Effect of hospital-community-home collaborative health management on symptoms, cognition, anxiety, and depression in high-risk individuals for stroke

Abstract

BACKGROUND

Effective health management for high-risk stroke populations is essential. The hospital-community-home (HCH) collaborative health management (CHM) model leverages resources from hospitals, communities, and families. By integrating patient information across these three domains, it facilitates the delivery of tailored guidance, health risk assessments, and three-in-one health education.

AIM

To explore the effects of the HCH-CHM model on stroke risk reduction in high-risk populations.

METHODS

In total, 110 high-risk stroke patients screened in the community from January 2019 to January 2023 were enrolled, with 52 patients in the control group receiving routine health education and 58 in the observation group receiving HCH-CHM model interventions based on routine health education. Stroke awareness scores, health behavior levels, medication adherence, blood pressure, serum biochemical markers (systolic/diastolic blood pressure, total cholesterol, and triglyceride), and psychological measures (self-rating anxiety/depression scale) were evaluated and compared between groups.

RESULTS

The observation group showed statistically significant improvements in stroke awareness scores and health behavior levels compared to the control group (P < 0.05), with notable enhancements in lifestyle and dietary habits (P < 0.05) and reductions in postintervention systolic blood pressure, diastolic blood pressure, total cholesterol, triglyceride, self-rating anxiety scale, and self-rating depression scale scores (P < 0.05).

CONCLUSION

The HCH-CHM model had a significant positive effect on high-risk stroke populations, effectively increasing disease awareness, improving health behavior and medication adherence, and appropriately ameliorating blood pressure, serum biochemical marker levels, and negative psychological symptoms.

Key Words: Hospital-community-home-collaborative health management model; High-risk populations for stroke; Stroke awareness score; Health behavior level; Hospital-community-home

Core Tip: High-risk stroke groups often present with obesity, advanced age, multiple comorbidities, low disease awareness, and limited prevention and treatment knowledge. They also suffer considerable burden from the disease. Hence, timely and effective health management interventions are essential. This study examines the impact of the hospital-community-family collaborative health management model on individuals at high-risk for stroke. Compared with routine health education, the hospital-community-family collaborative health management model demonstrates a substantial intervention effect, effectively raising patients’ disease awareness, enhancing their health behaviors and medication adherence, and improving their blood pressure, serum biochemical marker levels, and psychological well-being. These findings offer valuable insights and new strategies for optimizing the management of high-risk stroke populations.

INTRODUCTION

Stroke, or ischemia apoplexy, is typically ischemic (up to 87%) and ranks as the second leading cause of disability and death globally[1]. The disease is preventable, with declining risks of new occurrences and recurrences, and targeted changes in risk factors, such as smoking, obesity, overeating, malnutrition, and inactivity, can have a meaningful preventive effect[2,3]. In 2020, stroke affected nearly 20 million adults in China, resulting in almost 2.5 million deaths. This not only had major physical and mental impacts on patients but also increased the medical burden on patients and their families[4]. Generally, prevention is considered more effective than treatment for stroke, and providing effective, scientific health management can optimize patient management and outcomes[5]. Additionally, high-risk populations for stroke are often characterized by obesity, advanced age, multiple comorbidities, and limited awareness of the disease and its prevention, underscoring the need for timely and effective health management interventions[6,7].

Health management is a proactive strategy that facilitates the development of scientific and individualized health plans for patients based on disease risk factors and previous case data, helping to prevent or delay disease progression by improving habits and behaviors[8,9]. Currently, health management is applied across various medical scenarios to aid prevention and treatment. For example, health education for patients with diabetic foot has improved their understanding of the disease and led to healthier behaviors[10]. Oral health education for patients with pediatric tumors complicated by oral mucositis has effectively prevented ulcerative lesions and improved clinical outcomes[11]. Similarly, mixed health education for elderly patients undergoing total knee arthroplasty has markedly reduced postoperative pain and improved knee joint flexibility[12]. Routine health education for high-risk stroke populations typically involves creating health records, distributing brochures, sharing disease information during outpatient follow-ups, and advising on daily life, diet, exercise, and medication. However, this approach relies heavily on patients’ motivation and may fall short of the desired intervention outcomes[13,14]. In contrast, the hospital-community-home (HCH) collaborative health management (CHM) model integrates hospitals, communities, and families to offer patients personalized guidance, health risk assessments, and health education by integrating patient data from these three sources. This approach helps patients implement positive lifestyle changes and supports effective disease prevention[15]. We propose that the HCH-CHM model is a more practical solution for high-risk stroke populations and will yield better intervention outcomes. Therefore, we conducted a verification analysis and detailed report on its effectiveness.

