Browse > Article
http://dx.doi.org/10.12799/jkachn.2022.33.1.74

Identifying Trajectories of Health-related Quality of Life in Mid-life Transition Women: Secondary Data Analysis of Korean Longitudinal Survey of Women & Families  

Son, Miseon (Department of Nursing, Wonkwang University)
Publication Information
Research in Community and Public Health Nursing / v.33, no.1, 2022 , pp. 74-83 More about this Journal
Abstract
Purpose: The purpose of this study was to identify latent classes of health-related quality of life trajectories in middle-aged women and investigate predictors for latent classes. Methods: This study utilized data from the 2nd, the 4th to the 7th Korean Longitudinal Survey of Women & Families. The subjects included 1,351 women aged 40~45 years. The data was analyzed using latent class growth analysis and logistic regression. Results: Two trajectories were identified for health-related quality of life in middle-aged women; 'persistently good' and 'increasing' groups. Predictors for the 'increasing' group were lower economic status, higher depression, and lower perceived health status. Conclusion: This study showed that characteristics of the individual, symptom status, and health perceptions were associated with health-related quality of life in middle-aged women. It is necessary to provide effective intervention for latent classes of health-related quality of life trajectories based on physical, mental, and social factors.
Keywords
Middle aged; Health; Quality of life; Latent class analysis;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Lee SI. Validity and reliability evaluation for EQ-5D in Korea. Research Report. Cheongju: Korea Disease Control and Prevention Agency; 2011 December. Report No.: 2011-E33009-00.
2 Wickrama KK, Lee TK, O'Neal CW, Lorenz FO. Higher-order growth curves and mixture modeling with Mplus: A practical guide. New York, NY: Routledge; 2016. 326 p.
3 Lo Y, Mendell NR, Rubin DB. Testing the number of components in a normal mixture. Biometrika. 2001;88(3):767-778. https://doi.org/10.1093/biomet/88.3.767   DOI
4 Radloff LS. The CES-D scale. Applied Psychological Measurement. 1977;1(3):385-401. https://doi.org/10.1177/014662167700100306   DOI
5 Jung T, Wickrama KAS. An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass. 2008;2(1):302-317. https://doi.org/10.1111/j.1751-9004.2007.00054.x   DOI
6 Le Grande MR, Elliott PC, Murphy BM, Worcester MU, Higgins RO, Ernest CS, et al. Health related quality of life trajectories and predictors following coronary artery bypass surgery. Health and Quality of Life Outcomes. 2006;4(1):1-13. https://doi.org/10.1186/1477-7525-4-49   DOI
7 Yang Y, Wang S, Chen L, Luo M, Xue L, Cui D, et al. Socioeconomic status, social capital, health risk behaviors, and healthrelated quality of life among Chinese older adults. Health and Quality of Life Outcomes. 2020;18(1):1-8. https://doi.org/10.1186/s12955-020-01540-8   DOI
8 Shin HS, Lee EJ. Factors influencing quality of life in post-menopausal women. Korean Journal of Women Health Nursing. 2020;26(4):336-345. https://doi.org/10.4069/kjwhn.2020.11.14   DOI
9 Oh YK, Hwang SY. A path analysis on the effect of anxiety and depression on health-related quality of life of middle aged women. Journal of Digital Convergence. 2017;15(10):579-588. https://doi.org/10.14400/JDC.2017.15.10.579   DOI
10 Levinson DJ. The seasons of a woman's life. Kim AS, translator. Seoul: Ewha Womans University Press; 2004.
11 Bailis DS, Segall A, Chipperfield JG. Two views of self-rated general health status. Social Science & Medicine. 2003;56(2): 203-217. https://doi.org/10.1016/s0277-9536(02)00020-5   DOI
12 Kim KH. The factors influencing to quality of life of middle-aged women. Journal of the Korean Data Analysis Society. 2016;18(1):497-508.
13 Cleary P, Wilson I, Fowler F. A theoretical framework for assessing and analyzing health-related quality of life. In: Albrecht GL, Fitzpatrick R. Advances in Medical Sociology. Stamford, CT: JAI Press; 1994. p. 23-41.
