Abstract
Aim
To examine self-management behaviors and their key Determinants among young and middle-aged adults with ischemic stroke.
Methods
In this cross-sectional study, a convenience sample of 247 patients aged 18–59 years with ischemic stroke was recruited from a tertiary hospital in China between July 2023 and February 2025. Data were collected using a sociodemographic and clinical characteristics questionnaire, the Self-management Behavior Scale for Stroke, and the Barthel Index. Descriptive statistics and multivariable linear regression analyses were performed.
Results
The mean self-management score was 85.05 (SD = 16.61), indicating a moderate level of self-management. Resource utilization was the lowest-scoring dimension. Gender, educational level, activities of daily living, exercise habits and smoking status were identified as significant independent factors associated with self-management behaviors (adjusted R2 = 0.340).
Conclusion
Self-management behaviors among young and middle-aged adults with ischemic stroke were suboptimal, particularly with regard to the utilization of health resources. Sociodemographic and lifestyle factors play an important role in shaping self-management capacity in this population.
Implications for the Profession and/or Patient Care
Routine assessment of self-management capacity could be considered for integration into post-stroke nursing care for working-age adults. Nursing interventions that are gender-responsive and adapted to health literacy levels should be further explored. Although not directly evaluated in this study, nurse-led digital and peer-support strategies may serve as potential avenues to support self-management and secondary stroke prevention.
Keywords: ischemic stroke, self-management, young and middle-aged, nursing care
Introduction
Stroke is an acute neurologic deficit secondary to arterial ischemic, hemorrhagic, or venous-thrombotic insults to the cerebral tissue resulting in neuronal cell death and irreversible brain injury.1 This study specifically focuses on ischemic stroke, which accounts for the vast majority of all stroke cases. Compared to hemorrhagic stroke, ischemic stroke requires distinct secondary prevention strategies, such as strict adherence to antiplatelet or anticoagulant therapies and intensive lipid management, making sustained, disease-specific self-management particularly crucial for this subtype. Stroke remains a leading cause of mortality and long-term disability worldwide, and its burden among young and middle-aged adults has increased substantially over the past decade.2,3 In China, individuals younger than 60 years now account for nearly half of all stroke cases.4 In alignment with both World Health Organization classifications and the standard statutory retirement age in China, this study defines the “young and middle-aged” (working-age) population as adults aged 18 to 59 years. This demographic faces significant challenges for long-term disease management and nursing care delivery. Unlike older stroke survivors, working-age adults often face competing demands related to employment, family responsibilities, and social roles, alongside a prolonged lifetime risk of recurrence. Many belong to the so-called “sandwich generation”, balancing employment with caregiving responsibilities for both children and aging parents.5 These competing demands may reduce adherence to secondary prevention behaviors and engagement in sustained self-management, making sustained self-management following hospital discharge particularly critical for this population.
Self-management is defined as an individual’s ability to manage symptoms, treatment regimens, physical and psychosocial consequences, and lifestyle modifications associated with chronic illness.6 Evidence suggests that effective self-management after stroke is associated with improved functional outcomes, reduced recurrence risk, enhanced quality of life, and lower readmission rates.7,8 From a nursing perspective, self-management represents not only a set of patient behaviors but also a key outcome of supportive, educational, and empowering nursing interventions across the care continuum.
Despite its importance, research on stroke self-management has largely focused on older populations, with limited attention given to young and middle-aged adults. This gap is clinically relevant, as working-age stroke survivors differ substantially from older patients in health behaviors, psychosocial contexts, digital engagement, and patterns of healthcare resource utilization. Studies have shown that younger stroke survivors may experience higher levels of psychological distress, including anxiety, depression, and illness denial, which can further impede effective interaction with healthcare services.9 Emerging evidence suggests that younger stroke survivors often experience unmet needs related to age-appropriate information, vocational guidance, and peer support, which may undermine sustained self-management.10
Consequently, there remains insufficient understanding of how young and middle-aged adults with ischemic stroke manage their condition after discharge and which sociodemographic and health-related factors influence their self-management behaviors. Guided by Lorig and Holman’s self-management framework,6 this study conceptualizes self-management as a multidimensional construct encompassing symptom management, daily life regulation, rehabilitation engagement, and resource utilization. This framework is particularly appropriate for the working-age population because it emphasizes active problem-solving, self-efficacy, and resource utilization—skills essential for patients navigating the complex transition back to employment and multifaceted societal roles. To operationalize this framework, specific sociodemographic characteristics (eg, education level, gender) and health-related factors (eg, ADL capacity, lifestyle habits) were selected as independent variables. Within this framework, these selected variables were hypothesized to influence self-management behaviors through their effects on health literacy, functional capacity, and health-related decision-making. Addressing this gap is essential for the development of targeted, nursing-led interventions that are responsive to the specific needs of working-age adults and support long-term recovery and secondary stroke prevention.
Therefore, this study aims to: (1) Examine the current status of self-management behaviors among young and middle-aged ischemic stroke patients. (2) Identify the sociodemographic and health-related factors that influence these behaviors. (3) Provide evidence-based suggestions for creating targeted interventions to improve self-management in this vulnerable population.
