Health behaviours of patients with atrial fibrillation – a preliminary study

INTRODUCTION

Atrial fibrillation (AF) is one of the most prevalent cardiovascular disorders, particularly among the elderly [1]. It is characterised by abnormal and uncoordinated atrial excitation of the myocardium, often accompanied by rapid ventricular action [2]. Data from the Framingham Heart Study (FHS) indicates a threefold increase in the incidence of AF over the past 50 years. According to the Global Burden of Disease project, the prevalence of AF in 2016 was approximately 46.3 million cases worldwide [3, 4]. Common complications of AF include thromboembolic events such as stroke, pulmonary embolism, and myocardial failure, which reduce quality of life and necessitate anticoagulant therapy [5].
Health behaviours are defined as any actions or inactions that have a significant impact on an individual’s health and well-being. They can be classified into health-promoting behaviours, which support the maintenance of health and disease prevention, and health-detracting behaviours, which contribute to the onset of health disorders and negative consequences in emotional, physical, and psychosocial domains [6, 7].
Health behaviours encompass a variety of actions, habits, and practices that can either promote or harm health. In the case of AF, changing these behaviours is a crucial element of holistic treatment. Unhealthy habits such as smoking, excessive alcohol consumption, and poor diet contribute to the progression of AF, while promoting healthy habits like regular exercise, a balanced diet, and weight control can improve patient outcomes [8-10]. It is also important to treat coexisting conditions, such as hypertension, diabetes, sleep apnoea, and coronary artery disease, which can affect the course of AF [11].
Research on health behaviours is a significant focus for scientists globally, whose studies concentrate on various health-related activities undertaken by different population groups who view health as a paramount value [12]. Such activities include regular weight monitoring, adherence to proper dietary guidelines, limiting the use of stimulants, engaging in regular physical activity tailored to individual capabilities, stress and emotion management, and preventive measures in the broadest sense [13]. The American Heart Association’s guidelines emphasise the importance of modifying health behaviours and the role of both primary and secondary prevention as essential components of therapeutic strategies, alongside traditional treatments [14]. Comprehensive treatment of AF according to the guidelines of the European Society of Cardiology (ESC) and the European Association of Cardio-Thoracic Surgery (EACTS) emphasises the importance of a holistic approach. This approach includes managing cardiovascular risk factors and comorbid conditions, which involves modifying health behaviours. In the management of AF, a key component within the “C” pathway is the management of cardiovascular risk factors and the implementation of lifestyle changes for patients. The aim of these modifications is to reduce the disease burden and alleviate the symptoms of AF [15].
The most important recommendations include weight reduction, especially for obese patients who are candidates for invasive AF treatment. Obesity significantly increases the risk of developing AF and its related complications, such as stroke and thromboembolic events. Therefore, patients with AF and obesity are advised to systematically reduce their weight in conjunction with controlling other risk factors [16].
Another crucial aspect is the limitation of excessive alcohol and caffeine consumption, which can trigger episodes of AF. Regular, moderate physical activity is also recommended because it can help prevent AF recurrences, while avoiding intensive endurance exercises that may promote the development of this arrhythmia. Additionally, AF patients are advised to undergo screening for obstructive sleep apnoea (OSA) [17, 18].
Lifestyle changes in the treatment of AF also include systematic blood pressure control to reduce the risk of AF recurrences and complications such as stroke and bleeding. Combining these lifestyle changes with pharmacological therapy and medical interventions creates a comprehensive approach to managing AF, which improves the quality of life and long-term treatment outcomes for patients [19, 20].
The main aim of the study was to determine the index of health behaviours of patients with AF.

