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Factors associated with patient activation among patients with diabetes on hemodialysis: a multicenter cross-sectional study from a developing country

BMC Nephrology volume 25, Article number: 232 (2024) Cite this article

Abstract

Background

Diabetes mellitus (DM) is a major public health concern with considerable morbidity and mortality. DM affects patients’ quality of life and can lead to multiple complications, including chronic kidney disease (CKD) and the need for dialysis. Higher patient activation can improve health outcomes in hemodialysis patients with DM. This study aimed to explore the factors associated with higher patient activation and health-related quality of life (HRQoL) among hemodialysis patients with DM.

Methods

This was a cross-sectional, questionnaire-based study conducted on hemodialysis patients with DM in Palestine. The quota sampling method was utilized to draw samples from six dialysis centers. The questionnaire consists of three sections. The first section includes demographic, socioeconomic and clinical questions. The second section utilizes the patient activation measure-13 (PAM-13) to measure patient activation, while the third section assesses HRQoL using the EQ-5D-5 L tool and the visual analog scale (VAS). Mann‒Whitney and Kruskal‒Wallis tests were employed to examine the relationships between variables at the bivariate level, and multiple regression analysis was employed at the multivariate level.

Results

Of the 200 patients who were approached, 158 were included. The median PAM, EQ-5D index, and VAS score were low at 51.0, 0.58, and 60.0, respectively. A higher PAM score was independently associated with a higher household income level and taking medications independently. A higher EQ-5D index was associated with taking more than eight medications, taking medications independently, living with fewer than three comorbid conditions, and having a higher PAM. A higher VAS score was associated with being married, and receiving less than 3.5 hours of hemodialysis.

Conclusions

A higher patient activation level was associated with a higher income level and independence in taking medications. Interventions designed to improve patient activation, such as medication management programs, should address these factors among the target population. Longitudinal studies are needed to assess the time effect and direction of causation between health status and patient activation.

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Background

Diabetes mellitus (DM) is a leading global health problem associated with substantial morbidity and mortality. The global prevalence of DM doubled from 1990 to 2019, directly contributing to 1.5 million deaths [1]. DM affects the quality of life of patients due to its chronic nature and the need for continuous self-management, including blood glucose monitoring, following a certain diet, engaging in regular exercise and adhering to medication regimens [2]. Moreover, DM can lead to various complications, such as diabetic nephropathy and retinopathy, resulting in physical, functional and social challenges [3]. Of note, DM is among the most common causes of chronic kidney disease (CKD) [4]. The prevalence of CKD in patients with type 2 DM ranges from 23.1 to 41.7% [5,6,7]. Progression to dialysis is 2.7 times more common in patients with DM than in those without DM [8]. Furthermore, once dialysis is initiated, patients with DM have a higher mortality rate than those without DM [9].

The progression of DM to CKD is governed by several factors, such as tight glycemic control, systolic blood pressure, age, albuminuria and duration of DM [10]. In particular, intensive glycemic control has a well-established role in reducing the risk of progression to hemodialysis in patients with DM [11]. However, the recommended level of glycemic control for patients with DM on dialysis is different and variable, and evidence on reducing morbidity and mortality is scarce [12,13,14].

Patient activation is the readiness and ability of patients to manage their own health and well-being and adopt certain health behaviors to improve their conditions. To do so, patients need the motivation, knowledge, skills, and confidence necessary to make decisions and manage their own care [15, 16]. High activation in patients with DM was found to be associated with fewer admissions, better glycemic control, blood glucose monitoring, physical exercise, improved nutritional habits, and adherence to recommended eye examinations and foot care [17,18,19]. Likewise, patient activation was associated with increased home-based dialysis and better blood pressure control in hemodialysis patients [20, 21]. However, patients with CKD demonstrated less patient activation than patients with other chronic diseases, with even lower activation levels in those receiving inpatient dialysis [22, 23]. One study revealed a low level of patient activation among patients with DM on dialysis, which was associated with older age and poorer self-reported health [24].

Between 2006 and 2021, the number of patients receiving hemodialysis in the Palestinian West Bank almost quadrupled, peaking at 1554 patients [25, 26]. These patients were found to have a high prevalence of malnutrition and poor quality of life [27, 28]. In 2015, the average annual cost per patient was estimated at 16,085 USD, inclusive of medications, tests and outpatient visits [29]. This high cost is covered by the governmental healthcare system, which is underfunded, fragmented, and aid dependent [30]. As engaged patients demonstrate increased adherence to treatment and follow-up and improved self-care, patient activation reduces costs by decreasing costly service utilization, such as hospital admissions and emergency department visits [16, 31]. Therefore, patient activation provides an opportunity to improve health outcomes and save costs, especially for patients with conditions requiring considerable self-management, such as DM and the need for hemodialysis. Identifying the extent and drivers of patient activation among this population is key to informing interventions and designing guidelines aimed at enhancing patient activation. This study aimed to measure the level of patient activation among hemodialysis patients with DM and explore the factors associated with patient activation. The study also aimed to assess the health-related quality of life (HRQoL) of this population.

Methods

Study design and settings

This was a multicenter, cross-sectional study based on a self-administered questionnaire to measure patient activation, health-related quality of life (HRQoL) and associated factors among patients who were diagnosed with DM and receiving hemodialysis. The study was conducted in six dialysis centers in the northern West Bank in the cities of Nablus, Tulkarem, Jenin, Qalqilya, Tubas and Salfit.

Population and sampling

The study population comprises patients with DM currently receiving hemodialysis in the northern Palestinian West Bank. According to the annual health records published by the Palestinian Ministry of Health, 507 patients received hemodialysis in the northern governorates of the Palestinian West Bank. As almost 45% of those on hemodialysis are estimated to have DM, the estimated total population size is 230 patients [32]. To estimate the sample size, the Raosoft online sample size calculator was used (http://www.raosoft.com/samplesize.html). Using a margin of error of 5% and a confidence level of 99%, the sample size was calculated to be 171. The target sample size was increased to 200 to account for potential missing data. The respondents were chosen using the quota sampling method corresponding to the distribution of the population in the 6 centers.

Inclusion and exclusion criteria

Patients who (1) were over 18 years of age, (2) had been diagnosed with DM for more than one year prior to the study, or (3) had been receiving hemodialysis regularly for at least six months before the study were included. Patients with cognitive or mental limitations were excluded.

