Individual and health care provider factors influencing stroke self-management behavior: A cross-sectional study

2.1 Materials

An analytic observational design using a cross-sectional method was employed in this study. A sample size of 110 patients with ischemic stroke was selected from the neurology outpatient department of Universitas Airlangga Hospital from February 2023 to May 2023. Determining the large sample in this study used the Slovin formula. A simple random sampling approach was used in the selection process, ensuring that everyone within the population had an equal probability of being included in the sample. The sampling procedure included a random selection of individuals from the registration list of patients who sought medical care at the neurology outpatient department, according to predetermined inclusion and exclusion criteria. The inclusion criteria comprised individuals between 18 and 60 years old who had received a diagnosis from a neurologist. Additionally, respondents must be able to communicate either vocally or nonverbally with the researcher, without experiencing significant aphasia, speech impairment, or any other communication barriers. Furthermore, individuals must demonstrate their willingness to participate by providing informed permission via their signature. The following were the exclusion criteria: (1) individuals with impaired cognitive function or a documented history of mental illnesses and (2) those with significant medical difficulties, including heart, liver, lung, and renal disease. The exclusion and inclusion criteria were determined by neurologists and guided by medical records. This study was approved by the Ethics Committee of Universitas Airlangga Hospital, with the assigned number of 044/KEP/2023. Additionally, the study protocol was recorded under the identification number of UA-02-23026. Before performing the examination, we ensured that all patients provided written informed consent. During data collection, all participants (110 patients) completed the questionnaire, without any missing participant data (either withdrawals or incomplete data).

2.2 Data collection procedures

This study investigated individual factors of stroke patients such as age, gender, and place of residence. In addition, we also investigated health service provider factors, such as availability of information, health facilities, access, and collaboration between health professionals. In the first step, approval from the hospital was obtained for data collection on patients at the neurology outpatient department. Hospital supervisors assisted in determining outpatients who met inclusion and exclusion criteria to prevent selection bias in this study. In addition, researchers explained the aims and procedures of the study to patients and obtained their consent. Next, participants are asked to fill out the digital form of the demographic and health service provider questionnaires which takes around 10-20 minutes to complete.

The health service provider questionnaire consists of 4 parts, including questionnaires on availability of information, availability of health facilities, availability of access, and collaboration between professional health workers. These four questionnaires were scored using a four-level scoring method (1–4 points) for favorable questions if strongly agree = 4, agree = 3, disagree = 2, and strongly disagree = 1. In contrast, unfavorable questions were the opposite. In part one, the information availability questionnaire consists of 7 questions, including 4 favorable questions (number 1,2,3,7), and unfavorable questions (number 4,5,6). Scores for answers to the questionnaire are 7-15 = poor (1), 16-25 = fair (2), 26-35 = good (3). In the second part, the health facilities availability questionnaire was developed by the researchers themselves and adapted to the health service resource use questionnaire (Ilhan et al. 2009), which consists of eight questions, all of which are favorable questions. Scores for answers to the questionnaire are 8-18 = poor (1), 19-29 = sufficient (2), 30-40 = good (3). In the third part, the access availability questionnaire used the health access and utilization survey questionnaire (Harris et al. 2011), which consists of four questions, including favorable questions (number 1.4), and unfavorable questions (number 2.3). The score for answers to the access questionnaire is 4-8 = poor (1), 9-14 = fair (2), 15-20 = good (3). n the fourth part, a collaboration questionnaire between health workers was developed by the researchers themselves and adapted to the Perception of Interprofessional Collaboration Model Questionnaire (Légaré et al. 2011), which consists of eight questions (all favorable questions). Scores for answers to the questionnaire 8-18 = poor (1), 19-29 = sufficient (2), 30-40 = good (3). All these health service provider questionnaires were tested for validity and reliability and declared valid (r count > 0.361) and reliable, with a Cronbach’s alpha of 0.912.

The self-management behavior questionnaire was a development and modification questionnaire for the Hypertension Self-Management Behavior Questionnaire (Akhter 2010), which consisted of 29 questions. All favorable questions were scored as follows: very always = 4, sometimes = 3, rarely = 2, and very never = 1. which consists of 29 questions. All favorable questions were scored as follows: very always = 4, sometimes = 3, rarely = 2, and very never = 1. The score range was 29-116, where 29-58= poor, 59-87= fair, 88-116= good. All items in this questionnaire were declared valid (r count > 0.361) and reliable, with a Cronbach’s alpha of 0.926.

