ABSTRACT
Aim
To explore the latent classes and various characteristics of self‐management ability of patients with atrial fibrillation, and to analyse the influencing factors.
Design
A cross‐sectional study design.
Methods
A convenience sampling was used to select 208 patients with atrial fibrillation from 2 hospitals in Shandong Province, China between August 2022 and June 2023. The survey tools included the general data questionnaire, Brief Illness Perception Questionnaire and Self‐Management Ability Scale for patients with atrial fibrillation. Data were analysed using latent profile analysis, univariate analysis and binary logistic regression.
Results
The results of the latent profile analysis showed that the self‐management ability of patients with atrial fibrillation was divided into two different latent classes. Binary logistic regression analysis showed that disease duration, primary caregiver and illness perception were significantly associated with self‐management ability.
Conclusions
There are 2 potential categories of self‐management ability in patients with atrial fibrillation. Appropriate individualised health management interventions for patients with atrial fibrillation, focusing on the patient's disease duration, primary caregiver and illness perception, may improve self‐management in these patients.
Implications for Clinical Practice
This study is beneficial in providing information reference for medical staff. Medical staff can implement targeted interventions based on the categorical characteristics of the different profiles of self‐management ability in patients with atrial fibrillation to improve their self‐management ability.
Patient or Public Contribution
We thank all participants for taking part in the survey throughout the study.
Keywords: atrial fibrillation, illness perception, influencing factors, latent profile analysis, self‐management ability
1. Introduction
Atrial fibrillation (AF) is the most common type of arrhythmia in clinical practice, accounting for 45% of all arrhythmias, and is often characterised by recurrent episodes. In China, the first detection rate of AF is as high as 30.7%, with more than 4.87 million cases of AF occurring in people aged 35 years and older, for an overall prevalence of 0.77% (Wang et al. 2018). As China's population ages, the number of patients and overall prevalence will grow even more. The number of patients with AF worldwide is estimated to reach 60 million in 2050, with a disease growth rate more than 60% greater than that reported in 2017 (403 new cases per million inhabitants) (Cerasuolo et al. 2017; Lippi et al. 2021). A study has shown that smoking, alcohol consumption, insufficient exercise, sleep deficiency and increasing age can induce the onset of AF, leading to sharp increases in the incidence of AF (Groh et al. 2019).
AF, like other cardiovascular diseases, can lead to patients experiencing palpitations, fatigue, chest tightness, cognitive impairment, decreased exercise tolerance and other common clinical symptoms that can exacerbate heart problems and even lead to heart failure or death. In addition, ventricular fluctuations and arrhythmia irregularities in patients with AF can cause hemodynamic changes that result in thromboembolic events due to dislodgment of atrial appendage thrombi. AF is an important risk factor for inducing stroke. A study has found that patients with stroke due to AF account for 20%–30% of all stroke patients (Migdady et al. 2021). A 12‐year follow‐up study reported a higher mortality rate from stroke and cardiovascular disease in patients with AF than in patients without AF, with a nearly two‐fold higher risk of death (Goulart et al. 2022). A prospective study conducted in Singapore demonstrated that AF is a significant risk factor for stroke and death after surgery in patients with cardiac diseases (Wang et al. 2021). Recognising the importance of effective AF management strategies, as emphasised by the American Heart Association, is particularly important in the prevention and management of cardiovascular disease and stroke (Wilson et al. 2024). Therefore, to reduce the complications of AF, early enhancement of the ability of patients with AF to self‐manage their disease is essential and can greatly reduce their risk of future stroke and death.
2. Background
Self‐management is widely recognised as an important approach to maintaining and improving patient behaviour and health. Self‐management ability refers to the series of activity levels at which patients restrain and supervise their self‐physiology, psychological and social functions and lifestyle when dealing with disease treatment. Self‐health management is a positive health behaviour that is the foundation of secondary prevention of AF. Rosman et al. (2021) showed that a self‐management intervention program was effective in increasing patients' self‐confidence in disease management and adherence to guideline‐recommended self‐management of AF and could have beneficial effects on patients' self‐care, emotional well‐being and physical functioning. Such programs can also improve patient utilisation of healthcare resources, improve health, reduce symptom burden and improve quality of life. Early active lifestyle interventions and risk factor management are priorities to improve outcomes and reduce complications in patients with AF (Jobst et al. 2020; Wahlström and Stelling Risom 2022). Therefore, patients should recognise the symptoms of AF and its associated risk factors and cooperate with their doctors in the self‐management of the disease, including self‐monitoring (heart rate, rhythm and blood pressure), symptom recognition, compliance with medication and lifestyle modification to control the progression of AF.
However, despite multiple studies demonstrating the many benefits of self‐management for AF prevention and recovery, self‐management remains a challenge for patients with AF, who often neglect their health and do not have knowledge about AF‐related issues, such as symptoms, medication and prevention, leading to their passivity in disease management (Wilson et al. 2024; Rakhshan et al. 2019; Hsieh et al. 2021). Previous research has highlighted that more than one‐third of patients with AF in China are unaware of their condition, and only 6% of patients with AF and those at high risk of stroke are treated with warfarin anticoagulation (Du et al. 2021). Unfortunately, many patients with AF exhibit poor self‐management and inadequate management of critical anticoagulants. For example, a previous study found that Chinese patients with AF have insufficient self‐management ability, which increases the incidence of stroke (Shen et al. 2022). Ding et al. (2023) reported that only 30% of study participants had good self‐management skills within 3 months after radiofrequency catheter ablation in patients with AF and that more than half of the patients with AF had self‐management skills that required improvement.
Additionally, some factors influenced the self‐management abilities of patients with AF. Studies based on the information‐motivation‐behavioural skills model have shown that education level, occupation type and personal monthly income affect self‐management in these patients (Ding et al. 2023). A retrospective controlled study found that age and medication knowledge were independent risk factors for medication adherence in patients with AF and were detrimental to the development of self‐management skills (Zhang et al. 2022). Illness perceptions, an individual's belief about a disease, are also an important factor that influences self‐management and treatment adherence in patients with chronic diseases (Xia et al. 2022). A cross‐sectional study involving 168 patients undergoing cardiac implantation found that disease perception was moderately negatively correlated with self‐management (Ashley et al. 2015). A prospective study of multimorbid patients also found that disease perception was a key predictor of patients' level of self‐management and health‐related quality of life (Kenning et al. 2015). However, the current lack of clarity regarding the impact of disease perception on the self‐management skills of patients with AF remains to be further explored.