Data collection: procedures and tools

Permission was obtained to access medical records at each dialysis center to identify patients with DM undergoing hemodialysis who could participate in the study as per the inclusion and exclusion criteria. Participants, including those who could not read, were invited to participate, interviewed face-to-face, and assisted in filling out the questionnaire.

The questionnaire was developed in Arabic, and the tools were properly translated. A pilot study was carried out on a sample of 20 respondents, based on which the questionnaire was edited for ease, clarity and accuracy. The questionnaire is structured into three sections:

  • The first section included questions on demographic characteristics, including age, sex, height, weight; socioeconomic characteristics, including residency, marital status, education level, occupation, household income; and clinical characteristics, including dialysis vintage (< 4 or ≥ 4 years), duration of DM (≤ 10, 11–20, or > 20 years), frequency of dialysis (< 3 or ≥ 3 sessions/week), duration of each session (< 3.5 or ≥ 3.5 h/session), history of kidney transplantation (yes/no), number of chronic comorbid diseases (< 3 or ≥ 3), number of chronic medications (< 8 or ≥ 8 medications/day), and ability to take medications independently (yes/no). The variable categorization was based on previous similar studies [32,33,34].

  • The second section assessed patient activation using the Patient Activation Measure-13 short form (PAM-13), which is a 13-item questionnaire that measures the patient’s knowledge, skills, and confidence necessary for self-management of health and health conditions. Responses are based on a 4-point Likert scale (from strongly disagree to strongly agree). The final score is calculated based on these responses and ranges from 0 to 100. Then, the final score is used to assign respondents to one of four levels of patient activation, with levels 1 and 2 indicating lower patient activation and levels 3 and 4 indicating higher patient activation [35]. The PAM-13 is among the most widely translated and validated tools for measuring patient activation and has been tested for validity and reliability in different locations and for various populations [35,36,37,38], including patients with CKD [39].

  • The third section assesses HRQoL using the EuroQol tool (EQ-5D-5 L). This tool is easy and practical for clinical use and has been validated in patients with CKD before and after receiving hemodialysis [40]. It consists of two parts. The first part is the five-dimensional, five-level EuroQol tool (EQ-5D-5 L). This part measures health status in five dimensions (mobility, self-care, usual activities, pain and discomfort, and anxiety and depression), with each domain assessed at five levels of response (no problems, slight problems, moderate problems, severe problems, and extreme problems). Then, the health state index score is calculated using a formula that assigns an index value to each level of each dimension. The index value is different for different regions to reflect the societal perspective of different health states. The final health state index score ranges from 0 (health status of dead) to 1 (best health possible). The second part is the visual analog score (VAS), whereby respondents estimate their perceived health from 0 (the worst health status) to 100 (the best health status) [41].

Data analysis

Descriptive and inferential statistics were employed to analyze the data using the 26th version of the Statistical Package for the Social Sciences (SPSS). Cronbach’s alpha was used to test the internal consistency between the PAM and EQ-5D scale items, with a value greater than 0.70 indicating an acceptable level of internal consistency as a type of scale reliability [42]. Percentages and frequencies are reported for the categorical and ordinal independent variables of sociodemographic and clinical characteristics. The frequency, percentage, median, mean rank and/or mean (± standard deviation) were reported for the scale scores (PAM, VAS and EQ-5D).

The inferential statistical tests chosen to analyze the data were based on the median and mean rank as measures of central tendency, as parametric data assumptions, such as normality of the data and equality of variance, are not met. The values of the scale scores (PAM, VAS and EQ-5D) can be skewed for several reasons. First, the population consists of individuals with chronic conditions who, for instance, may demonstrate a low level of activation, leading to a left-skewed distribution [43]. The perception of health can be influenced by other population characteristics, such as cultural differences and social expectations of better health status. Furthermore, scales often have a limited range restricted by upper and lower limits, and thus, reporting of the highest and lowest possible scores is not uncommon, leading to ceiling and floor effects [44]. Additionally, as the exact numerical differences between the score values as ordinal data may not be meaningful, the data cannot be normally distributed; thus, the median is a more representative measure of central tendency [45, 46]. Therefore, the Mann‒Whitney test was used when comparing two groups, while the Kruskal‒Wallis test was used for more than two groups. Furthermore, to ensure the nonnormality of the data, the Shapiro‒Wilk test was used to test for normality. The mean rank, as the average based on ranking all observations, is used to calculate the H-value that is necessary for nonparametric tests, such as the Kruskal‒Wallis test [47]. The multivariate analysis employed a multiple linear regression model that included all the factors that demonstrated significant associations with the PAM, EQ-5D, and VAS scores at the bivariate level. The significance level was set at p < 0.05.

Results

Of the 200 patients with DM who were approached, eight declined to participate (4%), 22 were excluded based on the exclusion criteria (11%), and 12 were excluded because of considerable missing data (Fig. 1). Among the final sample of 158 patients, 99 (62.7%) were males, and 59 (37.3%) were females. The mean age of the participants was 60.92 years (SD ± 10.65), and almost half of the participants were younger than 60 years. The vast majority of participants were unemployed (93.7%), and almost two-thirds had a low monthly income.

Fig. 1
figure 1

CONSORT diagram of the included hemodialysis patients

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Only 29 patients (18.4%) had been diagnosed with DM for less than 10 years, while 57 patients (36.1%) had been diagnosed for more than 20 years. Most patients reported taking fewer than eight medications a day (62.7%) and taking their medications independently (60.1%). The majority had been on hemodialysis for less than 4 years (68.9%), had undergone hemodialysis more than 3 times a week (90.5%), and had undergone hemodialysis for more than 3.5 h per session (64.6%) (Table 1).

Table 1 Sociodemographic and clinical characteristics of the samples in this study
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The PAM, EQ-5D, and VAS scores were found to have a nonnormal distribution (p < 0.001). The Cronbach’s alpha values of the PAM and EQ-5D were 0.72 and 0.80, respectively, indicating an acceptable level of internal consistency. The median PAM score was 51.0 (IQR = 48.9–58.1). At the bivariate level, a higher household income level (p = 0.002), the use of more than eight medications (p = 0.037) and the ability to take medications independently (p = 0.026) were significantly associated with PAM. No significant associations were found with age, sex, education, employment status or marital status. At the multivariate level, a higher household income level (p < 0.001, β = 0.271) and ability to take medications independently (p = 0.001, β = 0.257) were significantly related to the PAM score, while the number of medications taken by the patient was not significantly different (p = 0.557, β = 0.588). The mean rank was highest for those with the highest household income (137.07) and lowest for those with no formal education (63.13) (Table 2).

