Ja Ok Lee; Rhayun Song

doi:10.4040/jkan.2015.45.6.846

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Original Article
Symptom Management to Predict Quality of Life in Patients with Heart Failure: A Structural Equation Modeling Approach: Ja Ok Lee¹, Rhayun Song²; Journal of Korean Academy of Nursing 2015;45(6):846-856.
DOI: https://doi.org/10.4040/jkan.2015.45.6.846
Published online: December 15, 2015

¹The Catholic University Daejeon St. Mary's Hospital, Daejeon

²College of Nursing, Chungnam National University, Daejeon, Korea

¹The Catholic University Daejeon St. Mary's Hospital, Daejeon

²College of Nursing, Chungnam National University, Daejeon, Korea

Address reprint requests to : Song, Rhayun College of Nursing, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon 35015, Korea Tel: +82-42-580-8331 Fax: +82-42-584-8915 E-mail: songry@cnu.ac.kr

• Received: May 11, 2015 • Revised: May 20, 2015 • Accepted: August 12, 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License. (http://creativecommons.org/licenses/by-nd/4.0) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

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Abstract
REFERENCES

Abstract

Purpose
The focus of this study was on symptom management to predict quality of life among individuals with heart failure. The theoretical model was constructed based on situation-specific theory of heart failure self-care and literature review.
Methods
For participants, 241 outpatients at a university hospital were invited to the study from May 19 to July 30, 2014. Data were collected with structured questionnaires and analyzed using SPSSWIN and AMOS 20.0.
Results
The goodness of fit index for the hypothetical model was .93, incremental fit index, .90, and comparative fit index, .90. As the outcomes satisfied the recommended level, the hypothetical model appeared to fit the data. Seven of the eight hypotheses selected for the hypothetical model were statistically significant. The predictors of symptom management, symptom management confidence and social support together explained 32% of the variance in quality of life. The 28% of variance in symptom management was explained by symptom recognition, heart failure knowledge and symptom management confidence. The 4% of variance in symptom management confidence was explained by social support.
Conclusion
The hypothetical model of this study was confirmed to be adequate in explaining and predicting quality of life among patients with heart failure through symptom management. Effective strategies to improve quality of life among patients with heart failure should focus on symptom management. Symptom management can be enhanced by providing educational programs, encouraging social support and confidence, consequently improving quality of life among this population.
Keywords: Heart failure; Symptom; Management; Quality of life

Figure 1.

Model of heart failure self-care [2].

Figure 2.

Path diagram of the hypothetical model.

Figure 3.

Path diagram of the hypothetical model including control variable.

Table 1.

Fit Index of the Hypothetical Model

Model	χ²	DF	p	χ²/df	GFI	SRMR	RMSEA	IFI	CFI
Evaluation criteria	−	−	> .05	<3	≥.90	≤.05	≤.10	≥.90	≥.90
Hypothetical model	88.15	29	< .001	3.04	.93	.06	.09	.90	.90

GFI=Goodness of fit index; SRMR=Standardized root mean residual; RMSEA=Root mean squared error of approximation; IFI=Incremental fit index; CFI=Comparative fit index.

Table 2.

Direct Effect, Indirect Effect, and Total Effect in Hypothetical Model (N=241)

Endogenous variables	Exogenous variables	Hypothetical model			Direct effect	Indirect effect	Total effect
Endogenous variables	Exogenous variables	SRW (SE)	C.R (p)	SMC	p	p	p
SM confidence	Social support	.20 (.04)	3.23 (.001)	.04	.20 (.004)		.20 (.004)
Symptom	S recognition	.34 (.13)	6.08 (.001)	.28	.34 (.021)		.34 (.021)
management	HF knowledge	.23 (.07)	4.05 (< .001)		.23 (.003)		.23 (.003)
	SM confidence	.24 (.04)	4.24 (< .001)		.24 (.008)		.24 (.008)
	Social support	.04 (.02)	0.64 (.525)		.04 (.676)	.05 (.004)	.09 (.116)
Quality of life	S recognition			.32		.09 (.007)	.09 (.007)
	HF knowledge					.06 (.002)	.06 (.002)
	SM confidence	.30 (.05)	4.51 (< .001)		.30 (.008)	.06 (.004)	.37 (.016)
	S management	.26 (.07)	4.02 (< .001)		.26 (.009)		.26 (.009)
	Social support	.26 (.03)	4.01 (< .001)		26 (.007)	.08 (.003)	.34 (.010)

SRW=Standardized regression weight; C.R=Critical ratio; SMC=Squared multiple correlation; HF=Heart failure; SM=Symptom management; S=Symptom.