Methods
Study Design and Setting
This cross-sectional study was conducted at two campuses of a tertiary hospital in China. Participant recruitment occurred July 2023 to February 2025. The research team comprised one neurologist and four neurology-specialized nurses, who played central roles in participant recruitment, patient communication, and questionnaire administration. The involvement of experienced stroke nurses ensured that data collection was conducted in a patient-centered and ethically sensitive manner, consistent with nursing practice standards.
Inclusion and Exclusion Criteria
In this study, the inclusion and exclusion criteria were as follows: Criteria for inclusion of participants. (1) met the World Health Organization’s definition of young and middle-aged people aged 18–59 year; (2) acute ischemic stroke diagnosis verified by computed tomography (CT) or magnetic resonance imaging (MRI) in accordance with the Chinese guidelines for diagnosis and treatment of acute ischemic stroke 2018;11 (3) disease duration of more than one week with stable vital signs; (4) clear consciousness and effective communication to complete questionnaires; and (5) voluntary participation with informed consent. Criteria for excluding participants: (1) concomitant traumatic brain injury; (2) life-threatening comorbidities such as acute myocardial infarction, major organ failure, malignant arrhythmias, or active cancer; (3) severe cognitive impairment that makes it impossible to reliably self-report (Mini-Mental State Examination <18); (4) severe aphasia that makes it impossible to complete questionnaires.
Study Sample
Sample size estimation followed Kendall’s principle, recommending 5–10 participants per variable included in regression analyses. In this study, 18 variables were included: (a) patients demographic and sociological information (7variables), including gender, age, marital status, occupation, education level, pre-stroke monthly income, and medical insurance type; (b) stroke-related information (11variables), including stroke frequency, Body mass index (BMI), ADL capacity, smoking, drinking, dietary habits, exercising, history of hypertension, diabetes, heart disease, and hyperlipidemia. With 18 variables to be analyzed, the minimum sample size required for this study was 90. Although the lack of an a priori effect-size-based power calculation represents a methodological limitation, the final sample size included in our study was 247 (a total of 270 eligible patients were approached, 20 declined participation, and 3 were excluded due to missing or invalid responses), which is generally considered adequate for an exploratory multivariable regression model (Figure 1).
Figure 1.
The Research Flowchart.
Measurements
General Sociological and Disease Characteristics
It was designed by the researchers themselves and included two sections with demographic and sociological information (gender, age, marital status, occupation, education level, pre-stroke monthly income, medical insurance type) and stroke-related information (stroke frequency, Body mass index (BMI), smoking, drinking, dietary habits, exercising, history of hypertension, diabetes, heart disease, and hyperlipidemia).
Self-Management Behavior Scale for Stroke (SBSFS)
This scale was compiled by Na et al.12 The Chinese SBSFS has 5 dimensions: symptom management, daily life management, emotional management, rehabilitation management, and resource management, with 26 items. A Likert 5-level scoring method was employed, with a score of 5 indicating “strongly agree”, 4 “agree”, 3 “neutral”, 2 “disagree”, and 1 “strongly disagree”. The total potential score was 130. The higher the scores, the better self-management behaviors. The SBSFS has been widely used in nursing research to assess behavioral self-management capacity among stroke survivors. Originally designed to evaluate self-management behaviors in young stroke patients, it was adapted for use with middle-aged and young ischemic Stroke patients after obtaining author permission. The scale demonstrated a Cronbach’s α coefficient of 0.908. Retest reliability yielded a Cronbach’s α coefficient of 0.868.
Barthel Index (BI)
The Barthel Index (BI) scale is an internationally recognized tool used to assess patients’ activities of daily living (ADL).13 It contains 10 items that evaluate self-care in the following areas: eating, washing, grooming, dressing, bowel control, urination, toileting, bed and wheelchair transfer, level walking, and stair walking. The scores fall into four categories, ranging from 0 to 100: complete independence (100), mild dependence (61–99), moderate dependence (41–60), and severe dependence (≤40). The BI demonstrated exceptional reliability in the present sample (Cronbach’s α=0.89).
Data Collection
The information was collected from the cerebrovascular ward. Hospital’s electronic medical records were used to select potential participants based on predetermined inclusion and exclusion criteria. If the patient was eligible, the researcher went to the patient’s bedside to explain the study’s goals and get signed informed consent. Questionnaires were administered face-to-face by the trained neurology nurses. Participants were encouraged to complete the forms independently, while the nurses were available to read items aloud or provide objective clarifications for those with mild visual or reading difficulties, ensuring no influence on their independent choices.
Ethical Considerations
All respondents were informed of the aims and objectives of the study and were provided the written consent form in the questionnaire itself. Ethical approval was officially granted prior to the commencement of participant recruitment and data collection (Ethics Approval No.: Ningbo First Hospital LS2022Y No.129A). Participants were aware that their participation was voluntary. The confidentiality of the participants was ensured.
