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DOI: 10.1055/a-1254-9616
Wearables in der Schlaganfallmedizin
Wearables in Stroke MedicineZusammenfassung
Unter Wearables versteht man in die Kleidung oder in tragbare Geräte integrierte Sensoren, die eine kontinuierliche Langzeitmessung von physiologischen Parametern, wie Herzfrequenz, Blutdruck, Atmung, Bewegung, Hautwiderstand usw. und/oder Bewegungsmustern ermöglichen. In der Schlaganfallmedizin eröffnen Wearables neue Optionen in der Diagnostik, Prävention und Rehabilitation.
Abstract
Wearables are sensors integrated in garments or designed as wearable accessories facilitating continuous long-term measurements of physiological parameters or movement patterns. Compared with a 12-lead ECG smartwatches showed a sensitivity of 93,0–98,9% and a specificity of 81,9–98,2% for the detection of atrial fibrillation. Compared with simultaneous recordings from an insertable cardiac monitor detection sensitivity was 97,5% and duration sensitivity was 97,7%. In the Huawei Heart Study resp. the Apple Watch Study notifications of atrial fibrillation were observed in 0,23% resp. 0,52% of study participants, atrial fibrillation was diagnosed in 0,12% resp. 0,04%. Wearables in stroke rehabilitation comprise inertial measurement units, surface EMG sensors, pressure sensors (“the intelligent shoe”), sensors integrated in garments (“intelligent garments”), robot-assisted systems, functional electrical stimulation, transcutaneous electrical stimulation, and finally home-based Rehabilitation-Internet-of-Things devices integrated with virtual reality and gaming programs.
Publication History
Article published online:
23 February 2021
© 2021. Thieme. All rights reserved.
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Literatur
- 1 Haeusler KG, Tütüncü S, Schnabel RB. Detection of Atrial Fibrillation in Cryptogenic Stroke. Curr Neurol Neurosci Rep 2018; 18: 66
- 2 Weng LC, Preis SR, Hulme OL. et al. Genetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation. Circulation 2018; 137: 1027-1038
- 3 Turakhia MP, Shafrin J, Bognar K. et al. Estimated prevalence of undiagnosed atrial fibrillation in the United States. PLoS One 2018; 13: e0195088
- 4 Raja JM, Elsakr C, Roman S. et al. Apple Watch, Wearables, and Heart Rhythm: where do we stand?. Ann Transl Med 2019; 7: 417
- 5 Marini C, De Santis F, Sacco S. et al. Contribution of atrial fibrillation to incidence and outcome of ischemic stroke: results from a population-based study. Stroke 2005; 36: 1115-1119
- 6 Turakhia MP, Desai M, Hedlin H. et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. Am Heart J 2019; 207: 66-75
- 7 Hart RG, Pearce LA, Aguilar MI. Meta-analysis: antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med 2007; 146: 857-867
- 8 Ruff CT, Giugliano RP, Braunwald E. et al. Comparison of the efficacy and safety of new oral anticoagulants with warfarin in patients with atrial fibrillation: a meta-analysis of randomised trials. Lancet 2014; 383: 955-962
- 9 Lin HJ, Wolf PA, Benjamin EJ. et al. Newly diagnosed atrial fibrillation and acute stroke. The Framingham Study. Stroke 1995; 26: 1527-1530
- 10 Hart RG, Diener HC, Coutts SB. et al. Embolic strokes of undetermined source: the case for a new clinical construct. Lancet Neurol 2014; 13: 429-438
- 11 Schöberl F, Ringleb PA, Wakili R. et al. Juvenile Stroke. Dtsch Ärztebl Int 2017; 114: 527-534
- 12 Hart RG, Sharma M, Mundl H. et al. Rivaroxaban for Stroke Prevention after Embolic Stroke of Undetermined Source. N Engl J Med 2018; 378: 2191-2201