Taken together, the levels and factors affecting self‐management ability are multifaceted.
The levels of self‐management ability in individuals are characterised by complexity, diversity and high heterogeneity. However, previous studies on self‐management in patients with AF focused on variables and determined the overall level of self‐management in these patients using self‐management questionnaire scores, which were then analysed to identify influencing factors. This method of analysis views all patients as homogeneous, independent individuals and does not consider inter‐individual heterogeneity, which makes it difficult for clinical providers to guide future interventional studies in this area.
Latent profile analysis (LPA) is an individual‐centered method that classifies samples based on different characteristics. Furthermore, the potential categorical variables can be used to identify differences between individual categories and category groups. Previous studies have used LPA to analyse health literacy categories in patients with heart failure, in which LPA classification focused more on inter‐individual heterogeneity and reflected individual differences compared with traditional analysis methods (Jiang et al. 2023); however, no studies have yet applied LPA to the assessment of self‐management in patients with AF. The use of this analysis in this study allowed for the division of the scores of study variables accordingly, better identifying the differences between the different categories of self‐management ability in patients with AF.
The common‐sense model of self‐regulation (CSM) is a theoretical framework that explains the process of individual behaviour (Leventhal et al. 2016). It emphasises that individuals can adjust their behaviour and emotions through self‐monitoring and self‐regulation when facing health challenges in order to achieve health goals. In other words, personal goal‐setting and self‐regulation of goals can transform intentions into behaviours, which serve as critical factors that influence individual behaviour. Positive illness perception can facilitate adaptive coping strategies, thereby maintaining effective self‐management behaviours. This study adopts the CSM as a theoretical framework, focusing on disease management in patients with AF. It evaluates the impact of disease perception as a self‐regulation factor on different categories of self‐management ability, aiming to provide a reference for developing targeted health management plans for different types of patients with AF. The initial conceptual framework is shown in Figure 1. The research hypotheses are as follows:
Hypothesis 1
The self‐management ability of patients with AF can be divided into different categories.
Hypothesis 2
High level of illness perception can have a positive impact on the self‐management ability of patients with perception.
Hypothesis 3
The proposed intervention strategies vary according to the different categories of self‐management ability in patients with AF.
FIGURE 1.
Conceptual framework.
3. The Study
This study aimed to: (1) To explore the potential categories of self‐management ability of Chinese patients with AF using LPA. (2) To analyse the characteristics and the influencing factors of the self‐management ability of different categories to provide a reference for the development of targeted health management programs for different types of patients with AF.
4. Methods
4.1. Design, Study Setting and Sampling
This was a descriptive, cross‐sectional study that included a convenience sample of 208 patients with AF who participated from two hospitals in Jinan, China, from August 2022 to June 2023. The sample size of the study participants was estimated using the cross‐sectional study calculation formula: n = 2 (Z α/2 + Z β )2/δ 2. In this study, we set the effect size δ of self‐management ability at 0.3, confidence level (CI) at 95%, and efficacy at 80%. Using Z α/2 = 1.96, Z β = 0.84 and δ = 0.3, the calculated sample size was 174. Considering a possible rate of 15% loss to follow‐up, the final estimated minimum sample size was 205. Finally, in this survey, we recruited a total of 253 patients with AF, 208 participants in the final analysis, with an effective rate of 82.21%. Figure 2 shows the selection process of the participants in this study.
FIGURE 2.
The selection process of participants.
4.2. Inclusion and/or Exclusion Criteria
The inclusion criteria were as follows: (1) All participants were aged ≥ 18 years; (2) diagnosed with AF according to the “The European Society of Cardiology Guidelines for the Management of Atrial Fibrillation 2020” (Hindricks et al. 2021). In other words, AF can be clinically diagnosed when a standard 12‐lead ECG recording or a single‐lead ECG tracing (≥ 30 s) shows the disappearance of normal P waves and their replacement by a series of F waves with varying morphology, size and intervals, as well as irregular RR intervals (which indicate normal atrioventricular conduction function); and (3) able to correctly communicate and express their true feelings. The exclusion criteria were as follows: (1) patients with serious diseases refer to individuals who may have schizophrenia, bipolar disorder, malignant tumours, paralysis, or unresponsive states; and (2) patients with mental or cognitive impairment unable to participate in this study.
4.3. Data Collection
Data collection was conducted by researchers using paper questionnaires in Chinese. Before starting the investigation, the consent of the head nurse of the department was first obtained. Subsequently, the investigator entered the ward to introduce the purpose and significance of the investigation to the patients and their families. After obtaining consent, the investigator administered a questionnaire to the patients at their bedsides. For those who had difficulty completing the questionnaire, such as those with low vision, the researcher made oral inquiries and filled it in for them. After the questionnaire was completed, the investigators reviewed it promptly on the spot.
4.4. Instruments
4.4.1. Patient General Information
We designed a general information questionnaire based on a literature review of studies reporting factors associated with self‐management ability. This questionnaire included 11 items on AF patients' sex, age, place of residence, career, marital status, education level, household income level (yuan/month), disease course (years), primary caregiver, type of AF and whether radiofrequency ablation was used for the treatment of AF.
4.4.2. Patient Perceptions of Illness
We assessed patient perceptions of their illness using the Brief Illness Perception Questionnaire (BIPQ) developed and validated by Broadbent et al. (2006) for the rapid assessment of negative mood and cognitive levels. The questionnaire consists of nine items: illness cognition (five items), emotion perception (two items), illness intelligibility (one item) and one open‐ended question (patients were asked to list three factors that they thought were important to their illness). Each item is scored from 0 to 10 points, and some items are scored inversely. The total score ranges from 0 to 80 points, with higher scores representing more negative emotions and disease cognition. The scale Cronbach's α coefficient is 0.77. In this study, the Cronbach's α coefficient was 0.785.
