Abstract
-
Purpose
This descriptive correlational study aimed to evaluate the impact of patient activation on self-management and explore the mediating role of shared decision-making (SDM) among patients on hemodialysis.
-
Methods
A cohort of 136 participants was recruited from hemodialysis units in Gwangju, South Korea, between August 9 and 22, 2024. Patient activation, self-management, and SDM were assessed using the Patient Activation Measure (PAM-13), the Hemodialysis Self-Management Instrument (HDMI-K), and the 9-item Shared Decision-Making Questionnaire (SDM-Q-9), respectively. Descriptive statistics, Pearson’s correlation analysis, and mediation analysis using the PROCESS macro were conducted to analyze the data.
-
Results
Patient activation, SDM, and self-management were positively correlated with one another. Mediation analysis showed that patient activation significantly predicted both SDM and self-management. SDM also significantly predicted self-management, confirming its partial mediating effect. The final model explained 54.5% of the variance in self-management. The indirect effect of patient activation on self-management through SDM was statistically significant (indirect effect=0.05, 95% confidence interval [CI]=0.02–0.10). The indirect effect of patient activation on self-management through SDM was statistically significant (indirect effect=0.05, 95% CI=0.02–0.10).
-
Conclusion
Patient activation directly and indirectly enhances self-management through SDM, verifying the partial mediating role of SDM. Integrating SDM into nursing interventions is essential for effectively supporting self-management in patients undergoing hemodialysis.
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Key Words: Hemodialysis; Self-management; Decision making, shared
INTRODUCTION
Chronic kidney disease (CKD) is characterized by persistent kidney dysfunction lasting over 3 months, resulting in a gradual decline in glomerular filtration rate and eventual loss of renal function. CKD can become life-threatening without renal replacement therapy [
1]. Advances in medical technology and increased access to treatment have led to a continual rise in the incidence and prevalence of CKD. Consequently, the proportion of patients undergoing hemodialysis as renal replacement therapy has also increased [
1].
Patients undergoing hemodialysis often experience a considerable disease burden, as the dialysis process itself contributes to cumulative physical and mental fatigue, thereby significantly impairing quality of life [
1,
2]. Treatment regimens typically necessitate hospital visits two to three times per week, with each session lasting approximately 4 hours, imposing substantial restrictions on daily activities. In addition to physical exhaustion, patients frequently encounter psychological and social challenges [
1-
3]. During the initial stages of hemodialysis, patients may experience shock following diagnosis, leading to denial or passive coping behaviors. Over time, adhering to dietary restrictions, fluid management, and medication regimens becomes challenging, making self-management increasingly difficult [
3,
4].
Self-management is defined as the process of maintaining health through health-promoting practices and effective illness management [
5]. This comprehensive concept extends beyond mere adherence to treatment, encompassing lifestyle modifications necessitated by the disease and psychosocial adjustments [
5,
6]. Inadequate self-management can result in complications and disease progression, underscoring the importance of proactive self-care among patients undergoing hemodialysis [
4]. Healthcare professionals play a pivotal role in strengthening patients’ self-management capabilities through continuous monitoring and support.
Patient activation refers to a patient’s possession of the necessary knowledge, skills, and confidence required to effectively manage their health [
7]. It serves as an essential precursor to successful self-management practices [
8]. Higher levels of patient activation correlate with increased patient engagement in health management and collaborative interactions with healthcare professionals in making treatment decisions [
8,
9]. Hibbard et al. [
10] identified patient activation as a key factor in enhancing self-management efficacy, whereas Hussein et al. [
9] observed that increased patient activation is associated with more proactive participation in self-care activities, particularly among patients on hemodialysis.
Shared decision-making (SDM) is a collaborative process wherein patients and healthcare providers exchange information and jointly determine the treatment plan [
11]. This approach is pivotal for improving patient health outcomes and satisfaction with care [
12]. SDM positively influences various treatment aspects in patients on hemodialysis, including dialysis modality selection and vascular access methods [
13]. Higher SDM levels correlate with improved patient understanding of their treatment and increased adherence to therapeutic regimens [
12,
13].
