Study on self-management of real-time and individualized support in stroke patients based on resilience: a protocol for a randomized controlled trial

(2023) 24:493 Jiang et al. Trials https://doi.org/10.1186/s13063-023-07475-x Trials Open Access STUDY PROTOCOL Study on self‑management of real‑time and individualized support in stroke patients based on resilience: a protocol for a randomized controlled trial N. Jiang1†, Y. Xv2†, X. Sun3†, L. Feng4†, Y. B. Wang5 and X. L. Jiang1* Abstract Background The transitional period from hospital to home is vital for stroke patients, but it poses serious challenges. Good self-management ability can optimize disease outcomes. However, stroke patients in China have a low level of self-management ability during the transitional period, and a lack of effective support may be the reason. With the rapid development of technology, using wearable monitors to achieve real-time and individualized support may be the key to solving this problem. This study uses a randomized controlled trial design to assess the efficacy of using wearable technology to realize real-time and individualized self-management support in stroke patients’ self-management behavior during the transitional period following discharge from hospital. Methods This parallel-group randomized controlled trial will be conducted in two hospitals and patients’ homes. A total of 183 adult stroke patients will be enrolled in the study and randomly assigned to three groups in a 1:1:1 ratio. The smartwatch intervention group (n = 61) will receive Real-time and Individualized Self-management Support (RISS) program + routine care, the wristband group (n = 61) will wear a fitness tracker (self-monitoring) + routine care, and the control group (n = 61) will receive routine stroke care. The intervention will last for 6 months. The primary outcomes are neurological function status, self-management behavior, quality of life, biochemical indicators, recurrence rate, and unplanned readmission rate. Secondary outcomes are resilience, patient activation, psychological status, and caregiver assessments. The analysis is intention-to-treat. The intervention effect will be evaluated at baseline (T0), 2 months after discharge (T1), 3 months after discharge (T2), and 6 months after discharge (T3). Discussion The cloud platform designed in this study not only has the function of real-time recording but also can push timely solutions when patients have abnormal conditions, as well as early warnings or alarms. This study could also potentially help patients develop good self-management habits through resilience theory, wearable devices, and individualized problem–solution library of self-management which can lay the foundation for long-term maintenance and continuous improvement of good self-management behavior in the future. Trial registration The ethics approval has been granted by the Ethics Committee of West China Hospital, Sichuan University (2022–941). All patients will be informed of the study details and sign a written informed consent form † Jiang N., Xv Y., Sun X., and Feng L. contributed equally to this work. *Correspondence: X. L. Jiang Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Jiang et al. Trials (2023) 24:493 Page 2 of 14 before enrollment. The research results will be reported in conferences and peer-reviewed publications. The trial registration number is ChiCTR2300070384. Registered on 11 April 2023. Keywords Self-management, Real-time support, Wearable devices, Stroke patients Introduction Stroke has become one of the major diseases endangering the health of individuals around the world, with the characteristics of high incidence, high disability rate, high mortality, and high recurrence rate. It is the third most deadly disease in Western countries [1], and it is also the leading cause of death and disability in Chinese adults [2]. With the rapidly aging population in China, the burden of stroke disease presents an explosive growth trend [2, 3]. The transition period from hospital to home is a very important part of disease care for stroke patients after discharge, but it faces severe challenges such as decreased self-care ability, lack of knowledge, poor treatment compliance, and lack of adequate follow-up support and service continuity, which leads to the increased risk of early readmission after discharge and endangers the safety of patients [4–6]. There are two major health tasks in the transition period of stroke patients. The first is functional rehabilitation, including rehabilitation exercise, drug therapy, diet management, and emotional management. The second is to prevent recurrence [7, 8]. These tasks are closely related to patients’ self-management behaviors. The better patients control their self-management behavior, the better their outcome indicators in terms of neurological recovery, ability to perform activities of daily living, and social ability [9]. However, studies have shown that the self-management ability of Chinese stroke patients remains at a lower-moderate level [10, 11]. Lack of effective support is the main reason [12]. With the rapid development of wearable technology, real-time and accurate self-management support using wearable technology may be the key to solving this problem [13, 14]. However, we found that the functions of wearable devices involved in stroke research were mainly intelligent rehabilitation training devices, and they were mostly used for rehabilitation treatment, mainly focusing on the recovery training of limb function [15], sensory stimulation [16], swallowing function [17], and support for cognitive impairment [18]. There were relatively few studies on the monitoring of physical indicators (such as heart rate, blood pressure, electrocardiogram, sleep) and selfmanagement behaviors (such as exercise, medication behavior, diet management) of stroke patients. As a disease with a high disability rate and high recurrence rate, how to combine wearable devices with disease monitor (...truncated)