DOI: https://doi.org/10.21203/rs.3.rs-1103422/v1
Although studies indicate that resilience is related to care burden, and depressive symptoms, the underlying mechanism between those variables remains unknown. Thus, the present study aimed to explore the potential mediating role of resilience between care burden and depressive symptoms.
A cross-sectional study was conducted with a convenience sample of 245 main family caregivers of stroke patients recruited from the neurology department of Tertiary A hospital of China. The self-designed demographic characteristics for patients and caregivers、Barthel Daily Living Activities Index (BDLAI)、Zarit Caregiver Burden Interview (ZBI)、Connor-Davidson Resilience Scale (CD-RISC), and Center for Epidemiological Studies Depression Scale (CES-D) were used for investigation. Structural equation modeling (SEM) was conducted to explore the relationships between care burden, resilience, and depressive symptoms among the main family caregivers of stroke patients༎
The average scores of care burden, resilience, and depressive symptoms for caregivers were 43.89 ± 13.40, 55.68 ± 11.01, and 22.33 ± 9.85, respectively. Pearson correlation analysis results showed that the care burden was positively related to depressive symptoms (r = 0.578, p < 0.01), resilience was significantly negatively related to both care burden (r = -0.264, p < 0.01) and depressive symptoms (r = -0.697, p < 0.01). Structural equation models (SEM) analysis indicated that resilience partially mediated the relationship between care burden and depressive symptoms with the mediation effect ratio of 23.8%.
Our study signifies that resilience plays a mediating role between care burden and depressive symptoms among the main family caregivers of stroke patients. This finding shows us that resilience can be a critical source to alleviate depressive symptoms.
Stroke is recognized as one of the leading causes of adult disability and mortality worldwide[1, 2], particularly in China[3]. Stroke survivors currently cope with significant physical, cognitive, and emotional impairments, and more than two-thirds of these survivors require help with normal daily activities[4]. However, due to the insufficient health care and economic burden of sports specialists (such as physical therapists), most stroke survivors are cared for and assisted by their families after hospital discharge[5]. Family caregivers subsequently have reported experiencing difficulties in maintaining employment, financial, sleep, leisure activities[6]and socialization[7], which can be detrimental to carers’ quality of life, fatigue level, physical and mental health[8, 9]. The care burden is the physical, psychological, and social disruption related to the negative caring experience that was divided into objective and subjective components[10]. Studies have reported that 68.4% of the caregivers in China had a moderate burden and above[11], rates that are higher than those reported among stroke patients[12, 13]. Previous research has shown that care burden in caregivers of stroke patients will increase over time[14]and even contribute to depression if the proper intervention is not provided[11].
It reported that 53.9% of the caregivers of stroke patients in China have varying degrees of depressive symptoms[11], which might be related to the care burden of family caregivers of stroke patients. Family caregivers are facing a tremendous financial burden, social pressure, and mental distress[15]. A study has shown that a high incidence of caregivers’ negative emotions, including low satisfaction with leisure time[16], loss of happiness, loneliness, frustration, and feelings of being prisoners in their own homes[17]. What is more, a heavy burden not only leads to caregivers’ emotional exhaustion but also reduces the enthusiasm of caregivers and affects the quality of care provided[18]. However, not every caregiver who has care burden will experience depression, which emphasizes the importance of some protective factors, such as resilience. Resilience is defined as the ability to adapt well in the face of trauma or adversity[19]. Studies have shown that people with higher resilience would actively cope with adversity and quickly adapt to changes[20]. Some recent researches have also found that resilience partially serves as a mediator between negative life events and depressive symptoms[21, 22]. However, there is a dearth of evidence on mediating the care burden and depressive symptoms among family caregivers of stroke patients.
Based on the above literature studies, it can be seen that there is a correlation between care burden and depressive symptoms, and this relationship may be regulated by resilience. Therefore, we assume that the resilience of family caregivers of stroke patients is negatively correlated with both care burden and depressive symptoms, the care burden positively associated with depressive symptoms, and resilience mediated the relationship between care burden and depressive symptoms.