14 Sohn JN. Factors influencing depression in middle aged women: Focused on quality of life on menopause. Journal of Health Informatics and Statistics. 2018;43(2):148-157. https://doi.org/10.21032/jhis.2018.43.2.148   DOI
15 Ko JA, Shim JW, Kim JS, Lee MS. Stress risk factors and mental health: Findings from 2011 Seoul mental health survey. The Mental Health. 2011;2(1):32-46.
16 Park HK, Chun SY, Choi Y, Lee SY, Kim SJ, Park EC. Effects of social activity on health-related quality of life according to age and gender: An observational study. Health and Quality of Life Outcomes. 2015;13(1):1-9. https://doi.org/10.1186/s12955-015-0331-4   DOI
17 Statistics Korea. Population projections for Korea [Internet]. Daejeon: Statistics Korea. 2019 [cited 2021 July 29]. Available from: https://www.index.go.kr/potal/main/EachDtlPageDetail.do?idx_cd=2758
18 Avis NE, Colvin A, Bromberger JT, Hess R. Midlife predictors of health-related quality of life in older women. The Journals of Gerontology: Series A. 2018;73(11):1574-1580. https://doi.org/10.1093/gerona/gly062   DOI
19 Wilson IB, Cleary PD. Linking clinical variables with health-related quality of life. The Journal of the American Medical Association. 1995;273(1):59-65. https://doi.org/10.1001/jama.1995.03520250075037   DOI
20 Kim MA, Choi SE, Moon JH. Effect of heath behavior, physical health and mental health on health-related quality of life in middle aged women: By using the 2014 Korea health panel data. Journal of Korean Academic Society of Home Health Care Nursing. 2019;26(1):72-80. https://doi.org/10.22705/JKASHCN.2019.26.1.72   DOI
21 Muthen LK, Muthen BO. Mplus: Statistical analyses with latent variables. User's guide. 7th ed. Los Angeles: Muthen & Muthen; 2012. 850 p.
22 Korean Longitudinal Survey of Women & Families (KLoWF). User's Guide of the first~seventh Korean longitudinal survey of women & families. Seoul: KLoWF; 2020. 164 p.
23 Lee YK, Nam HS, Chuang LH, Kim KY, Yang HK, Kwon IS, et al. South Korean time trade-off values for EQ-5D health states: Modeling with observed values for 101 health states. Value in Health. 2009;12(8):1187-1193. https://doi.org/10.1111/j.1524-4733.2009.00579.x   DOI
24 Shin TS. Review and application of missing data methods: Focusing on longitudinal achievement data. Journal of Educational Evaluation. 2014;27(3):693-725.
25 Park SY, Park SY. A longitudinal study on ecological determinants associated with middle-aged and elderly women's life satisfaction and depressive symptoms. Health and Social Welfare Review. 2018;38(4):129-163. https://doi.org/10.15709/hswr.2018.38.4.129   DOI
26 Muthen B. Latent variable analysis. In: Kaplan D. The SAGE handbook of quantitative methodology for the social sciences. California: Sage Publications, Inc; 2004. p. 345-368.
27 Yoo JY, Kim YS, Kim SS, Lee HK, Park CG, Oh EG, et al. Factors affecting the trajectory of health-related quality of life in COPD patients. The International Journal of Tuberculosis and Lung Disease. 2016;20(6):738-746. https://doi.org/10.5588/ijtld.15.0504   DOI
28 Breda J, Jakovljevic J, Rathmes G, Mendes R, Fontaine O, Hollmann S, et al. Promoting health-enhancing physical activity in Europe: Current state of surveillance, policy development and implementation. Health Policy. 2018;122(5):519-527. https://doi.org/10.1016/j.healthpol.2018.01.015   DOI
29 Jang EJ. Analysis of health-related quality of life using beta regression. Journal of the Korean Data and Information Science Society. 2017;28(3):547-557. https://doi.org/10.7465/jkdi.2017.28.3.547   DOI
30 Yeun EJ, Kwon YM, Lee YM. Comparison of influencing factors for self-rated health between middle aged and elderly. The Journal of the Korea Contents Association. 2016;16(2):200-210. https://doi.org/10.5392/jkca.2016.16.02.200   DOI