SDM and patient activation are closely related; patients with higher activation levels are more likely to actively participate in the SDM process [
14,
15]. Vitger et al. [
15] reported that SDM enhances patient activation, boosts confidence in communicating with healthcare providers, and promotes self-management. In hemodialysis, participation in SDM fosters a patient-centered care environment, improving dietary control, adherence to fluid restrictions, and proactive disease-management attitudes [
16]. Additionally, higher SDM levels have been linked to improved clinical outcomes, including better blood pressure control, blood glucose management, and mental health status through enhanced self-management [
15,
17].
The relationship among patient activation, SDM, and self-management can be theoretically explained by the Middle-Range Theory of Self-Care of Chronic Illness proposed by Riegel et al. [
5]. This theory emphasizes that self-care behaviors occur through decision-making processes. Within this framework, patient activation serves as a motivational foundation for engaging in self-care, while SDM functions as a facilitating mechanism, enabling patients to make informed judgments regarding changes in their health status. In this context, SDM can be interpreted as a mediating mechanism that translates patient activation into concrete self-management behaviors.
While previous studies have identified associations among patient activation, SDM, and self-management, most have primarily examined correlations. Given the theoretical connections among these variables, it is important to investigate whether SDM mediates the relationship between patient activation and self-management, particularly among patients undergoing hemodialysis. Mediation analysis is useful for determining whether SDM mediates the effect of patient activation on self-management and serves as a critical method for identifying effective nursing intervention strategies.
Accordingly, this study aimed to analyze the impact of patient activation on self-management and determine whether SDM serves as a mediator in this relationship, with the ultimate goal of informing practical nursing approaches to enhance self-management among patients undergoing hemodialysis.
METHODS
1. Study Design
A descriptive correlational design was employed to analyze the relationships among patient activation, self-management, and SDM in patients undergoing hemodialysis and to assess the mediating role of SDM. The manuscript was prepared in accordance with the STROBE statement for cross-sectional observational research.
2. Participants
Participants were recruited from the hemodialysis units of three general hospitals and one medical clinic located in Gwangju City. These institutions were chosen based on their high patient volumes, willingness to participate, and accessibility for data collection. Inclusion criteria comprised adult patients diagnosed with CKD undergoing regular hemodialysis two to three times weekly for at least 6 months, who understood the study’s purpose and consented to participate. Exclusion criteria included patients diagnosed with cognitive impairments such as dementia or psychiatric disorders, those unable to communicate, and those unable to respond to written surveys.
The required sample size was determined using G-Power 3.1.9.7 [
18], considering 10 predictor variables, a significance level of .05, a statistical power of .80, and an effect size of 0.15, classified as medium-to-large according to the literature [
19]. This calculation indicated that a minimum of 118 participants was necessary. Accounting for a potential dropout rate of 20%, 142 participants were recruited. After excluding six participants for insincere responses, data from 136 participants were retained. Mediation analysis was performed using bootstrapping. According to Fritz and MacKinnon [
20], a sample of this size is adequate to detect medium-sized indirect effects with 80% power, ensuring statistical validity for mediation analysis.
1) Patient activation
Patient activation was assessed using the Korean version of the Patient Activation Measure (PAM-13) [
21], originally developed by Hibbard et al. [
7]. This instrument evaluates an individual's knowledge, skills, and confidence related to health management. Each item is rated on a 4-point Likert scale, with higher scores indicating greater patient activation. Scores classify patient activation into four levels: level 1 (≤47.0), indicating no recognition of the need for an active health management role; level 2 (47.1–55.1), denoting insufficient knowledge, skills, and confidence to act proactively; level 3 (55.2–67.0), reflecting initiation of active health-related behavior; and level 4 (≥67.1), representing sustained proactive self-management, even under stress. The original instrument reported a Cronbach’s alpha of .87 [
7], while a previous study by Ahn et al. [
22] reported a reliability coefficient of .88. In this study, Cronbach’s alpha was .92.
2) Self-management
Self-management behaviors were measured using the Hemodialysis Self-Management Instrument-Korean version (HDMI-K), adapted from the original Hemodialysis Self-Management Instrument (HDMI) developed by Song and Lin [
23] and translated into Korean by Cha and Kang [
24]. The HDMI-K consists of 20 items across four sub-domains: problem-solving and communication, fluid and weight management, diet and dialysis management, and self-advocacy and emotional management. Responses were recorded using a 4-point Likert scale, with higher scores indicating better self-management practices. The original instrument showed a Cronbach’s alpha of .87 [
21], while Cha and Kang [
24] reported .89. The alpha coefficient in the present study was .92.
