Skip to main content
NCBI home page
International Journal of Nursing Sciences logoLink to International Journal of Nursing Sciences
. 2025 Dec 20;13(1):45–52. doi: 10.1016/j.ijnss.2025.12.012

Effectiveness of a stepped self-care program for stroke survivors: A quasi-experimental study

Zihao Ruan a, Dan Wang a, Wenna Wang a, Yongxia Mei a, Hui Wang b, Suyan Chen a, Qiushi Zhang a, Zhenxiang Zhang a,c,⁎
PMCID: PMC12891791  PMID: 41684614

Abstract

Objectives

This study aimed to evaluate the effectiveness of the stepped self-care program on the self-care, self-efficacy, and quality of life of stroke survivors.

Methods

This quasi-experimental study allocated 110 stroke survivors from two neurology wards into an intervention group (n = 55) who received the stepped self-care program and a control group (n = 55) who received usual care from June to December 2023. The Self-Care of Stroke Inventory, Stroke Self-Efficacy Questionnaire, and the short version of the Stroke Specific Quality of Life Scale were administered at baseline (T0), immediately post-intervention (T1), and at 1-month (T2) and 3-month (T3) follow-ups. Data were analyzed using repeated measures analyses of variance, and generalized estimating equations.

Results

A total of 48 participants in the intervention group and 50 participants in the control group completed the study. No statistically significant differences were observed at T0 in any of the measured indicators (all P > 0.05). The study showed significant group, time, and group × time interaction effects across the assessed outcomes (all P < 0.05). Follow-up between-group comparisons at T1, T2, and T3 indicated that the intervention group had significantly higher scores in self-care maintenance, self-care monitoring, self-care management, self-efficacy, and quality of life than the control group (all P < 0.001).

Conclusions

The stepped self-care program significantly improved self-care behaviors, self-efficacy, and quality of life among stroke survivors. These findings support the broader implementation of this approach in post-discharge home self-care.

Keywords: Quality of life, Self-care, Self-efficacy, Stepped care program, Stroke

What is known?

  • •

    Stroke survivors require long-term self-care to promote recovery and prevent recurrence.

  • •

    According to the Middle-Range Theory of Self-Care of Chronic Illness, self-care encompasses three core elements: self-care maintenance, self-care monitoring, and self-care management.

  • •

    Past studies on self-care among stroke survivors predominantly focus on a single dimension and employ a single intervention modality, thereby neglecting individual variability and the holistic enhancement of survivors' self-care behavior.

What is new?

  • •

    We developed a stepped self-care program for stroke survivors based on the Stepped Care Model and the Middle-Range Theory of Self-Care of Chronic Illness using WeChat and face-to-face support.

  • •

    The stepped self-care program significantly improved stroke survivors’ self-care, self-efficacy, and quality of life.

1. Introduction

According to the Global Burden of Disease report, stroke has impacted more than 100 million individuals globally, with over 80 million survivors living with long-term disabilities [1,2]. The disability-adjusted life years (DALYs) attributable to stroke have reached 143 million [1]. Each year, approximately 13 million new stroke cases occur, equating to one stroke every 40 s and one stroke-related death every 4 min, highlighting the substantial public health burden [3,4]. In China, stroke is the leading cause of both death and disability, with over 17.04 million individuals aged 40 and above having experienced a stroke [5]. The number of hospital discharges and the associated medical expenses, particularly for ischemic stroke, have been steadily increasing [6], reflecting a growing healthcare and economic burden at the national level.

Following acute care, most survivors return to their homes or communities [7]. However, 70 %–80 % continue to experience functional impairments, such as motor and swallowing deficits [8]. Post-discharge limitations in healthcare resources, transportation, and financial capacity substantially restrict survivors' and caregivers’ access to professional rehabilitation guidance, leading to diminished quality of life (QoL) and overall well-being [9]. In this context, the International Self-Care Research Center advocates active self-care among patients with chronic diseases [10]. Evidence [11,12] suggests that self-care can delay the onset and progression of stroke, improve rehabilitation outcomes and overall well-being, and reduce readmission and recurrence rates. Therefore, promoting self-care is crucial for enhancing both the physical and mental health outcomes of stroke survivors, as well as for reducing the overall burden of stroke.

Despite its benefits, self-care among stroke survivors remains suboptimal. Kraaijkamp et al. [13] found that older adult stroke survivors spend approximately 80 % of their waking hours in sedentary behavior during rehabilitation. Similarly, Jang et al. [14] reported that middle-aged stroke survivors demonstrated only moderate levels of self-care, underscoring the need for improvement. However, many current interventions [[15], [16], [17]] focus only on isolated components of self-care, thereby neglecting a holistic approach. To systematically interpret self-care behavior and guide the development of interventions, our study adopts the Middle-Range Theory of Self-Care of Chronic Illness [18] as a conceptual framework. This theory posits that self-care comprises three interrelated dimensions: self-care maintenance, monitoring, and management. Self-care maintenance involves daily activities that promote health (e.g., maintaining a healthy weight via balanced nutrition and acquiring stroke-related information). Self-care monitoring refers to consistent observation of physical or emotional changes (e.g., tracking changes in vital signs and symptoms). Self-care management involves responding appropriately to detected changes (e.g., modifying lifestyle or seeking medical assistance). These interrelated dimensions constitute a comprehensive cycle of chronic disease self-care.

In clinical settings, stroke self-care interventions are primarily implemented through face-to-face guidance, telephone follow-up, and digital remote support. Each modality exhibits distinct resource intensity, interactivity, and coverage characteristics, which determine its appropriateness for different contexts and patient requirements. Face-to-face guidance delivers the highest level of interaction but also involves substantial resource expenditure, rendering it suitable for addressing complex, individualized issues [16]. Telephone follow-up ensures convenient access but is limited by reduced efficiency in information transmission, making it most appropriate for routine reminders and emotional support [17]. Digital remote support offers high accessibility and potential for standardization but requires a certain degree of technical proficiency from users, making it suitable for daily monitoring and information dissemination [19]. However, existing research often employs a single, fixed intervention framework, attempting to meet patients’ evolving needs throughout rehabilitation with an undifferentiated approach, which inevitably magnifies the limitations of each modality [20]. Therefore, there is an urgent need to establish a comprehensive self-care program that concurrently addresses the three dimensions of self-care—maintenance, monitoring, and management—and flexibly integrates multiple intervention modalities in alignment with patient needs. Such integration would facilitate the systematic coordination of low-cost, accessible support with high-intensity, targeted interventions, thereby providing more effective and equitable care for stroke survivors.

