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Digital health self-management support for stroke patients following discharge: A randomized controlled trial
Ning Jiang1, Ling Feng2, Tian Tian3
1West China Hospital/West China School of Nursing, Sichuan University, Chengdu, Sichuan, China; School of Nursing, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, Shandong, China; Department of Neurology, Chengdu Second People's Hospital, Affiliated to Sichuan University, Chengdu, Sichuan, China.
Background:
Stroke is the leading cause of death and disability among adults in China. Effective self-management after hospital discharge can significantly improve patients' quality of life. However, the current level of self-management among stroke survivors in China remains suboptimal, highlighting an urgent need for innovative support strategies.
Objectives:
To develop an individualized and dynamic self-management support program based on wearable technology and the solution-focused model, evaluate its effectiveness, and provide new insights for advancing post-stroke care models.
Methods:
A total of 183 stroke patients scheduled for discharge were randomly assigned to one of three groups-smartwatch, wristband, or control-with 61 participants per group. Over an 8-week intervention period, participants in the smartwatch intervention group received support through a personalized cloud platform that integrated real-time data from smartwatches, patient-maintained self-management diaries, and semi-structured interviews, to identify dynamic issues and obtain tailored feedback. Outcome measures-including self-management behaviors, resilience, patient activation, social support, Barthel Index, depression, blood pressure, glucose concentration, and stroke recurrence-were assessed at baseline (T0) and at 2 (T1), 3 (T2), and 6 (T3) months post-discharge. Intervention effects were analyzed using linear mixed-effects models.
Results:
The cloud platform comprised a smartwatch interface, HMS Core application software, and a WeChat mini-program. The intervention significantly improved: self-management behaviors (T1: β = 27.496, p < 0.001; T2: β = 13.855, p = 0.004; T3: β = 10.649, p = 0.025), resilience (T1: β = 5.361, p < 0.001; T2: β = 3.498, p = 0.01; T3: β = 3.519, p = 0.01), patient activation (T1: β = 17.836, p < 0.001; T2: β = 14.302, p < 0.001; T3: β = 15.13, p < 0.001), and Barthel Index (T1: β = 5.225, p = 0.049; T3: β = 5.594, p = 0.035).
Conclusions:
The individualized and dynamic self-management support program effectively enhanced self- management behaviors, resilience, and patient activation in stroke survivors. It also showed promising trends toward reducing post-stroke depression and recurrence, and may contribute to better control of blood pressure and glucose concentration.
Registration:
Registered at chictr.org.cn, ChiCTR2300070384, on April 11, 2023, https://www.chictr.org.cn/showproj.html?proj=193286. The protocol version is ESC 1.0.
