Journal List > J Stroke > v.25(2) > 1516082878

TOOLS
Bonura, Motolese, Capone, Iaccarino, Alessiani, Ferrante, Calandrelli, Lazzaro, and Pilato: Smartphone App in Stroke Management: A Narrative Updated Review
CORRIGENDUM: Corrected Table & Reference

J Stroke 2023;25(2):320-324.

Published online: 30 May 2023

DOI: https://doi.org/10.5853/jos.2022.01410.e1

Smartphone App in Stroke Management: A Narrative Updated Review

Adriano Bonura1, Francesco Motolese1, Fioravante Capone1, Gianmarco Iaccarino1, Michele Alessiani1, Mario Ferrante1, Rosalinda Calandrelli2, Vincenzo Di Lazzaro1, Fabio Pilato1

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

Copyright © 2023 Korean Stroke Society

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
In the article, there is a mistake in the references in Table 1. On pages 326 and 327, the references 22, 25, 29-35, 39-44, 46, 50-56, 58-61, 67-72, and 74-80 from Table 1 were misplaced in the previous version of the Review, and the correct table is as follows;
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.
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.
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.
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-toneedle time in the treatment of acute ischemic stroke. J Stroke Cerebrovasc Dis 2020;29:105319.
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.
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.
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 App-based 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.
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.
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.
On page 328, there is a misplaced reference in the previous version of the article (56 instead of 55). The correct reference is as follows:
“Speed interactive pedalling training (SIPT), for example, using smartphone-based motion-tracking technology has been shown to improve strength, balance, and gait in stroke patients.55”
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.
We apologize for any inconvenience that this may have caused.