The Stepped Care Model (SCM) has emerged as a promising framework proposed by international scholars [21] to address these limitations. This model offers a structured, multi-step approach to care, typically comprising two to four levels, starting with low-intensity, accessible interventions and progressing to higher-intensity, specialist-based care tailored to individual needs and responses [20,21]. The SCM aims to optimize care delivery while avoiding both under- and over-treatment. As the Self-Care Model evolves, its applications have expanded to areas such as weight management, sleep improvement, pain control, dietary management, and mental health, yielding promising outcomes [22]. However, its effectiveness in post-stroke self-care remains underexplored.

Meanwhile, the rapid development of digital platforms has created new opportunities for self-care interventions. WeChat’s broad user base and multifunctionality (e.g., group messaging, official accounts, and applets) make it a promising tool for patient engagement and self-care interventions. Tan et al. [15] found that a WeChat-based continuous care model significantly improved self-care and treatment adherence, while also reducing complication rates and hospital readmissions. These findings suggest that WeChat-based digital platforms facilitate cost-effective disease management and information sharing, thereby enhancing sustainability, accessibility, and resource utilization in stroke self-care. Despite its proven efficacy in the medical field, the integration of this approach with the SCM for managing chronic diseases remains limited [23]. At the same time, relying solely on digital platforms may not adequately meet the complex and individualized needs of stroke survivors in later stages of recovery. Therefore, combining WeChat-based support with higher-intensity, face-to-face interventions better aligns with the principles of the SCM, ensuring both accessibility and personalization of care.

Grounded in the Middle-Range Theory of Self-Care of Chronic Illness, this study focuses on practical self-care behaviors and develops interventions centered on its three core elements. The study design strictly adheres to the SCM, integrating digital technology with traditional care methods while accounting for individual differences in participants' preferences and learning capacities. Specifically, it employs WeChat-based interventions in the initial stages and provides face-to-face support in the later phases. Therefore, this study aimed to evaluate the effectiveness of a stepped self-care program (SSCP) in improving self-care behaviors, self-efficacy, and QoL. This provides a novel perspective for personalized support and guides the development of stepped interventions for other chronic diseases.

2. Methods

2.1. Study design and setting

This prospective, two-arm, quasi-experimental study adhered to the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) statement (http://medbox.iiab.me/modules/en-cdc/www.cdc.gov/trendstatement/index.html) and was conducted in a tertiary hospital in Henan Province. A cluster design was employed, in which the two neurology wards, located on separate floors, were randomly allocated to either the intervention or control group using a coin-flip method.

2.2. Study participants

Potential participants were identified through convenience sampling by reviewing medical records and subsequently recruited from June to December 2023. Stroke survivors were eligible if they met the following criteria: 1) diagnosis of stroke as defined in the 2019 Diagnostic Points of Various Major Cerebrovascular Diseases in China [24], confirmed by cranial CT or MRI; 2) clinically stable condition; 3) age ≥18 years; 4) cognitively intact (Mini Mental State Examination Scale ≥27 [25]) and without significant communication disorders (Token Test Scale score ≥17 [26]); 5) partial self-care ability (Modified Barthel Index ≥60 [27]) but suboptimal self-care behaviors (scores <70 on any subscale of the Self-Care of Stroke Inventory [28]); and 6) able to use WeChat on a smartphone. Exclusion criteria included: 1) coexisting primary conditions (e.g., heart failure, respiratory failure, malignant tumors, and severe trauma); 2) participation in other clinical studies. Withdrawal criteria included: 1) clinical deterioration rendering participation unfeasible; 2) voluntary withdrawal for personal reasons.

2.3. Sample size

Sample size was calculated using the two-sample mean formula: n1 = n2 = 2[σ(Zα/2+Zβ)/δ]2 [29]. Using self-care behavior as the primary outcome, with values from a pre-intervention pilot study conducted as part of this research (σ = 7.69 and δ = 5.33), and setting α = 0.05, Zα/2 = 1.96, β = 0.1, Zβ = 1.28, the calculated sample size was 88 participants (44 per group). After accounting for a 20 % attrition rate, the required sample size was 110 participants (55 per group).

2.4. Interventions

2.4.1. Intervention group

The SSCP was conducted over 12 weeks, followed by a subsequent 3-month follow-up period. The program was structured into three progressively intensive steps (Appendix A). Participant progression was guided by the SCM, allowing advancement to the next step only when the previous one was deemed ineffective. The effectiveness of each step was assessed using the Stroke Self-Care Scale. Intervention success was defined as a score of 70 or higher on all three subscales (self-maintenance, monitoring, and management). Participants meeting this threshold did not advance further but continued receiving follow-up support, including ongoing knowledge dissemination. Those who did not meet the criterion advanced to the next step. The intervention was discontinued after Step 3 regardless of effectiveness. Outcome assessments were conducted after each step via WeChat voice calls.

2.4.1.1. Step 1: pre-discharge education and WeChat knowledge dissemination (Weeks 1–4)

The first stage encompasses a pre-discharge health education course and a four-week WeChat group-wide intervention post-discharge. Pre-discharge, researchers delivered a 30- to 45-min one-on-one bedside session or a departmental classroom-based, face-to-face group health education course for stroke survivors. This course outlines the risks and hazards of stroke recurrence through visual aids, such as pictures and videos, and explains self-care maintenance, monitoring, and management behaviors. It also encourages survivors to establish self-care goals in these areas after discharge. This course aims to foster an awareness of self-care among stroke survivors and facilitate setting self-care goals. Consequently, the content of this course differs somewhat from the routine health education provided to the control group. Detailed self-care guidance is provided through the WeChat knowledge push component for stroke survivors after discharge.