- 13 Diener HC, Sacco RL, Easton JD. et al. Dabigatran for Prevention of Stroke after Embolic Stroke of Undetermined Source. N Engl J Med 2019; 380: 1906-1917
- 14 Veltkamp R, Pearce LA, Korompoki E. et al. Characteristics of Recurrent Ischemic Stroke After Embolic Stroke of Undetermined Source: Secondary Analysis of a Randomized Clinical Trial. JAMA Neurol 2020; 77: 1-8
- 15 Häusler KG, Gröschel K, Köhrmann M. et al. Positionspapier zur Detektion von Vorhofflimmern nach ischämischem Schlaganfall. Akt Neurol 2018; 45: 93-106
- 16 Higgins P, MacFarlane PW, Dawson J. et al. Noninvasive cardiac event monitoring to detect atrial fibrillation after ischemic stroke: a randomized, controlled trial. Stroke 2013; 44: 2525-2531
- 17 Gladstone DJ, Spring M, Dorian P. et al. Atrial fibrillation in patients with cryptogenic stroke. N Engl J Med 2014; 370: 2467-2477
- 18 Wachter R, Gröschel K, Gelbrich G. et al. Holter-electrocardiogram-monitoring in patients with acute ischaemic stroke (Find-AF(RANDOMISED)): an open-label randomised controlled trial. Lancet Neurol 2017; 16: 282-290
- 19 Sanna T, Diener HC, Passman RS. et al. Cryptogenic stroke and underlying atrial fibrillation. N Engl J Med 2014; 370: 2478-2486
- 20 Stahrenberg R, Weber-Krüger M, Seegers J. et al. Enhanced detection of paroxysmal atrial fibrillation by early and prolonged continuous holter monitoring in patients with cerebral ischemia presenting in sinus rhythm. Stroke 2010; 41: 2884-2888
- 21 Grond M, Jauss M, Hamann G. et al. Improved detection of silent atrial fibrillation using 72-hour Holter ECG in patients with ischemic stroke: a prospective multicenter cohort study. Stroke 2013; 44: 3357-3364
- 22 Ritter MA, Kochhäuser S, Duning T. et al. Occult atrial fibrillation in cryptogenic stroke: detection by 7-day electrocardiogram versus implantable cardiac monitors. Stroke 2013; 44: 1449-1452
- 23 Poli S, Diedler J, Härtig F. et al. Insertable cardiac monitors after cryptogenic stroke--a risk factor based approach to enhance the detection rate for paroxysmal atrial fibrillation. Eur J Neurol 2016; 23: 375-381
- 24 Haeusler KG, Gröschel K, Köhrmann M. et al. Expert opinion paper on atrial fibrillation detection after ischemic stroke. Clin Res Cardiol 2018; 107: 871-880
- 25 Schnabel RB, Haeusler KG, Healey JS. et al. Searching for Atrial Fibrillation Poststroke: A White Paper of the AF-SCREEN International Collaboration. Circulation 2019; 140: 1834-1850
- 26 Lewalter T, Jilek C, Israel C. et al. Clinical differential indication: Wearables vs implantables. Herzschrittmacherther Elektrophysiol 2020; 31: 288-291
- 27 Strain T, Wijndaele K, Brage S. Physical Activity Surveillance Through Smartphone Apps and Wearable Trackers: Examining the UK Potential for Nationally Representative Sampling. JMIR MHealth UHealth 2019; 7: e11898
- 28 Marcus GM. The Apple Watch can detect atrial fibrillation: so what now?. Nat Rev Cardiol 2020; 17: 135-136
- 29 Sperzel J, Hamm CW, Hain A. Over- and undersensing-pitfalls of arrhythmia detection with implantable devices and wearables. Herzschrittmacherther Elektrophysiol 2020; 31: 273-287
- 30 Koshy AN, Sajeev JK, Nerlekar N. et al. Smart watches for heart rate assessment in atrial arrhythmias. Int J Cardiol 2018; 266: 124-127
- 31 Koshy AN, Sajeev JK, Nerlekar N. et al. Utility of photoplethysmography for heart rate estimation among inpatients. Intern Med J 2018; 48: 587-591
- 32 Isakadze N, Martin SS. How useful is the smartwatch ECG?. Trends Cardiovasc Med 2020; 30: 442-448