MATERIALS AND METHODS

Patient information

This retrospective study enrolled 110 high-risk stroke patients screened from the community between January 2019 and January 2023. Patients were grouped based on health education intervention methods, with 52 patients in the control group receiving only routine health education, and 58 patients in the observation group receiving the HCH-CHM model intervention based on routine health education.

Criteria for patient enrollment and exclusion

High-risk stroke patients identified through echocardiography or carotid ultrasound examination were enrolled[16]. These individuals presented with hypertension, diabetes, and dyslipidemia, with a smoking history (either continuous smoking or cumulative smoking for 6 months or longer), a family or previous history of stroke, prior transient ischemic attacks, and atrial fibrillation. They were also classified as significantly overweight or obese and exhibited a lack of physical activity. Excluded patients included those with impaired mobility or on long-term bed rest; aphasia, attention disturbances, or swallowing disorders; mental illness; an inability to participate in health management owing to environmental, physical, or mental conditions; pregnant or lactating women; cognitive dysfunction or communication barriers; and those severely ill or requiring critical care.

Methods

The control group received routine health education as follows: (1) Creating basic patient information files; (2) Introducing stroke-related information (such as stroke onset characteristics, risk factors, early signs, and emergency treatment) by distributing health brochures and conducting outpatient follow-ups; (3) Providing advice on performing moderate physical activity, maintaining a healthy lifestyle, and following balanced dietary habits; and (4) Giving instructions on taking medications regularly and making routine follow-up visits.

In addition, to these measures, the observation group received further intervention via the HCH-CHM model, as follows: (1) A multidisciplinary health management team was established, comprising the hospital, community medical institutions, and families, with 10 medical staff from the community service center and 6 hospital chief physicians. The community service center was responsible for health management, and hospital doctors provided relevant technical guidance and answered questions during the management process; (2) To enable HCH-collaborative stroke health risk assessments, hospital physicians managed quality control, and community healthcare workers conducted residents’ health risk assessments. These assessments covered patient information, lifestyle, family history of stroke, and relevant physical and laboratory tests. Family members helped patients develop action plans and provided timely feedback to medical staff; (3) The HCH-collaborative health education intervention was implemented by establishing comprehensive electronic information files and conducting monthly face-to-face follow-ups through home visits. Interventions were tailored to patients’ situations to monitor blood pressure and other indicators and assess risk factors. Family members were guided to support patients in health management, encouraging healthy lifestyle changes and providing targeted health education; (4) Patient management was reinforced using the regional intelligent reporting management system for disease control and prevention, promoting five major prescriptions and providing individual counseling to explain health management and stroke self-management methods, facilitating dynamic personalized health management; and (5) Face-to face follow-ups were enhanced through outpatient services, home and telephone visits, and online communication, collecting data on residents’ living conditions, symptoms, medication use, and cardiocerebrovascular events. Using follow-up and risk assessment data, patients received individualized guidance on knowledge, skills, and psychological counseling to promote healthier lifestyles and disease prevention.

Outcome measures

Stroke awareness: Patients’ awareness of stroke prevention was assessed with the stroke prevention knowledge questionnaire, covering risk factors, prevention knowledge, early symptom recognition, and emergency treatment. The questionnaire has 20 items (5 per dimension) with a total score of 100, where higher scores indicate better stroke prevention awareness.

Healthy behavior: Patient’s health behavior levels were evaluated using the Health-Promoting Lifestyle Profile, which includes subscales for health responsibility, exercise, nutrition, stress management, interpersonal relationships, and self-actualization, totaling 52 items. Responses were rated on a four-point scale, including “never” (1 point), “sometimes” (2 points), “always” (3 points), and “all the time” (4 points), with higher scores indicating better health behavior.

Standardized medication use: Adherence to prescribed antiplatelet drugs (APDs), statins, antihypertensive drugs (AHTDs), and hypoglycemic agents was evaluated.