4.4.3. Patient Self‐Management Abilities
We assessed patients' self‐management ability using the scale developed by Wen (2020), which included 22 items in five dimensions: compliance behaviour management (seven items), adverse habits management (three items), emotional and social management (five items), anticoagulant medication management (three items) and illness prevention and monitoring management (four items). The scale adopts a five‐point Likert scoring method, with scores from 1 to 5 indicating “not doing it”, “rarely doing it”, “sometimes doing it”, “often doing it” and “always doing it”, respectively. The total score on the scale ranges from 22 to 110, with higher scores indicating better self‐management of patients with AF. The Cronbach's α coefficient of the total table was 0.811, while those of the five dimensions ranged from 0.621 to 0.884. In this study, the Cronbach's α coefficient was 0.707.
4.5. Ethical Considerations
The study was approved by the ethics committee of the Shandong Provincial Hospital Affiliated to Shandong First Medical University (SWYX: No. 2023‐246) and was conducted following the tenets of the Declaration of Helsinki. All the patients included in the study were informed in advance of the study and had the right to withdraw from the study at any time.
4.6. Data Analysis
The data were analysed using IBM SPSS Statistics for Windows, version 25.0, and Mplus 8.3. Measurement data conforming to normal distributions are expressed as means ± standard deviations, while the count data are expressed as frequencies and percentages. The dimensions of self‐management ability were divided into explicit variables. The LPA was conducted using Mplus 8.3. Starting from the single‐category model, the number of categories in the model was gradually increased, and the fitness of the model was judged according to the model‐fit index until the model fit index reached the best value.
These model‐fitting indicators included the Akaike information criteria (AIC), Bayesian information criteria (BIC), and adjusted BIC (aBIC). The smaller the values of these criteria, the better the fit of the data to the model. Entropy ranges from 0 to 1, in which a value closer to 1 indicates a more accurate model classification. The Lo–Mendell–Rubin (LMR) and bootstrapped likelihood ratio tests (BLRT) were used to compare models. A significant P‐value (p < 0.05) indicated that the fit of the K‐class model was better than that of the K‐1 class model. The final number of categories was also determined considering the practical significance of the classification and the number of samples included in the categories.
Descriptive analyses of self‐management ability and illness perception in patients with AF were performed using SPSS. Group variables for each latent class of self‐management ability were analysed using the chi‐squared test or Fisher's exact probability method. Continuous variables were analysed using t‐tests, analysis of variance (ANOVA), or nonparametric tests. The factors influencing each latent class of self‐management abilities were analysed using binary logistic regression. The inspection level was set to α = 0.05.
LPA was performed based on the five dimensions of self‐management ability. The number of latent classes was gradually increased, starting with the number of latent classes 1, and the number of latent classes from 1 to 5 was established.
5. Results
5.1. Study Population and Questionnaire Response Rates
We analysed data from 220 recovered questionnaires. Among these, 208 questionnaires were valid, with an effective response rate of 94.55%. A total of 208 patients with AF were included in the study, ranging in age from 18 to 83 years (mean [SD], 65.43 [11.80]). Among them, 135 (64.9%) were male, 144 (69.2%) were aged ≥ 60 years, 107 (51.4%) lived in the city, 102 (49.0%) were retired, and 154 (74.0%) were married. As for the distribution of education level, 35 (16.8%) completed elementary school or below, 61 (29.3%) completed middle school, 84 (40.4%) completed high school or technical secondary school, and 28 (13.5%) held a college degree or higher. The number of patients with household income levels (Yuan/month) of less than 2000, 2000 to 4999 and 5000 or higher was 49 (23.6%), 51 (24.5%) and 108 (51.9%), respectively. 73 (35.1%) had a disease course of 10 years or longer. 112 (53.8%) primary caregivers were sons and daughters or parents. Of the participants, 143 (68.8%) had persistent AF and 118 (56.7%) underwent radiofrequency ablation for the AF.
Table 1 shows the average self‐management ability score as 74.07 (SD = 7.08) and the average illness perceptions score as 40.80 (SD = 6.63).
TABLE 1.
Self‐management behaviour and illness perception scores of patients with atrial fibrillation (n = 208, mean ± SD).
| Variable | Score | Items | Mean score |
|---|---|---|---|
| Total score for self‐management behaviour | 74.07 ± 7.08 | 22 | 3.37 ± 0.32 |
| Compliance medical behaviour management | 25.91 ± 2.80 | 7 | 3.70 ± 0.40 |
| Bad management | 11.86 ± 3.39 | 3 | 3.95 ± 1.13 |
| Emotional and social management | 16.61 ± 2.96 | 5 | 3.3.2 ± 0.59 |
| Anticoagulant drug management | 10.26 ± 1.67 | 3 | 3.42 ± 0.56 |
| Disease prevention and surveillance and management | 9.43 ± 1.95 | 4 | 2.36 ± 0.49 |
| Total score for illness perception | 40.80 ± 6.63 | 8 | 5.10 ± 0.83 |
| Illness cognition | 22.63 ± 5.35 | 5 | 4.53 ± 1.07 |
| Affective sensation | 13.71 ± 2.66 | 2 | 6.85 ± 1.33 |
| Understanding ability | 4.47 ± 2.08 | 1 | 4.47 ± 2.08 |
5.2. Latent Classes of Self‐Management Ability in Patients With AF
5.2.1. Latent Profile Analysis of Self‐Management Ability in Patients With AF
Latent profile analysis was performed based on five dimensions of self‐management ability. The number of latent classes was gradually increased, starting with the number of latent classes 1, and the number of latent classes from 1 to 5 was established in turn.
The model‐fit metrics are shown in Table 2. As the number of classes increased, the values of AIC, BIC and aBIC gradually decreased. When two latent classes were retained, the entropy value was the largest, and the p‐values of both LMR and BLRT were statistically significant; however, when three latent classes were retained, the model was not statistically significant despite BLRT < 0.001, but also with LMR > 0.05. When the four latent classes were retained, although the AIC, BIC and aBIC were all low (BLRT and LMR < 0.05), the probability of one class was only 5.8%, the sample size was 12, classification accuracy was poor, and there was no taxonomic significance. Therefore, the two‐class model was selected as the optimal classification result for the self‐management ability of patients with AF.
TABLE 2.