Table 2 Patient activation scores and sociodemographic and clinical variables among diabetic patients on hemodialysis (n = 158)
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The median EQ-5D index was 0.58 (IQR = 0.32–0.80). At the bivariate level, age less than 60 years (p = 0.047), male sex (p = 0.014), higher educational level (p < 0.001), household income (p = 0.040), being married (p = 0.007), living with fewer than three comorbid conditions (p = 0.030), taking eight medications or more (p = 0.003), taking medications alone (p = 0.023), and having a PAM (p = 0.002) were significantly associated with a higher EQ-5D index. At the multivariate level, the EQ-5D index retained significant associations with living with fewer than three comorbid conditions (p = 0.041, β =-0.160), taking more than eight medications (p = 0.003, β = 0.231), taking medications alone (p = 0.048, β = 0.154), and having a higher PAM level (p = 0.012, β = 0.190) while age (p = 0.195, β =-0.092), sex (p = 0.0339, β =-0.073), educational level (p = 0.159, β = 0.120), household income level (p = 0.647, β = 0.036), and marital status (p = 0.066, β = 0.134) showed no significant associations. The mean rank was highest for those with a history of kidney transplantation (131.00) and lowest for those with no formal education (42.17). Only two patients had a history of kidney transplantation (Table 3). The frequencies and proportions of patients’ responses across the domains of the EQ-5D tool are depicted in Table 4.

Table 3 Total EQ-5D score and sociodemographic and clinical variables among diabetic patients receiving hemodialysis (n = 158)
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Table 4 Frequencies and proportions of patients’ responses across the domains of the EQ-5D tool (n = 158)
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The median VAS score was 60.0 (IQR = 45.0–70.0). At the bivariate level, a higher household income level (p = 0.012), being married (p = 0.013), a hemodialysis session duration of 3.5 h or less (p = 0.019), taking eight chronic medications or more (p = 0.037), and a higher PAM level (p = 0.015) were significantly associated with a higher VAS score. At the multivariate level, being married (p = 0.020, β = 0.180), and having a hemodialysis session duration of less than 3.5 h (p = 0.018, β =-0.185) were significantly related to the VAS score, while household income level (p = 0.274, β = 0.087), the PAM level (p = 0.323, β = 0.078), and taking eight or more chronic medications (p = 0.059, β = 0.151) were not significantly related to the VAS score. The mean rank was highest for those with the highest household income (128.86) and lowest for those who were not married (55.85) (Table 5).

Table 5 Visual analog scale scores and sociodemographic and clinical variables among diabetic hemodialysis patients (n = 158)
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Discussion

This was the first study to measure patient activation in Palestine. In particular, this study aimed to assess patient activation and HRQoL among hemodialysis patients with DM. To date, only one study has measured patient activation among those with DM on dialysis globally [24]. The study findings revealed a low level of patient activation and HRQoL. A higher household income level and taking medications independently were associated with a higher PAM score, while other demographic constructs, such as educational level, employment and age, showed no significant association.

In this study, the median PAM was found to be low at 51.0. This is in line with similar studies conducted among patients on hemodialysis that reported measures ranging between 51 and 66.7 [23, 48,49,50]. In addition, other studies that were conducted among patients on dialysis indicated low activation, but comparisons to these studies are limited due to differences in methodology [24, 51]. Of note, these findings align with the prevailing pattern of lower patient activation in those with CKD than in those with other chronic conditions [22], which is even more pronounced in patients on hemodialysis than in those on peritoneal dialysis [52]. The complexity and chronicity of the specialized process of dialysis give rise to a unique culture where healthcare professionals are entirely responsible for service delivery, limiting patient engagement [53]. Moreover, dialysis imposes a substantial burden on patients’ physical and mental well-being due to the chronicity of the disease, presence of associated chronic conditions, and impact on quality of life [54,55,56,57]. Indeed, HRQoL was low among participants in our study and even lower in other studies conducted among patients on hemodialysis in Palestine [32, 33].

However, patients undergoing dialysis can be actively involved in self-management of certain aspects of the dialysis process. Patients can be assigned dialysis-related tasks that range in complexity from simple clinical observation, such as weight measurement, to complex tasks, such as dialysis machine programming [58, 59]. Moreover, the use of patient-reported outcome measures, whereby patients actively measure symptom burden and quality of life, can enhance patient agency and improve clinician-patient communication [60, 61]. Currently, two trials are testing the impact of using patient-reported outcome measures in hemodialysis care in Australia and Canada [62, 63].

The findings of this study revealed a positive association between household income level and patient activation. However, no correlation was found between education level or employment status and patient activation. Similar income associations were reported in most studies addressing patient activation generally and among those with DM [16, 18, 64,65,66]. In contrast to our study, education level was found to be associated with patient activation among adult patients [64, 66], patients undergoing dialysis [48, 67], and patients with DM [68, 69], heart failure [70], pre-dialysis CKD [71], and other chronic conditions [72]. Notably, the subconstructs of household income, educational level, and employment status may confound the relationship of each with patient activation. A study conducted among diabetic patients on dialysis revealed that socioeconomic status, a construct of income, education, and employment, was not associated with patient activation [24]. Similarly, a national survey of American adults revealed that the income association narrowed after controlling for education, which suggests that the income effect might be influenced by other social factors [64]. This highlights the importance of utilizing a more rigorous methodology in assessing social and economic constructs in relation to patient activation, whereby the multifaceted interactions between income, education, and employment are analyzed separately and collectively. This is especially crucial for translating research into real-world interventions because such social factors interact to shape patient activation. In addition, the study revealed that taking medications independently was associated with a higher PAM score, which corresponds with the definition of the active patient as capable and motivated to manage their own conditions [15, 35].

This study revealed no significant association between age and patient activation, which contradicts previous studies conducted on a similar population of patients with DM on dialysis [24] and other populations of patients with DM [18, 67,68,69], CKD [71], hypertension [73], osteoarthritis [36], and adult patients in general [64]. However, other studies conducted in different research settings have reported no such associations [65, 70, 74]. The impact of age on patient activation may vary among populations with different demographic and clinical characteristics. Even within populations sharing similar clinical characteristics, patient activation may be influenced by disparities in social, cultural and health-related factors, which may confound the effect of age. For instance, the presence of cognitive and physical challenges, social support, and health literacy can encourage or discourage patients from being able to manage their own conditions [75,76,77]. The lower patient activation in the younger population could be ascribed to the emotional distress resulting from the context of unexpected need for dialysis at a young age, which affects the willingness to actively engage in self-care. Moreover, the demanding lifestyle and career expectations of early- and middle-aged adults make younger patients less likely to devote their time and effort to their dialysis self-care.

