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Original Article
- Structural Equation Modeling on Quality of Life in Pre-dialysis Patients with Chronic Kidney Disease
- Hye Won Kim1, Smi Choi-Kwon2
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Journal of Korean Academy of Nursing 2012;42(5):699-708.
DOI: https://doi.org/10.4040/jkan.2012.42.5.699
Published online: October 12, 2012
1Department of Nursing, Konyang University, Daejeon, Korea
2College of Nursing, Seoul National University·Research Institute of Nursing Science, Seoul, Korea
1Department of Nursing, Konyang University, Daejeon, Korea
2College of Nursing, Seoul National University·Research Institute of Nursing Science, Seoul, Korea
- Address reprint requests to : Choi-Kwon, Smi College of Nursing, Seoul National University 28 Yeongeon-dong, Chongro-gu Seoul 110-799 Korea Tel: +82-2-740-8830 Fax: +82-2-765-4103 E-mail: smi@snu.ac.kr
• Received: January 10, 2012 • Revised: February 14, 2012 • Accepted: September 24, 2012
Copyright © 2012 Korean Society of Nursing Science
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
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Purpose
- This study was designed to test structural equation modeling of the quality of life of pre-dialysis patients, in order to provide guidelines for the development of interventions and strategies to improve the quality of life of patients with Chronic Kidney Disease (CKD).
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Methods
- Participants were patients who visited the nephrology outpatient department of a tertiary hospital located in Seoul. Data on demographic factors, social support, nutritional status, physical factors and biobehavioral factors and quality of life were collected between March 4 and March 31, 2011.
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Results
- In the final analysis 208 patients were included. Of the patients 42% were in a malnourished state. Anxious or depressed patients accounted for 62.0%, 72.6%, respectively. Model fit indices for the hypothetical model were in good agreement with the recommended levels (GFI= .94 and CFI= .99). Quality of life in pre-dialysis patients with CKD was significantly affected by demographic factors, social support, nutritional status, physical factors and biobehavioral factors. Biobehavioral factors had the strongest and most direct influence on quality of life of patients with CKD.
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Conclusion
- In order to improve the quality of life in pre-dialysis patients with CKD, comprehensive interventions are necessary to assess and manage biobehavioral factors, physical factors and nutritional status.
Table 1.General Characteristics of Participants (N=208)
Table 2.Descriptive Statistics of Observed Variables (N=208)
Table 3.Standardized Direct, Indirect and Total Effects for the Hypothetical Model
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XML DownloadStructural Equation Modeling on Quality of Life in Pre-dialysis Patients with Chronic Kidney Disease
Figure 1. Research framework.
Figure 2. Path diagram for the hypothetical model.
Figure 1.
Figure 2.
Structural Equation Modeling on Quality of Life in Pre-dialysis Patients with Chronic Kidney Disease
| Characteristics | Categories | n (%) or M±SD |
|---|---|---|
| Gender | Male | 125 (60.1) |
| Female | 83 (39.9) | |
| Age (year) | 60.9±15.4 | |
| ≤64 | 106 (51.0) | |
| 65-74 | 62 (29.8) | |
| ≥75 | 40 (19.2) | |
| Educational level | Elementary school | 37 (17.7) |
| Middle school | 32 (15.4) | |
| High school | 69 (33.2) | |
| ≥College | 70 (33.7) | |
| Occupation | Yes | 81 (38.9) |
| No | 127 (61.1) | |
| Monthly expenditure | <100 | 56 (26.9) |
| (10,000 won) | 100-199 | 136 (65.4) |
| ≥200 | 16 (7.7) |
| Variables | Categories | n (%) or M±SD | Range |
|---|---|---|---|
| Social support | 43.0±8.8 | 17.0-60.0 | |
| Nutritional status | Severely malnourished | 48 (23.1) | |
| Moderately malnourished | 40 (19.2) | ||
| Well nourished | 120 (57.7) | ||
| Glomerular filtration rate | 21.6±14.0 | 3-59 | |
| 30-59 mL/min/1.73 m2 | 59 (28.4) | ||
| 15-29 mL/min/1.73 m2 | 62 (29.8) | ||
| <15 mL/min/1.73 m2 | 87 (41.8) | ||
| Karnofsky performance scale | 78.6±9.7 | 50-100 | |
| 100-80 | 146 (70.2) | ||
| 79-60 | 57 (27.4) | ||
| <60 | 5 (2.4) | ||
| Uremic symptoms | 3.6±2.3 | 0-11.0 | |
| None | 15 (7.2) | ||
| 1-3 | 113 (54.3) | ||
| 4-6 | 50 (24.0) | ||
| ≥7 | 30 (14.5) | ||
| Comorbidities | 1.5±0.9 | 0-4.0 | |
| None | 21 (10.1) | ||
| 1-2 | 154 (74.0) | ||
| ≥3 | 33 (15.9) | ||
| Anemia | Yes | 162 (77.9) | |
| No | 46 (22.1) | ||
| Anxiety | 12.5±4.2 | 7.0-26.0 | |
| No | 18 (8.7) | ||
| Borderline | 61 (29.3) | ||
| Yes | 129 (62.0) | ||
| Depression | 14.0±4.6 | 7.0-25.0 | |
| No | 4 (1.9) | ||
| Borderline | 53 (25.5) | ||
| Yes | 151 (72.6) | ||
| Fatigue | 38.3±16.3 | 9.0-63.0 | |
| Sleep disorder | No | 101 (48.6) | |
| Yes | 107 (51.4) | ||
| Sleep quality | 48.3±14.8 | 12.0-79.0 | |
| Quality of life | 51.5±20.3 | 6.7-97.2 |
| Endogenous variables | Exogenous variables | Standardized direct effect | Standardized indirect effect | Standardized total effect | SMC |
|---|---|---|---|---|---|
| Quality of life | Demographic factors | −.23* | −.30 | −.53 | .537 |
| Social support | .15* | .15 | .30 | ||
| Nutritional status | .31** | −.47 | −.16 | ||
| Physical factors | .34** | .17 | .51 | ||
| Biobehavioral factors | .40** | .40 | |||
| Nutritional status | Demographic factors | .34** | .34 | .211 | |
| Social support | .31** | .31 | |||
| Physical factors | Demographic factors | −.42** | −.16 | −.58 | .527 |
| Social support | .27** | −.15 | .12 | ||
| Nutritional status | −.47** | −.47 | |||
| Biobehavioral factors | Demographic factors | −.07 | −.43 | −.50 | .807 |
| Social support | .15** | −.13 | .02 | ||
| Nutritional status | −.58** | −.19 | −.77 | ||
| Physical factors | .41** | .41 |
Table 1. General Characteristics of Participants (N=208)
Table 2. Descriptive Statistics of Observed Variables (N=208)
Table 3. Standardized Direct, Indirect and Total Effects for the Hypothetical Model
SMC=Squared multiple correlations. *