Table 2.
Summary of included Apps
App name/authors App store availability Study type Field of application App modality Main findings Summary
Stroke Riskometer [22,25] iOS, Android Ongoing trial (NCT04529681) Primary prevention Calculator, Video, Health info NA Calculates annual stroke risk through weight, age, diet and other risk factors data. Gives information on managing risk factors through videos and articles.
iLAMA [29] Not available NA Pre-hospital management Augmented reality NA Through the smartphone’s camera and the accelerometer allows the recognition of signs such as altered eye motility, dysmetria, facial paresis and strength deficit in the upper limbs
SPMIS [30] Not available Pilot study Pre-hospital management Data sharing App usability Patient details are entered into the App by emergency responders. The App transmits the data to hospital physicians.
FAST-ED [31-33] iOS, Android Pilot study Pre-hospital management GPS, CDSS NA Provides a series of questions to assess eligibility for revascularization therapy and it contains a GPS to find the nearest hospital.
ESN [34] iOS, Android Pilot study Pre-hospital management GPS, CDSS, Video-call Reduction in door-in. door-out, door-to-groin and door-toneedle times Provides a series of questions to assess eligibility for revascularization therapy, it contains a video communication system to connect medical teams, and a GPS to find the nearest hospital.
Stroke119 [35] iOS, Android Pilot study Pre-hospital management CDSS, Information, GPS NA It helps patients in self-screening stroke symptoms through clinical scales. It gives health information and has a GPS system to find hospital centers that perform thrombolysis.
JOIN [39-42] iOS, Android Validation study In-hospital management DICOM viewer, Video-call, Chat Reduction in door-to-needle time Allows sharing of images and clinical data between teams of specialists with chat and video-call systems. Records patient data chronologically in a timeline to simplify clinical management.
StopStroke [43] NA Retrospective study In-hospital management Chat, Video-call Reduction in door-to-needle time Allows to create group chats with other specialists to share patient images and clinical information. It also supports video calls.
Act-Fast [44] iOS, Android Pilot study In-hospital management CDSS, Chat Contains several clinical scales and checklists for revascularization therapy. Also presents sharing and messaging features among physicians.
Acute Stroke Evaluation [46] iOS Pilot study In-hospital management CDSS Reduction in door-to-needle time Digitized version of the checklist for revascularization therapies based on the U.S. stroke guidelines.
S3 Rehab [50] NA NA Rehabilitation Sensors NA Records data about the movement of the limbs through smartphone’s gyroscope and accelerometer.
GetMyROM [51] iOS Pilot study Rehabilitation Sensors App can reliably measure passive upper limb range of motion Records data on the range of movements of the upper limbs
ARMStroke [52] iOS Pilot study Rehabilitation Sensors No changes detected when using the App Records data on the range of movements of the upper limbs
Chae et al. [53] Android Clinical trial (KCT0004818) Rehabilitation Sensors, wearable devices Wearables and machine learning can improve home care of stroke survivors Records upper extremity range of motion data via smartwatch
Hou et al. [54] Android Pilot study Rehabilitation Sensors Feasibility of App-based measurement of balance in stroke patients Records balance and posture data
SIPT [55] iOS Clinical trial Rehabilitation Virtual reality, exergames Sitting balance, trunk control, gait improvement Uses smartphone’s motion-tracking technology to simulate pedalling.
MoU-Rehab [56] Tablet PC Clinical trial Rehabilitation Virtual reality, exergames Non-inferiority to conventional therapy Allows to play different exergames using smartphone motion-tracking technology
ViaTherapy [58] iOS, Android Quality improvement project Rehabilitation CDSS Increased accessibility to and use of evidence based practice Collects data entered by patients to assist them in establishing a rehabilitation program
Rehabilitation Guardian [59] NA NA Rehabilitation Calendars, Health info NA Gives reminders about physical exercises to be performed, allows the consultation of specialized articles and contains a progress diary.
Li et al. [60] iOS, Android Clinical trial (ChiCTR1900027626) Rehabilitation Telemedicine Feasibility and validity of App-based televisits Allows practitioners to make televisits.
VirTele [61] NA NA Rehabilitation Telemedicine, ExerGames NA Allows practitioners to evaluate the development of interactive rehabilitative exercises with the exergames via televisit
PRESTRO [67] Android Pilot study Rehabilitation Health info, Reminder App usage was associated with healthier lifestyle Contains medication reminder features and vital signs measurement. It gives health lifestyle info
KUHMS2 [68] Not available Clinical trial (KCT0001045) Rehabilitation Parameter registration Lowering of blood pressure and glycated hemoglobin Records vital parameters
Lose it [69,70] iOS, Android Clinical trial (NCT02531074) Chronic management Diet management No difference between intervention and control group Records patients’ food intake and gives information about the macronutrients.
MakeMyDay [71] Not available Multiple case study Chronic management Health info, Reminder High acceptability of the App among patients Gives reminders and info about correct and healthy lifestyle
Movies4Stroke [72] NA Clinical trial (NCT02202330) Chronic management Health videos No lowering of blood pressure, LDL cholesterol and glycosylated hemoglobin. Improved functional outcome Provides educational videos on stroke
AFib 2gether [79,80] iOS, Android Clinical trial (NCT04118270) Chronic management CDSS High usability and perceived usefulness of the App It gives clinical info and collects patient data about AF that can be viewed by the doctor prior to visit.
FibriCheck [74] iOS, Android Clinical trial (NCT03509493) AF detection Wearable devices High measurement compliance and patient satisfaction Provides heart rate monitoring via smartwatch
Santala et al. [75] NA Clinical trial (NCT03507335) AF detection Wearable devices High quality ECG recording. High accuracy of automatic arrythmia detection Provides heart rhythm monitoring via an ECG belt
AliveCor [76] iOS, Android Observational study (ACTRN 12616001293459) AF detection Wearable devices NA Uses miniaturized ECG to monitor heart rhythm
TEASE [77,78] NA Clinical trial (NCT03301662.) AF detection Wearable devices AF successfully detected in patients with cryptogenic stroke Uses miniaturized ECG to be placed in the chest to monitor heart rhythm

NA, not available; GPS, Global Positioning System; CDSS, clinical decision support system; LDL, low-density lipoprotein; AF, atrial fibrillation; ECG, electrocardiogram.

TOOLS
Similar articles