J Stroke.  2022 Sep;24(3):323-334. 10.5853/jos.2022.01410.

Smartphone App in Stroke Management: A Narrative Updated Review

Affiliations
  • 1Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Campus Bio Medico University of Rome, Rome, Italy
  • 2Neuroradiology and Radiology Unit, Diagnostic Imaging, Radiotherapy, Oncology, Haematology Department, Agostino Gemelli University Policlinic (Fondazione Policlinico Universitario Agostino Gemelli) IRCCS, Rome, Italy

Abstract

The spread of smartphones and mobile-Health (m-health) has progressively changed clinical practice, implementing access to medical knowledge and communication between doctors and patients. Dedicated software called Applications (or Apps), assists the practitioners in the various phases of clinical practice, from diagnosis to follow-up and therapy management. The impact of this technology is even more important in diseases such as stroke, which are characterized by a complex management that includes several moments: primary prevention, acute phase management, rehabilitation, and secondary prevention. This review aims to evaluate and summarize the available literature on Apps for the clinical management of stroke. We described their potential and weaknesses, discussing potential room for improvement. Medline databases were interrogated for studies concerning guideline-based decision support Apps for stroke management and other medical scenarios from 2007 (introduction of the first iPhone) until January 2022. We found 551 studies. Forty-three papers were included because they fitted the scope of the review. Based on their purpose, Apps were classified into three groups: primary prevention Apps, acute stroke management Apps, and post-acute stroke Apps. We described the aim of each App and, when available, the results of clinical studies. For acute stroke, several Apps have been designed with the primary purpose of helping communication and sharing of patients’ clinical data among healthcare providers. However, interactive systems Apps aiming to assist clinicians are still lacking, and this field should be developed because it may improve stroke patients’ management.

Keyword

Mobile applications; Smartphone; Stroke; Telemedicine; Technology; Primary and secondary prevention

Figure

  • Figure 1. Flow diagram of literature search and articles selection.