Statistical Analysis
All statistical analyzes were performed using SPSS version 25.0. To handle missing data, a complete case analysis approach was adopted; any questionnaires with missing or incomplete responses were deemed invalid and excluded prior to statistical evaluation. Continuous variables were expressed as mean ± standard deviation (SD), and categorical variables as frequencies and percentages. Independent sample t-test and one-way ANOVA followed by Tukey’s HSD post-hoc test were used to analyze the relationship between nominal data (eg age, gender, marital status) and self-management scores. To control for Type I error across multiple univariate tests, a Bonferroni correction was applied where appropriate. Variables entered into the multiple linear regression model were selected based on both statistical significance in univariate analyses and theoretical relevance to self-management behaviors as reported in previous nursing and stroke literature. Nominal categorical variables (eg, occupation, medical insurance type, dietary habits, and ADL capacity) were dummy-coded before inclusion. Regression diagnostics were performed to verify the assumptions of normality of residuals, homoscedasticity, linearity, and the absence of multicollinearity (Variance Inflation Factor < 5) and influential cases.
Results
Characteristics of the Participants
A total of 247 young and middle-aged patients with ischemic stroke were included. (see Table 1). Most participants were aged over 45 years (83.4%) and were predominantly male (75.7%). The majority had relatively low to moderate educational levels, with 36.8% having primary education or below and 40.5% junior high school education, while fewer had high school (15.0%) or college education and above (7.7%). Most participants were married (92.3%). In terms of occupation, 50.6% were manual workers, 25.9% were unemployed or laid off, and 23.5% were engaged in mental labor. Nearly half (45.7%) had a monthly income of 3000–5999 yuan, with smaller proportions in lower and higher income groups. Regarding medical insurance, most participants were covered by urban resident insurance (51.4%) or rural cooperative insurance (41.3%), while only a few had no insurance (4.5%) or civil servant insurance (2.8%).
Table 1.
Characteristics of Participants (N = 247)
| Variable | N (%) |
|---|---|
| Age | |
| ≤45 | 41 (16.6%) |
| >45 | 206 (83.4%) |
| Gender | |
| Male | 187 (75.7%) |
| Female | 60 (24.3%) |
| Education Level | |
| Elementary and below | 91 (36.8%) |
| Junior high school | 100 (40.5%) |
| High school | 37 (15%) |
| College and above | 19 (7.7%) |
| Marital Status | |
| Unmarried/Divorce/Widowed | 19 (7.7%) |
| Married | 228 (92.3%) |
| Occupation | |
| No job or laid off | 64 (25.9%) |
| Manual labor | 125 (50.6%) |
| Mental labor | 58 (23.5%) |
| Monthly Income (yuan) | |
| <1000 | 17 (6.9%) |
| 1000-2999 | 64 (25.9%) |
| 3000-5999 | 113 (45.7%) |
| 6000-8999 | 35 (14.2%) |
| >9000 | 18 (7.3%) |
| Medical Insurance Type | |
| None | 11 (4.5%) |
| Rural cooperative | 102 (41.3%) |
| Urban resident | 127 (51.4%) |
| Civil servant | 7 (2.8%) |
The Level of Self-Management Behaviors of Young and Middle-Aged Patients with Ischemic Stroke
The overall score of SBSFS of young and middle-aged patient was 85.05 ±16.61, scores for each dimension were as follows: symptom management (17.02±4.52), daily life management (35.17±8.76), emotional management (13.06±3.29), rehabilitation management (11.87±3.59), and resource management (7.93±3.13). A score indication reported as percentage was used to normalize scores for comparative reasons because each dimension of the scale had a different number of items. Scoring rate = Actual Score (Item/Dimension)/Maximum Score × 100% is the precise calculating method. Three layers comprised the assessment criteria: A score of more than 80% was categorized as good, between 60% and 80% as moderate, and less than 60% as poor. For middle-aged and younger ischemic stroke patients, the self-management behavior score index was 65.43%, which is marginally below average. When comparing dimensions, rehabilitation management received a high score, resource management received a low score, and daily life management, symptom management, and emotional management had slightly lower scores than normal. Detailed information is provided in Table 2.
Table 2.
Scores of Various Dimensions of SBSFS of Young and Middle-Aged Patients (N = 247)
| Dimensions | Min | Max | Mean±SD | Scoring Rate |
|---|---|---|---|---|
| Symptom management (5 items) | 5 | 25 | 17.02±4.52 | 68.08% |
| Daily life management (11 items) | 11 | 55 | 35.17±8.76 | 63.95% |
| Emotional management (4 items) | 4 | 20 | 13.06±3.29 | 65.32% |
| Rehabilitation management (3 items) | 3 | 15 | 11.87±3.59 | 79.11% |
| Resource management (3 items) | 3 | 15 | 7.93±3.13 | 52.85% |
| Total score | 26 | 130 | 85.05±16.61 | 65.43% |
Items with Poorest Performance
Poor performance was indicated by seven items with scores below 60%. Among these, one to the symptom management dimension, three items belonged to the daily life management dimension, one to the emotional management dimension, and two to the resource utilization management dimension. The details are shown in Table 3.
