Wearables als unterstützendes Tool für den Paradigmenwechsel in der Versorgung von Parkinson Patienten

Zusammenfassung

Tragbare Sensoren – „Wearables“ – eignen sich, Funktionsstörungen bei Parkinson Patienten zu erheben und werden zur Prävention, Prädiktion, Diagnostik und Therapieunterstützung genutzt. In der Forschung erhöhen sie die Reliabilität der erhobenen Daten und stellen bessere Studien-Endpunkte dar, als die herkömmlichen, subjektiven und wenig quantitativen Rating- und Selbstbeurteilungsskalen. Untersucht werden motorische Symptome wie Tremor, Bradykinese und Gangstörungen und auch nicht motorische Symptome. In der Home-Monitoringanwendung kann der Ist-Zustand des Patienten im realen Leben untersucht werden, die Therapie überwacht, die Adhärenz verbessert und die Compliance überprüft werden. Zusätzlich können Wearables interventionell zur Verbesserung von Symptomen eingesetzt werden wie z. B. Cueing, Gamification oder Coaching. Der Transfer von Laborbedingungen in den häuslichen Alltag ist eine medizinisch-technische Herausforderung. Optimierte Versorgungsmodelle müssen entwickelt werden und der tatsächliche Nutzen für den individuellen Patienten in weiteren Studien belegt werden.

Abstract

Wearables are portable sensors that are suitable for collecting data on functional disturbances in patients with Parkinson’s disease and are used for prevention, prediction, diagnosis and therapy. In research applications, they increase the reliability of the collected data and represent better study endpoints than traditional subjective and non-quantitative ratings and self-report scales. In this study, we examine motor symptoms such as tremor, bradykinesia and gait disturbances as well as non-motor symptoms. Home diagnostics can be used to examine the patient’s condition in real life, monitor therapy, improve adherence, and check compliance. Additionally, wearables can be used in interventions such as cueing, gamification or coaching in order to improve symptoms. Transfer from laboratory conditions to everyday life at home is a medical-technical challenge, optimized care models have to be developed and the actual benefit for the individual patient needs to be demonstrated in further studies.

Schlüsselwörter

Morbus Parkinson - Wearable - Ganganalyse - Value-based-medicine - Versorgungseffekt

Key words

Parkinson’s disease - wearables - gait analysis - value-based medicine - care effect