Due to this variation in the influence of demographic, socioeconomic and clinical factors across different populations and the interplay between these factors within the same population, it is crucial to interpret research findings with caution. Researchers and policymakers should navigate the nuances and complexities associated with these factors to enhance research methodology and build effective interventions. Such interventions are better informed by studies conducted on populations with similar characteristics. Therefore, the results of our research may inform interventions aimed at improving patient activation, especially among the target population, by focusing on the factors influencing active engagement. For example, developing a medication management program for patients receiving dialysis in Palestine can improve knowledge, empower decision-making and thus encourage patient activation. A medication management program may include medication reviews, medication reconciliation, self-monitoring for side effects and drug interactions, and educational programs tailored to the literacy level of each patient [78, 79].

The use of more than eight chronic medications was positively associated with HRQoL, as measured by the EQ-5D index and VAS score. Three studies examined the relationship between medication number and quality of life in Palestine. One study did not reveal a significant association among the same population of hemodialysis patients with DM [33]. Nonetheless, the other two studies, conducted among patients undergoing hemodialysis regardless of diabetic status, found that taking fewer medications was associated with better quality of life [32]. The impact of the number of medications on quality of life depends on the type and appropriateness of the medications. Personalized, effective medications can improve symptoms, control chronic conditions, enhance functionality, and improve psychological well-being. However, drawing conclusions solely based on the number of medications can be misleading, as the choice and number of medications should be tailored to individual needs.

Moreover, several other factors were also associated with each of the two HRQoL scores used in this study. Living with fewer than three chronic conditions, taking medications independently, and having a higher PAM were associated with a higher EQ-5D score, and marital status and a duration of hemodialysis less than 3.5 h were associated with a higher VAS score. The marital status association is consistent with a study conducted among the same patient population in Palestine [33], in addition to other local [80], regional [81] and global studies [82,83,84] conducted on patients with DM and CKD. The association between marital status and HRQoL can be explained by the social support provided by married partners, which can improve the self-care and psychological well-being of patients [85]. Notably, research indicates that marital quality can be more important than marital status, especially in patients with DM [86]. Furthermore, other studies conducted on patients with DM, similar to the present study, reported a positive association between self-reported health, a component of HRQoL, and the PAM, which adds to the evidence on the benefits of patient activation on improving health outcomes [67, 87, 88]. However, the direction of causation remains uncertain. It is unclear whether a higher patient activation level may lead to better HRQoL or whether patients with better HRQoL may be more motivated to manage their own conditions. As this relationship may be bidirectional and operate in a virtuous cycle, longitudinal studies exploring differences across time may be needed to better elucidate the effect.

Strengths and limitations

This was the first study exploring patient activation among hemodialysis patients with DM in Palestine, which can inform interventions and guide future research on patient activation. However, this study has a few limitations. First, the generalizability of the study findings is limited due to the potential influence of cultural, social, and clinical characteristics on patient activation. Second, the use of a subjective, self-reported scale might have introduced measurement errors resulting from personal perceptions, biases or social desirability. Third, some strata of the categorical variables, such as household income level and transplant history, had low frequencies, which might have affected the quality of analysis and the validity of the results. Finally, the cross-sectional design inherently restricts the ability to establish cause-and-effect relationships, thus limiting the applicability and understanding of how certain constructs, such as perceived health, affect patient activation.

Conclusions

Higher patient activation can improve health outcomes and reduce healthcare-associated costs in hemodialysis patients with DM. This study explored patient activation, HRQoL, and the factors influencing both among hemodialysis patients with DM in Palestine. These patients demonstrated low levels of patient activation and HRQoL. A higher household income level and independence in taking medications were associated with a higher PAM score. Interventions targeting health literacy and improving capability in regard to medications, such as medication management programs, have the potential to improve patient activation among the target population. Future research should employ rigorous methodologies to investigate the complex and confounding relationships between factors influencing patient activation. In addition, longitudinal studies are needed to examine the temporal effect and the presence and direction of causation, particularly between HRQoL and patient activation.

Data availability

Due to privacy concerns, the datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. This manuscript is part of a Doctor of Medicine graduation project submitted to An-Najah National University. The abstract was published as part of self-archiving institutional repositories (university repository: https://repository.najah.edu/bitstreams/5760440f-efcc-4650-a3c4-e34b18bb2300/download).

Abbreviations

DM:

diabetes mellitus

CKD:

chronic kidney disease

HRQo:

health-related quality of life

ESRD:

End-stage renal disease

PAM-13:

Patient activation measure-13

EQ-5D-5L:

the five-dimensional, five-level EuroQol tool

VAS:

visual analog scale

SBSS:

Statistical Package for the Social Sciences

IQR:

Interquartile Range

References

  1. Diseases GBD, Injuries C. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22.

    Article  Google Scholar 

  2. Shrivastava SR, Shrivastava PS, Ramasamy J. Role of self-care in management of diabetes mellitus. J Diabetes Metab Disord. 2013;12(1):14.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Papatheodorou K, Banach M, Bekiari E, Rizzo M, Edmonds M. Complications of Diabetes 2017. J Diabetes Res 2018, 2018:3086167.

  4. Kovesdy CP. Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl (2011). 2022;12(1):7–11.

    Article  PubMed  Google Scholar 

  5. Thomas MC, Weekes AJ, Broadley OJ, Cooper ME, Mathew TH. The burden of chronic kidney disease in Australian patients with type 2 diabetes (the NEFRON study). Med J Aust. 2006;185(3):140–4.

    Article  PubMed  Google Scholar 

  6. Plantinga LC, Crews DC, Coresh J, Miller ER 3rd, Saran R, Yee J, Hedgeman E, Pavkov M, Eberhardt MS, Williams DE, et al. Prevalence of chronic kidney disease in US adults with undiagnosed diabetes or prediabetes. Clin J Am Soc Nephrol. 2010;5(4):673–82.