Table 3.
Poorest-Performing Self-Management Items
| Items | Dimensions | Mean±SD | Scoring Rate |
|---|---|---|---|
| Item 2: Recognizing stroke warning signs | Symptom management | 2.69±1.41 | 53.77% |
| Item 9: Weight monitoring and control | Daily life management | 2.37±1.41 | 47.45% |
| Item 11: Daily oral hygiene maintenance | Daily life management | 2.77±1.46 | 55.47% |
| Item 16: Regular aerobic exercise | Daily life management | 2.34±1.36 | 46.72% |
| Item 18: Seeking emotional support from family/friends | Emotional management | 2.80±1.19 | 56.03% |
| Item 24: Seeking stroke information via internet/books | Resource management | 2.25±1.33 | 44.94% |
| Item 25: Exchanging experiences with stroke peers | Resource management | 2.48±1.23 | 49.64% |
Factors Associated with Self-Management Behaviors in Univariate Analysis
Univariate analyses revealed significant differences (p < 0.05) in self-management scores based on sociodemographic factors including age, gender, education level, occupation, and medical insurance type (Table 4). Furthermore, significant differences were also observed across disease and lifestyle characteristics, specifically ADL capacity, smoking, drinking, dietary habits, exercise habits, and hypertension (Table 5).
Table 4.
Comparison of Self-Management Behaviors Differences Among Young and Middle-Aged Ischemic Stroke Patients with Different Sociological Characteristics (N = 247)
| Variable | Participants (n) | SBSFS (Mean±SD) | t/F | p |
|---|---|---|---|---|
| Age | 2.635 | 0.009 | ||
| ≤45 | 41 | 91.22±16.64 | ||
| >45 | 206 | 83.83±16.36 | ||
| Gender | −3.943 | <0.001 | ||
| Male | 187 | 82.76±16.40 | ||
| Female | 60 | 92.20±15.29 | ||
| Education level | 7.115 | <0.001 | ||
| Elementary and below | 91 | 80.05±14.61 | ||
| Junior high school | 100 | 86.16±17.16 | ||
| High school | 37 | 88.22±15.86 | ||
| College and above | 19 | 97.00±16.60 | ||
| Marital Status | 0.517 | 0.606 | ||
| Unmarried/Divorce/Widowed | 19 | 86.95±17.86 | ||
| Married | 228 | 84.89±16.53 | ||
| Occupation | 4.942 | 0.008 | ||
| No job or laid off | 64 | 85.08±17.17 | ||
| Manual labor | 125 | 82.46±16.17 | ||
| Mental labor | 58 | 90.62±15.78 | ||
| Monthly income (yuan) | 2.159 | 0.074 | ||
| <1000 | 17 | 88.71±21.51 | ||
| 1000–2999 | 64 | 82.53±16.54 | ||
| 3000–5999 | 113 | 83.50±16.33 | ||
| 6000–8999 | 35 | 90.34±14.66 | ||
| >9000 | 18 | 90.06±14.72 | ||
| Medical insurance type | 4.184 | 0.007 | ||
| None | 11 | 79.73±20.49 | ||
| Rural cooperative | 102 | 81.95±16.37 | ||
| Urban resident | 127 | 87.21±15.91 | ||
| Civil servant | 7 | 99.43±15.14 |
Table 5.
Comparison of Self-Management Behaviors Differences Among Young and Middle-Aged Ischemic Stroke Patients with Different Disease Characteristics (N = 247)
| Variable | Participants (n) | SBSFS (Mean±SD) | t/F | p |
|---|---|---|---|---|
| Stroke Frequency | −0.462 | 0.646 | ||
| First time | 201 | 84.79±16.01 | ||
| Recurrence | 46 | 86.20±19.15 | ||
| BMI | 1.015 | 0.387 | ||
| Emaciation | 11 | 77.73±15.03 | ||
| Normal weight | 96 | 86.56±17.47 | ||
| Over weight | 97 | 84.64±16.82 | ||
| Obese | 43 | 84.49±14.31 | ||
| ADL Capacity | 8.816 | <0.001 | ||
| Severe dependence | 18 | 72.83±15.57 | ||
| Moderate dependence | 28 | 80.71±16.79 | ||
| Slight dependence | 99 | 82.92±15.18 | ||
| Independent | 102 | 90.47±16.31 | ||
| Smoking | −4.034 | <0.001 | ||
| No | 130 | 88.98±15.68 | ||
| Yes | 117 | 80.69±16.58 | ||
| Drinking | −2.298 | 0.022 | ||
| No | 162 | 86.80±15.79 | ||
| Yes | 85 | 81.73±17.69 | ||
| Dietary Habits | 3.403 | 0.035 | ||
| Vegetarianism | 25 | 88.64±19.23 | ||
| Omnivorous | 130 | 86.83±17.36 | ||
| Meat activists | 92 | 81.58±14.17 | ||
| Exercise | −6.865 | <0.001 | ||
| No | 151 | 79.56±13.94 | ||
| Yes | 96 | 93.70±16.85 | ||
| Hypertension | −2.417 | 0.016 | ||
| No | 102 | 88.07±17.66 | ||
| Yes | 145 | 82.93±15.54 | ||