3) Shared decision-making
The extent of SDM was evaluated using the 9-item Shared Decision-Making Questionnaire (SDM-Q-9) developed by Kriston et al. [
25]. This tool assesses the degree to which patients perceive involvement in healthcare providers' decision-making processes. Each item is rated on a 5-point Likert scale, with higher scores indicating greater SDM levels. The original instrument demonstrated a Cronbach’s alpha of .93 [
25]. A previous study by Shin [
19] using the Korean version reported reliability at 0.96; in this study, Cronbach’s alpha was .94.
Data collection occurred from August 9 to 22, 2024. The researcher visited three general hospitals and one internal medicine clinic in Gwangju to explain the study’s purpose and procedures to hospital directors, nephrology staff, and participants. These institutions were selected among those operating hemodialysis units based on patient volume, willingness to participate, and accessibility for data collection. Recruitment notices prepared by the research team were posted on bulletin boards at participating facilities, with hospital administrators’ permission. In some cases, hospital staff assisted in posting notices within dialysis units. Eligible patients on hemodialysis who voluntarily expressed interest through recruitment notices received detailed study information from research assistants. After providing written informed consent, participants completed the surveys independently, with research assistants offering clarification without influencing responses. Surveys were administered following participants’ outpatient dialysis sessions, after a 15-minute rest period post-dialysis, as recommended by nephrologists.
To ensure confidentiality, completed questionnaires were sealed in envelopes. A total of 142 questionnaires were distributed and collected. After excluding six insincere responses, 136 were included in the final analysis.
5. Ethical Considerations
This study was approved by the Institutional Review Board of Nambu University (approval No. 1041478-2024-HR-006). Participants were informed about the study’s purpose, procedures, potential discomfort, confidentiality measures, and their right to withdraw at any time without penalty. Surveys were processed anonymously, without identifying information, and data were stored in password-protected files accessible only to the principal investigator and research staff. Collected data will be retained for 3 years, after which physical documents will be shredded, and electronic files permanently deleted. Participants were offered a small token of appreciation for their involvement.
6. Data Analysis
The collected data were analyzed using SPSS Win version 29.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics, including frequencies, percentages, means, and standard deviations, were calculated to describe participants’ general characteristics. Independent t-tests and one-way analysis of variance were performed to examine differences in patient activation, self-management, and SDM across demographic variables, with Scheffé’s test conducted for post-hoc analyses. Pearson’s correlation coefficients were computed to assess relationships among patient activation, self-management, and SDM. The mediating effect of SDM on the relationship between patient activation and self-management was evaluated using Hayes’ PROCESS macro (model 4). Hayes developed the PROCESS macro [
26], a statistical tool used in SPSS and SAS for mediation, moderation, and conditional process model analyses, employing bootstrapping methods to determine the significance of indirect effects.
RESULTS
1. General Characteristics
The study included 136 participants with a mean age of 59.14±13.37 years. Men comprised 64.7% (n=88) of the sample, and women accounted for 35.3% (n=48). Regarding educational attainment, 72.8% (n=99) had completed high school or university-level education. The most common comorbidities among participants were hypertension (69.9%) and diabetes (48.5%). The average duration of hemodialysis treatment was 61.15±57.32 months. Additionally, 73.5% (n=100) had received dialysis self-management education, and 83.1% (n=113) had participated in decisions regarding their dialysis modality (
Table 1).
The analysis revealed significant differences in patient activation, self-management, and SDM scores based on education level, presence of comorbid conditions, experience with self-management education, and involvement in dialysis modality decision-making (
Table 1).
1) Patient activation
Patient activation scores differed significantly by education level (F=4.23, p=.017). Participants with less than a high school education reported significantly lower activation scores compared to those with high school to college education. Additionally, higher activation scores were observed among participants with hypertension (t=−2.47, p=.015), those who had received self-management education (t=3.51, p=.001), and those who participated in dialysis modality decision-making (t=3.58, p<.001).
2) Self-management
Self-management scores also significantly differed by education level (F=6.10, p=.003). Participants with less than a high school education reported significantly lower scores compared to those with high school to college education. Higher self-management scores were also observed among participants without diabetes (t=3.06, p=.003), those who had received self-management education (t=2.88, p=.005), and those involved in dialysis modality decision-making (t=3.19, p=.004).