Class information index.
| Model | K | AIC | BIC | aBIC | Entropy | LMR | BLRT | Class probabilities |
|---|---|---|---|---|---|---|---|---|
| 1 | 10 | 4845.896 | 4879.272 | 4847.587 | 1 | |||
| 2 | 16 | 4699.662 | 4753.062 | 4702.367 | 0.938 | 0.0037 | < 0.001 | 0.111/0.889 |
| 3 | 22 | 4636.392 | 4709.818 | 4640.111 | 0.886 | 0.2008 | < 0.001 | 0.111/0.159/0.731 |
| 4 | 28 | 4589.648 | 4683.099 | 4594.381 | 0.851 | 0.0074 | < 0.001 | 0.058/0.663/0.135/0.144 |
| 5 | 34 | 4562.145 | 4675.622 | 4567.893 | 0.854 | 0.1919 | < 0.001 | 0.058/0.341/0.159/0.111/0.332 |
5.2.2. Characteristics and Naming of Latent Classes of Self‐Management Ability in Patients With AF
The two latent classes of self‐management ability in patients with AF had different scores in the five dimensions and exhibited different characteristics. The mean score for each item in Class 1 (C1) was generally low; therefore, this category was defined as having low self‐management ability. The dimension items in Class 2 (C2) were generally high; therefore, they comprised high self‐management ability. The probabilities of the two classes in the sample were 11.1% (n = 23) and 88.9% (n = 185), respectively. The specific features are shown in Figure 3. The scores for the self‐management ability of patients in the two latent classes are shown in Table 3.
FIGURE 3.
Scores of two different self‐management ability clusters.
TABLE 3.
Score of self‐management ability of patients with different categories of AF (n = 208, Mean ± SD).
| Variables | Groups | |
|---|---|---|
| 1 | 2 | |
| Total score for self‐management ability | 60.61 ± 6.58 | 75.75 ± 5.07 |
| Compliance medical behaviour management | 21.30 ± 3.56 | 26.48 ± 2.08 |
| Bad management | 12.61 ± 2.66 | 11.77 ± 3.47 |
| Emotional and social management | 10.87 ± 2.60 | 17.32 ± 2.10 |
| Anticoagulant drug management | 7.87 ± 1.39 | 10.56 ± 1.45 |
| Disease prevention and surveillance and management | 7.96 ± 1.94 | 9.62 ± 1.88 |
5.3. Univariate Analysis of Self‐Management Ability Types in Patients With AF
Univariate analysis revealed significant differences in the distribution of self‐management ability classes among patients with different places of residence, disease durations, primary caregivers, and illness perceptions (Table 4).
TABLE 4.
Social‐demographic information of patients with AF (n = 208).
| Variable | N | Groups, n (%) | Test statistic | p | |
|---|---|---|---|---|---|
| Class 1 (n = 23) | Class 2 (n = 185) | ||||
| Gender | |||||
| Female | 73 | 11 (15.1) | 62 (84.9) | χ 2 = 1.840 | 0.175 |
| Male | 135 | 12 (8.9) | 123 (91.1) | ||
| Age | |||||
| < 60 | 64 | 7 (10.9) | 57 (89.1) | χ 2 = 0.001 | 0.971 |
| ≥ 60 | 144 | 16 (11.1) | 128 (88.9) | ||
| Place of residence | |||||
| Countryside | 66 | 7 (10.6) | 59 (89.4) | χ 2 = 6.364 | 0.042 |
| County town | 35 | 8 (22.9) | 27 (77.1) | ||
| City | 107 | 8 (7.5) | 99 (92.5) | ||
| Career | |||||
| Retired | 102 | 7 (6.9) | 95 (93.1) | χ 2 = 3.804 | 0.149 |
| Be on the job | 35 | 6 (17.1) | 29 (82.9) | ||
| Unemployed | 71 | 10 (14.1) | 61 (85.9) | ||
| Marital status | |||||
| Married | 154 | 20 (13) | 134 (87) | χ 2 = 2.245 | 0.134 |
| Unmarried/divorced/widowed | 54 | 3 (5.6) | 51 (94.4) | ||
| Education level | |||||
| Elementary school or below | 35 | 7 (20) | 28 (80) | χ 2 = 4.249 | 0.225 |
| Middle school | 61 | 7 (11.5) | 54 (88.5) | ||
| High school or technical secondary school | 84 | 8 (9.5) | 76 (90.5) | ||
| College degree or higher | 28 | 1 (3.6) | 27 (96.4) | ||
| Household income level (Yuan/month) | |||||
| < 2000 | 49 | 6 (12.2) | 43 (87.8) | χ 2 = 0.152 | 0.927 |
| 2000–5000 | 51 | 5 (9.8) | 46 (90.2) | ||
| > 5000 | 108 | 12 (11.1) | 96 (88.9) | ||
| Disease course (year) | |||||
| < 1 | 63 | 3 (4.8) | 60 (95.2) | χ 2 = 8.968 | 0.024 |
| 1–5 | 44 | 2 (4.5) | 42 (95.5) | ||
| 5–10 | 28 | 5 (17.9) | 23 (82.1) | ||
| > 10 | 73 | 13 (17.8) | 60 (82.2) | ||
| Primary caregiver | |||||
| Spouse | 65 | 12 (18.5) | 53 (81.5) | χ 2 = 6.073 | 0.048 |
| Sons and daughters or parents | 112 | 10 (8.9) | 102 (91.1) | ||
| Non‐family members | 31 | 1 (3.2) | 31 (96.8) | ||
| Type of AF | |||||
| Persistent AF | 143 | 19 (13.3) | 124 (86.7) | χ 2 = 2.312 | 0.128 |
| Paroxysmal AF | 65 | 4 (6.2) | 61 (93.8) | ||
| Whether using radiofrequency ablation of AF | |||||
| Yes | 118 | 17 (14.4) | 101 (85.6) | χ 2 = 3.110 | 0.078 |
| No | 90 | 6 (6.7) | 84 (93.3) | ||
| BIPQ (Mean ± SD) | — | 47.74 ± 7.33 | 39.94 ± 6.02 | t = 5.717 | < 0.001 |
5.4. Multivariate Analysis of Self‐Management Ability in Patients With AF
The results of the logistic regression model showed that disease duration, primary caregivers, and illness perceptions were related to the self‐management ability classification of patients with AF (all p < 0.05). The variable assignments are shown in Table 5, and the binary logistic regression results are shown in Table 6.
TABLE 5.