2.3 Data analysis

Data for the study materials were entered into an Excel spreadsheet following questionnaire collection. Statistical description and analysis were performed using Statistical Package for the Social Sciences (version 26, IBM, Armonk, NY, USA). General data, health service providers, and data regarding patients’ self-management were presented as percentages. Specifically, to examine the correlation between variables, the Chi-square test was employed with a significance level of α = 0.05. Additionally, to investigate the primary factor that influences the self-management behavior of patients with stroke, ordinal logistic regression was utilized with a significance level of α = 0.05. There were no missing data in the data analysis of this study.

4.1 Correlation between individual-related factors and self-management behavior

This study aimed to examine several individual-related factors or characteristics that have an impact on self-management behavior among patients with stroke. These factors included age, gender, and place of residence. Most respondents were between 26 and 45 years old, and a significant positive association was observed between age and the self-management behavior of patients with stroke. Globally, there is a growing tendency for stroke to occur at younger ages, as approximately 30%–50% of all patients with stroke are under 65 years old (Ren et al. 2020). The results of the present study are consistent with those of Elma et al. (2022) who reported that adult patients exhibit superior self-management abilities than older adult patients. Individuals in the younger age group who possess a greater potential for extended lifespan and increased life expectancy subsequent to experiencing a stroke may contemplate a reintegration into societal structures. Owing to the concurrent demands of employment and familial obligations among their peers, the youthful demographic has challenges in committing to extended rehabilitation (Shuqi et al. 2023). As young individuals with longevity and life expectancy following a stroke may consider returning to society more, adult patients have better self-management than older ones. It is challenging for the young population to devote themselves to long-term rehabilitation because their peers work and take on family responsibilities.

Most respondents in the study were male, and it was observed that they mostly exhibited good self-management behavior. Nevertheless, no significant association was observed between gender and self-management behavior. The results of this study align with those of Kuo et al. (2021) who indicated a lack of correlation between gender and the variables investigated in this study. The ability of both male and female patients to modify their self-management behavior is contingent upon their goals and self-efficacy.

This study observed no statistically significant association between the place of residence and patient self-management behavior. Most patients who were presented for follow-up resided in Surabaya, an urban locality. Lu et al. (2022) reported a disparity in the self-management behavior of urban and rural patients in China, highlighting a gap in their respective locations. There is no guarantee that older middle-aged individuals possess superior self-management behaviors than their counterparts in rural regions. Conversely, enhanced medication adherence is exhibited by urban inhabitants. The self-management behavior shown by individuals residing in urban regions tends to surpass that of their rural counterparts. This discrepancy may be attributed to the greater accessibility to private and government healthcare organizations, medical professionals, and medical facilities within urban settings. Conversely, healthcare services in rural regions are far from residential areas. The limited availability of healthcare practitioners and drugstores has a significant impact on self-management (Akhter 2010). This study was conducted in a government hospital situated in the city center, which offers convenient accessibility through several modes of transportation. Patients residing outside the city of Surabaya frequently opt to rent accommodations near the hospital, thereby facilitating ease of access during their treatment.

4.2 Correlation between health service provider-related factors and self-management behavior

This study also determined the health service provider-related factors (including availability of health facilities, availability of access, and collaboration between professional health workers) and self-management behavior among stroke patients. Several respondents expressed that the neurology outpatient department exhibited a satisfactory level of information accessibility. This perception was primarily based on the presence of informative pamphlets positioned in the periphery of the room, the utilization of digital platforms for disseminating information, and the active engagement with social media channels for facilitating information sharing. The results of this study are consistent with those of Koga et al. (2011) who indicated a clear correlation between knowledge and self-management behavior. In contrast to the assertions made by Sari and Airlanda (2022), it has been argued that the essential conditions for engaging in self-management behavior require patients to possess enough information and abilities pertaining to the illness and its treatment principles. Individuals who are provided with self-management health education before their discharge from medical facilities exhibit superior self-management behaviors than those who do not receive such education (Shuqi et al., 2023). Several independent studies have shown several benefits associated with health-related behaviors. These advantages include reduced use of medical services, lower smoking and alcohol consumption, improved diet, and a more positive attitude (Whitehead 2018). Most healthcare providers realized their role in self-management assistance as primarily that of coaching, whereas a minority believed that their job was to educate or support patients in achieving self-management. Bos-Touwen et al. (2017) reported that it has the potential to empower patients to make behavioral changes.