The post-discharge WeChat group-wide intervention involves the one-way dissemination of information to all survivors, with a focus on enhancing their mastery of self-care-related knowledge. In the first to fourth weeks after discharge, we disseminated knowledge within a WeChat group, covering six topics of self-care maintenance, including dietary guidance, exercise guidance, sleep management, smoking and alcohol cessation, medication compliance, and information sources; four topics of self-care monitoring, comprising weight, blood pressure, blood glucose, and recurrence; and four topics of self-care management, encompassing coping with recurrence, managing complications, activity and rehabilitation, and emotional health. This information was conveyed through electronic documents or videos through the WeChat group. Each topic was pushed every two days.

2.4.1.2. Step 2: WeChat interactive group sessions (Weeks 5–8)

Participants (6–8 per group) engaged in weekly 30–45-min WeChat group sessions. These included: 1) feedback on Step 1 progress; 2) identification of barriers and challenges; 3) resetting self-care goals; 4) two-way communication and case-based discussions; 5) survivor-led sharing of monitoring experiences; and 6) group exercises and stress-reduction techniques.

2.4.1.3. Step 3: face-to-face home visits (Weeks 9–12)

Weekly home visits (45–60 min) were conducted from weeks nine to twelve. This step encouraged stroke caregivers to play an active role in family support and supervision and emphasized: 1) supervision, where caregivers monitored and recorded behaviors; 2) environment, where home safety was promoted (e.g., anti-slip measures, bedside stroke monitoring records); 3) emergency preparedness, including role-playing and simulations for stroke recurrence; and 4) emotional support, including stress identification and joint coping strategies. Feedback was gathered from both survivors and caregivers, and ongoing support was maintained after the final visit.

2.4.2. Control group

Participants in the control group received usual care, which included health education and monthly post-discharge follow-ups. A Stroke Self-Care Health Education Manual was provided, covering four sections: introduction to self-care, self-care maintenance, self-care monitoring, and self-care management. Topics included dietary guidance, exercise, sleep hygiene, medication adherence, blood pressure and glucose monitoring, recurrence prevention, and emotion regulation. Individual bedside education was provided before discharge, covering 1) stroke types and the importance of medication adherence; 2) recognition of recurrence and emergency response; 3) principles of secondary stroke prevention; and 4) strategies to manage negative emotions and promote positive coping. Monthly follow-up calls assessed self-care performance, identified barriers, and collaboratively developed improvement strategies to address these issues. Caregivers were not involved in usual care.

2.5. Measures

2.5.1. Demographic data and disease-related information

The demographic questionnaire collected 14 variables, including gender, age, ethnicity, education level, marital status, employment, primary caregivers, per capita monthly income, disease duration (years), stroke type, stroke frequency (cumulative number of episodes since diagnosis), current health insurance type, comorbidities (e.g., hypertension, diabetes, heart disease), and cardinal symptom.

2.5.2. Self-care

Self-care was measured as the primary outcome using the Self-Care of Stroke Inventory, which was developed by Wang et al. [28]. This scale consisted of three subscales: Self-Care Maintenance (9 items), Self-Care Monitoring (6 items), and Self-Care Management (8 items). A 5-point Likert scale was used, with response options ranging from “never” (1 point) to “always” (5 points). Subscale scores were converted into standardized scores ranging from 0 to 100. Specifically, the scores of the Self-Care Maintenance of Stroke scale were calculated as (total scores−9) × (100/36), the Self-Care Monitoring of Stroke scale as (total scores −6) × (100/24), and the Self-Care Management of Stroke scale as (total scores−8) × (100/32). Higher scores indicate better self-care. The internal consistency of the three subscales was good, with Cronbach’s α coefficients of 0.83, 0.93, and 0.83, respectively.

2.5.3. Self-efficacy

Self-efficacy was measured using the Stroke Self-Efficacy Questionnaire (SSEQ). The Scale was developed by Jones et al. [30] in 2008 and revised into a Chinese version by Li et al., in 2015 [31]. It consisted of 13 items across two domains: functional performance (8 items) and self-management (5 items). Each item is rated on an 11-point Likert scale, ranging from “no confidence” (0 points) to “complete confidence” (10 points). Higher scores indicated stronger self-efficacy. The Cronbach’s α coefficient was 0.969, indicating high internal consistency.

2.5.4. Quality of life

QoL was measured using the short version of the Stroke Specific Quality of Life Scale (SS-QoL-12). The Scale was developed by Post et al. [32] in 2008 and revised into a Chinese version by Tang et al. [33] in 2021. It comprises 12 items across physical and psychosocial dimensions. Each item is rated on a 5-point Likert scale. Physical items are rated from “cannot do at all” (1 point) to “no difficulty at all” (5 points), and psychosocial items are rated from “strongly agree” (1 point) to “strongly disagree” (5 points). The total score ranges from 12 to 60, with higher scores indicating a better QoL. The Cronbach’s α coefficient was 0.882.

2.6. Data collection

With the assistance of the primary nurse, the researcher provided stroke patients with a face-to-face explanation of the study before discharge and obtained written informed consent. Baseline data (T0) were then collected. During face-to-face assessments, patients completed the questionnaires independently. For those with writing impairments, items were read aloud, patient understanding was confirmed, and the researchers recorded their responses. Research team members conducted follow-ups via WeChat voice calls to assess the effectiveness of the intervention during its first two stages, as well as immediately post-intervention (T1), at 1 month (T2), and 3 months (T3) after the intervention. Baseline questionnaires took 10–30 min to complete, and each follow-up lasted 6–20 min.

2.7. Quality control

To minimize implementation bias, all implementers received standardized training on intervention protocols, communication skills, and theoretical background. Data collectors were similarly trained to ensure consistency and reliability of data collection. Intervention materials (including WeChat content, animations, and expert-led videos) were developed from authoritative sources and reviewed by neurologists and nurses before dissemination to ensure scientific rigor and standardization. The entire intervention process was led by the principal investigator and supervised jointly by rehabilitation therapists and neurologists, with neurology nurses responsible for patient communication and reviewing educational content.