- 33 Tison GH, Sanchez JM, Ballinger B. et al. Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch. JAMA Cardiol 2018; 3: 409-416
- 34 Bumgarner JM, Lambert CT, Hussein AA. et al. Smartwatch Algorithm for Automated Detection of Atrial Fibrillation. J Am Coll Cardiol 2018; 71: 2381-2388
- 35 Rajakariar K, Koshy AN, Sajeev JK. et al. Accuracy of a smartwatch based single-lead electrocardiogram device in detection of atrial fibrillation. Heart 2020; 106: 665-670
- 36 Wasserlauf J, You C, Patel R. et al. Smartwatch Performance for the Detection and Quantification of Atrial Fibrillation. Circ Arrhythm Electrophysiol 2019; 12: e006834
- 37 Dörr M, Nohturfft V, Brasier N. et al. The WATCH AF Trial: SmartWATCHes for Detection of Atrial Fibrillation. JACC Clin Electrophysiol 2019; 5: 199-208
- 38 Guo Y, Wang H, Zhang H. et al. Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation. J Am Coll Cardiol 2019; 74: 2365-2375
- 39 Perez MV, Mahaffey KW, Hedlin H. et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med 2019; 381: 1909-1917
- 40 Curry SJ, Krist AH, Owens DK. et al. Screening for Atrial Fibrillation With Electrocardiography: US Preventive Services Task Force Recommendation Statement. JAMA 2018; 320: 478-484
- 41 Svennberg E, Engdahl J, Al-Khalili F. et al. Mass Screening for Untreated Atrial Fibrillation: The STROKESTOP Study. Circulation 2015; 131: 2176-2184
- 42 Halcox JPJ, Wareham K, Cardew A. et al. Assessment of Remote Heart Rhythm Sampling Using the AliveCor Heart Monitor to Screen for Atrial Fibrillation: The REHEARSE-AF Study. Circulation 2017; 136: 1784-1794
- 43 Alonso A, Krijthe BP, Aspelund T. et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc 2013; 2: e000102
- 44 Christophersen IE, Yin X, Larson MG. et al. A comparison of the CHARGE-AF and the CHA2DS2-VASc risk scores for prediction of atrial fibrillation in the Framingham Heart Study. Am Heart J 2016; 178: 45-54
- 45 Hannun AY, Rajpurkar P, Haghpanahi M. et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 2019; 25: 65-69
- 46 Jovanov E. Wearables Meet IoT: Synergistic Personal Area Networks (SPANs). Sensors (Basel) 2019; 19: 4295
- 47 Sim I. Mobile Devices and Health. N Engl J Med 2019; 381: 956-968
- 48 Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25: 44-56
- 49 Maceira-Elvira P, Popa T, Schmid AC. et al. Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment. J Neuroeng Rehabil 2019; 16: 142
- 50 Parker J, Powell L, Mawson S. Effectiveness of Upper Limb Wearable Technology for Improving Activity and Participation in Adult Stroke Survivors: Systematic Review. J Med Internet Res 2020; 22: e15981
- 51 Kwakkel G, van Peppen R, Wagenaar RC. et al. Effects of augmented exercise therapy time after stroke: a meta-analysis. Stroke 2004; 35: 2529-2539
- 52 Veerbeek JM, van Wegen E, van Peppen R. et al. What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PLoS One 2014; 9: e87987
- 53 Lee SI, Adans-Dester CP, Grimaldi M. et al. Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training. IEEE J Transl Eng Health Med 2018; 6: 2100411
- 54 Leuenberger K, Gonzenbach R, Wachter S. et al. A method to qualitatively assess arm use in stroke survivors in the home environment. Med Biol Eng Comput 2017; 55: 141-150
- 55 van Meulen FB, Klaassen B, Held J. et al. Objective Evaluation of the Quality of Movement in Daily Life after Stroke. Front Bioeng Biotechnol 2015; 3: 210
- 56 Wittmann F, Held JP, Lambercy O. et al. Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system. J Neuroeng Rehabil 2016; 13: 75