| Diabetes | −1.784 | 0.076 | ||
| No | 168 | 86.34±17.10 | ||
| Yes | 79 | 82.32±15.25 | ||
| Hyperlipidemia | −1.179 | 0.239 | ||
| No | 173 | 85.87±17.40 | ||
| Yes | 74 | 83.15±14.50 | ||
| Heart Disease | −0.087 | 0.930 | ||
| No | 229 | 85.08±16.66 | ||
| Yes | 18 | 84.72±16.32 |
Multiple Linear Regression Analysis of Influencing Factors of Self-Management Behaviors in Young and Middle-Aged Patients with Ischemic Stroke
The total SBSFS score of young and middle-aged patients with ischemic stroke was used as the dependent variable. Statistically significant general demographic data and variables related to disease-related characteristics (age [years old], gender, education level, occupation, medical insurance type, ADL capacity, smoking, drinking, dietary habits, exercise, and hypertension) were used as independent variables for multiple linear regression analysis. The independent variables were assigned as shown in Table 6. Although several variables were significant in the univariate analysis, factors such as age, occupation, medical insurance, drinking, dietary habits, and hypertension lost statistical significance in the multivariable model, likely due to confounding effects and shared variance among these associated factors. The linear regression results showed that gender, education level, ADL capacity, smoking, and exercise were independent influences on self-management behaviors in young and middle-aged patients with ischemic stroke (P < 0.05). The final model accounted for a moderate portion of the variance (R2 = 0.340), meaning that the variables together explained 34.0% of the total variation in young and middle-aged stroke patients, as shown in Table 7, leaving a substantial proportion of unexplained variability that suggests the likely involvement of other unmeasured psychosocial or environmental factors.
Table 6.
Assignment of Independent Variables
| Dimensions | Assignment Description |
|---|---|
| Age | ≤45 years=1, >45 years=2 |
| Gender | Male=1, female=2 |
| Education level | Elementary and below=1, junior high school=2, high school=3, college and above=4 |
| Occupation | No job or laid off=1, manual labor=2, mental labor=3 |
| Medical insurance type | None=1, rural cooperative=2, urban resident=3, civil servant=4 |
| ADL Capacity | Severe dependence=1, moderate dependence=2, slight dependence=3, independent=4 |
| Drinking | Yes=1, no=2 |
| Smoking | Yes=1, no=2 |
| Dietary Habits | Omnivorous=1, meat activists=2, vegetarianism=3 |
| Exercise | No=1, yes=2 |
| Hypertension | Yes=1, no=2 |
Table 7.
Multivariate Linear Regression Models for Prediction of Self-Management Behaviors in Young and Middle-Aged Patients with Ischemic Stroke. (N = 247)
| Dimensions | Unstandardized Coefficients | Standardized Coefficients | t | p | 95% CI | ||
|---|---|---|---|---|---|---|---|
| B | SE | β | Lower | Upper | |||
| Constant | 30.60 | 8.87 | 3.45 | <0.001 | 13.13 | 48.07 | |
| Age (Ref: ≤45) | −2.72 | 2.47 | −0.06 | −1.10 | 0.27 | −7.58 | 2.13 |
| Gender (Ref: Male) | 5.39 | 2.47 | 0.14 | 2.18 | 0.03 | 0.53 | 10.26 |
| Education level (Ref: Elementary and below) | 3.93 | 1.07 | 0.22 | 3.66 | <0.001 | 1.81 | 6.05 |
| Occupation (Ref: No job or laid off) | 1.14 | 1.25 | 0.05 | 0.92 | 0.36 | −1.31 | 3.60 |
| Medical insurance type (Ref: None) | 1.35 | 1.49 | 0.05 | 0.91 | 0.36 | −1.58 | 4.28 |
| ADL Capacity (Ref: Severe dependence) | 3.68 | 1.06 | 0.20 | 3.47 | <0.001 | 1.59 | 5.77 |
| Drinking (Ref: No) | 2.68 | 2.00 | 0.08 | 1.34 | 0.18 | −1.26 | 6.63 |
| Smoking (Ref: No) | 5.94 | 2.11 | 0.18 | 2.82 | 0.01 | 1.79 | 10.10 |
| Dietary Habits (Ref: Omnivorous) | −0.94 | 1.30 | −0.04 | −0.72 | 0.47 | −3.51 | 1.63 |
| Exercise (Ref: No) | 10.36 | 1.84 | 0.30 | 5.63 | <0.001 | 6.73 | 13.98 |
| Hypertension (Ref: No) | 1.02 | 1.79 | 0.03 | 0.57 | 0.57 | −2.51 | 4.55 |
Note: R = 0.608, Adjusted R2 = 0.340.