REFERENCES

1. The Korean Society of Circulation, Heart Failure Study Group. The manual of heart failure. Seoul: MMK Communications; 2007.
2. Riegel B, Lee CS, Dickson VV, Carlson B. An update on the self- care of heart failure index. The Journal of Cardiovascular Nursing. 2009;24(6):485–497. http://dx.doi.org/10.1097/JCN.0b013e3181b4baa0Article PubMed PMC
3. Son YJ, Kim SH, Kim GY. Factors influencing adherence to self care in patients with chronic heart failure. Korean Journal of Adult Nursing. 2011;23(3):244–254.
4. Statistics Korea. 2013 annual report on the cause of death statis- tics. Daejeon: Author; 2014.
5. Lee ES, Shin ES, Hwang SY, Chae MJ, Jeong MH. Effects of tai- lored supportive education on physical, emotional status and quality of life in patients with congestive heart failure. Korean Journal of Adult Nursing. 2013;25(1):62–73. http://dx.doi.org/10.7475/kjan.2013.25.1.62Article PDF
6. Ahmed A. Chronic heart failure in older adults. The Medical Clin- ics of North America. 2011;95(3):439–461. http://dx.doi.org/10.1016/j.mcna.2011.02.001
7. Blinderman CD, Homel P, Billings JA, Portenoy RK, Tennstedt SL. Symptom distress and quality of life in patients with advanced congestive heart failure. Journal of Pain and Symptom Manage- ment. 2008;35(6):594–603. http://dx.doi.org/10.1016/j.jpainsymman.2007.06.007Article
8. Zambroski CH, Moser DK, Bhat G, Ziegler C. Impact of symptom prevalence and symptom burden on quality of life in patients with heart failure. European Journal of Cardiovascular Nursing. 2005;4(3):198–206. http://dx.doi.org/10.1016/j.ejcnurse.2005.03.010PubMed
10. Jurgens CY, Lee CS, Reitano JM, Riegel B. Heart failure symptom monitoring and response training. Heart and Lung. 2013;42(4):273–280. http://dx.doi.org/10.1016/j.hrtlng.2013.03.005Article PubMed
11. Dodd M, Janson S, Facione N, Faucett J, Froelicher ES, Hum- phreys J, et al. Advancing the science of symptom management. Journal of Advanced Nursing. 2001;33(5):668–676. http://dx.doi.org/10.1046/j.1365-2648.2001.01697.xArticle PubMed
12. Riegel B, Dickson VV. A situation-specific theory of heart failure self-care. The Journal of Cardiovascular Nursing. 2008;23(3):190–196. http://dx.doi.org/10.1097/01.jcn.0000305091.35259.85Article PubMed
13. Gallagher R, Luttik ML, Jaarsma T. Social support and self-care in heart failure. The Journal of Cardiovascular Nursing. 2011;26(6):439–445. http://dx.doi.org/10.1097/JCN.0b013e31820984e1Article PubMed
14. Graven LJ, Grant JS. Social support and self-care behaviors in in- dividuals with heart failure: An integrative review. International Journal of Nursing Studies. 2014;51(2):320–333. http://dx.doi.org/10.1016/j.ijnurstu.2013.06.013Article PubMed
15. Lee SH, Ahn SH. Self care compliance, family support, and de- pression in patients with congestive heart failure. Journal of Korean Academy of Fundamentals of Nursing. 2005;12(2):186–194.
16. Lainscak M, Keber I. Validation of self assessment patient knowl- edge questionnaire for heart failure patients. European Journal of Cardiovascular Nursing. 2005;4(4):269–272.Article PubMed PDF
17. Kim YS. A path analysis model of health-related quality of life in patients with heart failure. Journal of Korean Academy of Adult Nursing. 2007;19(4):547–555.
18. Krethong P, Jirapaet V, Jitpanya C, Sloan R. A causal model of health-related quality of life in Thai patients with heart-failure. Journal of Nursing Scholarship. 2008;40(3):254–260. http://dx.doi.org/10.1111/j.1547-5069.2008.00235.xArticle PubMed
19. Yu JP. The concept and understanding of structural equation mod- eling. Seoul: Hannare Publishing Co.; 2012.
20. Cobb S. Presidential address-1976. Social support as a moderator of life stress. Psychosomatic Medicine. 1976;38(5):300–314.Article PubMed
21. Kang HS. An experimental study of the effects of reinforcement education for rehabilitation on hemiplegia patients’ self-care activ- ities [dissertation]. Seoul: Yonsei University; 1984.
22. Lee EH, Tahk SJ, Shin JH, Lee YW, Song R. Development and a psychometric evaluation of cardiovascular disease-specific quality of life scale for Koreans. Journal of Korean Academy of Nursing. 2007;37(3):313–323.Article PubMed PDF
23. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Devel- opment and validation. Journal of Chronic Diseases. 1987;40(5):373–383. http://dx.doi.org/10.1016/0021-9681(87)90171-8Article PubMed
24. Vellone E, Riegel B, D’Agostino F, Fida R, Rocco G, Cocchieri A, et al. Structural equation model testing the situation-specific the- ory of heart failure self-care. Journal of Advanced Nursing. 2013;69(11):2481–2492. http://dx.doi.org/10.1111/jan.12126Article PubMed PDF
25. Riegel B, Dickson VV, Cameron J, Johnson JC, Bunker S, Page K, et al. Symptom recognition in elders with heart failure. Journal of Nursing Scholarship. 2010;42(1):92–100. http://dx.doi.org/10.1111/j.1547-5069.2010.01333.xArticle PubMed
26. Mata J, Frank R, Gigerenzer G. Symptom recognition of heart at- tack and stroke in nine European countries: A representative sur- vey. Health Expectations. 2014;17(3):376–387. http://dx.doi.org/10.1111/j.1369-7625.2011.00764.xArticle PubMed
27. Cen CW, Haymore LB, Dolan-Soto D, Lin FC, Pignone M, Dewalt DA, et al. Self-care confidence mediates the relationship between perceived social support and self-care maintenance in adults with heart failure. Journal of Cardiac Failure. 2013;19(3):202–210. http://dx.doi.org/10.1016/j.cardfail.2013.01.009Article PubMed PMC
28. Kim HN. Symptom experience, symptom management strategies and health-related quality of life in patients with pancreatic cancer [master’s thesis]. Seoul: Yonsei University; 2013.
29. Kato N, Kinugawa K, Ito N, Yao A, Watanabe M, Imai Y, et al. Adherence to self-care behavior and factors related to this behav- ior among patients with heart failure in Japan. Heart and Lung. 2009;38(5):398–409. http://dx.doi.org/10.1016/j.hrtlng.2008.11.002Article PubMed
30. Song EK, Kim CJ, Yoo IY, Kim GY, Kim JH, Ha JW. Factors in-fluencing functional status in patients with heart failure. Journal of Korean Academy of Nursing. 2006;36(5):853–862.Article PubMed PDF