Reference

References

1. Mesko B, Győrffy Z. The rise of the empowered physician in the digital health era: viewpoint. J Med Internet Res. 2019; 21:e12490.
2. Pedicelli A, Valente I, Pilato F, Distefano M, Colosimo C. Stroke priorities during COVID-19 outbreak: acting both fast and safe. J Stroke Cerebrovasc Dis. 2020; 29:104922.
3. Wong BL, Maaß L, Vodden A, van Kessel R, Sorbello S, Buttigieg S, et al. The dawn of digital public health in Europe: implications for public health policy and practice. Lancet Reg Health Eur. 2022; 14:100316.
4. What is Digital Health? U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/digital-health-center-excellence/what-digital-health. 2020. Accessed August 5, 2022.
5. Ku JP, Sim I. Mobile Health: making the leap to research and clinics. NPJ Digit Med. 2021; 4:83.
6. Watson HA, Tribe RM, Shennan AH. The role of medical smartphone apps in clinical decision-support: a literature review. Artif Intell Med. 2019; 100:101707.
7. Ekeland AG, Bowes A, Flottorp S. Effectiveness of telemedicine: a systematic review of reviews. Int J Med Inform. 2010; 79:736–771.
8. Motolese F, Capone F, Magliozzi A, Vico C, Iaccarino G, Falato E, et al. A smart-device based secondary prevention program for cerebrovascular disease patients: a randomized trial. Research Square. 2022; Feb. 9. [Preprint]. https://doi.org/10.21203/rs.3.rs-1251620/v1.
9. Li J, Carayon P. Health Care 4.0: a vision for smart and connected health care. IISE Trans Healthc Syst Eng. 2021; 11:171–180.
10. How COVID-19 accelerated digital healthcare. International Telecommunication Union;https://www.itu.int/hub/2021/04/how-covid-19-accelerated-digital-healthcare/. 2021. Accessed August 5, 2022.
11. Katan M, Luft A. Global burden of stroke. Semin Neurol. 2018; 38:208–211.
12. Feigin VL, Roth GA, Naghavi M, Parmar P, Krishnamurthi R, Chugh S, et al. Global burden of stroke and risk factors in 188 countries, during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet Neurol. 2016; 15:913–924.
13. Luengo-Fernandez R, Violato M, Candio P, Leal J. Economic burden of stroke across Europe: a population-based cost analysis. Eur Stroke J. 2020; 5:17–25.
14. Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart disease and stroke statistics-2021 update: a report from the American Heart Association. Circulation. 2021; 143:e254–e743.
15. Owolabi MO, Thrift AG, Mahal A, Ishida M, Martins S, Johnson WD, et al. Primary stroke prevention worldwide: translating evidence into action. Lancet Public Health. 2022; 7:e74–e85.
16. Walter S, Kostopoulos P, Haass A, Keller I, Lesmeister M, Schlechtriemen T, et al. Diagnosis and treatment of patients with stroke in a mobile stroke unit versus in hospital: a randomised controlled trial. Lancet Neurol. 2012; 11:397–404.
17. Ebinger M, Winter B, Wendt M, Weber JE, Waldschmidt C, Rozanski M, et al. Effect of the use of ambulance-based thrombolysis on time to thrombolysis in acute ischemic stroke: a randomized clinical trial. JAMA. 2014; 311:1622–1631.
18. Saver JL, Fonarow GC, Smith EE, Reeves MJ, Grau-Sepulveda MV, Pan W, et al. Time to treatment with intravenous tissue plasminogen activator and outcome from acute ischemic stroke. JAMA. 2013; 309:2480–2488.
19. Kunz WG, Hunink MG, Sommer WH, Beyer SE, Meinel FG, Dorn F, et al. Cost-effectiveness of endovascular stroke therapy: a patient subgroup analysis from a US Healthcare Perspective. Stroke. 2016; 47:2797–2804.
20. Kunz WG, Hunink MG, Almekhlafi MA, Menon BK, Saver JL, Dippel DW, et al. Public health and cost consequences of time delays to thrombectomy for acute ischemic stroke. Neurology. 2020; 95:e2465–e2475.
21. Kleindorfer DO, Towfighi A, Chaturvedi S, Cockroft KM, Gutierrez J, Lombardi-Hill D, et al. 2021 Guideline for the prevention of stroke in patients with stroke and transient ischemic attack: a guideline from the American Heart Association/ American Stroke Association. Stroke. 2021; 52:e364–e467.
22. Krishnamurthi R, Hale L, Barker-Collo S, Theadom A, Bhattacharjee R, George A, et al. Mobile technology for primary stroke prevention. Stroke. 2019; 50:196–8.
23. Wolf PA, D’Agostino RB, Belanger AJ, Kannel WB. Probability of stroke: a risk profile from the Framingham Study. Stroke. 1991; 22:312–318.
24. O’Donnell MJ, Xavier D, Liu L, Zhang H, Chin SL, Rao-Melacini P, et al. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet. 2010; 376:112–123.