The present study used a cross-sectional study, and a convenient sampling method. The participants were recruited from the Neurology department ward of a tertiary hospital in Shenyang, China from November 2020 to July 2021. Ethical counsel permit (Approval number: 2020-402) was approved by the Medical Ethical Committee of the First Affiliated Hospital of China Medical University, and informed consent was received from each participant. Informed consent was also obtained from the participants under the Helsinki Accords.
Inclusion criteria were: 1) diagnosed as stroke by brain computed tomography angiography scan or magnetic resonance imaging; 2) the score of The Barthel Daily Living Activities Index ≤95; 3) caregivers were 18 years old and above; 4) the main caregiver was a family member of the patient; 5) who spent the longest time with patients per day; 6) unpaid care; 7) the care time was 3 months or more; 8) who voluntary participation in the study. The exclusion criteria were as follows: 1) The caregivers suffered from one or more stressful life events within 2 weeks (such as divorce、widowhood and loss of employment); 2) the caregiver had a serious physical illness, such as malignancy or intellectual-psychiatric problems; 3) incomplete investigation due to communication or reading and writing obstacles.
From November 2020 to July 2021, all 250 questionnaires were collected with 245 questionnaires included in data analyses. Five questionnaires were missing data and excluded (valid response rate = 98%). Data collection was done by one trained researcher using self-reported questionnaires. The researcher explained the aims of the study to participants and informed them that the collected data will be kept confidential, and they had the right to refuse participation. If they agree to participate, they will sign a written informed consent. Questionnaires were completed independently and collected immediately, which took approximately 20-25 min to complete. Those who wished to terminate participation midway through the study were allowed to do so to avoid answering deviations related to forced participation.
The demographic data collected from the patients included gender, age, onset times, chronic disease, insurance, stroke subtypes, side of hemiplegia, and ability of self-care. The demographic data collected from the caregivers were gender, age, education status, monthly income, working status, relationship with the patient, chronic disease, living with patients, care time per day, and duration of care time.
The BDLAI was developed by Mahoney in 1965, has been used to assess self-care activities including eating, bathing, grooming, dressing, toilet use, transferring from bed to chair, walking, stair climbing, bowel continence, and urinary continence[23]. The scale has a total of 10 items and is scored based on a five-point Likert. The total scores ranges from 0 (total dependence) to100 (total independence), with 0-20 points defining total dependence, 21-60 advanced level of dependence, 61-90 intermediate level of dependence, 91-95 mild level of dependence, and 95-100 total independence[23]. The validity and reliability of this tool for use in the Chinese population of older people have been well-established[24]. In this study, Cronbach’s alpha value was 0.869.
We used the Chinese version of the ZBI scale[25], which was designed to assess the stress experienced by family caregivers of older people [26]. The scale can be divided into two dimensions: role strain and personal strain[27], is a 5-point Likert-type scale with scores from “0” to “4”, never (0), seldom (1), sometimes (2) often (3), or almost always (4)[26]. The total score is from 0 to 88. The higher the total score, the higher the load. If the load is between 0 and 20 points, it implies little or no load; between 21 and 40, intermediate level of load; between 41 and 60, high level of load, and between 61 to 88 is rated as excessive load[26]. The Chinese version of ZBI has satisfactory psychometric properties[28].In this study, Cronbach’s alpha value was 0.934.
The CD-RISC scale was originally developed by Connor and Davidson[29] and translated into Chinese by Yu and Zhang[30], is one of the most widely used scales to measure resilience. The scale consists of 25-items, categorized into three factors-tenacity, strength, and optimism[31], a 5-point Likert-type scale where 0 indicated “not true at all” and 4 “true nearly all the time” was designed to collect the data[29]. The total scores range from 0 to 100, a higher score indicated a higher level of resilience[29]. The Chinese version of CD-RISC showed good reliability and validity[31]. In this study, Cronbach’s alpha value was 0.935.