2.8. Data analysis

Data were analyzed using SPSS version 26.0. Normality was assessed using the Shapiro–Wilk test. Continuous variables with a normal distribution are presented as mean and standard deviation, whereas non-normally distributed variables are presented as median (interquartile range). Categorical variables are summarized as frequencies and percentages. The Chi-square test or Fisher’s exact test was applied to compare categorical variables between the intervention and control groups. For between-group comparisons of continuous data with a normal distribution, an independent-samples t-test was utilized; otherwise, the Mann-Whitney U test was applied. Repeated measures analyses of variance (RM-ANOVA) and generalized estimating equation (GEE) models were used to compare the trends in scores for the two groups of survivors, collected at T0, T1, T2, and T3. For normally distributed data, repeated measures ANOVA was conducted; otherwise, the GEE model was applied for analysis. P < 0.05 was considered statistically significant unless otherwise specified. When a significant group × time interaction was detected, follow-up between-group comparisons were conducted at T1–T3 with Bonferroni adjustment; the adjusted significance level was set at α = 0.05/3 = 0.0167.

2.9. Ethical considerations

This study was approved by the Ethics Committee of Zhengzhou University (ZZUIRB 2021-115). All participants provided informed consent and were free to withdraw from the study at any time.

3. Results

3.1. Participants’ characteristics

The study flow diagram for the enrolled participants is illustrated in Appendix B. A total of 110 stroke survivors met the inclusion criteria, and they were assigned to either the intervention group (n = 55) or the control group (n = 55). We conducted a per-protocol analysis, including only participants who completed the intervention as assigned. Non-adherent participants (e.g., those who discontinued or were lost to follow-up) were excluded from the final analysis. Finally, 98 stroke survivors completed the study and were included in data analysis, yielding an overall attrition rate of 10.9 %, which falls within acceptable limits. The mean ages were 54.36 ± 5.84 years for the control group and 56.56 ± 6.24 years for the intervention group. No significant baseline differences were observed between the groups (P > 0.05) (Appendix C). No adverse events occurred during the study.

3.2. Self-care

The Shapiro–Wilk test indicated that self-care maintenance and self-care monitoring scores were normally distributed, whereas self-care management scores were not. Table 1 indicated that no statistically significant differences were found at T0 across the three self-care dimensions (all P > 0.05). Significant effects were noted for group, time, and group × time interaction across the three dimensions (all P < 0.05). Follow-up between-group comparisons with a Bonferroni correction showed that at T1, T2, and T3, the intervention group scored significantly higher than the control group across all dimensions (all P < 0.001).

Table 1.

Comparison of the scores for self-care, self-efficacy, and quality of life between two groups (n = 98).

Variables Pre-intervention (T0) Post-intervention
 Group effect
 Time effect
 Group×Time effect
T1 T2 T3 F/Wald χ2 P F/Wald χ2 P F/Wald χ2 P
Self-care
Self-care maintenance
Intervention group 63.15 ± 7.26 75.23 ± 5.29 82.04 ± 4.42 85.09 ± 4.10 133.86a <0.001 166.13 <0.001 14.12 <0.001
Control group 62.13 ± 5.62 69.82 ± 3.81 72.53 ± 4.35 74.22 ± 3.30
t 0.77 5.79 10.73 14.49
P 0.443 <0.001 <0.001 <0.001
Self-care monitoring
Intervention group 59.44 ± 6.82 77.57 ± 6.22 82.15 ± 5.93 86.04 ± 6.47 75.31a <0.001 243.71 <0.001 34.93 <0.001
Control group 61.73 ± 4.91 69.00 ± 5.18 72.64 ± 6.65 73.93 ± 5.78
t -1.90 7.42 7.46 9.77
P 0.061 <0.001 <0.001 <0.001
Self-care management
Intervention group 68.75 (60.00, 72.50) 80.00 (75.00, 85.00) 85.00 (85.00, 89.38) 87.50 (85.00, 90.00) 253.96b <0.001 33.68 <0.001 7.52 0.023
Control group 67.50 (65.00, 70.00) 75.00 (67.50, 77.50) 72.50 (70.00, 77.50) 75.00 (70.00, 80.00)
Z -0.48 -5.27 -7.58 -6.99
P 0.634 <0.001 <0.001 <0.001
Self-efficacy
Intervention group 102.00 (89.00, 113.25) 112.00 (104.00, 117.00) 117.00 (112.00, 124.50) 122.00 (120.00, 128.75) 79.90b <0.001 50.55 <0.001 12.51 0.002
Control group 97.00 (93.00, 108.50) 105.00 (99.00, 109.25) 107.00 (99.00, 109.00) 107.0 (101.25, 111.25)
Z -0.78 -4.19 -5.98 -6.92
P 0.438 <0.001 <0.001 <0.001
Quality of life
Intervention group 40.40 ± 6.37 48.79 ± 3.45 49.54 ± 2.25 48.92 ± 2.53 37.20a <0.001 72.37 <0.001 4.61 0.005
Control group 39.66 ± 4.56 45.08 ± 2.50 44.92 ± 2.72 44.72 ± 2.48
t 0.66 6.08 9.15 8.28
P 0.514 <0.001 <0.001 <0.001
Open in a new tab

Note: Data are Mean ± SD or Median (P25, P75). T1: immediately post-intervention. T2: 1 month post-intervention. T3: 3 months post-intervention. a Repeated measures analyses of variance. b Generalized estimating equation.

3.3. Self-efficacy and quality of life

Table 1 indicated that no significant differences were observed in the self-efficacy and QoL scores between the two groups at T0 (all P > 0.05). For self-efficacy, the GEE model indicated significant group, time, and group × time interaction effects (Wald χ2 = 79.90, 50.55, and 12.51, respectively; all P < 0.05). Follow-up between-group comparisons with a Bonferroni correction showed that the intervention group scored significantly higher than the control group at T1, T2, and T3 (Z = −4.19, −5.98, and −6.92, respectively; all P < 0.001). For QoL, RM-ANOVA similarly showed significant group, time, and group × time interaction effects (F = 37.20, 72.37, and 4.61, respectively; all P < 0.05). Follow-up between-group comparisons with a Bonferroni correction indicated significantly higher QoL scores in the intervention group at all three post-intervention assessments (t = 6.08, 9.15, and 8.28, respectively; all P < 0.001).

