4422(09)70291-1
pubmed: 19909914
Maetzler W et al (2013) Quantitative wearable sensors for objective assessment of Parkinson’s disease. Mov Disord 28(12):1628–1637
doi: 10.1002/mds.25628
pubmed: 24030855
Marek K, Jennings D, Lasch S, Siderowf A, Tanner C, Simuni T, Coffey C, Kieburtz K, Flagg E, Chowdhury S, Poewe W, Mollenhauer B, Klinik P-E, Sherer T, Frasier M, Meunier C, Rudolph A, Casaceli C, Seibyl J, Mendick S, Schuff N, Zhang Y, Toga A, Crawford K, Ansbach A, De Blasio P, Piovella M, Trojanowski J, Shaw L, Singleton A, Hawkins K, Eberling J, Brooks D, Russell D, Leary L, Factor S, Sommerfeld B, Hogarth P, Pighetti E, Williams K, Standaert D, Guthrie S, Hauser R, Delgado H, Jankovic J, Hunter C, Stern M, Tran B, Leverenz J, Baca M, Frank S, Thomas C-A, Richard I, Deeley C, Rees L, Sprenger F, Lang E, Shill H, Obradov S, Fernandez H, Winters A, Berg D, Gauss K, Galasko D, Fontaine D, Mari Z, Gerstenhaber M, Brooks D, Malloy S, Barone P, Longo K, Comery T, Ravina B, Grachev I, Gallagher K, Collins M, Widnell KL, Ostrowizki S, Fontoura P, Ho T, Luthman J, van der Brug M, Reith AD, Taylor P (2011) The Parkinson progression marker initiative (PPMI). Prog Neurobiol 95(4):629–635
doi: 10.1016/j.pneurobio.2011.09.005
pmcid: 9014725
Maserejian N, Vinikoor-Imler I, Dilley A. 2020 Estimation of the 2020 Global Population of Parkinson’s Disease (PD). International Congress of Parkinson’s Disease and Movement Disorders. In: htpps:// www.mdsabstracts.org/abstract/estimation-of-the-2020-global-population-of-parkinsons-disease-pb .
Monje MHG, Foffani G, Obeso J, Sanchez-Ferro A (2019) New sensor and wearable technologies to aid in the diagnosis and treatment monitoring Parkinson’s disease. Annu Rev Biomed Eng 21:111–143
doi: 10.1146/annurev-bioeng-062117-121036
pubmed: 31167102
Ossig C, Gandor F, Fauser M, Bosredon C, Churilov L, Reichmann H, Horne MK, Ebersbach G, Storch A (2016) Correlation of quantitative motor state assessment using a kinetograph and patient diaries in advanced PD: Data from an observational study. PLoS ONE 11:e0161559
doi: 10.1371/journal.pone.0161559
pubmed: 27556806
pmcid: 4996447
Papadopoulos A, Kyritsis K, Klingelhöfer L, Bostanjopoulou S, Chaudhuri KR, Delopoulos A (2019) Detecting Parkinsonian Tremor from IMU data collected in-the-wild using deep multiple-instance learning. IEEE J Biomed Health Inform 24(9):2559–2569
doi: 10.1109/JBHI.2019.2961748
pubmed: 31880570
Papadopoulos A, et al. 2019 Multiple-Instance Learning for In-the-Wild Parkinsonian Tremor Detection. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Eng Med Biol Mag. Berlin, Germany.
Papapetropoulos SS (2012) Patient diaries as a clinical endpoint in Parkinson’s disease clinical trials. CNS Neurosci Ther 18(5):380–387
doi: 10.1111/j.1755-5949.2011.00253.x
pubmed: 22070400
Ramsperger R, Meckler S, Heger T, An Uem J, Hucker S, Braatz U et al (2016) Continuous leg dyskinesia assessment in Parkinson’s disease-clinical validity and ecological effect. Parkinsonism Relat Disord 26:41–46
doi: 10.1016/j.parkreldis.2016.02.007
pubmed: 26952699
Ray Dorsey E, Elbaz A, Nichols E, Abd-Allah F, Abdelalim A, Adsuar JC, Snsha MG, Brayne C, Choi JYJ, Collado-Mateo D et al (2018) Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systemic analysis for the global burden of disease study 2016. Lancet Neurol 17:939–953
doi: 10.1016/S1474-4422(18)30295-3
Rodriguez-Martin D, Cabestany J, Perez-Lopez C, Pie M, Calvet J, Sama A, Capra C, Catala A, Rodriguez-Molinero A (2022) A new paradigm in Parkinson’s disease evaluation with wearable medical devices: a review of STAT-ON™. Front Neurol 13:912343
doi: 10.3389/fneur.2022.912343
pubmed: 35720090
pmcid: 9202426
Rossi A, Berger K, Chen H, Leslie D, Mailman RB, Huang X (2018) Projection of the prevalence of Parkinson’s disease in the coming decades: revisited. Mov Disord 33:156–159
doi: 10.1002/mds.27063
pubmed: 28590580
Rovini E, Maremmani C, Cavallo F (2017) How wearable sensors can support Parkinson’s disease diagnosis and treatment: a systematic review. Front Neurosci 11:555
doi: 10.3389/fnins.2017.00555
pubmed: 29056899
pmcid: 5635326
Schuepbach WMM, Rau J, Knudsen K, Volkmann J, Krack P, Timmermann L et al (2013) Neurostimulation for Parkinson’s disease with early motor complications. N Engl J Med 368(7):610–622
doi: 10.1056/NEJMoa1205158
pubmed: 23406026
Sweeney D, Quinlan I, Browne P, Richardson M, Meskell P, O’Laighin G et al (2019) Technological review of wearable cueing devices addressing freezing of gait in Parkinson’s disease. Sensors 19:1277
doi: 10.3390/s19061277
pubmed: 30871253
pmcid: 6470562
Tsiouris KM, Gatsios D, Rigas G, Miljkovic D, Karousic-Seljak B, Bohanec M, Arredondo MT, Antonini A, Konitsiotis S, Koutsouris D, Fotiadis DI (2017) PD_Manager: a mhealth platform for Parkinson’s disease patient management. Healthcare Technol Lett 4:102–108
doi: 10.1049/htl.2017.0007
Wanneveich M, Moisan F, Jacqmin-Gadda H, Elbaz A, Joly P (2018) Projections of prevalence, lifetime risk, and life expectancy of Parkinson’s disease (2010–2030) in France. Mov Disord 33:1449–1455
doi: 10.1002/mds.27447
pubmed: 30145805