3) Shared decision-making
SDM scores were significantly higher among male participants than among female participants (t=2.54, p=.013). Additionally, SDM scores varied significantly according to education level (F=7.23, p=.001), with participants having graduate-level education reporting significantly higher SDM scores compared to those with less than a high school education. Higher SDM scores were also significantly associated with having received self-management education (t=3.57, p=.001) and participating in dialysis modality decision-making (t=4.63, p<.001).
No significant differences were found based on age, the presence of cohabiting family members, the duration of hemodialysis, or other comorbidities such as coronary artery disease, malignancy, or autoimmune diseases.
3. Patient Activation, Self-Management, and SDM
Participants’ average scores for patient activation, self-management, and SDM were 61.10±16.25, 57.63±9.96, and 73.02±18.63, respectively. Patient activation levels were distributed as follows: level 1 included 25 participants (18.4%), level 2 included 25 participants (18.4%), level 3 included 39 participants (28.7%), and level 4 included 47 participants (34.5%) (
Table 2).
Patient activation was significantly positively correlated with self-management (r=0.67,
p<.001) and SDM (r=0.47,
p<.001). Additionally, self-management showed a significant positive correlation with SDM (r=0.52,
p<.001) (
Table 3).
Key assumptions were assessed to ensure the validity of the regression analysis. The Durbin-Watson statistic was 2.170, smaller than the critical value of 2.235 (du<d<4–du), indicating no autocorrelation and thus confirming the independence of observations. Multicollinearity was evaluated using the variance inflation factor (1.317), which remained below the threshold of 10, demonstrating the absence of multicollinearity. Homoscedasticity was verified through the Breusch-Pagan test (p=.267), confirming constant variance of residuals. These findings demonstrated that the regression model satisfied fundamental assumptions, supporting its appropriateness for the analysis.
The mediating effect of SDM on the relationship between patient activation and self-management was analyzed using Hayes’ PROCESS macro model 4 [
26]. The analysis controlled for variables that exhibited significant differences in self-management scores: education level, diabetes presence, self-management education experience, and involvement in dialysis modality decision-making. Patient activation was significantly associated with both SDM (β=.33,
p<.001) and self-management (β=.52,
p<.001). Furthermore, SDM exhibited a significant positive impact on self-management (β=.25,
p=.001). The overall model explained 54.5% of the variance in self-management (R²=.545) (
Table 4). Bootstrapping analysis with 5,000 resamples indicated a statistically significant indirect effect of patient activation on self-management through SDM (indirect effect=0.05, 95% confidence interval=0.02–0.10), confirming that SDM partially mediated the relationship between patient activation and self-management (
Table 5).
DISCUSSION
This study examined the mediating effect of SDM on the relationship between patient activation and self-management in patients undergoing hemodialysis. The analysis confirmed that SDM partially mediated this relationship, indicating that patient activation contributes directly and indirectly to self-management through SDM. These findings highlight that, while higher patient activation promotes self-management behaviors, the effectiveness and sustainability of these behaviors are enhanced when patients actively engage in SDM by communicating and collaborating with healthcare professionals. By empirically demonstrating the mediating role of SDM, this study provides critical insights into its underlying mechanism and underscores the importance of integrating SDM strategies into nursing interventions for patients undergoing hemodialysis.
In this study, the average activation level among patients undergoing hemodialysis corresponded to level 3, representing the initiation of active health-related behaviors, similar to scores previously reported for patients with chronic diseases [
10,
19]. However, there was considerable variation in activation levels among patients. Consequently, tailored interventions accounting for individual patient characteristics are necessary, and strategies such as employing digital tools [
15,
27] and peer support [
28] may effectively enhance patient activation. Additionally, while participants scored highly in the “problem-solving and communication” domain of self-management, their scores were lower in the “self-advocacy and emotional regulation” domain. This indicates that patients on hemodialysis are accustomed to managing their condition through interactions with healthcare professionals but may remain vulnerable when managing emotional burdens. Therefore, self-management education incorporating self-advocacy and emotion regulation is essential, along with providing support to help patients maintain emotional stability and strengthen self-advocacy skills [
29].