Discussion
This study provides important nursing evidence regarding self-management behaviors among young and middle-aged adults with ischemic stroke, a population that has received relatively limited attention in previous research. Unlike prior studies that predominantly focused on older populations, our research highlights the unique self-management profile of working-age adults. By contextualizing our findings within this demographic’s specific challenges—such as competing vocational and caregiving demands—we identified a distinct vulnerability in resource utilization, offering novel insights into age-specific barriers that require targeted post-stroke care planning. Overall, self-management levels were moderate, with particularly low performance in resource utilization.
Consistent with previous studies, our findings indicate that self-management among stroke survivors remains suboptimal during the recovery phase.8 Importantly, resource utilization emerged as the weakest dimension of self-management, suggesting that young and middle-aged patients may lack access to, awareness of, or engagement with appropriate post-stroke information and peer-support resources. The lowest-scoring items—seeking stroke-related information through digital or print sources and exchanging experiences with peers—reflect unmet needs that have been similarly reported in qualitative studies of younger stroke survivors.10 According to younger stroke survivors, age-appropriate vocational assistance and rehabilitation resources are scarce, and present stroke services are mostly targeted at older groups. However, it differs from global patterns. The studies showed that among young and middle-aged patients with ischemic stroke in Western nations, internet-based intervention options are highly acceptable and successful, indicating significant potential for digital health management platforms to improve resource utilization.14 Other studies highlighted that digital health technologies have the potential to improve self-management among stroke survivors, yet barriers such as limited digital health literacy, inadequate device access, and lack of user-centered design constrain the adoption of technology-based solutions.15 From a nursing systems perspective, these deficits point not only to individual-level barriers but also to structural shortcomings in how post-stroke education and support services are designed and delivered for working-age adults.
Gender differences in self-management behaviors were evident, with female patients demonstrating higher levels of engagement. This finding aligns with previous research showing that women tend to exhibit greater adherence to long-term rehabilitation and health-promoting behaviors following stroke.16 Sociocultural norms and gendered health practices may contribute to these differences, as women are often more proactive in health monitoring and lifestyle modification.17 In contrast, male patients in this working-age demographic often experience immense societal pressure as primary income earners. Driven by the urgency to return to work and restore financial stability, they may view time-consuming self-management behaviors as secondary to their occupational responsibilities, thereby underestimating long-term recurrence risks. These findings suggest the potential value of gender-sensitive nursing approaches that actively engage male stroke survivors and integrate self-management support into return-to-work planning and vocational rehabilitation, though the efficacy of such strategies remains a hypothesis to be tested in future trials.
Education level and ADL capacity were also positively associated with self-management behaviors. Higher educational attainment may facilitate health literacy, critical appraisal of information, and informed decision-making. Furthermore, for working-age adults, higher education may enhance their ability to navigate complex occupational health policies and negotiate workplace accommodations, which are critical for sustaining self-management while returning to work.18 Moreover, functional independence enables patients to carry out daily self-management tasks, such as medication adherence, symptom monitoring, and lifestyle adjustments.19 However, it is important to acknowledge the possibility of reverse causation; while ADL capacity may influence self-management, better self-management behaviors may concurrently foster better functional recovery. However, functional recovery is highly dynamic. Recent evidence examining latent trajectory patterns of care dependency after first stroke reveals that long-term nursing needs are highly heterogeneous, with certain patients experiencing persistent or worsening dependency over time.20 Therefore, patients with lower ADL capacity, particularly those on poor dependency trajectories, require dynamic, stage-specific nursing support rather than generic discharge education, to overcome physical and cognitive barriers to self-management.21 Simultaneously, visual aids should be provided to patients with low literacy rates, and teaching materials should be created for various reading levels.
Regular exercise emerged as one of the strongest associated factors of self-management behaviors, consistent with previous findings that physically active stroke survivors demonstrate greater engagement in health-promoting behaviors.8,22 Exercise may serve both as an indicator and a facilitator of self-management, reflecting greater health awareness, motivation, and self-efficacy. Because exercise is arguably a component of self-management itself, reverse causality is highly plausible, whereby individuals with inherently better self-management traits are more likely to maintain regular exercise habits. Conversely, smoking was independently associated with poorer self-management, reinforcing the importance of addressing modifiable lifestyle risk factors through nursing-led education and behavioral counselling.23
Strengths and Limitations of the Work
The research has some limitations that need to be addressed in future work. First, this was a cross-sectional study using a convenience sample conducted at a single tertiary hospital, which limits the generalizability of findings to other regions and healthcare settings. Additionally, the small sample sizes in certain subgroups (eg, civil servant medical insurance [n = 7] and severe ADL dependence [n = 18]) may reduce the statistical stability of comparisons across these specific categories. Second, the exclusion of patients with severe cognitive impairment or aphasia, while methodologically necessary to ensure the reliability of self-reported survey data, may have resulted in a sample biased toward higher-functioning patients. This selection bias prevents the capture of the full spectrum of post-stroke dependency and hinders our understanding of self-management issues among more severely impaired stroke survivors, who may be most vulnerable and in most need of comprehensive nursing care. Third, we did not evaluate certain potentially significant factors, including cognitive function, psychological status (depression, anxiety), social support, and family caregiver involvement. Furthermore, although our sample size met the minimum statistical requirements, the relatively large number of predictor variables entered into the model introduces a potential risk of overfitting. Lastly, the cross-sectional design limits causal inferences regarding the correlations between identified factors and self-management behaviors, and it prevents the observation of longitudinal changes or meaningful clinical outcomes over time. Specifically, we cannot rule out reverse causation, particularly for variables such as ADL capacity and exercise habits, which may share a bidirectional relationship with self-management.