Blood pressure and serum biochemical indices: Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured before and after the intervention using a blood pressure monitor. Additionally, fasting venous blood (3 mL) collected before and after intervention was centrifuged for serum analysis, measuring total cholesterol (TC) and triglyceride (TG) levels with an automatic biochemical analyzer.

Psychological emotions: Patients’ anxiety and depression levels were assessed before and after the intervention using the self-rating anxiety scale (SAS) and self-rating depression scale (SDS). Each scale includes 20 items, with a total score of 80, where higher scores indicate greater anxiety and depression severity.

Statistical analysis

The measurement data were described using mean ± SE, and between-group and within-group comparisons were conducted using independent sample t-tests and paired t-tests, respectively. Categorical data were presented as ratios (percentage), with comparisons between groups made using the χ²-test. The collected experimental data were analyzed using SPSS 21.0, with statistical significance reported at the P < 0.05 Level.

RESULTS

General data from the two groups at high risk of stroke

We comparatively analyzed patients’ general data, including age, sex, weight, education level, and the presence of hypertension, diabetes, and hyperlipidemia. We found no significant between-group differences (P > 0.05, Table 1).

Table 1 General data of the two groups at high risk of stroke, n (%).

Patient data	Control group (n = 52)	Observation group (n = 58)	χ2/t	P value
Age, years	59.27 ± 9.45	61.29 ± 10.73	1.043	0.300
Sex			0.736	0.391
Male	29 (55.77)	37 (63.79)
Female	23 (44.23)	21 (36.21)
Weight, kg	58.69 ± 8.69	59.67 ± 9.18	0.573	0.568
Educational level			0.666	0.717
Junior high school and below	14 (26.92)	12 (20.69)
Senior high school	22 (42.31)	28 (48.28)
University or above	16 (30.77)	18 (31.03)
Hypertension			2.628	0.105
With	30 (57.69)	42 (72.41)
Without	22 (42.31)	16 (27.59)
Diabetes			1.014	0.314
With	15 (28.85)	22 (37.93)
Without	37 (71.15)	36 (62.07)
Hyperlipidemia			0.822	0.365
With	26 (50.00)	24 (41.38)
Without	26 (50.00)	34 (58.62)

Stroke awareness scores from the two high-risk stroke groups

We evaluated the impact of the two intervention methods on disease awareness in high-risk stroke populations, focusing on risk factor knowledge, prevention knowledge, early symptom recognition, and emergency treatment. The observation group scored higher across all these areas, with statistically significant differences (P < 0.05, Figure 1 and Table 2).

Figure 1 Stroke awareness scores of two high-risk groups for stroke. A: The observation group had a statistically higher score of cognition of risk factors than the control group; B: The observation group had a statistically higher score of knowledge cognition than the control group; C: The observation group scored markedly higher in the early symptom cognitive dimension than the control group; D: The emergency cognitive score was significantly higher in the observation group than in the control group. ^bP < 0.01.

Table 2 Stroke awareness scores of two high-risk groups for stroke, mean ± SE.

Dimensions	Control group (n = 52)	Observation group (n = 58)	t	P value
Risk factor cognition	13.96 ± 3.51	19.9 ± 3.38	9.036	< 0.001
Prevention knowledge cognition	15.75 ± 2.93	22.36 ± 3.39	10.880	< 0.001
Early symptom cognition	16.94 ± 3.84	23.14 ± 3.25	9.169	< 0.001
Emergency treatment cognition	14.90 ± 2.58	18.19 ± 3.31	5.766	< 0.001

Health behavior levels in the two high-risk stroke groups

The influence of the two management models on health behaviors in high-risk stroke patients was assessed across six domains: Health responsibility, exercise, nutrition, stress management, interpersonal relationships, and self-actualization. The observation group scored significantly higher than the control group in all domains (P < 0.05, Figure 2 and Table 3).

Figure 2 Health behavior levels of two high-risk groups for stroke. A: The observation group showed a notably higher health responsibility score than the control group; B: The exercise score was markedly higher in the observation group than in the other; C: The nutrition score was statistically higher in the observation group vs the control group; D: The observation group had an obviously higher stress management score than the control group; E: The interpersonal relationships score was significantly higher in the observation group vs the control group; F: The self-actualization score of the observation group elevated markedly compared with the control group. ^aP < 0.05, ^bP < 0.01.

Table 3 Health behavior levels of two high-risk groups for stroke, mean ± SE.