4. Discussion

This study demonstrates the effectiveness of the SSCP in improving self-care, self-efficacy, and QoL among stroke survivors. The SSCP is a cost-effective and efficient approach that emphasizes patient engagement and provides tailored care. Recognizing that self-care trajectories differ across individuals [34], the SCM is applied to tailor the intervention intensity. The intervention initially delivered essential self-care information via WeChat group messaging. It was subsequently intensified through small-group or individualized sessions for participants showing limited progress, ensuring timely and tailored support. Compared with previous studies [35], the SSCP achieved comparable outcomes with fewer face-to-face sessions, optimizing resources while maintaining continuity of care. To enhance comprehensibility and engagement, content delivery was simplified and interactive components incorporated. As most participants were older adults with limited education, text materials were minimized, while animations and expert-led videos served as the primary teaching tools. Additionally, knowledge quizzes, role-model demonstrations, and case discussions encouraged active participation. Session feedback was analyzed to refine content and compiled into concise digital materials for patient review. To reinforce motivation and address prior ineffective experiences, preparatory interviews were conducted before the second and third steps to identify potential barriers and clarify solutions. During the home-based phase, regular clinic visits and telephone follow-ups supported coordination between survivors and caregivers, sustaining adherence and continuity of care. Overall, this intervention design provided continuous and individualized support, enhancing post-stroke self-care.

Our study demonstrated that the SSCP significantly improved survivors’ self-care compared with the control group. This improvement is likely attributable to the intervention's grounding in the Middle-Range Theory of Self-Care of Chronic Illness, which provides a structured and practical framework for designing self-care interventions. As defined by Riegel et al. [36], self-care interventions for adults with chronic conditions focus on behavior change by equipping patients with the knowledge and skills required to actively engage in and take responsibility for self-care maintenance, monitoring, and illness management. By structuring the SSCP around these three core dimensions, the program ensured both specificity and comprehensiveness across components, aligning closely with the operational definition of chronic illness self-care interventions [36]. Notably, previous research has highlighted that, although behavior change is central to self-care interventions, existing programs often underutilize behavior change techniques and rarely address the skill deficits individuals face when performing self-care in daily life [37]. Against this backdrop, the SSCP offers a flexible and individualized support structure. Specifically, online knowledge dissemination provided survivors with practical self-care skills (e.g., guidance on diet and medication use), addressing their immediate post-discharge “how-to” needs while maintaining high accessibility. The small-group component enhanced situational self-care skills through guided reflection, peer modeling videos, and case-based discussions, facilitating survivors’ navigation of common challenges in the home setting. Finally, acknowledging the critical role of family support in long-term self-care [38], face-to-face individualized sessions incorporated caregivers to foster a supportive home environment conducive to sustainable self-care behaviors. Therefore, the SSCP represents a theory-driven, multi-component intervention that offers a feasible and innovative approach to achieving sustainable improvements in self-care behaviors among stroke survivors.

Self-efficacy is defined as confidence in one’s ability to perform a behavior to achieve a specific outcome [39]. In this study, the SSCP significantly enhanced the self-efficacy of stroke survivors. This effect may be attributable to its individualized goal-setting approach and stepwise reinforcement strategies. At the beginning of each step, researchers and participants jointly established stage-specific goals, provided positive reinforcement for observed behavioral changes, and adjusted subsequent plans based on the attainment of these goals. Although previous studies have emphasized that enhancing self-care resources and knowledge is essential for improving self-efficacy, educational strategies alone often fail to produce meaningful changes in post-stroke self-care behaviors [19,40]. Building on an initial WeChat-based educational component, the present intervention progressively integrated structured group interactions and face-to-face family support. Activities such as sharing successful experiences and engaging in case discussions provided concrete behavioral models and reduced uncertainties related to attempting behavior change. At the same time, feedback from survivors and caregivers facilitated vicarious learning and improved the continuity of the intervention [19,41]. These elements collectively reinforced participants' confidence and motivation to engage in self-care. In addition, Rasyid et al. [42] have indicated that self-care and self-efficacy are closely interrelated and mutually reinforcing. Engaging in self-care activities can enhance the physical and cognitive functioning of stroke survivors, enabling them to develop a stronger sense of control over their recovery and overall health. This, in turn, can enhance their self-efficacy, further facilitating better self-care behaviors. As survivors gain confidence in their abilities, they are more likely to adopt healthy practices, ultimately establishing a beneficial, self-sustaining cycle that supports long-term self-care.

Our study findings suggest that the SSCP is more effective than the control group in improving QoL. Gurková et al. [43] reported that post-stroke clinical outcomes and various psychosocial factors primarily influence the QoL of stroke survivors. Positive self-care behaviors can enhance survivors’ functional status and increase their independence in performing daily activities, thereby contributing to improvements in the physical domain of QoL. In addition, as a core component of the SSCP, the SCM provides dynamically adjustable, stepped support based on survivors’ real-time feedback and self-care performance. This support fosters greater engagement in self-care, which subsequently strengthens survivors’ self-efficacy in condition-related self-care. Moreover, the emotional regulation education and skills training included in the intervention help survivors improve their sense of task control and reduce psychological distress associated with disease management, ultimately contributing to improved psychosocial aspects of QoL [44,45]. Together, these intervention components may synergistically enhance survivors’ overall QoL through both physiological and psychosocial pathways.