We used the Chinese version of the CES-D scale, which was designed to evaluate the depressive symptoms and risk of having a disorder of a (non-psychiatric population) person[32]. It is a 20-items self-reported questionnaire, assesses four factors-depressed feelings, somatic complaints, positive feelings, and international relationships, takes a 4-point Likert scale, ranging from “rarely or none of the time” with 0 points to “most of the time” with 3 points[32]. The total score ranges from 0 to 60, those with scores ≥ 16 were considered to have an elevated level of depressive symptoms[32]. Furthermore, those who scored 16-23 and those who scored ≥24 were classified as “moderate” and “severe” cases of depressive symptomatology[33]. The Chinese version of CES-D has been widely used in China and has good reliability and validity[34]. In this study, Cronbach’s alpha value was 0.945.
Statistical analyses were conducted utilizing SPSS version 26.0. Normal distribution tests were verified by using Kolmogorov-Smirnov and Shapiro-Wilk statistics. Continuous variables were presented as mean and standard deviation (SD), whereas categorical variables were presented as frequency and percentages (%). Independent sample t-test or single-factor variance to identify differences in depression concerning the characteristics of caregivers and stroke survivors. Pearson correlation analysis was used to explore the relationship between care burden, resilience, and depression. The structural equation modeling was used to determine the hypothetical mediation model and the relationship between variables was determined by using AMOS 26.0. A two-sided p < 0.05 was considered statistically significant.
As shown in Table 1, of stroke survivors, patients' ages ranged from 34 to 89 years (mean = 64.09, SD = 9.66), 66.53% were males and 90.20% needed help for daily activities. Of the caregivers, the ages ranged from 27 to 80 years (mean = 59.05, SD = 1.00), 78.78% were females and 75.10% were spouse. The Total Scores (BDLAI) was ≥ 80 (55.92%), 60-80 (24.49%), 40-60 (13.88%) and ≤ 40 (5.71%).
Moreover, there were significant differences in depressive symptoms according to patients’side of hemiplegia (F = 2.991, p = 0.032), total score (BDLAI) (F =10.175, p < 0.001) and caregivers education status (F = 8.641, p < 0.001), monthly income (F = 10.811, p < 0.001), working status (F = 4.489, p = 0.012), relationship with patients (F = 5.843, p < 0.001), care time per day (F = 7.086, p < 0.001), living with patients (F = 2.407, p = 0.017).
Pearson's correlation analysis results (shown in Table 2) revealed that care burden was positively associated with depressive symptoms (r = 0.578, p < 0.01), whereas resilience was negatively associated with care burden (r = -0.264, p < 0.01) and depressive symptoms (r = -0.697, p <0.01).
Regression analyses indicated that in the first step, care burden positively predicted depressive symptoms (β = 0.578, p < 0.01 Table3). In the second step, care burden negatively predicted resilience (β = -0.264, p < 0.001). In the third step, care burden (β = 0.424, p < 0.001) and resilience (β = -0.585, p < 0.001) positively predicted depressive symptoms. The results meant that resilience probably mediated the relation between care burden and depressive symptoms partially.
A structural equation model (SEM) was conducted to examine the mediating effect of resilience on care burden and depressive symptoms. A model was established with care burden as the independent variable, depressive symptoms as the dependent variable, and perceived resilience as the mediating variable. The path diagram and the path coefficient between the variables were shown in Figure1. The model fitting index showed that x2/df =1.589, the RMSEA = 0.049, the SRMR = 0.036 and the CFI, GFI, NFI and RFI values were higher than 0.900 (CFI = 0.990, GFI = 0.967, NFI = 0.975, RFI = 0.962). Indicating that the model had a good fit, as shown in Table 4. The path coefficients of care burden and depressive symptoms (β = 0.522, p < 0.001), care burden and resilience (β = -0.341, p < 0.001), and resilience and depressive symptoms (β = -0.478, p < 0.001) were all statistically significant.