Recommendations for Further Research
Based on the findings and limitations of this study, several avenues for future research are recommended. First, given the cross-sectional nature of this study, longitudinal studies are needed to track the trajectory of self-management behaviors from hospital discharge through the first year of recovery. This would help identify critical time windows where self-management compliance tends to decline. Second, intervention studies are urgently needed. Since “resource utilization” was the lowest-scoring dimension, future research should design and evaluate digital health interventions (eg, mobile apps, WeChat mini-programs) specifically tailored for young and middle-aged patients. These studies should verify whether increasing access to digital resources translates to better clinical outcomes. Third, qualitative or mixed-methods research is recommended to explore the reasons behind the gender disparity found in our study. Understanding why male patients demonstrate lower self-management engagement could inform the development of gender-sensitive educational strategies. Future studies should include caregiver variables and psychosocial factors (eg, depression, return-to-work anxiety) to build a more holistic prediction model for self-management in this working-age population.
Implications for Policy and Practice
These findings highlight the critical role of nurses in assessing self-management capacity among young and middle-aged stroke patients and tailoring interventions accordingly. Nursing practice should move beyond standardized discharge education toward individualized, behavior-focused coaching, particularly for male patients, smokers, and those with reduced ADL capacity. We hypothesize that integrating digital health literacy training and peer-support facilitation into routine nursing care may substantially improve resource utilization and long-term self-management outcomes.
Conclusion
This study reveals that self-management behaviors among young and middle-aged ischemic stroke patients in China are suboptimal, with a concerning deficit in resource utilization. Although direct comparisons with older stroke survivors were not conducted, these results suggest that working-age patients also experience significant challenges in health management engagement. Instead, specific subgroups, particularly men, individuals with lower education levels, and those with poor lifestyle habits, are associated with poorer self-management capacity. Improving self-management among this population requires shifting the focus from acute stabilization to long-term behavioral empowerment. While the single-center cross-sectional design of this study limits causal inferences and only demonstrates associations, our findings suggest that nursing professionals may help support the recovery trajectory of this vital demographic by implementing gender-sensitive, digitally enabled, and occupationally relevant interventions.
Funding Statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This study was supported by institutional research funds from the hospital-level research project of Ningbo First Hospital (H2022YJ006) and Zhejiang Province Medical and Health Science and Technology Plan Project (2025KY1330). The funders had no role in the study design, data collection, data analysis, data interpretation, or the preparation and writing of this manuscript.
Statement
An unauthorized version of the Chinese MMSE was used by the study team without permission; however, this has now been rectified with PAR.
The MMSE is a copyrighted instrument and may not be used or reproduced in whole or in part, in any form or language, or by any means without written permission of PAR (www.parinc.com).
Data Sharing Statement
The datasets used and/or analyzed during the current study are available from the corresponding author, Dr. Qinhong Xu, on reasonable request.
Disclosure
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References
- 1.Najjar M, Fleming M. Pediatric stroke: from emergent evaluation to rehabilitation. Curr Problem Pediatr Adolescent Health Care. 2025;55(11):101878. doi: 10.1016/j.cppeds.2025.101878 [DOI] [PubMed] [Google Scholar]
- 2.Gorelick PB. The global burden of stroke: persistent and disabling. Lancet Neurol. 2019;18(5):417–13. doi: 10.1016/s1474-4422(19)30030-4 [DOI] [PubMed] [Google Scholar]
- 3.Moosa A, Osama D, Alnidawi F, Algillidary S, Hussein A, Das P. Risk factors, incidence, and outcome of stroke: a retrospective cross-sectional hospital-based study comparing young adults and elderly. Cureus. 2023;15(6):e40614. doi: 10.7759/cureus.40614 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Si Y, Huang X, Zhao S, Ou Z, Pi M, Du J. Experiences of young and middle-aged stroke patients with hemiplegia who actively participate in rehabilitation exercises and their perceptions of well-being: a descriptive qualitative study. Risk Manag Healthc Policy. 2025;18:2049–2062. doi: 10.2147/rmhp.S509536 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Ma Z, He W, Zhou Y, et al. Global burden of stroke in adolescents and young adults (aged 15-39 years) from 1990 to 2019: a comprehensive trend analysis based on the global burden of disease study 2019. BMC Public Health. 2024;24(1):2042. doi: 10.1186/s12889-024-19551-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Lorig KR, Holman H. Self-management education: history, definition, outcomes, and mechanisms. Ann Behav Med. 2003;26(1):1–7. doi: 10.1207/s15324796abm2601_01 [DOI] [PubMed] [Google Scholar]