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Symptom Management to Predict Quality of Life in Patients with Heart Failure: A Structural Equation Modeling Approach

J Korean Acad Nurs. 2015;45(6):846-856. Published online December 15, 2015

DOI: https://doi.org/10.4040/jkan.2015.45.6.846

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Symptom Management to Predict Quality of Life in Patients with Heart Failure: A Structural Equation Modeling Approach

Figure 1. Model of heart failure self-care [2].

Figure 2. Path diagram of the hypothetical model.

Figure 3. Path diagram of the hypothetical model including control variable.

Figure 1.

Figure 2.

Figure 3.

Symptom Management to Predict Quality of Life in Patients with Heart Failure: A Structural Equation Modeling Approach

Model	χ²	DF	p	χ²/df	GFI	SRMR	RMSEA	IFI	CFI
Evaluation criteria	−	−	> .05	<3	≥.90	≤.05	≤.10	≥.90	≥.90
Hypothetical model	88.15	29	< .001	3.04	.93	.06	.09	.90	.90

Endogenous variables	Exogenous variables	Hypothetical model			Direct effect	Indirect effect	Total effect
Endogenous variables	Exogenous variables	SRW (SE)	C.R (p)	SMC	p	p	p
SM confidence	Social support	.20 (.04)	3.23 (.001)	.04	.20 (.004)		.20 (.004)
Symptom	S recognition	.34 (.13)	6.08 (.001)	.28	.34 (.021)		.34 (.021)
management	HF knowledge	.23 (.07)	4.05 (< .001)		.23 (.003)		.23 (.003)
	SM confidence	.24 (.04)	4.24 (< .001)		.24 (.008)		.24 (.008)
	Social support	.04 (.02)	0.64 (.525)		.04 (.676)	.05 (.004)	.09 (.116)
Quality of life	S recognition			.32		.09 (.007)	.09 (.007)
	HF knowledge					.06 (.002)	.06 (.002)
	SM confidence	.30 (.05)	4.51 (< .001)		.30 (.008)	.06 (.004)	.37 (.016)
	S management	.26 (.07)	4.02 (< .001)		.26 (.009)		.26 (.009)
	Social support	.26 (.03)	4.01 (< .001)		26 (.007)	.08 (.003)	.34 (.010)

Table 1. Fit Index of the Hypothetical Model

GFI=Goodness of fit index; SRMR=Standardized root mean residual; RMSEA=Root mean squared error of approximation; IFI=Incremental fit index; CFI=Comparative fit index.

Table 2. Direct Effect, Indirect Effect, and Total Effect in Hypothetical Model (N=241)

SRW=Standardized regression weight; C.R=Critical ratio; SMC=Squared multiple correlation; HF=Heart failure; SM=Symptom management; S=Symptom.