    Article  PubMed  PubMed Central  Google Scholar 

  7. van der Meer V, Wielders HP, Grootendorst DC, de Kanter JS, Sijpkens YW, Assendelft WJ, Gussekloo J, Dekker FW, Groeneveld Y. Chronic kidney disease in patients with diabetes mellitus type 2 or hypertension in general practice. Br J Gen Pract. 2010;60(581):884–90.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zhang X, Fang Y, Zou Z, Hong P, Zhuo Y, Xu Y, Wan J. Risk Factors for Progression of CKD with and without Diabetes. J Diabetes Res 2022, 2022:9613062.

  9. Hayashino Y, Fukuhara S, Akiba T, Akizawa T, Asano Y, Saito A, Bragg-Gresham JL, Ramirez SP, Port FK, Kurokawa K. Diabetes, glycaemic control and mortality risk in patients on haemodialysis: the Japan Dialysis outcomes and Practice Pattern Study. Diabetologia. 2007;50(6):1170–7.

    Article  CAS  PubMed  Google Scholar 

  10. Radcliffe NJ, Seah JM, Clarke M, MacIsaac RJ, Jerums G, Ekinci EI. Clinical predictive factors in diabetic kidney disease progression. J Diabetes Investig. 2017;8(1):6–18.

    Article  CAS  PubMed  Google Scholar 

  11. Perkovic V, Heerspink HL, Chalmers J, Woodward M, Jun M, Li Q, MacMahon S, Cooper ME, Hamet P, Marre M, et al. Intensive glucose control improves kidney outcomes in patients with type 2 diabetes. Kidney Int. 2013;83(3):517–23.

    Article  CAS  PubMed  Google Scholar 

  12. Kdoqi KDOQI. Clinical practice guidelines and clinical practice recommendations for diabetes and chronic kidney disease. Am J Kidney Dis. 2007;49(2 Suppl 2):S12–154.

    Google Scholar 

  13. Hill CJ, Maxwell AP, Cardwell CR, Freedman BI, Tonelli M, Emoto M, Inaba M, Hayashino Y, Fukuhara S, Okada T, et al. Glycated hemoglobin and risk of death in diabetic patients treated with hemodialysis: a meta-analysis. Am J Kidney Dis. 2014;63(1):84–94.

    Article  CAS  PubMed  Google Scholar 

  14. Bilous RW. Glycemic control and mortality in diabetic patients undergoing hemodialysis: much more to learn. Am J Kidney Dis. 2014;63(1):10–2.

    Article  PubMed  Google Scholar 

  15. Wagner EH. Chronic disease management: what will it take to improve care for chronic illness? Eff Clin Pract. 1998;1(1):2–4.

    CAS  PubMed  Google Scholar 

  16. Greene J, Hibbard JH. Why does patient activation matter? An examination of the relationships between patient activation and health-related outcomes. J Gen Intern Med. 2012;27(5):520–6.

    Article  PubMed  Google Scholar 

  17. Rask KJ, Ziemer DC, Kohler SA, Hawley JN, Arinde FJ, Barnes CS. Patient activation is associated with healthy behaviors and ease in managing diabetes in an indigent population. Diabetes Educ. 2009;35(4):622–30.

    Article  PubMed  Google Scholar 

  18. Begum N, Donald M, Ozolins IZ, Dower J. Hospital admissions, emergency department utilisation and patient activation for self-management among people with diabetes. Diabetes Res Clin Pract. 2011;93(2):260–7.

    Article  PubMed  Google Scholar 

  19. Almutairi N, Hosseinzadeh H, Gopaldasani V. The effectiveness of patient activation intervention on type 2 diabetes mellitus glycemic control and self-management behaviors: a systematic review of RCTs. Prim Care Diabetes. 2020;14(1):12–20.

    Article  PubMed  Google Scholar 

  20. Johnson ML, Zimmerman L, Welch JL, Hertzog M, Pozehl B, Plumb T. Patient activation with knowledge, self-management and confidence in chronic kidney disease. J Ren Care. 2016;42(1):15–22.

    Article  PubMed  Google Scholar 

  21. de Maar JS, de Groot MA, Luik PT, Mui KW, Hagen EC. GUIDE, a structured pre-dialysis programme that increases the use of home dialysis. Clin Kidney J. 2016;9(6):826–32.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Bos-Touwen I, Schuurmans M, Monninkhof EM, Korpershoek Y, Spruit-Bentvelzen L, Ertugrul-van der Graaf I, de Wit N, Trappenburg J. Patient and disease characteristics associated with activation for self-management in patients with diabetes, chronic obstructive pulmonary disease, chronic heart failure and chronic renal disease: a cross-sectional survey study. PLoS ONE. 2015;10(5):e0126400.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Van Bulck L, Claes K, Dierickx K, Hellemans A, Jamar S, Smets S, Van Pottelbergh G. Patient and treatment characteristics associated with patient activation in patients undergoing hemodialysis: a cross-sectional study. BMC Nephrol. 2018;19(1):126.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zimbudzi E, Lo C, Ranasinha S, Fulcher GR, Jan S, Kerr PG, Polkinghorne KR, Russell G, Walker RG, Zoungas S. Factors associated with patient activation in an Australian population with comorbid diabetes and chronic kidney disease: a cross-sectional study. BMJ Open. 2017;7(10):e017695.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Palestinian National Authority. OPT: Kidney dialysis supplies and medications to MoH hospitals - occupied Palestinian territory. 2006/05/17/ 2006. https://reliefweb.int/report/occupied-palestinian-territory/opt-kidney-dialysis-supplies-and-medications-moh-hospitals (accessed May 25 2024).

  26. Ahmed N, Khderat AH, Sarsour A, Taher A, Hammoudi A, Hamdan Z, Nazzal Z. The vulnerability of maintenance dialysis patients with COVID-19: mortality and risk factors from a developing country. Ann Med. 2022;54(1):1511–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Badrasawi M, Zidan S, Sharif I, Qaisiyha J, Ewaida S, Jaradat T, Samamra Y. Prevalence and correlates of malnutrition among hemodialysis patients at hebron governmental hospital, Palestine: cross-sectional study. BMC Nephrol. 2021;22(1):214.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Naseef HH, Haj Ali N, Arafat A, Khraishi S, AbuKhalil AD, Al-Shami N, Ladadweh H, Alsheikh M, Rabba AK, Asmar IT et al. Quality of Life of Palestinian Patients on Hemodialysis: Cross-Sectional Observational Study. ScientificWorldJournal 2023, 2023:4898202.