We used Bootstrap estimation procedures to explore the stability of the mediation variables. We adopted the method of random sampling to extract 5000 Bootstrap samples from the original data (N= 245). The results showed that the total effect of care burden on depressive symptoms was 0.685 [95%CI (0.538-0.839, p < 0.001)], the direct effect was 0.522 [95%CI (0.399-0.651, p < 0.001)] and the indirect effect was 0.163 [95%CI (0.053-0.266, p <0.01)], as shown in Table 5. The 95% confidence interval does not contain 0, indicating that resilience had a mediating role between care burden and depressive symptoms, and the mediating contribution rate was 23.80% (0.163/0.685).
The study sought to explain the relationship between care burden and depressive symptoms based on Kumpfer's resilience model. Firstly, our findings showed that the mean score of depressive symptoms value was 22.33±9.85, which was higher compared with the level of care burden reported in a study using the same tool among caregivers of patients with other diseases[35, 36]. The difference might be since a long disease duration and residual disability among stroke patients are likely more reliant on their family caregivers[37].
In our sample of caregivers, depressive symptoms were related to patients, side of hemiplegia, BDLAI and caregivers' education status, monthly income, working status, relationship with patients, care time per day, and living with patients. Patients with hemiplegia are associated with self-care abilities[38], which inevitably disrupt the normal life of the caregivers and further develop negative emotions[18]. Similar to the previous study[39], we found that caregivers with a higher level of education tend to experience fewer depressive symptoms, which may be due to better ways to insight into illness and seek help, resulting in lesser depression[40]. Unemployed people and lower-income families experience more depressive symptoms. It is well known that limited economic resources increase the burden of maintaining daily life and paying for health services. In addition, family caregivers who spent a long time on caregiving were associated with higher depression. It was assumed that the longer time spent, the more disruptions they experience in their normal social routines, which in turn causes more stress[41]. In our study, there was a trend that the levels of depression declined, starting from parents, spouses, and moving on to offspring, siblings. Perhaps because those parents are disposed to make sacrifices in taking care of children, making them especially susceptible. Another reason may be that older people are more vulnerable to most kinds of chronic diseases. Contrary to this finding, some studies stated that spouses reported more depressive symptoms[42, 43], which may be because those spouses are overwhelmed by conflicting demands such as work, children, and household chores[5]. Thus, early and ongoing assessment of influencing factors mentioned above for caregivers could be helpful to meet caregivers’ mental health needs.
Our findings demonstrate that the care burden had a direct effect on the depressive symptoms of main family caregivers. The direct effect can be explained by the features of the stroke itself. Specifically, due to the chronicity of stroke and the loss of limb function, family caregivers get insufficient time for sleep, socialization, and mental relaxation[44], sometimes feel desperate and hopeless when caring for a relative living with stroke, which inevitably develops negative emotion and further suffers from depression[11]. Hence the more care burden, the heavier depressive symptoms family caregivers may perceive. Also, our findings showed that the mean score of care burden value was 43.89±13.40, which was significantly higher than that of caregivers of patients with other diseases[45, 46]. The difference might be since that chronic course of and having some residual symptoms among stroke patients has a greater burden on caregivers[47].
Notably, our results showed that resilience could partly mediate the relationship between care burden and depressive symptoms. One possible explanation may be that people with a lower level of resilience tend to negatively confront adversity in unhealthy ways, such as mood disturbances, persisting fatigue, sleep changes[48]. Specifically, the psychobiological mechanisms underlying resilience has shown that resilience had a relation to neurochemical, neuropeptide, and hormonal when the response to stressful things[49, 50], people with higher resilience tend to reduce psychobiological allostatic load, balance neural systems, which could maintain normal psychological function and thus can confront stress actively[51]. Besides, individuals with higher resilience are better at coping with stressful events, they tend to make active attempts to adjust the relationship between the environment and individuals, make full use of various resources, and achieve a good state of adaptation[52, 53]. Therefore, resilience seemed to be one of the possible mechanisms to resist mental disorders who exposure to care burden, which confirmed Kumpfer's resilience model. Resilience is a dynamic phenomenon that can be altered at any moment[54]. Hence, it is possible to alleviate the depressive symptoms of caregivers via promoting the level of resilience. Specifically, social support is one of the important sources for the development of resilience, which may ultimately help lessen depressive symptoms[55]. Self-compassion and mindfulness training are also related to higher resilience[56]. Moreover, some research has shown that love for family, feeling responsible toward the family[57], ability to analyze the current situation, and capability to establish relationships[58] are some of the motivations for resilience. Similarly, care burden is related to mental health. We should assess the situation of care burden of caregivers, screen for its main influencing factors, and take effective programs such as social and financial support[59], increased post-traumatic, better patient-caregiver relationships, growth improvement in the competence and self-esteem of caregivers[60]. Overall, the sources of strength can provide intervention targets for promoting resilience and care burden, and thus alleviate the depressive symptoms .
