Smart Nursing-Based Tripartite Dual-Track Interactive Nursing in Elderly Patients with Coronary Heart Disease Complicated by the "Three Highs"

Summary

This study found that smart nursing-based tripartite dual-track interactive nursing helps to improve the quality of rehabilitation in elderly coronary heart disease patients with diabetes, hypertension, or hyperlipidemia.

Abstract

The objective of this study was to evaluate the effectiveness of the Tripartite Dual-Track Interactive Nursing (TDTIN) model, incorporating smart nursing technologies, in elderly patients with coronary heart disease (CHD) complicated by hypertension, hyperglycemia, and hyperlipidemia (collectively termed the "three highs"), and to verify its improvement effects on patients' self-care capacity, quality of life, and psychological well-being. A randomized controlled trial was conducted with 162 elderly CHD patients with the "three highs," allocated equally into an observation group (n = 81, receiving smart nursing-based TDTIN) and a conventional group (n = 81, receiving conventional care). Both groups were monitored for 3 months post-discharge. Standardized assessments included the Exercise of Self-Care Agency (ESCA) scale for self-care ability, the Barthel Index (BI) for activities of daily living, the Hamilton Anxiety (HAMA) and Depression (HAMD) scales for psychological status, a self-made scale for compliance, and a satisfaction survey. Compared to the conventional group, the observation group demonstrated significantly greater improvements in self-care ability (ESCA) and quality of life (BI), along with lower HAMA and HAMD scores (P < 0.05). The observation group also exhibited higher treatment compliance and satisfaction ratings (P < 0.05). Additionally, caregivers in the observation group showed enhanced caregiving skills and support scores (P < 0.05). The TDTIN model, leveraging smart nursing, significantly enhances self-care capacity, quality of life, treatment compliance, and psychological outcomes in elderly CHD patients with the "three highs," while strengthening family-community support systems.

Introduction

The accelerating pace of global aging has led to a growing prevalence of comorbidities, particularly the concurrent presence of coronary heart disease (CHD) and metabolic disorders-hypertension, hyperglycemia, and hyperlipidemia (collectively termed the "three highs")-among elderly populations^1,2. Epidemiological data reveal that in China, approximately 35% of individuals aged 60 and above suffer from CHD in combination with at least one of these metabolic abnormalities. Notably, the risk of cardiovascular events in such patients is 2-3 times higher compared to those with a single condition, while the five-year readmission rate escalates to a striking 40%^3,4. The clinical management of these patients is fraught with significant challenges, including intricate pathological mechanisms, multidrug interactions, and limited self-management capacity. These factors collectively contribute to suboptimal symptom control, poor treatment adherence, and fragmented care continuity across healthcare settings⁵. Consequently, there is an urgent imperative to develop and implement more effective integrated nursing paradigms to address these multifaceted issues.

Current traditional nursing models predominantly emphasize in-hospital acute-phase management or single-disease interventions, failing to address the dynamic interrelationships inherent in "CHD-three highs" comorbidities. Furthermore, systemic fragmentation arises due to poor collaboration among hospitals, communities, and households, undermining care continuity⁶. While smart nursing technologies such as wearable devices and remote monitoring platforms offer data-driven support for chronic disease management, their implementation among elderly populations remains limited. Key barriers include a misalignment between technological solutions and patient needs (e.g., interface complexity or delayed feedback), contributing to adoption rates below 30%⁷. Moreover, technology-centric approaches alone cannot overcome broader systemic challenges, including inadequate family support and disjointed community resources⁸. Most existing studies focus narrowly on isolated settings (e.g., hospitals or home care) or standalone technological modules (e.g., AI-based early warning systems)^9,10. Crucially, a comprehensive, multi-stakeholder collaborative nursing framework spanning the entire "prevention-intervention-rehabilitation" continuum has yet to be developed. This gap represents a major obstacle to achieving sustainable health outcomes for elderly patients with multimorbidity.