25. Mat Said Z, Musa KI, Tengku Ismail TA, Abdul Hamid A, Sahathevan R, Abdul Aziz Z, et al. The Effectiveness of Stroke Riskometer™ in improving stroke risk awareness in Malaysia: a study protocol of a cluster-randomized controlled trial. Neuroepidemiology. 2021; 55:436–446.
26. Hsia AW, Castle A, Wing JJ, Edwards DF, Brown NC, Higgins TM, et al. Understanding reasons for delay in seeking acute stroke care in an underserved urban population. Stroke. 2011; 42:1697–1701.
27. Mosley I, Nicol M, Donnan G, Patrick I, Kerr F, Dewey H. The impact of ambulance practice on acute stroke care. Stroke. 2007; 38:2765–2770.
28. Mowla A, Doyle J, Lail NS, Rajabzadeh-Oghaz H, Deline C, Shirani P, et al. Delays in door-to-needle time for acute ischemic stroke in the emergency department: a comprehensive stroke center experience. J Neurol Sci. 2017; 376:102–105.
29. Yao K, Wong KK, Yu X, Volpi J, Wong ST. An intelligent augmented lifelike avatar app for virtual physical examination of suspected strokes. Annu Int Conf IEEE Eng Med Biol Soc. 2021; 2021:1727–1730.
30. Nakae T, Kataoka H, Kuwata S, Iihara K. Smartphone-assisted prehospital medical information system for analyzing data on prehospital stroke care. Stroke. 2014; 45:1501–1504.
31. Nogueira RG, Silva GS, Lima FO, Yeh YC, Fleming C, Branco D, et al. The FAST-ED App: a smartphone platform for the field triage of patients with stroke. Stroke. 2017; 48:1278–1284.
32. Lima FO, Silva GS, Furie KL, Frankel MR, Lev MH, Camargo ÉC, et al. Field assessment stroke triage for emergency destination: a simple and accurate prehospital scale to detect large vessel occlusion strokes. Stroke. 2016; 47:1997–2002.
33. Frank B, Fabian F, Brune B, Bozkurt B, Deuschl C, Nogueira RG, et al. Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage. Ther Adv Neurol Disord. 2021; 14:17562864211057639.
34. Mansour OY, Ramadan I, Elfatatry A, Hamdi M, Abudu A, Hassan T, et al. Using ESN-smartphone application to maximize AIS reperfusion therapy in Alexandria Stroke Network: a stroke chain of survival organizational model. Front Neurol. 2021; 12:597717.
35. Nam HS, Heo J, Kim J, Kim YD, Song TJ, Park E, et al. Development of smartphone application that aids stroke screening and identifying nearby acute stroke care hospitals. Yonsei Med J. 2014; 55:25–29.
36. Mikulík R, Kadlecová P, Czlonkowska A, Kobayashi A, Brozman M, Svigelj V, et al. Factors influencing in-hospital delay in treatment with intravenous thrombolysis. Stroke. 2012; 43:1578–1583.
37. Jeon SB, Ryoo SM, Lee DH, Kwon SU, Jang S, Lee EJ, et al. Multidisciplinary approach to decrease in-hospital delay for stroke thrombolysis. J Stroke. 2017; 19:196–204.
38. Prabhakaran S, Ward E, John S, Lopes DK, Chen M, Temes RE, et al. Transfer delay is a major factor limiting the use of intra-arterial treatment in acute ischemic stroke. Stroke. 2011; 42:1626–1630.
39. Munich SA, Tan LA, Nogueira DM, Keigher KM, Chen M, Crowley RW, et al. Mobile real-time tracking of acute stroke patients and instant, secure inter-team communication: the Join App. Neurointervention. 2017; 12:69–76.
40. Martins SC, Weiss G, Almeida AG, Brondani R, Carbonera LA, de Souza AC, et al. Validation of a smartphone application in the evaluation and treatment of acute stroke in a comprehensive stroke center. Stroke. 2020; 51:240–246.
41. Takao H, Sakai K, Mitsumura H, Komatsu T, Yuki I, Takeshita K, et al. A smartphone application as a telemedicine tool for stroke care management. Neurol Med Chir (Tokyo). 2021; 61:260–267.
42. Sakai K, Sato T, Komatsu T, Mitsumura H, Iguchi Y, Ishibashi T, et al. Communication-type smartphone application can contribute to reducing elapsed time to reperfusion therapy. Neurol Sci. 2021; 42:4563–4568.
43. Andrew BY, Stack CM, Yang JP, Dodds JA. mStroke: “Mobile Stroke”-improving acute stroke care with smartphone technology. J Stroke Cerebrovasc Dis. 2017; 26:1449–1456.
44. Noone ML, Moideen F, Krishna RB, Pradeep Kumar VG, Karadan U, Chellenton J, et al. Mobile app based strategy improves door-to-needle time in the treatment of acute ischemic stroke. J Stroke Cerebrovasc Dis. 2020; 29:105319.
45. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, et al. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2019; 50:e344–e418.