Limitations and future research
This study had several potential limitations. First, this study is a cross-sectional study, and it is difficult to determine the causal connections between the variables. Therefore, future studies can use longitudinal research to explore the causal relationship between variables. Second, we used a self-rating questionnaire for screening depression instead of a clinical diagnosis from psychiatrists. Irrespective, the instrument is a validated depression screening tool. Finally, our study focused only on the association between care burden, resilience, and depression. Further investigation needs to be taken into consideration to explore other social psychology and emotional predictors for the level of depression in caregivers of stroke survivors, such as society, family environment factors, and so on.
This study explored the relationship between care burden, resilience, and depressive symptoms among the main family caregivers of stroke patients. The results showed that care burden was positively correlated with depressive symptoms, and caregivers with high care burden can alleviate depressive symptoms under the mediating influence of resilience. Thus, health professionals need to promptly assess the situation of caregivers’ mental health, to alleviate the depressive symptoms through further strengthening resilience is critical.
|
BDLAI |
Barthel Daily Living Activities Index |
|
ZBI |
Zarit Caregiver Burden Interview |
|
CD-RISC |
Connor-Davidson Resilience Scale |
|
CES-D |
Center for Epidemiological Studies Depression Scale |
|
SEM |
Structural Equation Modeling |
|
SD |
Standard Deviation |
|
B |
Non-Standardized Coefficient |
|
SE |
Standard Error |
|
CI |
Confidence Interval |
|
DF |
Degree of Freedom |
|
RMSEA |
Root-mean-square Error of Approximation |
|
SRMR |
Standardized root-mean-square Residual |
|
GFI |
Goodness-of-Fit Index |
|
CFI |
Comparative Fit Index |
|
NFI |
Normed Fit Index |
|
RFI |
Relative Fit Index |
Ethics approval and consent to participate
Ethical counsel permit (Approval number: 2020-402) was approved by the Medical Ethical Committee of the First Affiliated Hospital of China Medical University, and informed consent was received from each participant. All procedures performed in the study involving human participants were in accordance with the ethical standards of the hospital, national research committee, and the 1964 Helsinki declaration (as revised in Brazil 2013).
Acknowledgments
We would like to profoundly acknowledge the research participants in this study.
Funding
This research had no funding.
Availability of data and materials
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable
Authors' contributions
LLF and JZ conceptualized and designed the study. LLF collected the data. LLF and MYD analyzed the data and JZ reviewed the analyses. FLL prepared figure 1 and tables 1-5. FLL and MYD drafted the initial version of the manuscript. All authors contributed to revising, editing, and finalizing the manuscript. All authors read and approved the final manuscript.