Publikationsverlauf

Artikel online veröffentlicht:
23. Februar 2021

• Literatur

• 1 Hansen C, Sanchez-Ferro A, Maetzler W. How Mobile Health Technology and Electronic Health Records Will Change Care of Patients with Parkinson’s Disease. J Parkinsons Dis 2018; 8: S41-S45
• 2 Prell T, Siebecker F, Lorrain M. et al. Recommendations for Standards of Network Care for Patients with Parkinson’s Disease in Germany. J Clin Med 2020; 9: 1455
• 3 Odin P, Chaudhuri KR, Volkmann J. et al. Viewpoint and practical recommendations from a movement disorder specialist panel on objective measurement in the clinical management of Parkinson’s disease. NPJ Parkinsons Dis 2018; 4: 14
• 4 van Wamelen DJ, Hota S, Podlewska A. et al. Non-motor correlates of wrist-worn wearable sensor use in Parkinson’s disease: an exploratory analysis. NPJ Parkinsons Dis 2019; 5: 22
• 5 Silva de Lima AL, Smits T, Darweesh SKL. et al. Home-based monitoring of falls using wearable sensors in Parkinson’s disease. Mov Disord 2020; 35: 109-115
• 6 Zampogna A, Mileti I, Palermo E. et al. Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders. Sensors (Basel) 2020; 20: 3247
• 7 Pardoel S, Kofman J, Nantel J. et al. Wearable-Sensor-based Detection and Prediction of Freezing of Gait in Parkinson’s Disease: A Review. Sensors (Basel) 2019; 19: 5141
• 8 Bouça-Machado R, Jalles C, Guerreiro D. et al. Gait Kinematic Parameters in Parkinson’s Disease: A Systematic Review. J Parkinsons Dis 2020; 10: 843-853
• 9 Morgan C, Rolinski M, McNaney R. et al. Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson’s Disease in the Home or a Home-like Environment. J Parkinsons Dis 2020; 10: 429-454
• 10 di Biase L, Di Santo A, Caminiti ML. et al. Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring. Sensors (Basel) 2020; 20: 3529
• 11 Klucken J, Winkler J, Krüger R. et al. Die Geschichte des ’Freezing-of-gait’ beim Parkinson-Syndrom – vom Phänomen zum Symptom. Fortschr Neurol Psychiatr 2020; 88: 573-581
• 12 Gaßner H, Raccagni C, Eskofier BM. et al. The Diagnostic Scope of Sensor-Based Gait Analysis in Atypical Parkinsonism: Further Observations. Front Neurol 2019; 10: 5
• 13 van den Heuvel L, Dorsey RR, Prainsack B. et al. Quadruple Decision Making for Parkinson’s Disease Patients: Combining Expert Opinion, Patient Preferences, Scientific Evidence, and Big Data Approaches to Reach Precision Medicine. J Parkinsons Dis 2020; 10: 223-231
• 14 Klucken J, Krüger R, Schmidt P. et al. Management of Parkinson’s Disease 20 Years from Now: Towards Digital Health Pathways. J Parkinsons Dis 2018; 8: S85-S94
• 15 Klucken J, Gladow T, Hilgert JG. et al. “Wearables” in der Behandlung neurologischer Erkrankungen – wo stehen wir heute? [Wearables in the treatment of neurological diseases-where do we stand today?]. Nervenarzt. 2019; 90: 787-795
• 16 Erb MK, Karlin DR, Ho BK. et al. mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson’s disease. NPJ Digit Med 2020; 3: 6
• 17 Del Din S, Elshehabi M, Galna B. et al. Gait analysis with wearables predicts conversion to parkinson disease. Ann Neurol 2019; 86: 357-367
• 18 Pahwa R, Bergquist F, Horne M. et al. Objective measurement in Parkinson’s disease: a descriptive analysis of Parkinson’s symptom scores from a large population of patients across the world using the Personal KinetiGraph^® . J Clin Mov Disord 2020; 7: 5
• 19 Espay AJ, Hausdorff JM, Sánchez-Ferro Á. et al. Movement Disorder Society Task Force on Technology. A roadmap for implementation of patient-centered digital outcome measures in Parkinson’s disease obtained using mobile health technologies. Mov Disord 2019; 34: 657-663
• 20 Rovini E, Maremmani C, Cavallo F. How Wearable Sensors Can Support Parkinson’s Disease Diagnosis and Treatment: A Systematic Review. Front Neurosci 2017; 11: 555
• 21 Silva de Lima AL, Hahn T, Evers LJW. et al. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson’s disease. PLoS One 2017; 12: 12
• 22 Zampogna A, Manoni A, Asci F. et al. Shedding Light on Nocturnal Movements in Parkinson’s Disease: Evidence from Wearable Technologies. Sensors (Basel) 2020; 20: 5171
• 23 Dorsey ER, Okun MS, Bloem BR.Care. Convenience, Comfort, Confidentiality, and Contagion: The 5 C’s that Will Shape the Future of Telemedicine. J Parkinsons Dis 2020; 10: 893-897
• 24 Zajki-Zechmeister T, Kögl M, Kalsberger K. et al. Quantification of tremor severity with a mobile tremor pen. Heliyon. 2020; 6: e04702
• 25 Sturchio A, Dwivedi AK, Marsili L. et al. Kinematic but not clinical measures predict falls in Parkinson-related orthostatic hypotension. J Neurol 26.09.2020; 26
• 26 van de Weijer SCF, Duits AA, Bloem BR. et al. Feasibility of a Cognitive Training Game in Parkinson’s Disease: The Randomized Parkin’Play Study. Eur Neurol 2020; 83: 426-432
• 27 Lee A, Hellmers N, Vo M. et al. Can google glass™ technology improve freezing of gait in parkinsonism? A pilot study. Disabil Rehabil Assist Technol 2020; 1-11
• 28 van der Kolk NM, de Vries NM, Kessels RPC. et al. Effectiveness of home-based and remotely supervised aerobic exercise in Parkinson’s disease: a double- blind, randomised controlled trial. Lancet Neurol 2019; 18: 998-1008
• 29 Evans L, Mohamed B, Thomas EC. Using telemedicine and wearable technology to establish a virtual clinic for people with Parkinson’s disease. BMJ Open Qual 2020; 9: e001000
• 30 Silva de Lima AL, Evers LJW, Hahn T. et al. Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review. J Neurol 2017; 264: 1642-1654
• 31 Muthukrishnan N, Abbas JJ, Shill HA. et al. Cueing Paradigms to Improve Gait and Posture in Parkinson’s Disease: A Narrative Review. Sensors (Basel) 2019; 19: 5468
• 32 Marxreiter F, Gaßner H, Borozdina O. et al. Sensor-based gait analysis of individualized improvement during apomorphine titration in Parkinson’s disease. J Neurol 2018; 265: 2656-2665