46. Rubin MN, Fugate JE, Barrett KM, Rabinstein AA, Flemming KD. An acute stroke evaluation app: a practice improvement project. Neurohospitalist. 2015; 5:63–69.
47. Motolese F, Capone F, Di Lazzaro V. New tools for shaping plasticity to enhance recovery after stroke. Handb Clin Neurol. 2022; 184:299–315.
48. Coleman ER, Moudgal R, Lang K, Hyacinth HI, Awosika OO, Kissela BM, et al. Early rehabilitation after stroke: a narrative review. Curr Atheroscler Rep. 2017; 19:59.
49. Pollock A, Farmer SE, Brady MC, Langhorne P, Mead GE, Mehrholz J, et al. Interventions for improving upper limb function after stroke. Cochrane Database Syst Rev. 2014; 2014:CD010820.
50. Zhang MW, Chew PY, Yeo LL, Ho RC. The untapped potential of smartphone sensors for stroke rehabilitation and after-care. Technol Health Care. 2016; 24:139–143.
51. Lin NC, Hayward KS, D’Cruz K, Thompson E, Li X, Lannin NA. Validity and reliability of a smartphone inclinometer app for measuring passive upper limb range of motion in a stroke population. Disabil Rehabil. 2020; 42:3243–3249.
52. Lawson S, Tang Z, Feng J. Supporting stroke motor recovery through a mobile application: a pilot study. Am J Occup Ther. 2017; 71:7103350010p1–7103350010p5.
53. Chae SH, Kim Y, Lee KS, Park HS. Development and clinical evaluation of a web-based upper limb home rehabilitation system using a smartwatch and machine learning model for chronic stroke survivors: prospective comparative study. JMIR Mhealth Uhealth. 2020; 8:e17216.
54. Hou YR, Chiu YL, Chiang SL, Chen HY, Sung WH. Development of a smartphone-based balance assessment system for subjects with stroke. Sensors (Basel). 2019; 20:88.
55. Cai H, Lin T, Chen L, Weng H, Zhu R, Chen Y, et al. Evaluating the effect of immersive virtual reality technology on gait rehabilitation in stroke patients: a study protocol for a randomized controlled trial. Trials. 2021; 22:91.
56. Lee K. Speed-interactive pedaling training using smartphone virtual reality application for stroke patients: single-blinded, randomized clinical trial. Brain Sci. 2019; 9:295.
57. Choi YH, Paik NJ. Mobile game-based virtual reality program for upper extremity stroke rehabilitation. J Vis Exp. 2018; 133:56241.
58. Hancock NJ, Collins K, Dorer C, Wolf SL, Bayley M, Pomeroy VM. Evidence-based practice ‘on-the-go’: using ViaTherapy as a tool to enhance clinical decision making in upper limb rehabilitation after stroke, a quality improvement initiative. BMJ Open Qual. 2019; 8:e000592.
59. Xu J, Qian X, Yuan M, Wang C. Effects of mobile phone Appbased continuing nursing care on self-efficacy, quality of life, and motor function of stroke patients in the community. Acta Neurol Belg. 2021; Mar. 16. [Epub]. https://doi.org/10.1007/s13760-021-01628-y.
60. Li L, Huang J, Wu J, Jiang C, Chen S, Xie G, et al. A mobile health app for the collection of functional outcomes after inpatient stroke rehabilitation: pilot randomized controlled trial. JMIR Mhealth Uhealth. 2020; 8:e17219.
61. Allegue DR, Kairy D, Higgins J, Archambault P, Michaud F, Miller W, et al. Optimization of upper extremity rehabilitation by combining telerehabilitation with an exergame in people with chronic stroke: protocol for a mixed methods study. JMIR Res Protoc. 2020; 9:e14629.
62. Mohan KM, Wolfe CD, Rudd AG, Heuschmann PU, Kolominsky-Rabas PL, Grieve AP. Risk and cumulative risk of stroke recurrence: a systematic review and meta-analysis. Stroke. 2011; 42:1489–1494.
63. Antithrombotic Trialists’ Collaboration. Collaborative meta-analysis of randomised trials of antiplatelet therapy for prevention of death, myocardial infarction, and stroke in high risk patients. BMJ. 2002; 324:71–86.
64. Lakhan SE, Sapko MT. Blood pressure lowering treatment for preventing stroke recurrence: a systematic review and meta-analysis. Int Arch Med. 2009; 2:30.
65. Sandercock PA, Counsell C, Kane EJ. Anticoagulants for acute ischaemic stroke. Cochrane Database Syst Rev. 2015; 2015:CD000024.
66. Ní Chróinín D, Asplund K, Åsberg S, Callaly E, Cuadrado-Godia E, Díez-Tejedor E, et al. Statin therapy and outcome after ischemic stroke: systematic review and meta-analysis of observational studies and randomized trials. Stroke. 2013; 44:448–456.
67. Fruhwirth V, Berger L, Gattringer T, Fandler-Höfler S, Kneihsl M, Schwerdtfeger A, et al. Evaluation of a newly developed smartphone app for risk factor management in young patients with ischemic stroke: a pilot study. Front Neurol. 2022; 12:791545.