  29. Younis M, Jabr S, Al-Khatib A, Forgione D, Hartmann M, Kisa A. A cost analysis of kidney replacement therapy options in Palestine. Inquiry. 2015;52:0046958015573494.

    PubMed  PubMed Central  Google Scholar 

  30. World Health Organization. Palestine: Health Systems Profile. 2017 2017. https://rho.emro.who.int/sites/default/files/Profiles-briefs-files/PAL-Health-System-Profiles-2018.pdf (accessed May 26 2024).

  31. Mitchell SE, Gardiner PM, Sadikova E, Martin JM, Jack BW, Hibbard JH, Paasche-Orlow MK. Patient activation and 30-day post-discharge hospital utilization. J Gen Intern Med. 2014;29(2):349–55.

    Article  PubMed  Google Scholar 

  32. Mousa I, Ataba R, Al-ali K, Alkaiyat A, Zyoud SH. Dialysis-related factors affecting self-efficacy and quality of life in patients on haemodialysis: a cross-sectional study from Palestine. Ren Replace Ther. 2018;4(1):21.

    Article  Google Scholar 

  33. Khatib ST, Hemadneh MK, Hasan SA, Khazneh E, Zyoud SH. Quality of life in hemodialysis diabetic patients: a multicenter cross-sectional study from Palestine. BMC Nephrol. 2018;19(1):49.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kobayashi N, Takahara M, Iida O, Soga Y, Kodama A, Hirano K, Nakano M, Yamauchi Y, Komai H, Azuma N. Impact of Dialysis Vintage and renal biomarkers on mortality in Dialysis-dependent patients with critical limb ischemia undergoing revascularization. J Endovasc Ther. 2021;28(5):716–25.

    Article  PubMed  Google Scholar 

  35. Hibbard JH, Mahoney ER, Stockard J, Tusler M. Development and testing of a short form of the patient activation measure. Health Serv Res. 2005;40(6 Pt 1):1918–30.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Ahn YH, Kim BJ, Ham OK, Kim SH. Factors associated with patient activation for self-management among Community residents with osteoarthritis in Korea. J Korean Acad Comun Health Nurs. 2015;26(3):303–11.

    Article  Google Scholar 

  37. Packer TL, Kephart G, Ghahari S, Audulv A, Versnel J, Warner G. The patient activation measure: a validation study in a neurological population. Qual Life Res. 2015;24(7):1587–96.

    Article  PubMed  Google Scholar 

  38. Skolasky RL, Mackenzie EJ, Riley LH 3rd, Wegener ST. Psychometric properties of the patient activation measure among individuals presenting for elective lumbar spine surgery. Qual Life Res. 2009;18(10):1357–66.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Lightfoot CJ, Wilkinson TJ, Memory KE, Palmer J, Smith AC. Reliability and validity of the patient activation measure in kidney disease: results of Rasch Analysis. Clin J Am Soc Nephrol. 2021;16(6):880–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Cleemput I, Kesteloot K, Moons P, Vanrenterghem Y, Van Hooff JP, Squifflet JP, De Geest S. The construct and concurrent validity of the EQ-5D in a renal transplant population. Value Health. 2004;7(4):499–509.

    Article  PubMed  Google Scholar 

  41. EuroQol Research Foundation. EQ-5D user guides – EQ-5D. In. Euro QOL Group; 2020.

  42. Nunnally J, Bernstein I. Psychometric Theory 3rd edition New York: MacGraw-Hill; 1994.

  43. Newland P, Lorenz R, Oliver BJ. Patient activation in adults with chronic conditions: a systematic review. J Health Psychol. 2021;26(1):103–14.

    Article  PubMed  Google Scholar 

  44. Chyung SY, Hutchinson D, Shamsy JA. Evidence-based Survey Design: Ceiling effects Associated with Response scales. Perf Improv. 2020;59(6):6–13.

    Article  Google Scholar 

  45. Manikandan S. Measures of central tendency: median and mode. J Pharmacol Pharmacother. 2011;2(3):214–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Nair AS, Diwan S. Pain scores and statistical analysis—the conundrum. Ain Shams J Anesthesiol. 2020;12(1):35.

    Article  Google Scholar 

  47. Dodge Y. Kruskal-Wallis Test. The Concise Encyclopedia of statistics. edn. New York, NY: Springer New York; 2008. pp. 288–90.

    Google Scholar 

  48. Hussein WF, Bennett PN, Carrasco A, Sun S, Reiterman M, Watson E, Schiller B. Changes in patient activation in people starting dialysis: a prospective longitudinal, observational study. Hemodial Int. 2022;26(3):435–48.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lightfoot CJ, Wilkinson TJ, Patel NA, Jones CR, Smith AC. Patient activation and psychological coping strategies to manage challenging circumstances during the COVID-19 pandemic in people with kidney disease. J Nephrol. 2024;37(2):353–64.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ramírez-Sánchez S, Soriano-Munuera MJ, Gras-Colomer EL, Cana-Poyatos A, García-Martínez T, Ortiz-Ramon R, Linares-Aguayo S, García-Testal A. Activation and disease control of patients on chronic hemodialysis: an observational study. Nefrologia (Engl Ed) 2024.

  51. Hussein WF, Bennett PN, Sun SJ, Reiterman M, Watson E, Farwell IM, Schiller B. Patient activation among prevalent hemodialysis patients: an observational cross-sectional study. J Patient Exp. 2022;9:23743735221112220.

    PubMed  PubMed Central  Google Scholar 

  52. UK Renal Registry. Final Report Published for Transforming Participation in Chronic Kidney Disease. 2019/01/10/T19:15:01 + 00:00 2019. https://www.thinkkidneys.nhs.uk/ckd/news/final-report-published-for-transforming-participation-in-chronic-kidney-disease/ (accessed May 26 2024).

  53. Andersen-Hollekim T, Solbjor M, Kvangarsnes M, Hole T, Landstad BJ. Narratives of patient participation in haemodialysis. J Clin Nurs. 2020;29(13–14):2293–305.

    Article  PubMed  Google Scholar 

  54. Montalescot L, Dorard G, Speyer E, Legrand K, Ayav C, Combe C, Stengel B, Untas A. Patient perspectives on chronic kidney disease and decision-making about treatment. Discourse of participants in the French CKD-REIN cohort study. J Nephrol. 2022;35(5):1387–97.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Ronksley PE, Hemmelgarn BR. Optimizing care for patients with CKD. Am J Kidney Dis. 2012;60(1):133–8.