5. Implications for clinical practice and future research

In clinical practice, stroke self-care interventions should consistently encompass the three dimensions of self-care—maintenance, monitoring, and management—to ensure both comprehensiveness and effectiveness [14]. The SCM offers a feasible framework for delivering dynamic, staged, and individualized interventions by allocating additional resources to suboptimal responders, thereby enhancing precision care and promoting improvements in self-care behaviors. As self-care improvements tended to plateau during follow-up, integrating family and primary healthcare resources (particularly family physician services) is crucial for providing continuous guidance and supervision, thereby enhancing adherence and long-term sustainability [36]. Future research should tailor intervention content to patients’ specific needs, recovery stages, and rehabilitation goals to further refine individualized strategies. Behavioral incentive mechanisms (e.g., feedback-based check-in systems or point-based rewards) could be integrated to enhance motivation and sustain long-term engagement [46]. Moreover, digital interactive tools—such as mobile applications or AI-powered chatbots—merit further exploration as scalable platforms for continuous monitoring and patient support [47].

6. Limitations

This study has several limitations. First, participants were recruited from a single tertiary hospital in Henan Province, which may limit the generalizability of the findings. Second, the intervention group had a relatively high attrition rate of 12.73 %, primarily during the first two steps of the online intervention. Future research should investigate survivors’ preferences during the pre-intervention phase, diversify online intervention formats to enhance both flexibility and engagement, while ensuring continuous follow-up throughout the intervention intervals. Third, due to design constraints, blinding of survivors and outcome assessors was not feasible, which may have introduced measurement bias. Fourth, the two hospital wards were randomly assigned to the intervention and control groups using a simple coin-flip method. Although this approach provided basic randomization and baseline differences between groups were not statistically significant, the limited number of clusters may have reduced the ability to detect potential baseline imbalances. Future studies should consider conducting multicenter cluster-randomized controlled trials, leveraging the coordinating role of leading hospitals within a regional healthcare network. Finally, the study period was limited to three months, which precluded assessment of long-term effects. Extended follow-up is warranted in future studies.

7. Conclusions

Grounded in the Middle-Range Theory of Self-Care of Chronic Illness and the SCM, the SSCP was developed as a structured, multi-component, and continuous post-discharge intervention to support stroke survivors in facilitating effective self-care practices. The program effectively enhances self-care, self-efficacy, and QoL in individuals who have experienced a stroke. Its flexible, individualized design allows for real-time adaptation based on patient responses, making it both acceptable and practical. Collectively, these findings indicate that the SSCP offers a practical and scalable framework for promoting sustained self-care engagement and enhancing post-discharge home self-care among stroke survivors.

CRediT authorship contribution statement

Zihao Ruan: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing. Dan Wang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - review & editing. Wenna Wang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - review & editing, Supervision, Project administration. Yongxia Mei: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Supervision, Writing - review & editing. Hui Wang: Conceptualization, Methodology, Validation, Investigation, Data curation, Resources, Writing-review & editing. Suyan Chen: Conceptualization, Methodology, Validation, Investigation, Resources, Writing - review & editing. Qiushi Zhang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing - review & editing. Zhenxiang Zhang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Funding acquisition, Writing - review & editing, Supervision, Project administration.

Data availability statement

The datasets generated during and/or analyzed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author upon reasonable request.

Funding

The National Natural Science Foundation of China [72174184] provided policy and financial support for this research.

Declaration of competing interest

The authors have declared no conflict of interest.

Acknowledgments

We sincerely thank the neurologists, nurses, and rehabilitation therapists at Huaxian People’s Hospital in Henan Province for their invaluable support and assistance in this study. Furthermore, we gratefully acknowledge all stroke survivors and their caregivers for their invaluable cooperation and participation in this research.

Footnotes

Peer review under responsibility of Chinese Nursing Association.

Appendices

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijnss.2025.12.012.

Appendices. Supplementary data

The following are the Supplementary data to this article:

Multimedia component 1
mmc1.docx (80.8KB, docx)
Multimedia component 2
mmc2.docx (13KB, docx)