MATERIAL AND METHODS

The study included 236 patients diagnosed with AF hospitalised in the Cardiology Department of the Municipal Specialist Hospital in Radom, Poland. The study employed a prospective research model in which patients were enrolled based on established criteria. The researchers did not interfere with the diagnostic or therapeutic processes of the patients. The research took place between July and December 2023, utilising a diagnostic survey method and questionnaire technique. Throughout the survey, principles of anonymity, protection of respondents’ privacy, and the option to withdraw from the survey at any time were upheld. To ensure consistency in measurements, analyses, and interpretation of the data obtained, all assessments were carried out by 2 independent researchers.
Ethics Committee approval was not obtained for this study. The Ethics Committee provided an opinion stating that survey studies, which do not interfere with the privacy of participants and do not pose any risk to them, can be conducted without the need for formal approval from the Committee. This study was conducted in accordance with the guidelines set forth in the Declaration of Helsinki. All research procedures carried out were in compliance with ethical guidelines.
The minimum required sample size for this study was 384 patients, calculated based on a 95% confidence level and a 5% margin of error [21]. Although the sample consists of 236 respondents, which is smaller than the required number, the data obtained are valuable and significant. This group is sufficient to draw preliminary conclusions about the health behaviours of patients with AF. Practical limitations, such as patient availability, research resources, and time, significantly impacted the ability to collect data. Future studies are planned to be conducted on a larger scale.

INCLUSION CRITERIA

The inclusion criteria for the study included patients over 18 years of age with a diagnosis of AF, who provided informed consent to participate.

EXCLUSION CRITERIA

The exclusion criteria encompassed factors such as anticipated lack of patient cooperation, refusal to participate in the survey, and other comorbidities that could impact the study results (active or past cancer disease, alcohol or substance abuse, and anticipated survival of less than 12 months).

QUESTIONNAIRE DESIGN

The assessment of health behaviours in this study was conducted using Juczyński’s validated standardised tool, the Health Behaviour Inventory (HBI) [22]. This instrument is a subjective assessment tool comprising 24 statements that cover a wide range of health-related behaviours. Through these statements, participants reflected on their habits and actions concerning physical well-being, preventive measures, lifestyle practices, and mental outlook.
The Health Behaviour Inventory enabled the calculation of an intensity index for health-promoting behaviours (ZZ) as well as for 4 specific categories of health-related behaviours.
These categories included the following:
• Good Eating Habits (PN1): Assessing dietary choices, portion sizes, meal frequency, and nutritional quality to determine the extent to which participants engaged in healthy eating practices.
• Preventive Behaviour (ZP): Evaluating actions taken by individuals to prevent illnesses, injuries, or complications, such as adherence to vaccinations, screenings, and health check-ups.
• Health Practices (PZ): Examining habits related to physical activity, rest, proper hygiene, and other health-promoting behaviours that contribute to overall well-being.
• Positive Mental Attitude (PN2): Focusing on emotional well-being, stress management, coping strategies, and overall outlook on life to gauge participants’ mental health practices.
The overall index of health behaviour intensity derived from this scale ranged from 24 to 120 points, with a higher score indicating a greater intensity of declared health-promoting behaviours and a more proactive approach to wellness.
To ensure consistency and accuracy in interpreting the results, the obtained points were converted into sten scores. These scores were categorised into 3 levels: low (1-4 sten), medium (5-6 sten), and high (7-10 sten), providing a clear framework for assessing the intensity of health-promoting behaviours demonstrated by the respondents.
In addition to the comprehensive assessment of health behaviours using the HBI, the study also employed a custom survey questionnaire to gather specific demographic and clinical information from the participants. This questionnaire consisted of 8 closed-ended questions aimed at capturing essential details about the characteristics and medical history of patients with AF.