Variable valuation.
| Variable | Valuation |
|---|---|
| Self‐management ability | Low self‐management ability type = 1, Good self‐management ability type = 2 |
| Place of residence | Countryside = 0, County town = 1, City = 2 |
| Disease course (year) | < 1 year = 0, 1 ≤ time < 5 = 1, 5 ≤ time < 10 = 2, time ≥ 10 = 3 |
| Primary caregiver | Spouse = 0, Sons and daughters or parents = 1, Non‐family members = 2 |
| BIPQ | Measured value |
TABLE 6.
Logistic regression analysis of 2 latent self‐management behaviour classes.
| Item | β | SE | Wald χ 2 | p | OR | 95% CI |
|---|---|---|---|---|---|---|
| Place of residence | 0.543 | 0.349 | 2.415 | 0.120 | 1.721 | (0.868, 3.413) |
| Disease course (year) | −0.864 | 0.287 | 9.059 | 0.003 | 0.421 | (0.240, 0.740) |
| Primary caregiver | 1.400 | 0.532 | 6.913 | 0.009 | 4.055 | (1.428, 11.514) |
| BIPQ | −0.287 | 0.062 | 21.447 | < 0.001 | 0.751 | (0.665, 0.848) |
| Constant | 13.914 | 3.009 | 21.379 | < 0.001 |
6. Discussion
Our study is the first cross‐sectional study of the status, LPA, and influencing factors of self‐management ability in Chinese patients with AF. It was found that the self‐management ability of Chinese patients with AF was above the medium level, and two potential categories of self‐management ability were categorised as low (C1) and high (C2). By analysing the factors influencing the potential categories of self‐management ability, we observed that the following variables predicted two categories: disease durations, primary caregivers, and illness perceptions. Moreover, our study reflects methodological advancements that bring a novel and unique perspective to understanding the relationship between different classes of self‐management ability.
The mean self‐management ability score in patients with AF was (3.37 ± 0.32), which was at the upper average level compared with the mid‐score of 3 points. A previous study measuring self‐management ability in patients with AF observed deficiencies in knowledge and self‐management behaviours (McCabe et al. 2008); however, the measurement tool differed from that used in the present study. Therefore, our results cannot be directly compared with those of previous studies. The measurement tool used in this study was a localised specific AF patient self‐management ability assessment scale developed by Chinese scholars based on the Chinese cultural background, with an internal consistency coefficient of 0.811 and high credibility (Wen 2020).
In this study, the dimensions of disease prevention and surveillance management ranked lowest on the self‐management scale, similar to the findings reported by Chen et al. (2020). This may be related to the low educational level of most of the patients in this study, as 86.5% of patients had a high school or secondary school education. Previous studies (Wireklint et al. 2021; Amaral et al. 2021; Hernández Madrid et al. 2016) have reported that patients with low education levels have poor disease awareness and disease management, a low understanding of disease knowledge, limited ways to actively obtain information about disease treatment, and an inability to self‐monitor and prevent further disease development, thus affecting the establishment of healthy behaviours. Thus, healthcare workers should use different forms and levels of health education to meet the needs of patients at all literacy levels to access and understand disease management, improve disease monitoring and prevention, and establish positive self‐management abilities.
This study identified two potential categories of self‐management ability in patients with AF, consistent with the findings reported by Guo et al. (2020). In other words, self‐management ability varies across different individuals within the same population, which may be categorised into several types. The self‐management abilities among individuals in the same population with AF in this study were categorised as low (C1) and high (C2). The high self‐management ability category included 88.9% of participants, indicating that most patients with AF demonstrated relatively high self‐management ability.
In the category of high self‐management ability, the high score for the dimension related to unhealthy habit management indicated that patients with AF and high self‐management ability rarely had unhealthy habits such as smoking, drinking alcohol, and drinking strong tea, consistent with the results reported by Wang (2020). One explanation for this finding may be that most patients with AF in this category reside in the city. The dangers of unhealthy habits are better publicised in cities than in rural areas. For example, China's cities have strong tobacco control in public places, with no‐smoking signs and the dangers of smoking posted in many places, which makes the urban population more health‐conscious and less likely to smoke. Thus, urban populations have fewer unhealthy habits.
Among participants with low self‐management ability, the lowest‐scoring dimension was disease prevention and surveillance management, while the highest‐scoring dimension was unhealthy habit management. This finding suggests a large variation in the scores of these dimensions and a large imbalance in the different aspects of patients' self‐management ability, similar to the results for patients with chronic diseases (Ladner et al. 2022). Therefore, healthcare workers should develop appropriate health education to address the weaknesses of self‐management ability in these patients and to improve their self‐management ability in other dimensions. In addition, scores on the unhealthy habit management dimension for patients with low self‐management ability were much higher than those with high self‐management ability. Therefore, patients with AF and high self‐management may have worse habits and should be a priority population for interventions. This finding may be related to the predominance of female patients in the low self‐management category. A previous study reported that Chinese women have relatively healthy lifestyles and that most do not have potentially harmful habits such as smoking and heavy alcohol consumption (Du et al. 2021).
Compared with the traditional self‐management ability scores, the classification of self‐management abilities of patients with AF used in the present study not only helps to determine the level of self‐management ability of patients but also helps to identify the characteristics of different categories of patients, which helps healthcare professionals to implement more targeted interventions to improve self‐management in these patients.
The results of this study showed that disease duration was a factor influencing the latent classes of self‐management abilities in patients with AF. Thus, patients with a longer disease duration were less likely to develop a higher class of self‐management ability, contrary to the findings of a previous study on patients with obstructive sleep apnea‐hypopnea syndrome (Yu et al. 2022). This may be related to the fact that 69.23% of the patients in the present study were older adults, and more than one‐third had a disease duration of > 10 years. Due to limited information sources and frequent neglect of care for older adult patients, the longer they are ill, the more likely these patients are to have negative emotions during long‐term disease management (such as lifelong lifestyle and drug interventions), resulting in decreased treatment compliance and consequently affecting their self‐management. In addition, as the disease duration increases, patients experience more pain from chronic complications, reduced hope for recovery, and decreased confidence in their self‐management ability (Ladner et al. 2022).
Patients with shorter disease durations were more active in acquiring AF‐related knowledge (including information on diets, medications, exercise, risk factors, disease monitoring, review schedules, etc.) and taking the initiative to fully mobilise or utilise internal and external resources to generate high self‐management ability (Shan et al. 2020). Medical staff should enhance communication with patients with longer disease durations, provide them with effective options to cope with their diseases and help them build confidence and actively participate in the management of their AF treatment.