References

  • 1.GBD 2019 Stroke Collaborators Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet Neurol. 2021;20(10):795–820. doi: 10.1016/S1474-4422(21)00252-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Duncan P.W., Bushnell C., Sissine M., Coleman S., Lutz B.J., Johnson A.M., et al. Comprehensive stroke care and outcomes: time for a paradigm shift. Stroke. 2021;52(1):385–393. doi: 10.1161/STROKEAHA.120.029678. [DOI] [PubMed] [Google Scholar]
  • 3.Feigin V.L., Brainin M., Norrving B., Martins S., Sacco R.L., Hacke W., et al. World Stroke Organization (WSO): global stroke fact sheet 2022. Int J Stroke. 2022;17(1):18–29. doi: 10.1177/17474930211065917. [DOI] [PubMed] [Google Scholar]
  • 4.Adeoye O., Nyström K.V., Yavagal D.R., Luciano J., Nogueira R.G., Zorowitz R.D., et al. Recommendations for the establishment of stroke systems of care: a 2019 update. Stroke. 2019;50(7):e187–e210. doi: 10.1161/STR.0000000000000173. [DOI] [PubMed] [Google Scholar]
  • 5.Wang L.D., Ji X.M., Kang D.Z., Li T.X., Liu J.M., Zhao G.G., et al. Brief report on stroke center in China, 2020 report on stroke center in China writing group. Chin J Cerebrovasc Dis. 2021;18(11):737–743. doi: 10.3969/j.issn.1672-5921.2021.11.001. [In Chinese] [DOI] [Google Scholar]
  • 6.National Health Commission of the People’s Republic of China China health statistics yearbook. 2022. https://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/202305/49beded3bd984669bfe9089c6f231cf5.shtml [In Chinese] [DOI] [PMC free article] [PubMed]
  • 7.Mu X., Li J., Liu R.R., Zheng X.Y. The current status and influencing factors of self-management behaviors in patients with first stroke. Chin J Nurs. 2016;51(3):289–293. doi: 10.3761/j.issn.0254-1796.2016.03.006. [In Chinese] [DOI] [Google Scholar]
  • 8.Zhang T.Y., Liu X.H., Zhang Z.X. Psychological experience of caregivers of stroke patients with severe disability at home: a qualitative study. Mil Nurs. 2019;36(12):8–11. doi: 10.3969/j.issn.1008-9993.2019.12.003. [In Chinese] [DOI] [Google Scholar]
  • 9.Wang W.N., Lin B.L., Zhang Z.X., Zhou B., Chen G., Lv P., et al. Meta-synthesis of qualitative research on the real experience of telerehabilitation for stroke survivors. Chin J Nurs. 2021;56(2):199–206. doi: 10.3761/j.issn.0254-1769.2021.02.007. [In Chinese] [DOI] [Google Scholar]
  • 10.Riegel B., Dunbar S.B., Fitzsimons D., Freedland K.E., Lee C.S., Middleton S., et al. Self-care research: where are we now? Where are we going? Int J Nurs Stud. 2021;116 doi: 10.1016/j.ijnurstu.2019.103402. [DOI] [PubMed] [Google Scholar]
  • 11.Martínez N., Connelly C.D., Pérez A., Calero P. Self-care: a concept analysis. Int J Nurs Sci. 2021;8(4):418–425. doi: 10.1016/j.ijnss.2021.08.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Ruksakulpiwat S., Zhou W.D. Self-management interventions for adults with stroke: a scoping review. Chronic Dis Transl Med. 2021;7(3):139–148. doi: 10.1016/j.cdtm.2021.03.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Kraaijkamp J.J.M., Geerars M., Chavannes N.H., Achterberg W.P., van Dam van Isselt E.F., Punt M. Changes in physical activity and sedentary behaviour following geriatric rehabilitation in older adults with stroke. BMC Geriatr. 2025;25(1):357. doi: 10.1186/s12877-025-06007-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Jang D.E., Shin J.H. Self-care performance of middle-aged stroke patients in Korea. Clin Nurs Res. 2019;28(3):263–279. doi: 10.1177/1054773817740670. [DOI] [PubMed] [Google Scholar]
  • 15.Tan C.Y., Qin Y., Liao C.L., Liu J.H., Peng Q., Jiang W., et al. Effect of continuous nursing model based on WeChat public health education on self-management level and treatment compliance of stroke patients. Iran J Public Health. 2022;51(5):1040–1048. doi: 10.18502/ijph.v51i5.9419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Messina R., Dallolio L., Fugazzaro S., Rucci P., Iommi M., Bardelli R., et al. The Look After Yourself (LAY) intervention to improve self-management in stroke survivors: results from a quasi-experimental study. Patient Educ Counsel. 2020;103(6):1191–1200. doi: 10.1016/j.pec.2020.01.004. [DOI] [PubMed] [Google Scholar]
  • 17.Chen Y., Wei Y.Y., Lang H.J., Xiao T., Hua Y., Li L., et al. Effects of a goal-oriented intervention on self-management behaviors and self-perceived burden after acute stroke: a randomized controlled trial. Front Neurol. 2021;12 doi: 10.3389/fneur.2021.650138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Riegel B., Jaarsma T., Strömberg A. A middle-range theory of self-care of chronic illness. ANS Adv Nurs Sci. 2012;35(3):194–204. doi: 10.1097/ANS.0b013e318261b1ba. [DOI] [PubMed] [Google Scholar]
  • 19.Sahely A., Sintler C., Soundy A., Rosewilliam S. Feasibility of a self-management intervention to improve mobility in the community after stroke (SIMS): a mixed-methods pilot study. PLoS One. 2024;19(8) doi: 10.1371/journal.pone.0286611. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Lynch E.A., Nesbitt K., Gulyani A., Chan R.J., Bidargaddi N., Cadilhac D.A., et al. Do self-management interventions improve self-efficacy and health-related quality of life after stroke? A systematic review. Int J Stroke. 2025;20(7):786–800. doi: 10.1177/17474930251340286. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Smith W.M. ‘Stepped care: ‘treatment toward a specific goal. Urban Health. 1976;5(3):48–53. [PubMed] [Google Scholar]
  • 22.Wang D., Wang J.J., Wang Q., Zhang Z.X., Wang W.N. Application progress of stepped care model in chronic disease health management. Chin J Nurs. 2023;58(16):2034–2038. doi: 10.3761/j.issn.0254-1769.2023.16.019. [In Chinese] [DOI] [Google Scholar]
  • 23.Shah A., Hussain-Shamsy N., Strudwick G., Sockalingam S., Nolan R.P., Seto E. Digital health interventions for depression and anxiety among people with chronic conditions: scoping review. J Med Internet Res. 2022;24(9) doi: 10.2196/38030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Dong Y., Guo Z.N., Li Q., Ni W., Gu H.Q., Gu Y.X., et al. Chinese Stroke Organization guidelines for clinical management of cerebrovascular disorders: executive summary and 2019 update of clinical management of spontaneous subarachnoid haemorrhage. Stroke Vasc Neurol. 2019;4(4):176–181. doi: 10.1136/svn-2019-000296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Shi Y., Gao X.X., Tan X., Xue X.L., Zhang Y., Yuan X.F., et al. Comparative analysis of brief assessment of impaired cognition (Chinese version) and mini mental state examination scales in cognitive assessment of stroke survivors. J Chongqing Med Univ. 2021;46(11):1310–1314. doi: 10.13406/j.cnki.cyxb.002918. [In Chinese] [DOI] [Google Scholar]