STATISTICAL ANALYSIS OF DATA OBTAINED IN THE STUDY

The STATISTICA 13.3 PL software package was used as the main tool for the statistical analysis of the data obtained in the study. This comprehensive statistical software provided the necessary functions and algorithms for effectively analysing the collected data and drawing valuable conclusions from the study results.
Descriptive statistics were calculated to summarise and present the key characteristics of the dataset. Measures such as mean, median, standard deviation, and range were used to describe central tendency, variability, and distribution of the studied variables. Additionally, percentages were calculated for qualitative variables to determine the prevalence or distribution of specific characteristics within the sample population.
Various statistical methods were employed to examine relationships between variables and test hypotheses. Student’s t-test was used to compare the mean values of 2 independent groups or samples to determine if the observed differences were statistically significant. The significance level was set at p < 0.05, meaning that results with a probability below this threshold were considered statistically significant.
Moreover, analysis of variance (ANOVA) was used to analyse differences between multiple groups. Correlations between variables were assessed using Pearson’s correlation coefficient.

CHARACTERISTICS OF THE STUDY GROUP

The research results present sociodemographic data obtained from an original questionnaire completed by 236 patients with AF. Among them, 15% (n = 35) were under 40 years old, 35% (n = 83) were aged between 40 and 59 years, and 50% (n = 118) were 60 years or older. The average age of the patients was 58 years (SD = 12.4), with a median of 59 years and quartiles Q1 = 48 years and Q3 = 68 years. The majority of patients with AF are older individuals (60+ years), confirming the association between age and the occurrence of this condition. Among the studied patients, 56.77% (n = 134) were women and 43.22% (n = 102) were men, with the gender proportion difference being statistically significant (χ²[1, N = 236] = 4.76, p = 0.029).

RESULTS

Patients from urban areas (82.19%, n = 194) had better access to healthcare and a higher Health Behaviour Inventory (M = 85, SD = 10) compared to rural residents (17.80%, n = 42, M = 67, SD = 14), t(234) = 9.45, p < 0.001. Most patients had a long history of AF: 18.64% (n = 44) were diagnosed less than a year ago, 26.69% (n = 63) had been diagnosed 1-5 years ago, 14.82% (n = 35) had been diagnosed 6-10 years ago, and 39.83% (n = 94) had been living with the diagnosis for more than 11 years. ANOVA test showed significant differences in the Health Behaviour Inventory depending on the duration of diagnosis (F[2, 232] = 4.56, p = 0.012). Pharmacotherapy was the most commonly used treatment method (86.42%, n = 204), with significant differences in the distribution of therapeutic methods among different age groups (χ²[4, N = 236] = 10.57, p = 0.032). Ablation was less common (11.02%, n = 26), which may be due to its invasiveness and the availability of specialised care.
Patients with higher education (41%, n = 97) had an average Health Behaviour Inventory of 90 (SD = 12), while patients with primary education (11%, n = 26) had an average index of 70 (SD = 15). ANOVA analysis showed significant differences depending on the level of education (F[3, 232] = 6.89, p < 0.001). The majority of patients (51.69%, n = 122) were not professionally active, and 25% (n = 59) performed physical work, but the chi-square test showed no significant differences in health behaviours depending on the type of work (χ²[2, N = 236] = 1.23, p = 0.54). Patients with higher incomes had better access to healthcare and better health outcomes, confirmed by a positive correlation between income level and the Health Behaviour Inventory (r = 0.42, p < 0.001).
To assess health behaviours in patients with AF, the HBI was utilised. A high HBI value was reported by 27.88% of respondents. More than half of the respondents (54.65%) exhibited a medium level of health behaviours. A low level of health behaviours was reported by only 17.47% of respondents. The data are presented in Table 1. The average overall health behaviour severity index was 87.3 points for women and 80.6 points for men. The median value of this index was 89 points for women and 83 points for men. Both the mean and median values were higher among women, suggesting that, overall, women are more likely to engage in positive health behaviours compared to men.
The small difference between the mean and median health behaviour severity index for women (87.3 points vs. 89 points) suggests that the distribution of results in this group is relatively symmetrical. In contrast, the larger difference between the mean and median for men (80.6 points vs. 83 points) may indicate a more skewed distribution of scores. The 6-point difference in the median health behaviour severity index between women and men suggests notable gender differences in attitudes towards health.
The study revealed that women are more likely to demonstrate moderate to high levels of health behaviours compared to men. This may be attributed to their greater awareness of health issues, regular medical check-ups, and caregiving responsibilities. Conversely, men are more likely to exhibit low levels of Health Behaviour Inventory. A weak correlation was found between gender and health behaviour levels, while no significant associations were observed with age, education, or place of residence. Urban residents were more likely to have average to high levels of HBI compared to rural residents. These results are presented in Table 2.
The study results indicate a statistically significant association between gender, place of residence, and the level of health behaviours. Women had a higher average Health Behaviour Inventory (98 points) scores compared to men (63 points), with a p-value of 0.03. Urban residents had a higher average index (85 points) compared to rural residents (67 points), with a p-value of 0.02. Other variables, such as marital status, education level, and age, did not show significant differences. Understanding these factors is crucial for developing interventions that promote healthier lifestyles and improve health outcomes.