This study found educational level to be significantly associated with patient activation, SDM, and self-management. This finding is consistent with previous research [
9,
30] and suggests that higher educational attainment is linked to improved ability to understand and utilize health information, along with increased active participation in decision-making processes. Conversely, patients with lower educational attainment may have reduced health literacy and a more passive approach to self-management and decision-making, necessitating tailored health education strategies. Developing educational content using visual materials and digital tools and providing individualized health education through healthcare professionals are recommended strategies [
28,
31].
Patient activation, SDM, and self-management were significantly correlated in this study, validating the theoretical and statistical assumptions required for mediation analysis. Although these correlations indicate meaningful associations, they do not fully explain how patient activation translates into improved self-management. Therefore, this study further explored the mediating role of SDM in this relationship, confirming that SDM significantly mediates the relationship between patient activation and self-management in patients undergoing hemodialysis. This finding suggests that SDM functions not merely as a method of treatment participation but as a core mechanism supporting the internalization and sustained practice of self-management behaviors.
First, higher patient activation levels were associated with greater engagement in SDM, indicating that patients who exhibit greater interest and responsibility for their health tend to actively participate in decision-making processes with healthcare professionals. These results align with previous studies [
14,
15] and emphasize that patient activation is not confined to knowledge or attitudes but also encompasses psychological readiness leading to behavioral changes [
7]. Highly activated patients proactively seek treatment-related information, ask questions, and suggest options, thereby becoming active partners in the decision-making process.
In addition, patient activation demonstrated a significant direct effect on self-management. This finding indicates that patient activation independently enhances self-management behaviors, beyond its impact through SDM. Patients with higher activation levels are more likely to engage in proactive health behaviors, exhibit greater self-awareness, and assume responsibility for managing their condition. These results are consistent with previous studies identifying patient activation as a significant predictor of self-management [
8,
10,
19].
The finding that SDM significantly impacts self-management behaviors indicates that through information sharing and collaborative discussions, patients can fully understand and accept their health status and treatment options, facilitating the integration of self-management into daily life. Previous studies also report that active patient involvement through SDM improves self-efficacy and strengthens self-management behaviors [
32]. This finding aligns with prior research emphasizing the role of SDM, particularly for chronic conditions requiring long-term, complex self-management [
12,
13,
16,
19].
The indirect effect of patient activation on self-management through SDM was statistically significant, supporting SDM’s role as a critical mediating pathway enhancing patient engagement in health management. This finding suggests that willingness alone to manage health does not sufficiently translate into actual self-management behaviors. Rather, patients must first fully understand their health conditions and treatment options and then make informed decisions based on their values through the SDM process. This result aligns with prior research involving patients with hypertension, demonstrating the mediating role of SDM in the relationship between patient activation and self-management [
19].
Although previous literature has primarily highlighted SDM’s effects on patients’ cognitive understanding and emotional satisfaction [
12], the present study extends this perspective by empirically demonstrating SDM as a mediating variable within a structural pathway leading to behavioral change, specifically self-management. In other words, for highly activated patients undergoing hemodialysis, receiving disease-specific information and empowerment through SDM enhances self-efficacy. This increased self-efficacy subsequently fosters the confidence and motivation needed to implement self-management behaviors [
16]. Such psychological reinforcement can translate into actual improvements in self-management capacity, which is especially crucial for patients undergoing hemodialysis, where highly structured and sustained self-management is essential.
These findings suggest that SDM serves not merely as a means of information exchange or formal patient participation, but also as a mechanism that stimulates patients’ intrinsic motivation and facilitates their execution of self-management behaviors. Accordingly, in nursing practice, SDM should be recognized not simply as a communication technique but as a strategic approach to enhancing patients’ self-management capabilities. This perspective is particularly relevant for patients undergoing hemodialysis, who must routinely perform complex self-care tasks—such as dietary and fluid restriction, medication adherence, and regular health monitoring—alongside attending treatment sessions two to three times weekly. In such contexts, the importance of effectively implementing SDM becomes even more pronounced.