  • 26.Xie Y.X., Shen S., Yu H. A screening method for aphasia: the token test. Chin J Rehabil Med. 1999;14(2):89–91. doi: 10.3969/j.issn.1001-1242.1999.02.016. [in Chinese] [DOI] [Google Scholar]
  • 27.Zhou X.X., Huang Y.L., Fang Y.C., Yan M.F., Lin S.S., Ou Y.J., et al. Effects of the heath coaching on knowledge level and self-management of patients with first stroke. Chin J Nurs Educ. 2022;19(1):81–85. doi: 10.3761/j.issn.1672-9234.2022.01.014. [In Chinese] [DOI] [Google Scholar]
  • 28.Wang W.N., Mei Y.X., Vellone E., Zhang Z.X., Liu B.W., Zhou C.X., et al. Development and psychometric testing of the self-care of stroke inventory. Disabil Rehabil. 2024;46(6):1178–1187. doi: 10.1080/09638288.2023.2196093. [DOI] [PubMed] [Google Scholar]
  • 29.Nie P., Chen J.L., Liu N. The sample size estimation in quantitative nursing research. Chin J Nurs. 2010;45(4):378–380. doi: 10.3761/j.issn.0254-1769.2010.04.037. [In Chinese] [DOI] [Google Scholar]
  • 30.Jones F., Partridge C., Reid F. The stroke self-efficacy questionnaire: measuring individual confidence in functional performance after stroke. J Clin Nurs. 2008;17(7B):244–252. doi: 10.1111/j.1365-2702.2008.02333.x. [DOI] [PubMed] [Google Scholar]
  • 31.Li H.Y., Fang L., Bi R.X., Cheng H.L., Huang J., Feng L.Q. The reliability and validity of the Chinese version of stroke self-efficacy questionnaire. Chin J Nurs. 2015;50(7):790–794. doi: 10.3761/j.issn.0254-1769.2015.07.005. [In Chinese] [DOI] [Google Scholar]
  • 32.Post M.W.M., Boosman H., van Zandvoort M.M., Passier P.E.C.A., Rinkel G.J.E., Visser-Meily J.M.A. Development and validation of a short version of the stroke specific quality of life scale. J Neurol Neurosurg Psychiatry. 2011;82(3):283–286. doi: 10.1136/jnnp.2009.196394. [DOI] [PubMed] [Google Scholar]
  • 33.Tang B.L., Yi Y.W., He X.L., Yao N., Zhao J., Sun H.J. Reliability and validity of adapted Chinese version of stroke specific quality of life scale. Mod Clin Nurs. 2021;20(2):33–39. doi: 10.3969/j.issn.1671-8283.2021.02.006. [DOI] [Google Scholar]
  • 34.Kim D.Y., Son Y.J. Longitudinal patterns and predictors of self-care behavior trajectories among Korean patients with heart failure: a 6-month prospective study. J Nurs Scholarsh. 2023;55(2):429–438. doi: 10.1111/jnu.12833. [DOI] [PubMed] [Google Scholar]
  • 35.Kalav S., Bektas H., Ünal A. Effects of chronic care Model-based interventions on self-management, quality of life and patient satisfaction in patients with ischemic stroke: a single-blinded randomized controlled trial. Jpn J Nurs Sci. 2022;19(1) doi: 10.1111/jjns.12441. [DOI] [PubMed] [Google Scholar]
  • 36.Riegel B., Westland H., Freedland K.E., Lee C.S., Stromberg A., Vellone E., et al. Operational definition of self-care interventions for adults with chronic illness. Int J Nurs Stud. 2022;129 doi: 10.1016/j.ijnurstu.2022.104231. [DOI] [PubMed] [Google Scholar]
  • 37.Riegel B., Westland H., Iovino P., Barelds I., Bruins Slot J., Stawnychy M.A., et al. Characteristics of self-care interventions for patients with a chronic condition: a scoping review. Int J Nurs Stud. 2021;116 doi: 10.1016/j.ijnurstu.2020.103713. [DOI] [PubMed] [Google Scholar]
  • 38.Zeren F.G., Canbolat O. The relationship between family support and the level of self care in type 2 diabetes patients. Prim Care Diabetes. 2023;17(4):341–347. doi: 10.1016/j.pcd.2023.04.008. [DOI] [PubMed] [Google Scholar]
  • 39.Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191–215. doi: 10.1037//0033-295x.84.2.191. [DOI] [PubMed] [Google Scholar]
  • 40.Jeddi H., Aghebati N., Ghavami V., Rezaeitalab F. The effect of self-care nurturance using the theory of modeling and role-modeling on self-efficacy in stroke patients: a randomized controlled trial. Holist Nurs Pract. 2023;37(2):E24–E35. doi: 10.1097/HNP.0000000000000567. [DOI] [PubMed] [Google Scholar]
  • 41.Lo S.H.S., Chang A.M., Chau J.P.C. Stroke self-management support improves survivors’ self-efficacy and outcome expectation of self-management behaviors. Stroke. 2018;49(3):758–760. doi: 10.1161/STROKEAHA.117.019437. [DOI] [PubMed] [Google Scholar]
  • 42.Rasyid A., Pemila U., Aisah S., Harris S., Wiyarta E., Fisher M. Exploring the self-efficacy and self-care-based stroke care model for risk factor modification in mild-to-moderate stroke patients. Front Neurol. 2023;14 doi: 10.3389/fneur.2023.1177083. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Gurková E., Štureková L., Mandysová P., Šaňák D. Factors affecting the quality of life after ischemic stroke in young adults: a scoping review. Health Qual Life Outcome. 2023;21(1):4. doi: 10.1186/s12955-023-02090-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Park H.Y., Yeom I.S., Kim Y.J. Telehealth interventions to support self-care of stroke survivors: an integrative review. Heliyon. 2023;9(6) doi: 10.1016/j.heliyon.2023.e16430. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Aminuddin H.B., Jiao N., Jiang Y., Hong J., Wang W. Effectiveness of smartphone-based self-management interventions on self-efficacy, self-care activities, health-related quality of life and clinical outcomes in patients with type 2 diabetes: a systematic review and meta-analysis. Int J Nurs Stud. 2021;116 doi: 10.1016/j.ijnurstu.2019.02.003. Epub 2019 Feb 8. [DOI] [PubMed] [Google Scholar]
  • 46.Szeto S.G., Wan H., Alavinia M., Dukelow S., MacNeill H. Effect of mobile application types on stroke rehabilitation: a systematic review. J NeuroEng Rehabil. 2023;20(1):12. doi: 10.1186/s12984-023-01124-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Kurniawan M.H., Handiyani H., Nuraini T., Hariyati R.T.S., Sutrisno S. A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness. Ann Med. 2024;56(1) doi: 10.1080/07853890.2024.230298. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.docx (80.8KB, docx)
Multimedia component 2
mmc2.docx (13KB, docx)

Data Availability Statement

The datasets generated during and/or analyzed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author upon reasonable request.

Articles from International Journal of Nursing Sciences are provided here courtesy of Chinese Nursing Association

RESOURCES