- 7.Ansong R, Gazarian P. Healthcare self-management support of stroke patients after discharge: a conceptual analysis using Rodger’s evolutionary approach. J Adv Nurs. 2024;80(11):4436–4447. doi: 10.1111/jan.16078 [DOI] [PubMed] [Google Scholar]
- 8.Sun Y, Liu C, Zhang N, et al. Effect of self-management of stroke patients on rehabilitation based on patient-reported outcome. Front Neurosci. 2022;16:929646. doi: 10.3389/fnins.2022.929646 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Ignacio KHD, Diestro JDB, Medrano JMM, et al. Depression and anxiety after stroke in young adult filipinos. J Stroke Cerebrovasc Dis. 2022;31(2):106232. doi: 10.1016/j.jstrokecerebrovasdis.2021.106232 [DOI] [PubMed] [Google Scholar]
- 10.Amoah D, Prior S, Mather C, Schmidt M, Bird ML. Exploring the unmet needs of young adults with stroke in Australia: can technology help meet their needs? A qualitative study. Int J Environ Res Public Health. 2023;20(15):6450. doi: 10.3390/ijerph20156450 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Wu J, Lu AD, Zhang LP, Zuo YX, Jia YP. [Study of clinical outcome and prognosis in pediatric core binding factor-acute myeloid leukemia]. Zhonghua Xue Ye Xue Za Zhi. 2019;40(1):52–57. doi: 10.3760/cma.j.issn.0253-2727.2019.01.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Na X, Yi J, Zhan-ying M. Development and psychometric test of a self-management behavior scale for young stroke patients. J Chin J Nurs. 2016;51(1):103–107. doi: 10.3761/j.issn.0254-1769.2016.01.022 [DOI] [Google Scholar]
- 13.Valero-Chillerón MJ, Llagostera-Reverter I, Luna-Aleixós D, Moreno-Casbas M, Andreu-Pejó L, González-Chordá VM. Exploring the construct validity of the Barthel index in a sample of Spanish hospitalised patients. Enfermeria Clinica. 2023;33(5):370–374. doi: 10.1016/j.enfcle.2023.06.005 [DOI] [PubMed] [Google Scholar]
- 14.Barcheus IM, Ranner M, Månsson Lexell E, Jacobsson L, Larsson-Lund M. The internet-based intervention strategies for empowering activities in everyday life: qualitative study of experiences of clients with stroke. JMIR Format Res. 2024;8:e56189. doi: 10.2196/56189 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Bally ELS, Cheng D, van Grieken A, et al. Patients’ perspectives regarding digital health technology to support self-management and improve integrated stroke care: qualitative interview study. J Med Internet Res. 2023;25:e42556. doi: 10.2196/42556 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Carcel C, Wang X, Sandset EC, et al. Sex differences in treatment and outcome after stroke: pooled analysis including 19,000 participants. Neurology. 2019;93(24):e2170–e2180. doi: 10.1212/wnl.0000000000008615 [DOI] [PubMed] [Google Scholar]
- 17.Dimova ED, Swanson V, Evans JM. Gender and diet management in type 2 diabetes. Chronic Illness. 2021;17(4):362–376. doi: 10.1177/1742395319873375 [DOI] [PubMed] [Google Scholar]
- 18.Otter CEM, Hoogerduijn JG, Keers JC, Hagedoorn EI, de Man-van Ginkel JM, Schuurmans MJ. Older patients’ motives of whether or not to perform self-management during a hospital stay and influencing factors✰. Geriatric Nurs. 2019;40(2):205–211. doi: 10.1016/j.gerinurse.2018.10.004 [DOI] [PubMed] [Google Scholar]
- 19.Arias López MDP, Ong BA, Borrat Frigola X, et al. Digital literacy as a new determinant of health: a scoping review. PLOS Digital Health. 2023;2(10):e0000279. doi: 10.1371/journal.pdig.0000279 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Wenqian H, Yuanli G, Lianke W, et al. Analysis of potential categories and influencing factors of care dependency trajectories among first stroke patients. Chin J Nurs. 2023;58(7):829–835. [Google Scholar]
- 21.Oh HX, De Silva DA, Toh ZA, Pikkarainen M, Wu VX, He HG. The effectiveness of self-management interventions with action-taking components in improving health-related outcomes for adult stroke survivors: a systematic review and meta-analysis. Disability Rehabil. 2022;44(25):7751–7766. doi: 10.1080/09638288.2021.2001057 [DOI] [PubMed] [Google Scholar]
- 22.Kuo NY, Lin YH, Chen HM. Continuity of care and self-management among patients with stroke: a cross-sectional study. Healthcare. 2021;9(8). doi: 10.3390/healthcare9080989 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Afrin M, Khan SU, Das SC, Huq K, Moriyama M. Effectiveness of a health education program for patients who had a stroke and their caregivers by controlling modifiable risk factors to reduce stroke recurrence in a tertiary hospital in Bangladesh: protocol for a randomized controlled trial. JMIR Res Protocol. 2023;12:e51178. doi: 10.2196/51178 [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.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author, Dr. Qinhong Xu, on reasonable request.