- 57 Cheung VC, Piron L, Agostini M. et al. Stability of muscle synergies for voluntary actions after cortical stroke in humans. Proc Natl Acad Sci U S A 2009; 106: 19563-19568
- 58 Li Y, Zhang X, Gong Y. et al. Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors. Sensors (Basel) 2017; 17: 582
- 59 Kim JH. The effects of training using EMG biofeedback on stroke patients upper extremity functions. J Phys Ther Sci 2017; 29: 1085-1088
- 60 Donoso Brown EV, Dudgeon BJ, Gutman K. et al. Understanding upper extremity home programs and the use of gaming technology for persons after stroke. Disabil Health J 2015; 8: 507-513
- 61 Davies RJ, Parker J, McCullagh P. et al. A Personalized Self-Management Rehabilitation System for Stroke Survivors: A Quantitative Gait Analysis Using a Smart Insole. JMIR Rehabilitation Assist Technol 2016; 3: e11
- 62 Mawson S, Nasr N, Parker J. et al. A Personalized Self-Management Rehabilitation System with an Intelligent Shoe for Stroke Survivors: A Realist Evaluation. JMIR Rehabil Assist Technol 2016; 3: e1
- 63 Wang Q, Markopoulos P, Yu B. et al. Interactive wearable systems for upper body rehabilitation: a systematic review. J Neuroeng Rehabil 2017; 14: 20
- 64 Wang Q, Timmermans A, Chen W. et al. Stroke Patients’ Acceptance of a Smart Garment for Supporting Upper Extremity Rehabilitation. IEEE J Translat Eng Health Med 2018; 6: 2101009
- 65 Veerbeek JM, Langbroek-Amersfoort AC, van Wegen EE. et al. Effects of Robot-Assisted Therapy for the Upper Limb After Stroke. Neurorehab Neural Repair 2017; 31: 107-121
- 66 Lee HJ, Lee SH, Seo K. et al. Training for Walking Efficiency With a Wearable Hip-Assist Robot in Patients With Stroke: A Pilot Randomized Controlled Trial. Stroke 2019; 50: 3545-3552
- 67 Awad LN, Bae J, O'Donnell K. et al. A soft robotic exosuit improves walking in patients after stroke. Sci Translat Med 2017; 9: eaai9084
- 68 Marquez-Chin C, Popovic MR. Functional electrical stimulation therapy for restoration of motor function after spinal cord injury and stroke: a review. Biomed Eng Online 2020; 19: 34
- 69 Eraifej J, Clark W, France B. et al. Effectiveness of upper limb functional electrical stimulation after stroke for the improvement of activities of daily living and motor function: a systematic review and meta-analysis. Syst Rev 2017; 6: 40
- 70 Lee SH, Lee JY, Kim MY. et al. Virtual Reality Rehabilitation With Functional Electrical Stimulation Improves Upper Extremity Function in Patients With Chronic Stroke: A Pilot Randomized Controlled Study. Arch Phys Med Rehabil 2018; 99: 1447-1453.e1
- 71 Mahmood A, Veluswamy SK, Hombali A. et al. Effect of Transcutaneous Electrical Nerve Stimulation on Spasticity in Adults With Stroke: A Systematic Review and Meta-analysis. Arch Phys Med Rehabil 2019; 100: 751-768
- 72 Sharififar S, Shuster JJ, Bishop MD. Adding electrical stimulation during standard rehabilitation after stroke to improve motor function. A systematic review and meta-analysis. Ann Phys Rehabil Med 2018; 61: 339-344
- 73 Marcolino MAZ, Hauck M, Stein C. et al. Effects of transcutaneous electrical nerve stimulation alone or as additional therapy on chronic post-stroke spasticity: systematic review and meta-analysis of randomized controlled trials. Disabil Rehabil 2020; 42: 623-635
- 74 Sonde L, Kalimo H, Fernaeus SE. et al. Low TENS treatment on post-stroke paretic arm: a three-year follow-up. Clin Rehabil 2000; 14: 14-19
- 75 Dobkin BH. A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training. Neurorehabil Neural Repair 2017; 31: 217-227