Dimensions	Control group (n = 52)	Observation group (n = 58)	t	P value
Health responsibility	18.67 ± 5.68	21.60 ± 4.84	2.920	0.004
Exercise	18.60 ± 2.97	24.38 ± 3.78	8.846	< 0.001
Nutrition	18.12 ± 5.38	24.71 ± 6.17	5.939	< 0.001
Stress management	19.79 ± 4.71	26.33 ± 4.72	7.263	< 0.001
Interpersonal relationships	19.04 ± 5.30	21.69 ± 4.62	2.802	0.006
Self-actualization	14.73 ± 3.80	18.81 ± 4.90	4.839	< 0.001

Standardized medication uses in the two high-risk stroke groups

We assessed the effectiveness of the two management models on patients’ standardized use of APDs, statins, AHTDs, and hypoglycemic agents. The observation group showed significantly better standardized medication adherence across all categories compared to the control group (P < 0.05, Table 4).

Table 4 Standardized medication of two high-risk groups for stroke, n (%).

Standardized medication	Control group (n = 52)	Observation group (n = 58)	χ2	P value
Antiplatelet drugs	42 (80.77)	55 (94.83)	5.200	0.023
Statins	40 (76.92)	54 (93.10)	5.775	0.016
Antihypertensive drugs	41 (78.85)	57 (98.28)	10.650	0.001
Hypoglycemic agents	42 (80.77)	56 (96.55)	7.027	0.008

Blood pressure and serum biochemical indices in the two high-risk stroke groups

We investigated the effects of the two management interventions on blood pressure and serum biochemical indices, measuring SBP, DBP, TG, and TC levels in both groups. No significant between-group differences were noted before intervention. After intervention, the observation group showed statistically significant reductions in SBP, DBP, TG, and TC levels, which were lower than those in the control group (P < 0.05, Figure 3 and Table 5).

Figure 3 Blood pressure and serum biochemical indices of two high-risk groups for stroke. A: The observation group showed notably reduced systolic blood pressure after intervention, lower than the pre-interventional level and that in the control group; B: After intervention, the diastolic blood pressure level in the observation group was significantly lower compared to the pre-interventional level and the control group; C: After intervention, the triglyceride level in the observation group was significantly lower compared to before intervention and the control group; D: The observation group showed notably reduced total cholesterol after intervention, lower than the pre-interventional level and that in the control group. ^aP < 0.05, ^bP < 0.01. SBP: Systolic blood pressure; DBP: Diastolic blood pressure; TG: Triglyceride; TC: Total cholesterol.

Table 5 Blood pressure and serum biochemical indices of two high-risk groups for stroke.

Indicators	Control group (n = 52)	Observation group (n = 58)	t	P value
SBP
Before intervention	130.83 ± 10.53	129.57 ± 12.31	0.574	0.568
After intervention	125.87 ± 9.70	122.41 ± 6.26	2.245	0.027
DBP
Before intervention	84.87 ± 8.13	86.52 ± 9.92	0.947	0.346
After intervention	79.54 ± 7.66	76.03 ± 6.22	2.649	0.009
TG
Before intervention	2.28 ± 0.50	2.23 ± 0.52	0.513	0.609
After intervention	1.95 ± 0.46	1.45 ± 0.35	6.454	< 0.001
TC
Before intervention	4.58 ± 1.37	4.72 ± 1.39	0.531	0.597
After intervention	4.38 ± 1.27	3.80 ± 1.40	2.266	0.025

SBP: Systolic blood pressure; DBP: Diastolic blood pressure; TG: Triglyceride; TC: Total cholesterol.

Psychological and emotional changes in the two high-risk stroke groups

Psychological and emotional changes in the two high-risk groups were evaluated using the SAS and SDS scales. No significant between-group differences were found before intervention (P > 0.05). However, postintervention scores on both scales decreased significantly, with the observation group showing even lower SAS and SDS scores (P < 0.05, Figure 4 and Table 6).

Figure 4 Psychological and emotional changes in two high-risk groups for stroke. A: In the observation group, the self-rating anxiety scale score after intervention was significantly reduced compared to the pre-interventional level and the control group; B: The post-interventional self-rating depression scale score of the observation group was significantly reduced compared to the pre-interventional level and the control group. ^aP < 0.05, ^bP < 0.01. SAS: Self-rating anxiety scale; SDS: Self-rating depression scale.