RESULTS OF THE STUDY CONSIDERING VARIOUS CATEGORIES OF HEALTH BEHAVIOURS

In total 236 patients with AF participated in the study, among whom 60% (n = 142) maintained good dietary habits (PN1) and regularly consuming vegetables, fruits, and whole grain products. 25% (n = 59) had moderate dietary habits, and 15% (n = 35) did not follow healthy dietary habits. The average score for this category was 74 points (SD = 12), with a median of 75 points. In the domain of preventive behaviours (ZP), 70% of patients (n = 165) regularly attended check-up appointments with their doctor, 20% (n = 47) attended occasionally, and 10% (n = 24) rarely attended check-ups, with an average score of 80 points (SD = 13) and a median of 81 points. In the category of health practices (PZ), 50% of patients (n = 118) engaged in regular, moderate physical activity, 30% (n = 71) engaged occasionally, and 20% (n = 47) did not engage in physical activity, achieving an average score of 78 points (SD = 11) and a median of 77 points. Regarding positive mental attitude (PN2), 65% of patients (n = 154) practised stress management techniques, 25% (n = 59) practised occasionally, and 10% (n = 24) did not practise any techniques, achieving an average score of 85 points (SD = 14) and a median of 84 points.
Additionally, in the analysis of differences in health behaviours between groups, the average Health Behaviour Inventory score was higher among women (98 points, SD = 17.32) compared to men (63 points, SD = 14.62), which is statistically significant (p = 0.03, t[234] = 9.45). Patients from urban areas had a higher average score (85 points, SD = 12.13) than patients from rural areas (67 points, SD = 13.30), which is also statistically significant (p = 0.02, t[234] = 6.89). Patients with higher education obtained an average score of 90 points (SD = 12) compared to patients with primary education (70 points, SD = 15), which was statistically significant (p < 0.001, ANOVA: F(3,232) = 6.89). Correlation analysis showed that income level positively correlated with the Health Behaviour Inventory (r = 0.42, p < 0.001). Furthermore, patients with a longer history of AF had higher scores in health behaviours (ANOVA: F[2,232] = 4.56, p = 0.012). These results indicate significant differences in health behaviours among different demographic groups, which may have a substantial impact on health management strategies and patient education for those with AF.