Nurses, as healthcare professionals most closely interacting with patients during the hemodialysis process, are uniquely positioned to implement SDM in clinical practice. Previous study [
33] has emphasized nurses’ critical role as both information providers and mediators who incorporate patient preferences into clinical decision-making. Iida et al. [
16] further recommended that nurses fully engage in the SDM process by setting shared goals with patients, providing tailored information, and conducting ongoing evaluations and consultations. Although Marriott-Statham et al. [
34] noted practical challenges to effectively implementing SDM in clinical nursing settings, the roles of information provider, mediator, and facilitator align inherently with nurses’ professional responsibilities in chronic illness care. When nurses actively involve patients in care planning and incorporate their preferences, SDM becomes a feasible and meaningful practice in real-world clinical environments.
To enhance self-management among patients with chronic illnesses, nurses can apply several key SDM strategies. These include providing individualized information, collaboratively setting care goals, and integrating patients’ values into care decisions. Additionally, by offering continuous feedback, coordinating multidisciplinary care, and fostering emotional support and self-efficacy, nurses can strengthen patients’ motivation and active engagement in their health management. Given their essential roles as information providers, facilitators, and care coordinators, nurses are ideally positioned to integrate SDM into their practice, thereby promoting sustained patient self-management.
For SDM-based nursing interventions to be effectively implemented, it is crucial first to provide competency-based training for healthcare providers [
35] to enhance their relevant capabilities. Moreover, the development and implementation of standardized decision-making guidelines and practical decision aids tailored for clinical use are required. Institutional-level strategies must also be established to ensure the consistent and sustained application of SDM in clinical practice. Such strategies may include allocating sufficient consultation time, implementing supportive decision-making systems, and developing policy-linked institutional frameworks to facilitate SDM across healthcare environments.
This study has several limitations. First, its cross-sectional design precludes causal inference regarding the relationships among patient activation, SDM, and self-management. Future longitudinal or experimental studies are needed to clarify the directionality of these associations. Second, data were collected using self-reported questionnaires, which may introduce response or recall bias, potentially affecting the accuracy of the findings. Third, participants were recruited from a limited number of dialysis centers, potentially restricting the generalizability of the findings to other settings or patient populations. Therefore, caution is advised when applying these results to broader clinical contexts. Despite these limitations, this study provides meaningful evidence regarding the role of SDM in hemodialysis care and suggests practical approaches for enhancing patient-centered interventions.
CONCLUSION
This study confirmed that SDM serves as a significant mediator in the relationship between patient activation and self-management. Consequently, it is essential to systematically incorporate SDM elements into patient education and self-management intervention programs by establishing appropriate educational and support systems for healthcare professionals. Future research should focus on developing effective nurse-patient SDM interventions and identifying specific strategies suitable for clinical application.
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CONFLICTS OF INTEREST
The authors declared no conflict of interest.
-
AUTHORSHIP
Study conception and/or design acquisition - SYY; analysis - MYK and SYY; interpretation of the data - MYK and SYY; and drafting or critical revision of the manuscript for important intellectual content - MYK and SYY.
-
FUNDING
None.
-
ACKNOWLEDGEMENT
This article is a condensed form of the first author’s master’s thesis from Nambu University.
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DATA AVAILABILITY STATEMENT
The data can be obtained from the corresponding authors.