    Article  PubMed  Google Scholar 

  56. Kahn LS, Vest BM, Madurai N, Singh R, York TR, Cipparone CW, Reilly S, Malik KS, Fox CH. Chronic kidney disease (CKD) treatment burden among low-income primary care patients. Chronic Illn. 2015;11(3):171–83.

    Article  PubMed  Google Scholar 

  57. Reid C, Seymour J, Jones C. A thematic synthesis of the experiences of adults living with Hemodialysis. Clin J Am Soc Nephrol. 2016;11(7):1206–18.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Fotheringham J, Barnes T, Dunn L, Lee S, Ariss S, Young T, Walters SJ, Laboi P, Henwood A, Gair R, et al. Rationale and design for SHAREHD: a quality improvement collaborative to scale up Shared Haemodialysis Care for patients on centre based haemodialysis. BMC Nephrol. 2017;18(1):335.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Morfin JA, Yang A, Wang E, Schiller B. Transitional dialysis care units: a new approach to increase home dialysis modality uptake and patient outcomes. Semin Dial. 2018;31(1):82–7.

    Article  PubMed  Google Scholar 

  60. Davison SN, Klarenbach S, Manns B, Schnick-Makaroff K, Buzinski R, Corradetti B, Short H, Johnson JA. Patient-reported outcome measures in the care of in-centre hemodialysis patients. J Patient Rep Outcomes. 2021;5(Suppl 2):93.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Jablonski A. The multidimensional characteristics of symptoms reported by patients on hemodialysis. Nephrol Nurs J. 2007;34(1):29–37. quiz 38.

    PubMed  Google Scholar 

  62. Johnson JA, Al Sayah F, Buzinski R, Corradetti B, Davison SN, Elliott MJ, Klarenbach S, Manns B, Schick-Makaroff K, Short H, et al. A cluster randomized controlled trial for the evaluation of routinely measured PATient reported outcomes in HemodialYsis care (EMPATHY): a study protocol. BMC Health Serv Res. 2020;20(1):731.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Duncanson E, Bennett PN, Viecelli A, Dansie K, Handke W, Tong A, Palmer S, Jesudason S, McDonald SP, Morton RL, et al. Feasibility and acceptability of e-PROMs data capture and feedback among patients receiving haemodialysis in the Symptom monitoring WIth Feedback Trial (SWIFT) pilot: protocol for a qualitative study in Australia. BMJ Open. 2020;10(11):e039014.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Center For Studying Health System Change. Health Tracking Household Survey, 2007 [United States]: Version 1. 2009 2009. https://www.icpsr.umich.edu/web/HMCA/studies/26001/versions/V1 (accessed May 25 2024).

  65. Fowles JB, Terry P, Xi M, Hibbard J, Bloom CT, Harvey L. Measuring self-management of patients’ and employees’ health: further validation of the patient activation measure (PAM) based on its relation to employee characteristics. Patient Educ Couns. 2009;77(1):116–22.

    Article  PubMed  Google Scholar 

  66. Smith SG, Pandit A, Rush SR, Wolf MS, Simon CJ. The role of patient activation in Preferences for Shared decision making: results from a National Survey of U.S. adults. J Health Commun. 2016;21(1):67–75.

    Article  PubMed  Google Scholar 

  67. de Leon EB, Campos HLM, Santos NB, Brito FA, Almeida FA. Patient activation levels and socioeconomic factors among the Amazonas population with diabetes: a cross-sectional study. BMC Health Serv Res. 2024;24(1):169.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Hendriks M, Rademakers J. Relationships between patient activation, disease-specific knowledge and health outcomes among people with diabetes; a survey study. BMC Health Serv Res. 2014;14(1):393.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Abukhdeir HF, Caplan LS, Reese L, Alema-Mensah E. Factors affecting the prevalence of chronic diseases in Palestinian people: an analysis of data from the Palestinian Central Bureau of Statistics. East Mediterr Health J. 2013;19(4):307–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Do V, Young L, Barnason S, Tran H. Relationships between activation level, knowledge, self-efficacy, and self-management behavior in heart failure patients discharged from rural hospitals. F1000Res 2015, 4:150.

  71. Lunardi LE, Matricciani RKLL, Xu LA, Britton Q, Jesudason A, Bennett S. Patient activation in advanced chronic kidney disease: a cross-sectional study. J Nephrol. 2024;37(2):343–52.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Gleason KT, Tanner EK, Boyd CM, Saczynski JS, Szanton SL. Factors associated with patient activation in an older adult population with functional difficulties. Patient Educ Couns. 2016;99(8):1421–6.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Ryvicker M, Feldman PH, Chiu YL, Gerber LM. The role of patient activation in improving blood pressure outcomes in black patients receiving home care. Med Care Res Rev. 2013;70(6):636–52.

    Article  PubMed  Google Scholar 

  74. Lubetkin EI, Lu WH, Gold MR. Levels and correlates of patient activation in health center settings: building strategies for improving health outcomes. J Health Care Poor Underserved. 2010;21(3):796–808.

    Article  PubMed  Google Scholar 

  75. Blakemore A, Hann M, Howells K, Panagioti M, Sidaway M, Reeves D, Bower P. Patient activation in older people with long-term conditions and multimorbidity: correlates and change in a cohort study in the United Kingdom. BMC Health Serv Res. 2016;16(1):582.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Zhu Y, Song Y, Wang Y, Ji H, Wang D, Cai S, Wang A. Relationships among social support, self-efficacy, and patient activation in community-dwelling older adults living with coronary heart disease: a cross-sectional study. Geriatr Nurs. 2022;48:139–44.

    Article  PubMed  Google Scholar 

  77. Corbett CF, Daratha KB, McPherson S, Smith CL, Wiser MS, Vogrig BK, Murphy SM, Cantu R, Dyck DG. Patient activation, depressive symptoms, and Self-Rated Health: Care Management intervention effects among High-Need, medically complex adults. Int J Environ Res Public Health. 2021;18(11):5690.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Pai AB, Cardone KE, Manley HJ, St Peter WL, Shaffer R, Somers M, Mehrotra R, Dialysis Advisory Group of American Society of N. Medication reconciliation and therapy management in dialysis-dependent patients: need for a systematic approach. Clin J Am Soc Nephrol. 2013;8(11):1988–99.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Wigneswaran J, St Peter WL, Nissenson AR, Krishnan M, Faris R, Becker B, Lorch J. Redefining Medication Management in Dialysis: a kidney pharmacy quality pyramid. Kidney Med. 2019;1(5):307–14.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Eljedi A, Mikolajczyk RT, Kraemer A, Laaser U. Health-related quality of life in diabetic patients and controls without diabetes in refugee camps in the Gaza strip: a cross-sectional study. BMC Public Health. 2006;6:268.

