Nervenheilkunde 2022; 41(10): 658-665
DOI: 10.1055/a-1929-1684
Schwerpunkt

Digitale Unterstützung in Diagnose und Management von Parkinsonerkrankungen

ParkinsonCompanionDigital support for diagnosis and management of parkinson’s diseaseParkinsonCompanion
Jasmin Henze
1   Fraunhofer-Institut für Software- und Systemtechnik ISST, Gesundheitswesen, Dortmund
,
Pinar Bisgin
1   Fraunhofer-Institut für Software- und Systemtechnik ISST, Gesundheitswesen, Dortmund
,
Anja Burmann
1   Fraunhofer-Institut für Software- und Systemtechnik ISST, Gesundheitswesen, Dortmund
,
Christina Haubrich
2   Neuro Praxis Düsseldorf, Neurovegetative Diagnostik (ANS Clinic), Düsseldorf
› Author Affiliations

ZUSAMMENFASSUNG

Zur Unterstützung der Früherkennung, Diagnose und Begleitung der Parkinson-Erkrankung wurde der ParkinsonCompanion entwickelt. Ziel des Systems ist es, erstmals nicht motorische Symptome der Parkinson-Erkrankung, d. h. Störungen des Rapid-Eye-Movement (REM)-Schlafes und des Vegetativums, in einem patientennahen Monitoring zu berücksichtigen.

Ergebnis ist ein modularer Demonstrator bestehend aus mobilem Messgerät mit gekoppeltem Tablet sowie einer Webapplikation für Patienten (App) mit begleitender Analysesoftware, welche die Analyse von vegetativen Funktionen, Schlaf, Bewegung und kognitiven Funktionen integriert.

Das Ergebnis ist die Kombination eines mobilen Messgeräts zum Schlafmonitoring nach den Kriterien der American Academy of Sleep Medicine (AASM) mit Nachtkamera, neurovegetativen Tests sowie eines elektronischen Patienten-Tagebuches für zu Hause. Das Patienten-Tagebuch, die neurovegetative Diagnostik und das Schlafmonitoring könnten unabhängig voneinander zum Einsatz kommen.

ABSTRACT

The ParkinsonCompanion was developed to support the early detection, diagnosis and monitoring of Parkinson’s disease. Its goal is to enable usage of non-motor symptoms of Parkinson’s disease, i. e., disorders of rapid eye movement sleep and vegetative state, in a close-to-patient monitoring for the first time.

The result is a modular demonstrator consisting of a mobile measuring device with a connected tablet and a web application for patients (app) with accompanying analysis software, which integrates the analysis of vegetative functions, sleep, movement, and cognitive functions. The combination of a mobile measuring device for sleep monitoring according to AASM criteria, a night camera, tablet-based neurovegetative tests and an electronic patient diary is mobile and can also be used by patients at home. The patient diary, neurovegetative diagnostics and sleep monitoring could also be used independently of one another.



Publication History

Article published online:
14 October 2022

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  • Literatur

  • 1 Iranzo A, Fernandez-Arcos A, Tolosa E. et al Neurodegenerative Disorder Risk in Idiopathic REM Sleep Behavior Disorder: Study in 174 Patients. PLOS ONE 2014; 09 (02) e89741
  • 2 Magerkurth C, Schnitzer R, Braune S. Symptoms of autonomic failure in Parkinson’s disease: prevalence and impact on daily life. Clin Auton Res 2005; 15 (02) 76-82
  • 3 Maier A, Reetz K, Schiefer J. et al Störungen des REM-Schlafes und Vegetativer Nervenfunktionen im Prodromalstadium der Parkinsonerkrankung. Nervenheilkunde 2014; 34: 177-178
  • 4 Santiago A, Langston JW, Gandhy R. et al Qualitative Evaluation of the Personal KinetiGraphTM Movement Recording System in a Parkinson’s Clinic. J Parkinsons Dis 2019; 09 (01) 207-219
  • 5 Tsamis KI, Rigas G, Nikolas K. et al Accurate Monitoring of Parkinson’s Disease Symptoms With a Wearable Device During COVID-19 Pandemic. In Vivo 2021; 35 (04) 2327-2330
  • 6 Rodríguez-Martín D, Pérez-López JC, Pie M. et al A New Paradigm in Parkinson’s Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONTM. Front Neurol 2022 DOI: 10.3389/fneur.2022.912343
  • 7 Larkin HD. Apple Watch Parkinson Disease Symptom Monitor Is Cleared. JAMA 2022; 328 (05) 416
  • 8 Van Warmelen DJ, Sringean J, Trivedi D. et al Digital health technology for non-motor symptoms in people with Parkinson’s disease: Futile or future?. Parkinsonism Relat Disord 2021; 89: 186-194
  • 9 Chaudhuri KR, Martinez-Martin P, Brown RG. et al The metric properties of a novel non-motorsymptomsscale for Parkinson’s disease: Resultsfrom an international pilotstudy. Mov Disord 2007; 22: 1901-1911
  • 10 Mischley LK, Lau RC, Weiss NS. Use of a self-rating scale of the nature and severity of symptoms in Parkinson’s Disease (PRO-PD): Correlation with quality of life and existing scales of disease severity. NPJ Parkinson Dis 2017; 03: 20
  • 11 Palma JA, Gomez-Esteban JC, Norcliffe-Kaufmann L. et al Orthostatic Hypotension in Parkinson Disease: How Much You Fall or How Low You Go?. Movement Disorders 2015; 30: 5
  • 12 Bisgin P. et al REM Sleep Stage Detection of Parkinson’s Disease Patients with RBD. In: International Conference on Business Information Systems. Berlin: Springer; 2020
  • 13 Prashanth R, Dutta Roy S, Mandal P. et al High-Accuracy Detection of Early Parkinson’s Disease through Multimodal Features and Machine Learning. Int J Med Inform 2006; 90: 13-21
  • 14 Rovini E, Maremmani C, Cavallo F. How wearable sensors can support parkinson’s disease diagnosis and treatment: A systematic review. Front Neurosci 2017: 11
  • 15 Boeve BF.. Sleep Behavior Disorder: Updated Review of the Core Features, the RBD-Neurodegenerative Disease Association, Evolving Concepts, Controversies, and Future Directions Ann N Y Acad Sci. 2010 1184 15-54
  • 16 Sixel-Döring F, Trautmann E, Mollenhauer B. et al Associated factors for REM sleep behavior disorder in Parkinson disease. Neurology 2011; 77 (11) 1048-54
  • 17 Ziemssen T, Siepmann T. The Investigation of the Cardiovascular and Sudomotor Autonomic Nervous System-A Review. Front Neurol 2019; 10: 53