In this study, non‐family caregivers had a facilitative effect on the development of self‐management abilities in patients with AF, similar to the findings reported by Tian (2021). In the present study, the non‐family caregivers of patients were more often professional care assistants. Care assistants are managed by nursing companies and are posted to positions only after passing training. Thus, these assistants have relatively more relevant knowledge and skills compared with patients' families. As a result, they can provide patients with fine‐standard, high‐quality, and efficient self‐management guidance and play a role in encouraging patients to improve treatment compliance.
In this study, 177 (85.1%) patients with family members as primary caregivers had low self‐management abilities, which may be related to the time problems of the patients' family members. Patients with AF require regular visits to outpatient clinics for check‐ups and maintenance medications. Family members may be too busy with the patient's daily care to focus on managing their conditions, leading to a lack of patient self‐management. Therefore, the self‐management abilities of patients whose primary caregivers are family members should not be ignored in clinical practice. Nursing staff should not only enhance guidance and assistance for these patients but also encourage patients' family caregivers to promptly care for and help the patients, and also strengthen their companionship and support to reduce patients' psychological helplessness and loneliness and improve their self‐management ability.
Previous studies have confirmed that low patient self‐management ability is associated with a poor understanding of the disease (Kim et al. 2022; Xia et al. 2022). The results of the present study found that higher disease perception in patients with AF (i.e., the disease is perceived as more threatening by patients) had a significant impact on self‐management ability, which is similar to the previous findings in patients with type 2 diabetes (Bezo et al. 2020). Bezo et al. (2020) further reported that excessive illness perceptions lead to incorrect or negative perceptions of illness and its treatment and lead to pessimistic emotional responses by the patients, which can cause them to engage less in self‐management practices. These findings suggest the critical need to assess patient illness perceptions to design effective self‐management strategies.
The results of a study including Indonesian patients with chronic renal failure reported that appropriate interventions can improve perceptions of early patient illnesses (Suarilah and Lin 2022). Therefore, we suggest that doctors and nurses provide group‐based training and social support or establish remote online counselling platforms to help patients with early AF better recognise and understand the disease, thus facilitating an early emphasis on cultivating self‐management ability. Sawyer et al. (2019) proposed that a multidisciplinary approach can help improve patients' perceptions of a positive illness. For example, peer support groups including patients with increased disease knowledge in multiple wards can help convey and maintain positive illness perceptions to patients with AF, reduce their uncertainty about the multiple complications of the disease to enhance their disease self‐efficacy, and improve their self‐management ability.
7. Strengths and Limitations
A significant strength of our study is that the results highlight the utility of LCA in elucidating the heterogeneity of self‐management abilities in Chinese patients with AF. To our knowledge, few current studies have reported the LCA of self‐management ability in patients with AF. As outlined in the discussion, our study was innovative in several senses, such as methodology and perspective, which bring a different perspective to our understanding of self‐management in Chinese patients with AF.
This study has some limitations. First, the cross‐sectional design prevented the determination of the causal relationship between the classes of self‐management ability and influencing factors. Future longitudinal studies are needed to assess causation. Second, the participants were enrolled in two hospitals in the same city in China; thus, the findings may have been influenced by geographical and cultural factors, which may have affected the generalizability of the findings. Finally, the questionnaire was completed only from the perspective of patients with AF, and the views represented were one‐sided and subjective. Future studies considering relevant assessment data from the perspectives of the patient's spouses, children, parents, and caregivers are needed to objectively measure different classes of patients' self‐management abilities.
8. Recommendations for Further Research
Additional research is needed to validate the findings of the present study in a broader range of populations and to address the study limitations, including longitudinal studies to directly assess causation and consider the perspectives of the patient's spouse, children, parents, and caregivers to assess different classes of self‐management abilities more objectively among patients with AF.
9. Implications for Policy and Practice
The study results emphasise the utility of LCA in elucidating the heterogeneity of self‐management abilities in patients with AF. The influencing factors identified in this study can be used to personalise the design and implementation of self‐management intervention programs for patients with AF. We propose the development of more targeted interventions based on the characteristics of the different categories of patients with AF, with a focus on the patient's disease course, primary caregiver, and illness perceptions.
10. Conclusion
This study demonstrated the relatively high self‐management ability of Chinese patients with AF. The findings also confirmed two latent classes of self‐management ability in patients: low self‐management—the best type of bad addiction management—and high self‐management. Among them, high self‐management ability accounted for the largest proportion of patients. Disease duration, primary caregiver, and illness perception were important factors associated with the classes of self‐management ability in patients with AF. Patients with AF with high self‐management ability had a shorter disease duration, less family care, and more positive illness perceptions compared with patients with low self‐management.
Author Contributions
R.A. and Y.W. developed the research design. R.A. wrote the manuscript. J.G., X.W. and Y.Z. were responsible for data collection. R.A. data analysis. Y.W. critically revised the manuscript. J.G. and Y.W. have checked to make sure that our submission conforms as applicable to the Journal.
Funding
The authors have nothing to report.
Ethics Statement
The study was approved by the ethics committee of Shandong provincial hospital (SWYX:NO. 2023‐246) and was conducted following the tenets of the Declaration of Helsinki. All the patients included in the study were informed in advance of the study and had the right to withdraw from the study at any time.