Authors' information
Linlin Fang, Email: [email protected]
Mengyuan Dong, Email: [email protected]
JinZheng, Email: [email protected]
Table1 Descriptive statistics for demographic characteristics and differences in
depressive symptoms (N=245)
|
Variable |
N % |
Depressive symptoms |
||
|
M±SD |
F or t （P） |
|||
|
Patients |
|
|
|
|
|
Gender |
|
|
|
1.398 (0.163) |
|
Male |
163 |
66.53 |
22.96±10.21 |
|
|
Female |
82 |
33.47 |
21.10±9.01 |
|
|
Age(years) |
|
|
|
1.796 (0.149) |
|
<55 |
45 |
18.37 |
23.71±11.45 |
|
|
55-64 |
70 |
28.57 |
23.79±9.42 |
|
|
65-74 |
105 |
42.86 |
21.46±9.71 |
|
|
≥75 |
24 |
9.80 |
19.48±7.74 |
|
|
Onset times |
|
|
|
0.760 (0.518) |
|
1 |
23 |
9.39 |
19.83±8.68 |
|
|
2 |
169 |
68.98 |
22.53±9.85 |
|
|
3 |
35 |
14.29 |
22.06±9.93 |
|
|
>3 |
18 |
7.35 |
24.28±11.17 |
|
|
Number of Chronic diseases |
|
|
|
0.847 (0.469) |
|
None |
31 |
12.65 |
23.48±9.56 |
|
|
1 |
115 |
46.94 |
22.34±9.17 |
|
|
2 |
73 |
29.80 |
21.14±10.33 |
|
|
≥3 |
26 |
10.61 |
24.31±11.64 |
|
|
Health insurance |
|
|
|
-0.781 (0.436) |
|
Yes |
232 |
94.69 |
22.22±9.94 |
|
|
No |
12 |
4.90 |
24.50±7.79 |
|
|
Stroke subtypes |
|
|
|
1.210 (0.300) |
|
Ischemic stroke |
204 |
83.33 |
22.40±10.14 |
|
|
Hemorrhagic stroke |
18 |
7.35 |
24.72±8.24 |
|
|
Both |
23 |
9.39 |
20.00±8.06 |
|
|
Side of Hemiplegia |
|
|
|
2.991 (0.032) |
|
None |
11 |
4.49 |
20.64±10.24 |
|
|
Left |
108 |
44.08 |
21.22±9.14 |
|
|
Right |
74 |
30.20 |
21.72±10.34 |
|
|
Both |
52 |
21.22 |
25.88±9.91 |
|
|
Ability of self-care |
|
|
|
0.504 (0.605) |
|
Total independence |
5 |
2.04 |
24.60±9.34 |
|
|
Relative dependence |
221 |
90.20 |
22.45±9.85 |
|
|
Total dependence |
19 |
7.76 |
20.42±10.19 |
|
|
Caregivers |
|
|
|
|
|
Gender |
|
|
|
-0.752 (0.453) |
|
Males |
52 |
21.22 |
21.42±8.82 |
|
|
Females |
193 |
78.78 |
22.58±10.11 |
|
|
Age |
|
|
|
0.231 (0.875) |
|
<55 |
83 |
33.88 |
21.96±9.59 |
|
|
55-64 |
77 |
31.43 |
22.36±9.50 |
|
|
65-74 |
71 |
28.98 |
23.00±10.89 |
|
|
≥75 |
14 |
5.71 |
21.00±8.21 |
|
|
Education status |
|
|
|
8.641 (<0.001) |
|
Primary school at most |
50 |
20.41 |
27.08±10.46 |
|
|
Junior high school |
80 |
32.65 |
23.63±9.42 |
|
|
High school/Technical school |
80 |
32.65 |
19.71±8.19 |
|
|
College and above |
35 |
14.29 |
18.60±10.42 |
|
|
Monthly income |
|
|
|
10.811 (<0.001) |
|
＜2500 |
58 |
23.67 |
27.93±10.14 |
|
|
2500-3500 |
93 |
37.96 |
21.72±8.76 |
|
|
3500-4500 |
70 |
28.57 |
20.33±9.17 |
|
|
＞4500 |
24 |
9.80 |
17.04±9.61 |
|
|
Working status |
|
|
|
4.489 (0.012) |
|
Employed |
91 |
37.14 |
22.54±9.60 |
|
|
Unemployed |
60 |
24.49 |
25.10±10.87 |
|
|
Retired |
93 |
37.96 |
20.32±8.99 |
|
|
Relationship with patient |