Based on these findings, the study proposes the "Tripartite Dual-Track Interactive Nursing (TDTIN)" model. At the theoretical level, this model represents a breakthrough from the traditional disease-centered approach by establishing a three-dimensional collaborative framework that integrates hospital specialty leadership, community resource linkage, and family empowerment support. Through clearly defined role divisions and shared responsibility mechanisms, it ensures seamless coordination across diagnosis, treatment, management, and support. On the technical level, the study introduces an innovative "dual-track" interactive system. The online track uses a remote platform to integrate dynamic physiological monitoring, personalized health education, and risk early-warning functions, effectively resolving issues related to data fragmentation. Meanwhile, the offline track enhances the accessibility and continuity of nursing services by implementing standardized training for family caregivers, establishing community nurse-led "health circle" support groups, and creating a hospital-community bidirectional referral green channel. These two tracks operate in synergy, forming a closed-loop system through real-time data sharing and multi-directional feedback, thereby overcoming the technical limitations of traditional nursing models. Similarly, the study by Cheluvappa R et al.¹¹ also proposed the importance of multi-dimensional nursing to promote the health of the elderly. And Schnipper's study also confirmed that various nursing interventions can reduce the adverse events of patients after discharge¹². This study aims to evaluate the model's efficacy in improving physiological indicator control, boosting self-management efficacy, and reducing hospital readmission rates among elderly patients with CHD and comorbid "three highs". The results are expected to offer an innovative and practical solution for restructuring chronic disease management systems in aging populations.

Protocol

This study has completed clinical trial registration (No: ChiCTR2500104288) and has been approved by the ethics committee of the hospital, and all participants remained unaware of their group allocation throughout the study period.

Study subjects
Patients with CHD complicated by the "three highs" who were admitted to the hospital between August 2023 and January 2025 were selected as the study participants. Sample size calculation was performed using GPower 3.1.9.7. Assuming a medium effect size (Cohen's d=0.5), α=0.05, and power (1-β)=0.80, a total sample size of 164 participants (n = 70/group) was required. In addition, a dropout rate of 10% was considered, so the final recruitment target was 182 patients. These patients were randomly assigned to two groups via a random number table method: a conventional group receiving conventional care and an observation group receiving smart nursing-based TDTIN interventions, with 81 patients in each group (randomization was implemented using a computer-generated random number sequence with allocation concealment via sequentially numbered opaque envelopes).

Inclusion and exclusion criteria
Inclusion criteria: (1) a confirmed diagnosis of CHD by coronary angiography, accompanied by at least one of the specified chronic conditions (hypertension, diabetes, or hyperlipidemia); (2) age ≥ 65 years; (3) availability of complete clinical data; and (4) provision of signed informed consent. Exclusion criteria: (1) presence of cognitive or neurological dysfunction; (2) concurrent infectious or autoimmune diseases; (3) severe liver or kidney dysfunction; (4) poor treatment compliance leading to inability to cooperate with researchers in completing the study; or (5) patients with missing information or who were lost to follow-up.

Nursing interventions
Following admission, patients received group-specific nursing interventions (Conventional Nursing Care and Smart Nursing-Based TDTIN Model) maintained throughout hospitalization and continued for 3 months post-discharge (Figure 1).

Conventional nursing care:
Patients in this group received comprehensive routine nursing care during their hospital stay. During hospitalization, the nursing staff ensures timely medication administration by reminding patients in accordance with the prescriptions and dosage instructions provided by their attending physicians. Additionally, nurses educate both patients and their families on CHD rehabilitation, necessary precautions, and the critical role of family involvement in supervision and support. The nursing team also closely monitors patients' basic physiological parameters, including blood pressure, heart rate, and blood glucose levels. In cases of abnormal readings, they promptly notify the attending physician and assist in implementing appropriate interventions. Furthermore, they supervise patients' daily dietary intake and offer psychological support to patients and their families. Before discharge, patients and their families receive detailed guidance on post-discharge care. Patients are also instructed to return for a follow-up examination 3 months after discharge to evaluate their recovery progress.

Smart nursing-based TDTIN model:
Tripartite collaboration mechanism: The model integrates three collaborative entities to deliver comprehensive care: (i) Hospital Entity: A multidisciplinary team-including cardiovascular specialists, nurses, nutritionists, and psychologists-is responsible for acute-phase diagnosis and treatment, personalized care planning, medication guidance, and high-risk patient screening. (ii) Community Entity: Nurses and general practitioners perform daily monitoring, follow-ups, and targeted health interventions. They reinforce patient compliance through regular home visits and structured health education sessions. (iii) Family Entity: Family caregiver training adopted a three-stage mode of "theoretical teaching + simulation training + certification assessment", including theoretical training (a four-week series of lectures by cardiovascular specialist nurses, covering disease knowledge, monitoring skills, emergency treatment and other content, once a week; After class, the online test was completed through WeChat mini program) and simulation training (scenario simulation training was carried out in the clinical skills center of the hospital, and 12 core skills such as angina pectoris attack treatment and hypoglycemia correction were practiced by simulated people).

Dual-track interactive pathways: The care delivery operates through two integrated pathways: (i) Online Track: A smart nursing platform facilitates dynamic monitoring and real-time data sharing (Table of Materials). Wearable devices track vital parameters (e.g., blood pressure, blood glucose, heart rate), triggering automatic alerts for abnormal values (Table of Materials), and reminders for daily medication were set in the device. A healthy diet recommendation table was made by dietitians and pushed to patients through WeChat mini program. (ii) Offline Track: A hospital-community bidirectional referral system ensures rapid response during acute episodes. Community-organized quarterly health management group activities reinforce skills through face-to-face guidance, while families regularly participate in hospital-led "Multimorbidity Management of Three Highs" workshops to enhance long-term self-care capacity. In order to solve the use barriers of smart devices in elderly patients, a multi-dimensional technical support network was established. (1) On the first day of admission, the ward specialist nurses carried out "one-to-one" equipment operation training (introducing the wearing and operation of the wearable band according to the instructions), and the scenario simulation teaching method was used to demonstrate the process of device wearing, data upload, and abnormal alarm processing. (2) Each patient was equipped with an Intelligent Device User Manual (including graphic and voice version), which highlighted large font warning signs and one-touch call function instructions; (3) Establishment of a three-level "hospital-community-family" technical support system, community nurses synchronously carried out equipment maintenance and operation assessment when carrying out household follow-up every week, and the hospital remote support center provided 7 × 24 h telephone guidance.

Core intervention measures: (i) Intelligent Monitoring and Risk Management: Patients are equipped with smart wristbands and home-based blood pressure/blood glucose monitoring devices. These devices transmit real-time data to both hospital and community healthcare platforms, enabling dynamic cardiovascular risk assessment. Based on the monitoring of patients' blood glucose and blood pressure, personalized intervention suggestions and drug dose adjustment suggestions were edited by medical staff and pushed to each patient through WeChat mini program. (ii) Medication Adherence Management: Pharmacists actively monitored medication records through a WeChat mini program. When the patient was found to have missed or mistaken medication, the medical staff directly contacted the patient or informed the family members through WeChat. (iii) Lifestyle Management: we push exercise plans (e.g., 30-min walk daily) to patients through the WeChat mini program, and monitor exercise progress in real time through the Mi band, with the data fed back to the WeChat mini program. Additionally, psychologists conduct regular online counseling sessions to address anxiety or depression, while community nurses perform in-home mental status evaluations during visits and facilitate specialist referrals when clinically indicated.

Closed-loop data management: The smart platform integrates multi-source health data to maintain comprehensive patient records and dynamically refine care plans. On a monthly basis, interdisciplinary teams comprising hospital clinicians, community healthcare providers, and family caregivers participate in virtual case conferences. These meetings serve to evaluate intervention efficacy and optimize intervention strategies for common issues.

Observation indicators
Self-care ability assessment: The Exercise of Self-Care Agency Scale (ESCA)¹³ was administered to evaluate patients' self-care ability before and after the nursing intervention. The ESCA consists of four dimensions: self-concept, self-care responsibility, self-care skills, and health knowledge. Higher scores on this scale indicate better self-care ability.

Quality of life evaluation: Patients' quality of life was assessed using three standardized scales: the Barthel Index (BI)¹⁴, and the Mini-Mental State Examination (MMSE)¹⁵. The BI evaluates their mobility, and the MMSE assesses cognitive function.

Psychological state assessment: The Hamilton Anxiety Scale (HAMA) and the Hamilton Depression Scale (HAMD)¹⁶ were used to measure patients' psychological well-being. Higher scores on these scales indicate more severe anxiety or depressive symptoms.

Family member evaluation: A 5-point scoring system was employed to assess the status of patients' family members in two key areas: Caregiving skills (rated as: 1 = not at all mastered, 2 = unmastered, 3 = basically mastered, 4 = mastered, 5 = completely mastered). Support level (rated as: 1 = not at all supportive, 2 = unsupportive, 3 = basically supportive, 4 = supportive, 5 = completely supportive).

Treatment adherence assessment: At the end of the nursing intervention, the treatment compliance of patients was evaluated by our hospital's self-made compliance scale. This scale referred to the study by Yin S et al., and was modified by our hospital¹⁷. This 8-item scale yields a total score ranging from 0 to 14, with classifications as follows: 14 = fully adherent, 9–13 = partially adherent, and 0–8 = non-adherent. The adherence rate was calculated as the sum of the full adherence rate and the partial adherence rate. 

Patient satisfaction evaluation: The Newcastle Satisfaction with Nursing Scale (NSNS)¹⁸ was administered post-intervention to assess patient satisfaction. The total score was 95, with the following interpretation: 76-95 = very satisfied, 57-75 = moderately satisfied, 38-56 = dissatisfied, 19-37 = very dissatisfied. Satisfaction rate = very satisfied rate + moderately satisfied rate.

Statistical analysis
All statistical analyses were performed using SPSS 25.0. Categorical variables were expressed as numbers and percentages, and group comparisons were assessed using the chi-square (χ²) test. We used the Shapiro-Wilk test to check the distribution of the data. For normally distributed continuous variables, data were presented as ( ± s), and differences between groups were evaluated using independent samples t-tests or paired t-tests. Non-normally distributed continuous variables were summarized as median (interquartile range, IQR), and comparisons were performed using the Mann-Whitney U test or Wilcoxon rank-sum test. Results with P < 0.05 were deemed statistically significant.

Results

General information
First, we compared the clinical baseline data of the two groups, and the results showed that there were no differences in clinical baseline data between the two groups (P > 0.05) (Table 1).

Comparison of self-care skills
Comparison results of ESCA scale findings showed that there was no difference between the baseline scores of the two groups (P > 0.05), the scores of self-care responsibility and self-care ski...

Discussion

This study evaluated the effectiveness of smart nursing-based TDTIN versus conventional nursing in elderly patients with CHD complicated by the "three highs". The results demonstrated that the observation group exhibited significantly greater improvements in self-care capacity, quality of life, emotional regulation, family caregiver competence, treatment adherence, and nursing satisfaction compared to the conventional group receiving conventional care. These robust findings underscore the clinical value and potential sup...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Blood glucose meter 701	Jump of fish	20162400064	Used to detect the glucose concentration in capillary whole blood in hospital and home.
Blood pressure meter U701	Omron	20152070051	Blood pressure was monitored by smart compression technology
GPower	Heinrich-Heine-Universität Düsseldorf	v.3.1.9.7	G*Power was used for sample size calculation, statistical power analysis, and effect size estimation.
Mi Band	Xioami	https://www.mi.com/ae-en/product/xiaomi-smart-band-7/
SPSS 25.0	IBM	https://www.ibm.com/products/spss-statistics	Statistical analysis software
Wechat	Tencent	https://weixin.qq.com/	A free application that provides instant messaging services for smart terminals.