DISCUSSION

Health behaviours are crucial for human well-being. Understanding the types of health-promoting behaviours and strategies to modify detrimental habits can significantly improve the health status of individuals and society as a whole. The effectiveness of health education and promotion also depends on the positive experiences of individuals and the availability of attractive models to promote healthy behaviour.
Health Behaviours Inventory are categorised into those that promote health (pro-health, positive) and those that are detrimental (self-destructive, negative). Positive behaviours aim to enhance health, prevent disease, and support the therapeutic recovery process. Conversely, detrimental behaviours contribute to the development of health disorders, negatively impacting the body holistically.
A high score in the Health Behaviour Inventory is influenced by several factors and encompasses well-defined attitudes towards various aspects of health, disease prevention, and maintaining optimal functioning. Our study revealed that only 27.88% of patients exhibited a high level of Health Behaviour Inventory, significantly lower than the 44.8% reported in a similar study by Piotrkowska et al. [23].
Research conducted by Szypulska et al. on primary care patients found that women generally exhibit a higher score in the of Health Behaviour Inventory [24]. Studies by Kurpas et al. and Kurowska et al. also demonstrated a correlation between gender and Health Behaviour Inventory [25, 26]. Grochulska, in her study on the health behaviours of individuals post-myocardial infarction, concluded that women are more proactive than men in maintaining their health [27]. Our study yielded similar results, with women more likely than men to display medium and high scores in the Health Behaviour Inventory. This is corroborated by Nowicki et al., who found that detrimental health behaviour is more prevalent among men [28].
Women often prioritise their health due to their roles as caregivers and mothers. Women’s health is vital for the well-being of the entire family, motivating them to take preventive measures and maintain fitness. Additionally, women tend to be more aware of their bodies and understand the importance of regular health checks for early disease detection. Social and media pressures also encourage women to focus on their appearance and fitness, prompting them to stay in shape. Furthermore, women are generally more open about their psychological and emotional needs, which drives them to care for both their physical and mental health.
However, a study by Kawalec et al. on patients with obesity and overweight found no significant relationship between gender and Health Behaviour Inventory [29]. This suggests that further exploration is necessary to broaden the scientific perspective and identify other key relationships.
In the studies conducted by Ladwig et al. [30] it was demonstrated that lifestyle changes, particularly dietary improvements and regular physical activity, are crucial in reducing episodes of AF. Our own research also highlighted the importance of healthy habits, with particular attention given to gender differences. It was noted that men are less likely than women to engage in health-promoting behaviours, indicating a need for additional interventions.
Research published by BMC Psychology indicates that cognitive-behavioural therapy can significantly enhance the quality of life for patients with AF, aiding them in managing the condition and adopting healthier habits. Similar conclusions emerged from our own studies, confirming the need for psychological support, particularly for men, who tend to exhibit lower levels of health-promoting behaviours [31].
In contrast, the work by Nalliah et al. [32] focuses on specific factors such as sleep quality and dietary habits that may trigger AF. Our research, however, emphasises overall health behaviours, providing a broader perspective on patients’ health habits but offering less detail regarding specific triggers of AF.
The findings of this study provide important insights for nursing practice, especially in caring for AF patients. Nurses play a key role in patient education and promoting healthy behaviours, particularly for male patients who are less likely to engage in such activities. This study highlights the need for personalised interventions that consider gender, socioeconomic status, and cultural factors. By focusing on tailored education, lifestyle changes, and psychological support, nurses can help patients adopt healthier habits, improving long-term outcomes and reducing the risk of AF complications.

LIMITATIONS

The study has several limitations. Firstly, the limited number of respondents may affect the objectivity of assessing the health behaviours of AF patients. Extending the survey to a national scale could yield more reliable results. Additionally, survey methods may introduce subjective responses and data distortion. Furthermore, the lack of analysis of causal relationships between different factors limits the understanding of health behaviours among AF patients. Future research involving a larger population and more comprehensive statistical analyses could provide a deeper understanding of these behaviours.

CONCLUSIONS

The analysis of health behaviours among patients with AF showed that only a small percentage exhibited high levels of healthy behaviours. Women were more likely than men to demonstrate medium or high levels, possibly due to greater health awareness, while men were more prone to low levels of healthy behaviours. Key recommendations include enhancing nutritional education, promoting regular moderate physical activity, and providing psychological support to improve overall health outcomes. Additionally, treatment and education should be individualised, with special attention given to men and rural residents, who are less likely to engage in health-promoting behaviours.

Disclosures

This research received no external funding.
Institutional review board statement: Not applicable.
The authors declare no conflict of interest.

References