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgements
Thanks to all the participants and researchers involved in this study.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
- Amaral, V. R. S. , Ribeiro Í. J. S., and Rocha R. M.. 2021. “Factors Associated With Knowledge of the Disease in People With Type 2 Diabetes Mellitus.” Investigación y Educación en Enfermería 39, no. 1: e02. 10.17533/udea.iee.v39n1e02. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ashley, L. , Marti J., Jones H., Velikova G., and Wright P.. 2015. “Illness Perceptions Within 6 Months of Cancer Diagnosis Are an Independent Prospective Predictor of Health‐Related Quality of Life 15 Months Post‐Diagnosis.” Psycho‐Oncology 24, no. 11: 1463–1470. 10.1002/pon.3812. [DOI] [PubMed] [Google Scholar]
- Bezo, B. H. , Huang Y. T., and Lin C. C.. 2020. “Factors Influencing Self‐Management Behaviours Among Patients With Type 2 Diabetes Mellitus in the Solomon Islands.” Journal of Clinical Nursing 29, no. 5–6: 852–862. 10.1111/jocn.15139. [DOI] [PubMed] [Google Scholar]
- Broadbent, E. , Petrie K. J., Main J., and Weinman J.. 2006. “The Brief Illness Perception Questionnaire.” Journal of Psychosomatic Research 60, no. 6: 631–637. 10.1016/j.jpsychores.2005.10.020. [DOI] [PubMed] [Google Scholar]
- Cerasuolo, J. O. , Montero‐Odasso M., Ibañez A., Doocy S., Lip G. Y., and Sposato L. A.. 2017. “Decision‐Making Interventions to Stop the Global Atrial Fibrillation‐Related Stroke Tsunami.” International Journal of Stroke 12, no. 3: 222–228. 10.1177/1747493016687579. [DOI] [PubMed] [Google Scholar]
- Chen, Y. , Fu G., Liang F., Wei J., He J., and Bai J.. 2020. “Symptoms, Hope, Self‐Management Behaviors, and Quality of Life Among Chinese Preoperative Patient With Symptomatic Valvular Heart Diseases.” Journal of Transcultural Nursing 31, no. 3: 284–293. 10.1177/1043659619864157. [DOI] [PubMed] [Google Scholar]
- Ding, Y. , Li F., Fan Z., et al. 2023. “Factors Influencing Self‐Management Behavior During the “Blanking Period” in Patients With Atrial Fibrillation: A Cross‐Sectional Study Based on the Information‐Motivation‐Behavioral Skills Model.” Heart & Lung 58: 62–68. 10.1016/j.hrtlng.2022.11.006. [DOI] [PubMed] [Google Scholar]
- Du, X. , Guo L., Xia S., et al. 2021. “Atrial Fibrillation Prevalence, Awareness and Management in a Nationwide Survey of Adults in China.” Heart 107, no. 7: 535–541. 10.1136/heartjnl-2020-317915. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Goulart, A. C. , Olmos R. D., Santos I. S., et al. 2022. “The Impact of Atrial Fibrillation and Long‐Term Oral Anticoagulant Use on All‐Cause and Cardiovascular Mortality: A 12‐Year Evaluation of the Prospective Brazilian Study of Stroke Mortality and Morbidity.” International Journal of Stroke 17, no. 1: 48–58. 10.1177/1747493021995592. [DOI] [PubMed] [Google Scholar]
- Groh, C. A. , Faulkner M., Getabecha S., et al. 2019. “Patient‐Reported Triggers of Paroxysmal Atrial Fibrillation.” Heart Rhythm 16, no. 7: 996–1002. 10.1016/j.hrthm.2019.01.027. [DOI] [PubMed] [Google Scholar]
- Guo, L. , Liu Y., Zhu Y., and Wei M.. 2020. “Identification of Health Behaviour Clusters Among People at High Risk of Stroke: A Latent Class Profile Analysis.” Journal of Advanced Nursing 76, no. 11: 3039–3047. 10.1111/jan.14523. [DOI] [PubMed] [Google Scholar]
- Hernández Madrid, A. , Potpara T. S., Dagres N., et al. 2016. “Differences in Attitude, Education, and Knowledge About Oral Anticoagulation Therapy Among Patients With Atrial Fibrillation in Europe: Result of a Self‐Assessment Patient Survey Conducted by the European Heart Rhythm Association.” Europace 18, no. 3: 463–467. 10.1093/europace/euv448. [DOI] [PubMed] [Google Scholar]
- Hindricks, G. , Potpara T., Dagres N., et al. 2021. “2020 ESC Guidelines for the Diagnosis and Management of Atrial Fibrillation Developed in Collaboration With the European Association for Cardio‐Thoracic Surgery (EACTS): The Task Force for the Diagnosis and Management of Atrial Fibrillation of the European Society of Cardiology (ESC) Developed With the Special Contribution of the European Heart Rhythm Association (EHRA) of the ESC.” European Heart Journal 42, no. 5: 373–498. 10.1093/eurheartj/ehaa612. [DOI] [PubMed] [Google Scholar]
- Hsieh, H. L. , Kao C. W., Cheng S. M., and Chang Y. C.. 2021. “A Web‐Based Integrated Management Program for Improving Medication Adherence and Quality of Life, and Reducing Readmission in Patients With Atrial Fibrillation: Randomized Controlled Trial.” Journal of Medical Internet Research 23, no. 9: e30107. 10.2196/30107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jiang, S. , Zhang X., Li X., et al. 2023. “Exploring Health Literacy Categories in Patients With Heart Failure: A Latent Class Analysis.” Journal of Cardiovascular Nursing 38, no. 1: 13–22. 10.1097/JCN.0000000000000889. [DOI] [PubMed] [Google Scholar]
- Jobst, S. , Leppla L., and Köberich S.. 2020. “A Self‐Management Support Intervention for Patients With Atrial Fibrillation: A Randomized Controlled Pilot Trial.” Pilot and Feasibility Studies 6: 87. 10.1186/s40814-020-00624-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kenning, C. , Coventry P. A., Gibbons C., Bee P., Fisher L., and Bower P.. 2015. “Does Patient Experience of Multimorbidity Predict Self‐Management and Health Outcomes in a Prospective Study in Primary Care?” Family Practice 32, no. 3: 311–316. 10.1093/fampra/cmv002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kim, H. , Sereika S. M., Albert S. M., Bender C. M., and Lingler J. H.. 2022. “Do Perceptions of Cognitive Changes Matter in Self‐Management Behaviors Among Persons With Mild Cognitive Impairment?” Gerontologist 62, no. 4: 577–588. 10.1093/geront/gnab129. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ladner, J. , Alshurafa S., Madi F., et al. 2022. “Factors Impacting Self‐Management Ability in Patients With Chronic Diseases in the United Arab Emirates, 2019.” Journal of Comparative Effectiveness Research 11, no. 3: 179–192. 10.2217/cer-2021-0177. [DOI] [PubMed] [Google Scholar]
