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.