The results of our study showed that information accessibility was satisfactory; however, a deficiency in patient health literacy existed. Moreover, the results indicated that patients preferred to engage in conversations with other patients instead of engaging with educational videos or perusing health-related leaflets. Several studies reported that individuals with little health literacy who have had a stroke tend to have worse overall health outcomes and greater rates of death. The impact of health literacy encompasses an individual’s preferred mode of learning, specific requirements, and the extent to which patients can accurately comprehend and use available healthcare resources. Aran et al. (2022) stated that there is evidence supporting the notion that enhanced health literacy is linked to improved self-management, thereby resulting in advantageous outcomes for both patients and the healthcare system. Cultural ideas and social stigma influence knowledge-related behavioral alterations. Alqahtani (2015) stated that a significant number of individuals who have had a stroke believe that divine retribution, stress, and spiritual possession contribute to their condition. Consequently, these individuals tend to place more faith in spiritual healing and traditional medicinal practices as means of attaining recovery, as opposed to relying only on conventional medical interventions. Cultural ideas within society influence health information reception. In some instances, patients with stroke may place more trust in information provided by religious leaders, including clerics, priests, monks, or traditional authorities, as opposed to healthcare professionals.

The results of this study demonstrated that the presence and accessibility of healthcare services do not influence self-management behavior. The results are inconsistent with those of a study by Dwyer et al. (2020) who demonstrated that the extent of hospital infrastructure has a crucial role in evaluating patient adherence to control measures. This finding aligns with that of Wahyuningrum (2011) who postulated that the presence of adequate facilities has a considerable favorable impact on patient self-management behavior and overall satisfaction. The impediments faced by patients in seeking medical care are not just attributable to facility availability and accessibility but are also influenced by the financial burden of public transportation, including taxi fares (which may occasionally be mitigated by ride-sharing), the wait time for ambulance services, and the challenges associated with walking (Smythe et al. 2022). The provision of family support in patient care, which includes the act of escorting and following patients throughout treatment, affects patient self-management behavior (Alqahtani 2015).

The results of this study suggested that a positive level of cooperation is noted among health professionals; however, notably, this collaboration does not demonstrate a significant relationship with self-management behavior. Ding et al. (2023) reported that individuals have the capacity to modify their behavior inside a communal environment. The inclusion of community healthcare providers in the healthcare system is crucial for the effective stroke management, as highlighted by Yang et al. (2022). During the aforementioned period, 62.6% of primary healthcare providers who collaborated with nurses in primary healthcare settings have knowledge about the inclusion of loss of consciousness as a symptom of stroke (Ding et al. 2023).

Pearce et al. (2015) reported that studies have emphasized the significance of collaborative and educational connections between healthcare professionals and patients or carers. These relationships include professionals actively listening to the needs of patients and carers, offering expert advice, providing relevant information, and addressing any inquiries. This is consistent with a key element of self-management relating to human interactions as it represents the fundamental foundation of self-management. Dineen-Griffin et al. (2019) contended that the efficacy of interpersonal connections plays a significant role in attaining optimal outcomes and cultivating a more profound sense of fulfillment in life. Dobkin (2016) reported that it is essential to include elements such as education, continuous engagement, targeted information, goal establishment, monitoring, feedback, and motivational enhancement to achieve self-management and create behavioral change. Moreover, the enhancement of self-management behavior is influenced by the effect of self-efficacy (Lo, Chang, and Chau 2018). Individuals who possess elevated degrees of self-efficacy have enhanced abilities to cope with the adverse consequences associated with their medical condition and the corresponding therapeutic interventions. Szczepańska-Gieracha and Mazurek (2020) reported that self-efficacy seems to have more significance in the context of diseases that hinder physical functioning and need extended rehabilitation periods.

We now better understand the factors that predict self-management behavior in patients with stroke and identify which factors are the most significant. Therefore, nurses must focus on these factors when performing discharge planning. To identify health service provider-related factors that influence self-management behavior in patients with other chronic illnesses, more studies are needed. Therefore, for the self-management behavior of patients with stroke, understanding the factors that hinder changes in their self-management behavior is essential.

This study had several limitations. First, it only included respondents who had experienced ischemic stroke and did not have a previous stroke history; therefore, individuals with hemorrhagic stroke or multiple stroke occurrences were excluded from the analysis. Considering the epidemiological evidence indicating their substantial representation among patients with stroke, expanding the scope of the research group to include a representative sample of the total stroke population is imperative. Second, there may have been recall bias among respondents who answered the stroke self-management question. Although the selected participants did not have cognitive impairment, they had difficulty remembering the frequency of diet, medication, monitoring blood pressure and blood sugar levels. Third, a subset of older individuals encountered challenges while completing digital form-based questions. Consequently, researchers helped these individuals throughout the questionnaire administration process since they may have low visual acuity and have difficulties in using smartphones. Lastly, the data collection procedure was characterized by a lengthier duration. Therefore, our staff provided support and guidance in the process of completing the questionnaire via the use of a smartphone.