Table 1.General Characteristics of Participants and Differences in Patient Activation, Shared Decision-Making, and Self-Management According to General Characteristics (N=136)
|
Characteristics |
Categories |
n (%) or M±SD |
Patient activation |
Self-management |
Shared decision-making |
|
M±SD |
t or F (p) |
M±SD |
t or F (p) |
M±SD |
t or F (p) |
|
Age (year) |
|
59.14±13.37 |
|
|
|
|
|
|
|
Sex |
Men |
88 (64.7) |
61.92±16.00 |
0.79 (.430) |
58.16±9.86 |
0.85 (.399) |
76.26±15.51 |
2.54 (.013) |
|
Women |
48 (35.3) |
59.61±16.77 |
56.65±10.17 |
67.08±22.27 |
|
Education†
|
Less than high schoola
|
31 (22.8) |
54.15±18.26 |
4.23 (.017) |
52.52±11.35 |
6.10 (.003) |
62.44±23.73 |
7.23 (.001) |
|
High school to collegeb
|
99 (72.8) |
63.49±15.30 |
a<b†
|
59.34±9.14 |
a<b†
|
75.91±16.04 |
a<c†
|
|
Graduate schoolc
|
6 (4.4) |
57.68±10.76 |
|
55.67±6.50 |
|
80.00±5.79 |
|
|
Cohabitation Status |
Alone |
35 (25.7) |
60.06±18.19 |
–0.44 (.662) |
56.86±10.96 |
-0.53 (.598) |
71.05±18.03 |
–0.73 (.469) |
|
With family |
101 (74.3) |
61.49±15.61 |
57.89±9.63 |
73.71±18.88 |
|
Comorbidities |
Diabetes |
Yes |
66 (48.5) |
58.72±17.26 |
1.67 (.097) |
55.02±10.46 |
3.06 (.003) |
73.81±19.71 |
0.51 (.614) |
|
No |
70 (51.5) |
63.35±15.03 |
60.09±8.85 |
72.19±17.53 |
|
Hypertension |
Yes |
95 (69.9) |
63.32±15.50 |
–2.47 (.015) |
58.48±8.91 |
–1.54 (.126) |
74.74±16.53 |
–1.64 (.103) |
|
No |
41 (30.1) |
55.96±16.97 |
55.63±11.93 |
69.05±22.51 |
|
Coronary disease |
Yes |
25 (18.4) |
60.48±14.63 |
0.21 (.834) |
57.60±9.17 |
0.01 (.989) |
75.64±18.74 |
–0.78 (.438) |
|
No |
111 (81.6) |
61.24±16.66 |
57.63±10.17 |
72.43±18.64 |
|
Dialysis duration (month) |
61.15±57.32 |
|
|
|
|
Self-management education experience |
Yes |
100 (73.5) |
63.92±15.51 |
3.51 (.001) |
59.06±9.07 |
2.88 (.005) |
76.60±16.66 |
3.57 (.001) |
|
No |
36 (26.5) |
53.26±15.91 |
53.64±11.29 |
63.09±20.40 |
|
Participated in dialysis modality selection |
Yes |
113 (83.1) |
63.26±15.88 |
3.58 (<.001) |
59.03±8.97 |
3.19 (.004) |
76.87±15.17 |
4.63 (<.001) |
|
No |
23 (16.9) |
50.49±13.95 |
50.74±11.80 |
54.11±22.55 |
Table 2.Patient Activation, Shared Decision-Making, and Self-Management among Participants (N=136)
|
n (%) or M±SD |
Min |
Max |
Range |
|
Patient activation |
61.10±16.25 |
26 |
100 |
1–100 |
|
Level 1 (≤47.0) |
25 (18.4) |
|
|
|
|
Level 2 (47.1–55.1) |
25 (18.4) |
|
|
|
|
Level 3 (55.2–67.0) |
39 (28.7) |
|
|
|
|
Level 4 (≥67.1) |
47 (34.5) |
|
|
|
|
Self-management |
57.63±9.96 |
26 |
78 |
20–80 |
|
Shared decision-making |
73.02±18.63 |
0.0 |
100 |
0–100 |
Table 3.Correlations between Patient Activation, Self-Management and Shared Decision-Making (N=136)
|
r (p) |
|
Patient activation |
Self-management |
Shared decision-making |
|
Patient activation |
1 |
|
|
|
Self-management |
.67 (<.001) |
1 |
|
|
Shared decision-making |
.47 (<.001) |
.52 (<.001) |
1 |
Table 4.Mediation Analysis of Patient Activation, Self-Management, and Shared Decision-Making (N=136)
|
Variables |
Shared decision-making |
Self-management |
|
B |
SE |
β |
t |
p
|
B |
SE |
β |
t |
p
|
|
Patient activation |
0.38 |
0.09 |
.33 |
4.33 |
<.001 |
0.32 |
0.04 |
.52 |
7.36 |
<.001 |
|
Shared decision-making |
|
|
|
|
|
0.13 |
0.04 |
.25 |
3.33 |
.001 |
|
F (p) |
12.96 (<.001) |
21.90 (<.001) |
|
R2
|
.376 |
.545 |
Table 5.Significance Test of the Mediation Effect (N=136)
|
Effect |
BootSE |
95% CI |
|
BootLLCI |
BootULCI |
|
Total effect |
0.37 |
0.04 |
0.28 |
0.45 |
|
Direct effect |
0.32 |
0.04 |
0.23 |
0.40 |
|
Indirect effect |
0.05 |
0.02 |
0.02 |
0.01 |
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