|
|
|
5.843 (0.001) |
|
Spouse |
184 |
75.10 |
22.55±9.58 |
|
|
Offspring |
52 |
21.22 |
20.31±9.48 |
|
|
Parents |
5 |
2.04 |
38.60±10.90 |
|
|
Sibling |
4 |
1.63 |
18.5O±7.94 |
|
|
Duration of care time(month) |
|
|
|
0.677 (0.567) |
|
3-6 |
89 |
36.33 |
21.34±9.68 |
|
|
6-12 |
45 |
18.37 |
22.58±8.50 |
|
|
12-36 |
52 |
21.22 |
22.31±9.96 |
|
|
＞36 |
59 |
24.08 |
23.68±10.96 |
|
|
Care time per day(hours) |
|
|
|
7.086 (<0.001) |
|
＜4 |
102 |
41.63 |
19.30±7.54 |
|
|
4-8 |
98 |
40.00 |
23.53±10.16 |
|
|
8-16 |
34 |
13.88 |
26.21±11.79 |
|
|
＞16 |
11 |
4.49 |
27.82±11.75 |
|
|
Living with patients |
|
|
|
2.407 (0.017) |
|
Yes |
232 |
94.69 |
22.69±9.78 |
|
|
No |
13 |
5.31 |
16.00±9.22 |
|
|
Number of Chronic diseases |
|
|
|
0.506 (0.679) |
|
None |
136 |
55.51 |
21.67±9.52 |
|
|
1 |
89 |
36.33 |
23.03±10.17 |
|
|
2 |
17 |
6.94 |
23.94±9.50 |
|
|
≥3 |
3 |
1.22 |
22.67±19.14 |
|
|
Total Scores (BDLAI) |
|
|
|
10.175 (<0.001) |
|
＜40 |
14 |
5.71 |
26.71±7.27 |
|
|
40-60 |
34 |
13.88 |
27.88±12.24 |
|
|
60-80 |
60 |
24.49 |
24.52±11.10 |
|
|
＞80 |
137 |
55.92 |
19.55±7.71 |
|
Note: SD, standard deviation; BDLAI, The Barthel Daily Living Activities Index.
Table2 Correlations between care burden, resilience, and depressive symptoms
|
Variable |
M±SD |
1 |
2 |
3 |
|
1.care burden |
43.89±13.40 |
- |
|
|
|
2.resilience |
55.68±11.01 |
-0.264** |
- |
|
|
3.depressive symptoms |
22.33±9.85 |
0.578** |
-0.697** |
- |
** P < 0.01.
Table3 The multiple linear regression models among relevant variables
|
Variable |
Step1 |
step2 |
step3 |
||||
|
β |
SE |
β |
SE |
β |
SE |
||
|
Care burden |
0.578*** |
0.052 |
-0.264*** |
0.062 |
0.424*** |
0.039 |
|
|
Resilience |
|
|
|
|
-0.585*** |
0.046 |
|
|
R² |
0.335 |
0.070 |
0.653 |
||||
|
F |
122.127*** |
18.152*** |
227.787*** |
||||
*** P < 0.001.
Table4 Correlation matrix for care burden, resilience, and depressive symptoms (N=245)
|
|
x2 |
df |
x2/df |
RMSEA |
SRMR |
CFI |
GFI |
NFI |
RFI |
|
Model |
38.135 |
24 |
1.589 |
0.049 |
0.036 |
0.990 |
0.967 |
0.975 |
0.962 |
Note: DF, degree of freedom; RMSEA, root-mean-square error of approximation; SRMR, standardized root-mean-square residual; CFI, comparative fit index; GFI, goodness-of-fit index; NFI, normed Fit Index; RFI, relative fit index.
Table5 Bootstrap analysis of mediation effect significance test（N=245）
|
Effect |
B |
SE |
95%CI |
P |
|
Indirect effect |
0.163 |
0.054 |
0.053-0.266 0.399-0.651 0.538-0.839 |
0.004 |
|
Direct effect |
0.522 |
0.064 |
<0.001 |
|
|
Total effect |
0.685 |
0.077 |
<0.001 |
Note:5000 bootstrap samples; B, non-standardized coefficient; SE, standard error; CI, confidence interval.