68. Seo WK, Kang J, Jeon M, Lee K, Lee S, Kim JH, et al. Feasibility of using a mobile application for the monitoring and management of stroke-associated risk factors. J Clin Neurol. 2015; 11:142–148.
69. Ifejika NL, Bhadane M, Cai CC, Noser EA, Grotta JC, Savitz SI. Use of a smartphone-based mobile app for weight management in obese minority stroke survivors: pilot randomized controlled trial with open blinded end point. JMIR Mhealth Uhealth. 2020; 8:e17816.
70. Ifejika NL, Noser EA, Grotta JC, Savitz SI. Swipe out stroke: feasibility and efficacy of using a smart-phone based mobile application to improve compliance with weight loss in obese minority stroke patients and their carers. Int J Stroke. 2016; 11:593–603.
71. Patomella AH, Farias L, Eriksson C, Guidetti S, Asaba E. Engagement in everyday activities for prevention of stroke: feasibility of an mHealth-supported program for people with TIA. Healthcare (Basel). 2021; 9:968.
72. Kamal A, Khoja A, Usmani B, Magsi S, Malani A, Peera Z, et al. Effect of 5-minute movies shown via a mobile phone app on risk factors and mortality after stroke in a low- to middle-income country: randomized controlled trial for the stroke caregiver dyad education intervention (Movies4Stroke). JMIR Mhealth Uhealth. 2020; 8:e12113.
73. Ntaios G. Embolic stroke of undetermined source: JACC review topic of the week. J Am Coll Cardiol. 2020; 75:333–340.
74. Beerten SG, Proesmans T, Vaes B. A heart rate monitoring app (FibriCheck) for atrial fibrillation in general practice: pilot usability study. JMIR Form Res. 2021; 5:e24461.
75. Santala OE, Halonen J, Martikainen S, Jäntti H, Rissanen TT, Tarvainen MP, et al. Automatic mobile health arrhythmia monitoring for the detection of atrial fibrillation: prospective feasibility, accuracy, and user experience study. JMIR Mhealth Uhealth. 2021; 9:e29933.
76. Tu HT, Chen Z, Swift C, Churilov L, Guo R, Liu X, et al. Smartphone electrographic monitoring for atrial fibrillation in acute ischemic stroke and transient ischemic attack. Int J Stroke. 2017; 12:786–789.
77. Magnusson P, Lyren A, Mattsson G. Diagnostic yield of chest and thumb ECG after cryptogenic stroke, Transient ECG Assessment in Stroke Evaluation (TEASE): an observational trial. BMJ Open. 2020; 10:e037573.
78. Magnusson P, Koyi H, Mattsson G. A protocol for a prospective observational study using chest and thumb ECG: transient ECG assessment in stroke evaluation (TEASE) in Sweden. BMJ Open. 2018; 8:e019933.
79. Kapoor A, Hayes A, Patel J, Patel H, Andrade A, Mazor K, et al. Usability and perceived usefulness of the AFib 2gether mobile app in a clinical setting: single-arm intervention study. JMIR Cardio. 2021; 5:e27016.
80. Kapoor A, Andrade A, Hayes A, Mazor K, Possidente C, Nolen K, et al. Usability, perceived usefulness, and shared decision-making features of the AFib 2gether mobile app: protocol for a single-arm intervention study. JMIR Res Protoc. 2021; 10:e21986.
81. Faria GS, Polese JC, Ribeiro-Samora GA, Scianni AA, Faria CD, Teixeira-Salmela LF. Validity of the accelerometer and smartphone application in estimating energy expenditure in individuals with chronic stroke. Braz J Phys Ther. 2019; 23:236–243.
82. Polese JC, E Faria GS, Ribeiro-Samora GA, Lima LP, Coelho de Morais Faria CD, Scianni AA, et al. Google fit smartphone application or Gt3X Actigraph: which is better for detecting the stepping activity of individuals with stroke?: a validity study. J Bodyw Mov Ther. 2019; 23:461–465.
83. Mathews SC, McShea MJ, Hanley CL, Ravitz A, Labrique AB, Cohen AB. Digital health: a path to validation. NPJ Digit Med. 2019; 2:38.
84. Guidance for Industry and Food and Drug Administration Staff. Policy for device software functions and mobile medical applications. U.S. Food and Drug Administration;https://www.fda.gov/regulatory-information/search-fda-guidance-documents/policy-device-software-functions-and-mobile-medical-applications. 2019. Accessed August 5, 2022.
85. Regulation (EU) 2017/745 (EU MDR): The EU MDR entered into application on 26 May 2021. The European Union Medical Device Regulation;https://eumdr.com/. 2021. Accessed August 5, 2022.
Full Text Links
  • JOS
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr