The effect of a self-management program through a mobile application on lipid profile among persons with coronary artery disease: A randomized controlled trial study in Thailand

Abstract

Background

Cholesterol, triglycerides, and low-density lipoprotein levels are key indicators of patients’ self-care in coronary artery disease; although medications can improve lipid profiles, the results often remain above targets. Implementing mobile self-management programs is crucial for reducing the incidence of coronary artery disease, preventing cardiac events, and enhancing well-being.

Objective

This study aimed to evaluate the impact of a self-management program on cholesterol, triglycerides, and low-density lipoprotein levels in people with coronary artery disease.

Methods

A randomized controlled trial design was employed at a tertiary hospital in Thailand between June 2022 and June 2023. Forty-eight people were randomly assigned to one of two groups: control or experimental. The experimental group used applications and the telephone for four weeks to self-manage cholesterol, triglyceride, and low-density lipoprotein levels, while the control group received conventional care. Data were gathered about cholesterol, triglyceride, and low-density lipoprotein levels. Descriptive statistics, independent t-tests, and dependent t-tests were employed to analyze between-group and within-group differences. Effect sizes (Cohen’s d) with 95% confidence intervals were calculated.

Results

Results showed that the experimental group had a statistically significant reduction in cholesterol (t = 2.121, df = 46, p = 0.039), triglyceride (t = 2.060, df = 46, p = 0.046), and low-density lipoprotein levels (t = 2.115, df = 46, p = 0.040) compared to the control group. This study demonstrates that a mobile application self-management program can lower lipid levels.

Conclusions

The findings indicate that a mobile application–based self-management program can significantly reduce cholesterol, triglyceride, and low-density lipoprotein levels in people with coronary artery disease in the short term. These findings highlight the crucial role of nurses applying these strategies to enhance patient care.

Trial Registry Number

Thai Clinical Trials Registry (TCTR20250129001)

Background

Coronary artery disease (CAD) is a significant public health concern, with a mortality rate of up to 150 per 100,000 people and a global prevalence of 126.5 million (American Heart Association, 2023). According to the Division of Non-Communicable Disease - Ministry of Public Health (2024), the CAD death rate per 100,000 individuals in Thailand increased from 31.82% to 33.54% between 2020 and 2024. These numbers indicate a yearly increase in the number of patients. Furthermore, a survey by The Heart Association of Thailand (2020) found that, after one year, the mortality rate among patients with acute CAD was 39%. Thus, CAD is a severe health risk that requires prompt action.

Even though pharmacological and surgical treatments have advanced significantly, risk variables continue to be independent predictors of cardiovascular death in CAD patients (Jankowski et al., 2021). One of the most notable conditions is hypercholesterolemia, and the utilization of lipid-lowering medications has been linked to improved prognostic outcomes. However, the lipid profile levels exceed the recommended target.

An individual’s lipid profile plays a crucial role in determining the likelihood of CAD advancement, and an atypical lipid profile is regarded as a substantial risk factor for CAD (Jankowski et al., 2021; Wu et al., 2021). High levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and low levels of high-density lipoprotein cholesterol (HDL-C) are the main risk factors for CAD (Jankowski et al., 2021; Shabana et al., 2020; Zhao et al., 2021), leading to the narrowing and widening of coronary arteries, and high LDL-C levels that can cause arteriosclerosis due to the collection of plaque in the intima-media of arteries. This buildup increases thrombocytopoiesis, increasing the risk of atherosclerotic cardiovascular disease (Wu et al., 2021; Zhao et al., 2021).

A comprehensive literature review found that practical self-management skills can lower blood lipid, blood pressure, and BMI (Wang et al., 2016). Qiu et al. (2020) found that dietary control, smoking cessation, and addressing risk factors, such as TC, TG, and LDL-C can lower blood lipid levels. Li et al. (2022) noted that blood pressure and LDL-C levels dropped significantly in an experimental group. Palacios et al. (2017) discovered that self-management helps patients change their behaviors, improving exercise, blood pressure, cholesterol, and quality of life. Moreover, the study by Liu et al. (2018) found that eating more fruits and vegetables and exercising regularly reduced risk factors, and that this approach may lower blood lipids and improve health.

Therefore, self-management plays a crucial role in the success of individuals with CAD who effectively care for themselves. Lorig and Holman (2003) defined self-management as controlling one’s actions, thoughts, and emotions. As a result, people with strong self-management skills can handle a variety of situations. CAD management involves medical, role, and emotional management. To succeed, patients require six essential self-management skills: problem-solving, decision-making, resource utilization, patient/health care provider cooperation, action, and self-tailoring (Lorig & Holman, 2003). Fortunately, patients diagnosed with CAD have exhibited positive results in other elements of lifestyle change. For example, a large percentage of patients with CAD were able to quit smoking successfully (87.5%) and demonstrate a high level of compliance with their recommended drug regimens (96.0%) (Ergatoudes et al., 2016). Nevertheless, the rates of regular exercise (45.5%) and adherence to dietary adjustments (19.5-26.0%) were not equally successful.

Furthermore, it is essential to acknowledge that patients often made temporary alterations to their dietary habits for six months, following which they returned to their prior unhealthy eating behaviors. This resulted in elevated lipid profile levels. The topic of self-management of lipid profiles among persons with CAD is not new. However, the differences in culture and context may lead to improved self-management and a lower lipid profile among CAD patients.

Thailand’s National Plan for the Prevention and Control of Non-communicable Diseases (NCDs) (BE 2023 - 2027) aims to prevent and manage NCDs. One of the strategies of an innovative NCD network is to promote technology and innovation in managing NCDs (Division of Non-Communicable Disease - Ministry of Public Health, 2024). Among Thai people, the most accessible technology is a mobile application. Using a mobile application for self-management can help CAD patients access information on managing their lifestyle. This mobile application provides quick, evidence-based resources at the point of care, boosting knowledge retention and efficacy. As a result, self-management applications can help CAD patients reduce the incidence of CAD, which has the highest death rate among NCDs (Division of Non-Communicable Disease - Ministry of Public Health, 2024). Thus, gaining insights from cultural and contextual differences is critical to broadening our understanding of how to enhance individuals’ self-management abilities and to enabling patients to actively participate in their care. For example, in the program, participants receive information about the Thai context, including guidelines on what cardiovascular patients should and shouldn’t eat, proper cooking methods such as boiling and grilling, and the recommendation to eat vegetables in every meal. Moreover, Thai people should be mindful of the types of carbohydrates they consume, such as choosing brown rice over sticky rice. Additionally, strategies for managing stress were suggested, such as meditation, a simple and widespread practice in Thailand.

In addition, this study is a two-way communication using the app to communicate, monitor, and consult with patients 24 hours a day, ensuring that participants have high self-confidence to take care of themselves, resulting in sustainable health. Thus, this study investigates the impact of mobile application self-management programs on lipid profiles in CAD patients, as this technology is up-to-date and widely accessible in Thailand. Thus, the idea is to encourage patients’ ongoing self-care and provide easy access to information as needed to improve self-management among CAD patients.

Methods

Study Design

A randomized controlled experiment was conducted to investigate the effects of self-management programs, specifically mobile applications, on lipid profiles in people diagnosed with CAD. CONSORT guidelines (Schulz et al., 2010) were followed to ensure transparent reporting of the trial's design, participant flow, and analysis.

Sample/Participants

All CAD patients were recruited from the cardiology outpatient department at the Police General Hospital (Figure 1). The inclusion criteria included Thai patients aged 20 years or older diagnosed with CAD who were receiving lipid-lowering therapy or had a previous diagnosis of dyslipidemia: cholesterol >200 milligrams per deciliter (mg/dL), TG >150 mg/dL, or LDL-C >130 mg/dL. Participants also had to have no cognitive impairment, be classified as Class I-III by the Canadian Cardiovascular Society for angina pectoris, and have no comorbidities such as chest pain, syncope, or dyspnea. Individuals were required to be literate in Thai. The study excluded participants with a documented history of mental health disorders or other medical conditions, such as heart failure or renal failure.

Figure 1.

Flow of participants’ selection

Patients with CAD were randomly assigned to the control and experimental groups, using permuted blocks of four with randomly assigned blocking numbers. Following the enrollment of the first participant, building on the inaugural block began. Participants were randomly assigned to the experimental or control group based on the selected block, starting from the first and ending with the last registered individual. Participants who receive the intervention will be explained in a private room, only to the participants and the researcher. Participants in the control group received conventional treatment. In contrast, those in the experimental group participated in a four-week self-management program that included access to and use of a customized mobile phone application and conventional care.

The sample size was calculated using power analysis and effect size determination, as described by Cohen (1988), Munro (2005), and Polit and Beck (2006). Significant criteria were used, including significance level α = 0.05, a power of 0.80, and an effect size (d) of 0.79 by Hisni et al. (2019). The power analysis determined that a sample size of 21 participants per group would be necessary. According to Gray and Grove (2021), the attrition rate for each group was 10%. Thus, the total sample size for this study was 48.

Instruments

Two instruments were used in this study:

1) Socio-demographic and clinical profile. The researchers assessed the participants’ socio-demographic characteristics, encompassing age, gender, marital status, education level, income, BMI, and type of health insurance. The Cardiac Canadian Society Class (CCSC) data, comorbidities, and lipid profiles, namely cholesterol level, TG, and LDL-C, were derived from clinical sources.

2) Self-management questionnaires for coronary artery disease. A 9-item questionnaire assessed CAD self-management. Participants rated their medical, role, and emotional self-management on a 5-point scale, with 1 indicating no self-management and 5 indicating daily practice. Intermediate ratings were 2 for irregular, 3 for frequent, and 4 for near-daily practice. Higher ratings indicated better self-management within the study’s 9-45 rating scale. A 0.90 Cronbach’s alpha coefficient indicated internal consistency in the self-management survey (Tippinit & Polsook, 2023). This study used Cronbach’s alpha of 0.77 to assess internal consistency reliability.

Interventions

Self-management is defined as the control of one’s behaviors, thoughts, and emotions. Self-management entails recognizing and precisely carrying out one’s responsibilities in various aspects of life. Self-managers can handle a variety of situations. This program includes three self-management tasks. CAD management entails medical, role, and emotional management, and CAD patients experience stress, worry, and apprehension, making the latter especially important. Lorig and Holman (2003) identified six essential patient self-management skills: problem-solving, decision-making, resource utilization, patient/healthcare provider cooperation, action, and self-tailoring. The intervention in this study highlights educational patient self-management, self-management skill training, and self-management monitoring. The mobile application enabled two-way communication between participants and the research team, allowing participants to seek advice and receive feedback at any time. This ongoing support aimed to strengthen participants’ confidence and adherence to self-management practices.

1) This study focused on patients’ self-management, including healthy eating, risk factor modification, symptom management, stress management, and medication, by providing educational resources and role models at the initial visit.

2) Self-management skill training teaches CAD patients

• Problem-solving uses video to teach patients how to deal with dietary, physical activity, medication, and stress management. The findings include the patient’s anticipated challenges, the family’s, society’s, and nurses’ solutions, and problem-solving strategies. To help patients learn how to handle challenges, they must be informed about the application and its functionalities.

• Decision-making is crucial for CAD patients because they face changing medical situations that require constant evaluation and self-care choices. Many CAD patients were unsure how to evaluate clinical changes or side effects. Since they lacked self-regulation, patients were guided in practicing decision-making skills through virtual simulations. Patients methodically learned and used decision-making skills. This stage builds patients’ confidence in their decision-making and empowers them to handle the issue competently and responsibly. These interventions (1, 2) were designed to last 45 minutes, including questions and answers.

• Resource utilization supports patient decision-making. Scholars have suggested clear, user-friendly protocols for accessing data sources, along with a diverse array of significant, indispensable, and pertinent sources to give patients many options for finding information. Access to relevant information for decision-making is essential to solving problems in research team applications.

• Establishing the patient-healthcare provider relationship requires patient and provider commitment. Educational interventions emphasized the importance of healthcare provider-patient partnerships. These interventions encouraged patients to actively participate in their healthcare, including sharing health information with the team. The patients actively participated in treatment planning and agreed on a treatment plan with healthcare staff.

• Taking Action, Action executing short-term plans. Researchers advised on planning, where simplicity of implementation is vital to success. Expect immediate results when starting practice, then gradually extend the duration. The interventions helped the patient self-manage their diet and communicate food-related information to the research team to evaluate a healthy diet. The researchers also required participants to exercise 30 minutes per day, five days a week, totaling 150 minutes per week. The software connected the application to patients to support medication adherence and stress management, facilitating information sharing and patient monitoring.

• Assessment is an integral part of a successful self-management regime. The self-tailoring phase is assessed four weeks after installation to identify appropriate activities for each patient. This section led to more effective self-management, appropriate behavioral adjustments, and the development of patient self-management guidelines.

3) Self-management monitoring: In the second and third weeks following project initiation, the researchers engaged in ongoing self-management monitoring by undertaking two telephone follow-up sessions (Figure 2).

Figure 2.

Self-management intervention process

Data Collection

After receiving ethics committee approval, research assistants were trained to administer questionnaires and obtain informed consent. They then met with all patients in the cardiac outpatient department and, after obtaining their consent, invited participants who met the inclusion criteria to participate in the program. All study participants signed the consent form. Experiment conducted June 2022-June 2023. Participants were randomly assigned to experimental or control groups using sequentially numbered, opaque, sealed envelopes.

The experimental group received self-management strategies in addition to cardiac outpatient care. Participants received the intervention, which had three parts: establishing relationships and preparing patients for the self-management program, including training in self-management skills, monitoring self-management with the application, and two follow-up calls. Follow-up cardiology outpatient visits included lipid profile lab tests.

These studies occurred during participants’ first program participation and subsequent follow-ups. The cardiac outpatient department provided the control group with conventional care. These studies occurred during the participants’ initial participation program and were monitored via telephone or video call in week 2 and week 3. Follow-up occurred after completing the intervention in week 5. During the initial day of the intervention, relevant socio-demographic and clinical information was collected. Baseline data were collected prior to intervention initiation. Follow-up lipid profiles were obtained three months after baseline, while self-management questionnaires were administered one month after completion of the four-week intervention.

Participants are given a password to access the mobile application and answer the questions. This password is exclusively shared between research assistants and participants. At the start and finish of the program. Participants followed up, and the lab analyzed the lipid profiles. Outcome assessors who retrieved laboratory lipid profile data were not blinded to group allocation, as the study was conducted within a single clinical service. The self-management questionnaire was administered one-month post-intervention using the program. After the intervention, the participants received education and a booklet similar to that of the experimental group.

Data Analysis

The data were analyzed using IBM SPSS 28. Descriptive statistics summarized the demographic and clinical data, including frequency, percentages, averages, and standard deviations. After the test, the assumption of normality was assessed using the Kolmogorov-Smirnov test; the results indicated that the data were normally distributed (p > 0.05). Although multivariate analyses were considered during the study planning phase, only independent and dependent t-tests were conducted due to the sample size and study objectives. Independent t-tests were used to compare post-intervention lipid profile outcomes between the experimental and control groups, while dependent t-tests were used to assess within-group changes from baseline to follow-up. Effect sizes were calculated using Cohen’s d for all t-test comparisons. Cohen’s d values of 0.2, 0.5, and 0.8 were interpreted as small, medium, and large effects, respectively (Cohen, 1988). Effect sizes with corresponding 95% confidence intervals were reported to indicate the magnitude and precision of the intervention effects.

Ethical Considerations

The Police General Hospital ethics committee approved this study (Approval number: Oh33/65 on 13 June 2022). Participants received information about the research and were assured of anonymity. Participants could leave the study at any time without affecting their treatment. A password secured the researchers’ computers, and the questionnaire file cabinet was locked. Limited data was available to researchers.

Results

Socio-Demographic and Clinical Characteristics of the Participants

The control group consisted of individuals with a mean age of 58.6 years and a standard deviation of 9.6. Precisely 50.0% of the participants were classified as male. Approximately 54.2% of the participants were married. Additionally, a considerable portion (37.5%) indicated having attained a high school education. About 37.5% of the participants had monthly incomes from 0 to 10,000 baht, comparable to 0 to 287.61 USD. Half of the participants (50%) showed that they assumed responsibility for their health through government coverage. Precisely 50.0% of the subjects demonstrated the presence of one comorbidity. Around 54.2% of the participants also exhibited a BMI within the normal range (18.51-24.99 kg/m²).

The average age of the participants in the experimental group was 59.3 years, with a standard deviation of 5.9. Roughly 58.3% of the participants identified as male, while nearly 54.2% reported being married. Approximately 45.8% of the participants had completed high school. Nearly one-third (29.2%) of the participants in the study reported earning income between 10,001 and 20,000 baht, which is approximately 575.26 to 862.85 USD. More than half of the participants (54.2%) had health care provided through government coverage. Approximately 41.7% of the subjects had two or more comorbidities. About 58.3% of the individuals were found to have a BMI within the normal range of 18.51-24.99 kg/m² (Table 1).

Table 1.

Demographic and clinical characteristics of the patients (N = 48)

Characteristics	Control Group (n = 24)	Experimental Group (n = 24)	Total (n = 48)	χ2	p
	n (%)	n (%)	n (%)
Gender				0.336	0.562
Male	12 (50.0)	14 (58.3)	26 (54.2)
Female	12 (50.0)	10 (41.7)	22 (45.8)
Age (years)				4.376	0.224
42-44	2 (8.3)	-	2 (4.2)
45-60	12 (50.0)	14 (58.3)	26 (54.1)
61-75	8 (33.4)	10 (41.7)	18 (37.5)
76-90	2 (8.3)	-	2 (4.2)
Average age	58.6 (9.6)	59.3 (5.9)	58.9 (7.9)
Status				2.392	0.495
Single	9 (37.5)	8 (33.3)	17 (35.3)
Married	13 (54.2)	13 (54.2)	26 (54.2)
Widowed	2 (8.3)	3 (12.5)	5 (10.4)
Education				3.311	0.769
No education	2 (8.3)	-	2 (4.2)
Primary school	3 (12.5)	3 (12.5)	6 (12.5)
Secondary school	1 (4.2)	1 (4.2)	2 (4.2)
High school	9 (37.5)	11 (45.8)	20 (41.7)
Diploma	4 (16.7)	5 (20.8)	9 (18.8)
Bachelor’s degree	4 (16.7)	4 (16.7)	8 (16.7)
Master’s degree	1 (4.2)	-	1 (2.1)
Income (Baht)				5.129	0.400
0 – 10,000	9 (37.5)	3 (12.5)	12 (25.0)
10,001 – 20,000	5 (20.8)	5 (20.8)	10 (20.8)
20,001 – 30,000	4 (16.7)	7 (29.2)	11 (22.9)
30,001 – 40,000	4 (16.7)	5 (20.8)	9 (18.8)
40,001 – 50,000	2 (8.3)	3 (12.5)	5 (10.4)
More than 50,000	-	1 (4.2)	1 (2.1)
Type of health care coverage				0.240	0.917
Universal Coverage Scheme
(the 30-Baht Scheme)	2 (8.3)	2 (8.3)	4 (8.3)
Social Security	7 (29.2)	7 (29.2)	14 (29.2)
Pay by themselves	3 (12.5)	2 (8.3)	5 (10.3)
Government coverage	12 (50.0)	13 (54.2)	25 (52.1)
Comorbidity				4.634	0.327
None	3 (12.5)	6 (25.0)	9 (18.8)
One Comorbidity	12 (50.0)	8 (33.3)	20 (41.7)
≥ Two Comorbidity	9 (37.5)	10 (41.7)	19 (39.5)
BMI (kg/m2)				0.370	0.831
18.51-24.99	13 (54.2)	14 (58.3)	27 (56.3)
25.00-29.99	9 (37.5)	9 (37.5)	18 (37.5)
≥ 30.00	2 (8.3)	1 (4.2)	3 (6.2)

Note. BMI = Body Mass Index.

The Chi-square test (χ2) showed no significant between demographic and clinical characteristics between groups (control and experimental group) [gender (χ2 = 0.336, p = 0.562), age, status (χ2 = 4.376, p = 0.224), education, income (χ2 = 3.311, p = 0.769), type of health care coverage(χ2 = 0.240, p = 0.971), comorbidity(χ2 = 4.634 p = 0.327), and BMI (χ2 = 0.370, p = 0.831).

The Mean of Cholesterol, Triglyceride (TG), and Low-Density Lipoproteins (LDL) in the Group

Before receiving conventional care, the control group had an average cholesterol level of 169.39 (standard deviation [SD] = 29.10). After implementing conventional care, the average decreased to 160.67 (SD = 25.41). The findings revealed no statistically significant difference at the 0.05 level in the average cholesterol levels of the control group before and after receiving conventional care (t = 1.587, df = 23, p = 0.126). In contrast, the mean cholesterol level of the experimental group before the intervention was 165.04 (SD = 38.43), and after the program’s execution, it fell to 144.11 (SD = 28.58). The findings suggest a statistically significant difference at the 0.05 level in the experimental group’s average cholesterol levels before and after the intervention (t = 4.877, df = 23, p < 0.001).

The mean TG level in the control group before receiving conventional care was 144.23 (SD = 51.76). After three months of conventional medical treatment, the mean TG level decreased to 133.35 (SD = 48.99). The findings indicate that there was a statistically significant difference in the mean TG level of the control group before and after its members underwent conventional treatment (t = 2.189, df = 23, p = 0.039) at a significance level of .05. The experimental group exhibited an average TG level of 135.75 (SD = 40.37) before the initiation of the program. However, following their participation in the program, the average for this group decreased to 108.95 (SD = 31.09). The findings indicate a statistically significant difference in the average TG levels of the experimental group before and after the program (t = 5.659, df = 23, p = 0.001) at the significance level of .05. Although the control group also showed a modest reduction in TG level, the between-group comparison demonstrated a greater reduction in the experimental group.

The control group’s mean LDL level before receiving conventional therapy was 93.40 (SD = 32.79). After three months of conventional medical treatment, the mean LDL level dropped to 91.76 (SD = 29.91). The findings indicate no statistically significant difference in the average LDL levels of the control group before and after they received conventional treatment at the .05 level (t = 0.495, df = 23, p = 0.626). Before the program’s implementation, the experimental group exhibited an average LDL level of 92.38 (SD = 38.28). However, following their participation in the program, the average for this group decreased to 74.66 (SD = 25.98). The findings indicate a statistically significant difference in the average LDL levels of the experimental group before and after the program (t = 4.421, df = 23, p = 0.001) at the 0.05 significance level (Table 2).

Table 2.

Dependent t-test of lipid profile in a group (N = 48)

	Mean	SD	df	t	p
Cholesterol, mg/dL
Control Group (n = 24)
Pre-intervention	169.39	29.10	23	1.587	0.126
Post-intervention follow-up at 3 months	160.67	25.41
Experimental Group (n = 24)
Pre-intervention	165.04	38.43	23	4.877	<0.001*
Post-intervention follow-up at 3 months	144.11	28.58
Triglyceride, mg/dL
Control Group (n = 24)
Pre-intervention	144.23	51.76	23	2.189	0.039*
Post-intervention follow-up at 3 months	133.35	48.99
Experimental Group (n = 24)
Pre-intervention	135.75	40.37	23	5.659	<0.001*
Post-intervention follow-up at 3 months	108.95	31.09
LDL, mg/dL
Control Group (n = 24)
Pre-intervention	93.40	32.79	23	.495	0.626
Post-intervention follow-up at 3 months	91.76	29.91
Experimental Group (n = 24)
Pre-intervention	92.38	38.28	23	4.421	<0.001*
Post-intervention follow-up at 3 months	74.66	25.98

Note. LDL= low-density lipoproteins; mg/dL= Milligrams per deciliter;

^*< 0.05

The Effectiveness of the Program

After completing the intervention, the self-management questions were assessed by participants. The control group had a mean score of 26.67 (SD = 6.39). In contrast, the experimental group recorded a higher mean score of 36.50 (SD = 3.64), indicating superior self-management in the experimental group. Moreover, this study on cholesterol levels encompassed the examination and comparison of experimental and control groups. The findings of the independent t-test revealed a statistically significant reduction in the average cholesterol level of the experimental group after the intervention (t = 2.121, df = 46, p = 0.039) (Table 3). The mean score analysis and a 95% confidence interval indicate a significant difference between the two groups. The lower limit of the interval was calculated to be 0.843, while the upper bound was determined to be 32.274.

Table 3.

Independent t-test of lipid profile between groups (N = 48)

	Mean	SD	df	t	p
Cholesterol, mg/dL
Pre-intervention
Control Group (n = 24)	169.39	29.10	46	0.442	0.661
Experimental Group (n = 24)	165.04	38.43
Post-intervention follow-up at 3 months
Control Group (n = 24)	160.67	25.41	46	2.121	0.039*
Experimental Group (n = 24)	144.11	28.58
Triglyceride, mg/dL
Pre-intervention
Control Group (n = 24)	144.23	51.76	46	0.633	0.530
Experimental Group (n = 24)	135.75	40.37
Post-intervention follow-up at 3 months
Control Group (n = 24)	133.35	48.99	46	2.060	0.046*
Experimental Group (n = 24)	108.95	31.09
LDL, mg/dL
Pre-intervention
Control Group (n = 24)	93.40	32.79	46	0.099	0.921
Experimental Group (n = 24)	92.38	38.28
Post-intervention follow-up at 3 months
Control Group (n = 24)	91.76	29.91	46	2.115	0.040*
Experimental Group (n = 24)	74.66	25.98

Note. LDL= low-density lipoproteins, mg/dL= milligrams per deciliter,

^*< 0.05

The TG levels were evaluated in both the experimental and control cohorts. The findings of the independent t-test revealed a statistically significant reduction in the TG level of the experimental group after the intervention (t = 2.060, df = 46, p = 0.046) (Table 3). The mean difference between the two groups was estimated to be between 0.554 and 48.273, with a 95% confidence level. The LDL levels were also measured in both the experimental and control groups. The findings of the independent t-test reveal a statistically significant reduction in the post-test LDL level of the experimental group (t = 2.115, df = 46, p = 0.040) (Table 3). The 95% confidence interval for the difference between the two groups’ mean scores ranged from 0.824 to 33.385. The magnitude of the intervention effects was moderate across outcomes. The effect size for post-intervention cholesterol reduction was Cohen’s d = 0.61 (95% CI: 0.03–1.18), for TG reduction was d = 0.59 (95% CI: 0.01–1.17), and for LDL reduction was d = 0.61 (95% CI: 0.02–1.18), indicating clinically meaningful short-term effects.

Discussion

Principal Findings

Most experimental and control group participants were married, with mean ages of 59.3 and 58.6 years, respectively. Approximately 54.2% of individuals in the experimental group had government coverage for medical costs, while approximately 50% of individuals in the control group had a comparable healthcare arrangement. The socio-demographic features within this sample provide a comprehensive understanding of its composition and yield significant insights into the community under investigation.

The findings indicate that participants in the experimental group who completed the four-week self-management program exhibited significantly greater reductions in cholesterol, TG, and LDL levels than those receiving conventional care alone. Providing conventional nursing care can also help lower participants’ lipid profiles. Additionally, combining the conventional nursing care with our intervention will be more effective for participants to enhance self-management to take care of themselves, to control and improve their lipid profiles, further improving outcomes by reducing lipid levels. The findings of the study are preliminary, focused on short-term efficacy, and suggest that longer-term trials are necessary to confirm the durability of improvements in lipid profiles and sustained adherence to self-management behaviors.

The observed results can be explained by the program’s effectiveness, which the researchers implemented via mobile and telephone applications, in enhancing participants’ comprehension and skill in lifestyle adjustment. The implemented intervention significantly affected participants’ perceptions of security, empowerment, and confidence, reducing fear and anxiety. In a study of self-management by Lorig and Holman (2003), the participants in the experimental group were taught six essential self-management skills, resulting in improved medical, role, and emotional management.

For medical management, the participants were instructed to consume heart-healthy foods, such as vegetables, fruits, baked or steamed foods, lean meat, fish, and nuts. Additionally, they could choose cooking methods that are good for one’s heart, such as baking, steaming, or boiling. They could also choose foods that are good for one’s heart, such as vegetables, fruits, baked or steamed foods, lean meat, fish, and nuts. Concerning role management, they could plan and prepare meals each day. They also observed unusual symptoms, such as chest pain and dizziness.

Lastly, regarding emotional management, they learned to handle stress by eating foods high in vitamin C, such as kale, moringa leaves, bell peppers, broccoli, and fruits like oranges. They could also seek advice from doctors, nurses, and nutritionists when stressed by the challenges of diet changes, treatment plans, etc. The findings of this study are consistent with prior research emphasizing the importance of tailored educational interventions for persons with CAD, particularly in preparing for discharge (Barnason et al., 2012).

The study’s results also reveal that the experimental group exhibited higher levels of self-management than the control group. Furthermore, a substantial percentage of participants in both groups were married, a common characteristic in Thai culture, where individuals often live with extended family. To enhance the program’s effectiveness, it would be advantageous for nurses to incorporate coaching and skills training specifically aimed at lowering cholesterol, TG, and LDL levels. In addition, telephone follow-ups have enhanced patient self-assurance and encouraged adherence to self-management techniques (Peterson et al., 2014; Qiu et al., 2020).

The findings of this study align with the research carried out by Li et al. (2022), which investigated the impact of adhering to mobile app–based self-management digital treatments among individuals diagnosed with CAD. The results indicate that the group receiving the intervention demonstrated a statistically significant improvement in achieving blood pressure management (namely, systolic blood pressure below 140 mmHg and diastolic blood pressure below 90 mmHg) and lower LDL-C and respiratory rate levels after 12 months. In addition, research conducted by Mohamed Mohamed Soliman (2022) investigated the effects of a tele-nursing-based self-management intervention on the levels of cardiac self-efficacy and heart health indicators in individuals diagnosed with myocardial infarction. The study findings indicate statistically significant differences in the average scores for TG, HDL-C, TC, and LDL-C (p < 0.001), as well as in blood pressure levels (p < 0.001), before and after 3 months of telenursing. Furthermore, Huo et al. (2017) conducted a study in which mobile health treatments were implemented to develop the Cardiovascular Health and Text Messaging (CHAT) and the CHAT-Diabetes Mellitus (CHAT-DM) studies. The findings indicate that more patients in the intervention group achieved systolic blood pressure below 140 mmHg, lower LDL-C levels, greater physical activity engagement, better medication adherence, lower BMI, and successful smoking cessation.

This study is consistent with the findings of Palacios et al. (2017), who conducted a systematic review of the effectiveness of internet-delivered self-management support in improving outcomes for people with coronary heart disease. The results suggest that implementing self-management techniques enables patients to effectively adjust their habits, leading to enhanced exercise adherence, lower blood pressure and cholesterol levels, and a favorable correlation with improved quality of life.

Limitations of the Study

Several limitations may affect the generalizability of the study’s findings to other contexts, as they are constrained by distinct cultural and environmental elements specific to Thailand. Thailand’s cultural and social norms, self-management attitudes and abilities, and lifestyle modification dimensions may be distinctive compared to other regions or countries. Therefore, it is prudent to exercise caution when extrapolating these findings to diverse groups. Another limitation of this study is the relatively brief intervention duration and the small number of participants. The potential influence of the study’s short timeframe on the measurement of self-management should be acknowledged, as it relied on self-reports that are prone to participant bias and errors. A prolonged intervention period would have enabled a more comprehensive evaluation of cholesterol, TG, and LDL levels, as well as their sustained effectiveness over time. A large sample size is needed to be confident of the intervention’s change and effectiveness. The lack of assessor blinding may have introduced a risk of measurement bias, particularly for self-reported self-management outcomes.

Implications for Nursing Practice and Recommendations

The study’s findings indicate that implementing a self-management program among patients can yield positive outcomes when utilizing an application. During the evaluation, improvements or reductions in cholesterol, TG, and LDL levels were observed. The data suggest that pre-discharge self-management training may improve long-term management of cholesterol, TG, and LDL in patients with CAD. Future studies must focus on the program’s long-term effects on cholesterol, TG, and LDL levels after patients leave the hospital. Furthermore, nurses, clinicians, and clinical researchers must evaluate the program’s impact on diverse clinical outcomes, including symptom management, re-hospitalization rates, and the overall quality of life among individuals diagnosed with CAD. By meticulously examining these elements, individuals may gain a comprehensive understanding of the software's benefits and potential limitations. This knowledge can then guide future improvements and breakthroughs in CAD administration.

Conclusion

This study shows how important it is for people with CAD to use a self-management program to lower their cholesterol, TG, and LDL levels. The results highlight the efficacy of interventions administered via mobile applications in enhancing participants’ self-management, knowledge of lifestyle modification, proficiency, and engagement in self-care behaviors. Participants understood the importance of self-management and sustained lifestyle changes to improve their health. These findings emphasize the importance of nurses using these strategies to improve patient care.

Funding Statement

Funding This Research is funded by the Thailand Science Research and Innovation Fund, Chulalongkorn University (Grant number: CU_FRB65_hea (13)_019_36_03). Recipient: Rapin Polsook.

Declaration of Conflicting Interest

Dr. Yupin Aungsuroch is a member of the Editorial Advisory Board of Belitung Nursing Journal. However, she has no role in decision-making, and the authors and peer reviewers are blind to the authors’ identities.

Author Contribution

All authors contributed to and approved the final manuscript. R.P. designed the study, collected and analyzed the data, and wrote and revised the manuscript. Y.A. designed the study, wrote and revised the manuscript.

Author Biography

Rapin Polsook, PhD, RN, is an Associate Professor at the Faculty of Nursing, Chulalongkorn University.

Yupin Aungsuroch, PhD, RN, is an Associate Professor and a former Dean at the Faculty of Nursing, Chulalongkorn University.

Data Availability

The dataset generated and analyzed during the current study is available from the corresponding author upon request.

Declaration of Use of AI in Scientific Writing

The authors used Grammarly in the writing process to improve readability and remove grammatical errors. However, they took full responsibility for the content.