- Leventhal, H. , Phillips L. A., and Burns E.. 2016. “The Common‐Sense Model of Self‐Regulation (CSM): A Dynamic Framework for Understanding Illness Self‐Management.” Journal of Behavioral Medicine 39, no. 6: 935–946. 10.1007/s10865-016-9782-2. [DOI] [PubMed] [Google Scholar]
- Lippi, G. , Sanchis‐Gomar F., and Cervellin G.. 2021. “Global Epidemiology of Atrial Fibrillation: An Increasing Epidemic and Public Health Challenge.” International Journal of Stroke 16, no. 2: 217–221. 10.1177/1747493019897870. [DOI] [PubMed] [Google Scholar]
- McCabe, P. J. , Schad S., Hampton A., and Holland D. E.. 2008. “Knowledge and Self‐Management Behaviors of Patients With Recently Detected Atrial Fibrillation.” Heart & Lung 37, no. 2: 79–90. 10.1016/j.hrtlng.2007.02.006. [DOI] [PubMed] [Google Scholar]
- Migdady, I. , Russman A., and Buletko A. B.. 2021. “Atrial Fibrillation and Ischemic Stroke: A Clinical Review.” Seminars in Neurology 41, no. 4: 348–364. 10.1055/s-0041-1726332. [DOI] [PubMed] [Google Scholar]
- Rakhshan, M. , Najafi H., and Valizadeh G. A.. 2019. “Lifestyle of Patients With Atrial Fibrillation Following Self‐Management Interventions: A Randomized Clinical Trial.” Journal of Caring Sciences 8, no. 2: 83–88. 10.15171/jcs.2019.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rosman, L. , Armbruster T., Kyazimzade S., et al. 2021. “Effect of a Virtual Self‐Management Intervention for Atrial Fibrillation During the Outbreak of COVID‐19.” Pacing and Clinical Electrophysiology 44, no. 3: 451–461. 10.1111/pace.14188. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sawyer, A. T. , Harris S. L., and Koenig H. G.. 2019. “Illness Perception and High Readmission Health Outcomes.” Health Psychology Open 6, no. 1: 2055102919844504. 10.1177/2055102919844504. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shan, Z. , Huang H., and Tang S.. 2020. “Effect of Diversified and Staged Health Education on the Compliance of Elderly Patients With Coronary Heart Disease.” Annals of Palliative Medicine 9, no. 6: 4074–4080. 10.21037/apm-20-1995. [DOI] [PubMed] [Google Scholar]
- Shen, Q. , Zhang C., Liu T., Zhu H., Zhang Z., and Li C.. 2022. “A Descriptive Cross‐Sectional Study of Self‐Management in Patients With Nonvalvular Atrial Fibrillation.” Medicine 101, no. 40: e30781. 10.1097/MD.0000000000030781. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Suarilah, I. , and Lin C. C.. 2022. “Factors Influencing Self‐Management Among Indonesian Patients With Early‐Stage Chronic Kidney Disease: A Cross‐Sectional Study.” Journal of Clinical Nursing 31, no. 5–6: 703–715. 10.1111/jocn.15930. [DOI] [PubMed] [Google Scholar]
- Tian, H. L. 2021. “Study on Self‐Management Intervention Based on the Theory of Enablement to Young and Middle‐aged Stroke Patients With Hemiplegia [Master, Zhengzhou University].” 10.27466/d.cnki.gzzdu.2021.001431. [DOI]
- Wahlström, M. , and Stelling Risom S.. 2022. “The Importance of Self‐Management Programmes in a Comprehensive Care Approach for Patients With Atrial Fibrillation.” European Journal of Cardiovascular Nursing 21, no. 2: 95–96. 10.1093/eurjcn/zvab068. [DOI] [PubMed] [Google Scholar]
- Wang, G. N. 2020. “Analysis of Influencing Factors of Self‐Management in Patients With Atrial Fibrillation Taking Warfarin Based on the Theory of Planned Behavior [Master, Qingdao University].” 10.27262/d.cnki.gqdau.2020.002276. [DOI]
- Wang, K. K. P. , Liu W., Chew S. T. H., Ti L. K., and Shen L.. 2021. “New‐Onset Atrial Fibrillation After Cardiac Surgery Is a Significant Risk Factor for Long‐Term Stroke: An Eight‐Year Prospective Cohort Study.” Journal of Cardiothoracic and Vascular Anesthesia 35, no. 12: 3559–3564. 10.1053/j.jvca.2021.07.003. [DOI] [PubMed] [Google Scholar]
- Wang, Z. , Chen Z., Wang X., et al. 2018. “The Disease Burden of Atrial Fibrillation in China From a National Cross‐Sectional Survey.” American Journal of Cardiology 122, no. 5: 793–798. 10.1016/j.amjcard.2018.05.015. [DOI] [PubMed] [Google Scholar]
- Wen, X. 2020. “Development, Reliability and Validity of the Self Management Ability Scale for Atrial Fibrillation Patients [Master, Nanjing University].” 10.27235/d.cnki.gnjiu.2020.000718. [DOI]
- Wilson, R. E. , Burton L., Marini N., et al. 2024. “Assessing the Impact of Atrial Fibrillation Self‐Care Interventions: A Systematic Review.” American Heart Journal Plus: Cardiology Research and Practice 43: 100404. 10.1016/j.ahjo.2024.100404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wireklint, P. , Hasselgren M., Montgomery S., et al. 2021. “Factors Associated With Knowledge of Self‐Management of Worsening Asthma in Primary Care Patients: A Cross‐Sectional Study.” Journal of Asthma 58, no. 8: 1087–1093. 10.1080/02770903.2020.1753209. [DOI] [PubMed] [Google Scholar]
- Xia, B. Y. , Yu J. M., and Wu X. Y.. 2022. “Illness Uncertainty and Its Associated Factors in Coronary Heart Disease Patients With Heart Stent Implantation: A Cross‐Sectional Study.” Annals of Palliative Medicine 11, no. 7: 2410–2421. 10.21037/apm-22-528. [DOI] [PubMed] [Google Scholar]
- Yu, H. , Gao Y., Tong T., et al. 2022. “Self‐Management Behavior, Associated Factors and Its Relationship With Social Support and Health Literacy in Patients With Obstructive Sleep Apnea‐Hypopnea Syndrome.” BMC Pulmonary Medicine 22, no. 1: 352. 10.1186/s12890-022-02153-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang, M. , Chen J., Gao C., Gu S., Yang P., and Xu Z.. 2022. “Application Value of Whole‐Course Health Management for Patients With Nonvalvular Atrial Fibrillation With Oral Warfarin Treatment.” American Journal of Translational Research 14, no. 5: 3269–3277. [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 data that support the findings of this study are available from the corresponding author upon reasonable request.