Table 6 Psychological and emotional changes in two high-risk groups for stroke.

Indicators	Control group (n = 52)	Observation group (n = 58)	t	P value
SAS score
Before intervention	52.58 ± 16.35	53.74 ± 12.70	0.418	0.677
After intervention	45.58 ± 9.69	39.76 ± 7.60	3.523	< 0.001
SDS score
Before intervention	54.77 ± 13.81	50.74 ± 10.89	1.708	0.091
After intervention	46.96 ± 11.61	42.47 ± 8.82	2.298	0.024

SAS: Self-rating anxiety scale; SDS: Self-rating depression scale.

DISCUSSION

As a sudden cerebrovascular disease, stroke has a high rate of disability and mortality[17,18]. This study focused on high-risk stroke populations, analyzing the influence of the HCH-CHM model on this group to provide insights for the clinical management of patients with elevated stroke risk.

Regarding disease awareness, the observation group showed significantly higher scores compared with the control group in areas such as risk factor knowledge, prevention knowledge, early symptom recognition, and emergency treatment awareness. This suggests that the HCH-CHM model effectively enhances disease awareness across these areas, likely attributed to its integration of health risk assessment and follow-up data, which guides patients in disease knowledge and skills[19]. Similarly, research by Shi et al[20] demonstrated that the HCH-CHM model effectively raised disease awareness among patients with hypertension, lowering their blood pressure and reducing anxiety and depression, aligning with our findings. For health behaviors, the observation group also outperformed the control group in health responsibility, exercise, nutrition, stress management, interpersonal relationships, and self-actualization, indicating that the HCH-CHM model significantly improves health behaviors in patients at high risk of stroke. This improvement may result from patients’ active participation, family support in health management, and encouragement from family members[21]. Supporting these results, Jiang et al[22] found that HCH-CHM intervention enhanced lifestyle and health behaviors and improved medication adherence among patients with atrial fibrillation.

In terms of standardized medication adherence, the observation group demonstrated significantly better adherence to APDs, statins, AHTDs, and hypoglycemic agents compared with the control group, showing the HCH-CHM model’s efficacy in promoting standardized medication use among high-risk stroke populations. Gao et al[23] also reported that the HCH-CHM model significantly improved medication compliance in children with epilepsy, consistent with our findings. In the comparative analysis of blood pressure and serum biochemical indices, we found that postintervention levels of SBP, DBP, TG, and TC in the observation group were significantly lower than those in the control group, suggesting that the HCH-CHM model effectively improves blood pressure and serum indices, aiding in restoring metabolic balance in high-risk patients. The HCH-CHM model’s ability to help establish comprehensive electronic patient files holding pathological data and monitor biochemical indices contributes to timely and targeted interventions, leading to scientific management of these indices[24]. Research by Guo et al[25] showed that HCH-CHM intervention significantly reduces cases of hyperglycemia and dyslipidemia while improving health cognition and behaviors, aligning with observations.

From a psychological perspective, the observation group’s SAS and SDS scores decreased markedly after intervention, being lower than those in the control group, thereby highlighting the HCH-CHM model’s positive impact on alleviating negative emotions in high-risk stroke populations. This effect may stem from the model’s emphasis on psychological support from the multidisciplinary health management team and families. Feng et al[26] also found that using the HCH-CHM model in home rehabilitation for disabled elderly patients with stroke improved medical compliance and alleviated negative emotions, aligning with our results. Additionally, the HCH-CHM model combined with motor imagery therapy has shown major benefits in motor function, balance, daily living activities, and quality of life in patients with cerebral infarction[27].

This study has several limitations. First, its single-center design may limit the applicability of the results to other healthcare environments or patient demographics. Future multicenter studies could help validate these findings across diverse populations and settings. Second, the study did not include long-term follow-up data, which could further clarify the HCH-CHM model’s long-term clinical benefits. Finally, additional data on factors such as sleep and quality of life would expand our understanding of the HCH-CHM model’s potential clinical advantages. Future research will address these areas to further enhance the current findings.

CONCLUSION

In conclusion, the HCH-CHM model demonstrates strong intervention effects in high-risk stroke populations by improving disease awareness, enhancing health behaviors, supporting standardized medication use, effectively regulating blood pressure and serum biochemical indices, and alleviating negative emotions. These results offer valuable insights for optimizing the management of high-risk stroke populations.