    Article  PubMed  PubMed Central  Google Scholar 

  81. AbuAlhommos AK, Alturaifi AH, Al-Bin Hamdhah AM, Al-Ramadhan HH, Al Ali ZA, Al Nasser HJ. The Health-Related Quality of Life of Patients with type 2 diabetes in Saudi Arabia. Patient Prefer Adherence. 2022;16:1233–45.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Trief PM, Wade MJ, Britton KD, Weinstock RS. A prospective analysis of marital relationship factors and quality of life in diabetes. Diabetes Care. 2002;25(7):1154–8.

    Article  PubMed  Google Scholar 

  83. Papadopoulos AA, Kontodimopoulos N, Frydas A, Ikonomakis E, Niakas D. Predictors of health-related quality of life in type II diabetic patients in Greece. BMC Public Health. 2007;7:186.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Molsted S, Wendelboe S, Flege MM, Eidemak I. The impact of marital and socioeconomic status on quality of life and physical activity in patients with chronic kidney disease. Int Urol Nephrol. 2021;53(12):2577–82.

    Article  PubMed  Google Scholar 

  85. Wang ZF, Cheng YC, Zhang NH, Luo R, Guo KL, Ge SW, Xu G. Effect of Marital Status on Depression and Mortality among patients with chronic kidney disease from National Health and Nutrition Examination Survey 2005–2014. Kidney Dis (Basel). 2021;7(5):391–400.

    Article  PubMed  Google Scholar 

  86. Liu H, Waite L, Shen S. Diabetes risk and Disease Management in later life: a National Longitudinal Study of the role of Marital Quality. J Gerontol B Psychol Sci Soc Sci. 2016;71(6):1070–80.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Almutairi N, Gopaldasani V, Hosseinzadeh H. Relationship between patient activation and type 2 diabetes Mellitus Self-management and clinical outcomes in Saudi Arabian primary care setting. Am J Health Promot 2023:8901171231224889.

  88. Nair R, Meadows E, Sheer R, Lipkovich I, Poon JL, Zhao Z, Benneyworth B, Pasquale M. Activation, physical activity, and outcomes among individuals with T2D. Am J Manag Care. 2022;28(8):374–80.

    Article  PubMed  Google Scholar 

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Acknowledgements

We are deeply grateful to the An-Najah National University Administration, and the An-Najah National University Hospital for their invaluable collaboration and generous permission to utilize their data. This access proved instrumental to our research and would not have been possible without their support.

Funding

No funding was received for this study.

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Authors and Affiliations

  1. Department of Medicine, College of Medicine and Health Sciences, An-Najah National University, Nablus, 44839, Palestine

    Jehad M. Zeidalkilani, Yazan A. Milhem & Reem N. Shorafa

  2. An-Najah Global Health Institute (GHI), An-Najah National University, Nablus, 44839, Palestine

    Sari Taha

  3. Department of Public Health, Faculty of Medicine and Health Sciences, An-Najah National University, P.O. Box 7, Nablus, Palestine

    Sari Taha

  4. Department of Anatomy, Biochemistry and Genetics, An-Najah National University, Nablus, 44839, Palestine

    Sari Taha

  5. Department of Clinical and Community Pharmacy, College of Medicine and Health Sciences, An-Najah National University, Nablus, 44839, Palestine

    Amer A. Koni, Samah W. Al-Jabi & Sa’ed H. Zyoud

  6. Division of Clinical Pharmacy, Department of Hematology and Oncology, An-Najah National University Hospital, Nablus, 44839, Palestine

    Amer A. Koni

  7. Poison Control and Drug Information Center (PCDIC), College of Medicine and Health Sciences, An-Najah National University, Nablus, 44839, Palestine

    Sa’ed H. Zyoud

  8. Clinical Research Centre, An-Najah National University Hospital, Nablus, 44839, Palestine

    Sa’ed H. Zyoud

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  1. Jehad M. Zeidalkilani
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  3. Reem N. Shorafa
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  7. Sa’ed H. Zyoud
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Contributions

J.M.Z., Y.A.M., and R.N.S. collected and analyzed data, reviewed the literature, and initially drafted the manuscript. S.W.A. contributed to study design, ensured data integrity, and participated in data analysis. S.T. managed the research enterprise, oversaw analytical methods application, data management (including extracting, or cleaning data), and manuscript preparation, including manuscript writing and analysis, designing tables and managing the publications process. A.A.K. assisted in manuscript writing and analysis, providing critical intellectual input. S.H.Z. conceived the study idea, designed the research, extensively revised the manuscript for intellectual content, and contributed to final manuscript preparation. All authors have reviewed and approved the final manuscript.

Corresponding author

Correspondence to Sa’ed H. Zyoud.

Ethics declarations

Ethics approval and consent to participate

The Institutional Review Board (IRB) of An-Najah National University, along with local health authorities, granted approval for all facets of the study procedure. An-Najah National University Hospital and Palestinian Ministry of Health granted permission to aid in data collection. Furthermore, explicit consent was acquired from all participating patients. It is affirmed that the collected data were solely utilized for clinical research, and the provided information will remain confidential, exclusively serving this research. Participants were assured of data privacy, and the confidentiality of the data for research purposes was emphasized. As our interviews did not gather any identifiable information and our study posed minimal risk to participants, the IRB of An-Najah National University approved only verbal informed consent from the study participants. We confirm that all experiments and methods were performed in accordance with relevant guidelines and regulations.

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The authors declare no competing interests.

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Zeidalkilani, J.M., Milhem, Y.A., Shorafa, R.N. et al. Factors associated with patient activation among patients with diabetes on hemodialysis: a multicenter cross-sectional study from a developing country. BMC Nephrol 25, 232 (2024). https://doi.org/10.1186/s12882-024-03674-z

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Keywords

BMC Nephrology

ISSN: 1471-2369

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