Sung Reul Kim1, Hye Young Kim2, Ju-Hee Nho3, Eun Ko4, Kyung-Sub Moon5, Tae-Young Jung6. 1. College of Nursing, Korea University Nursing Research Institute, Korea University, Seoul, South Korea. Electronic address: srkim74@korea.ac.kr. 2. College of Nursing, Jeonbuk Research Institute of Nursing Science, Jeonbuk National University, Jeonju, South Korea. Electronic address: tcellkim@jbnu.ac.kr. 3. College of Nursing, Jeonbuk Research Institute of Nursing Science, Jeonbuk National University, Jeonju, South Korea. Electronic address: jhnho@jbnu.ac.kr. 4. Department of Nursing, College of Life Science and Natural Resources, Sunchon National University, Suncheon, South Korea. Electronic address: eunko@scnu.ac.kr. 5. Department of Neurosurgery, Chonnam National University Research Institute of Medical Sciences, Chonnam National University Medical School and Hwasun Hospital, Hwasun, South Korea. Electronic address: moonks@jnu.ac.kr. 6. Department of Neurosurgery, Chonnam National University Research Institute of Medical Sciences, Chonnam National University Medical School and Hwasun Hospital, Hwasun, South Korea. Electronic address: jung-ty@chonnam.ac.kr.
Abstract
PURPOSE: The aims of this study were to explore the relationship among symptoms, resilience, post-traumatic growth, and quality of life, and to identify the influence of these variables on quality of life in patients with glioma. METHODS: A correlational, cross-sectional research design was used. A convenience sample of 120 patients was recruited from an outpatient neurosurgery clinic. Data analyses included descriptive statistics, independent t-test, one-way ANOVA, Pearson's correlation coefficient, and hierarchical regression analysis and were performed with the SPSS WIN 25.0 program. RESULTS: Quality of life positively correlated with the duration of disease diagnosis and resilience and negatively correlated with age, age at onset, severity of symptoms, and interference in symptoms. Resilience was negatively correlated with severity of symptoms and interference with symptoms, and was positively correlated with post-traumatic growth. Hierarchical regression analysis showed that demographic and clinical factors explained 39.3% of the variance in quality of life in glioma patients. The explanatory power increased by 22.1% and 15.1%, respectively, when interference in symptoms and resilience were considered. CONCLUSIONS: Assessment of quality of life in patients with glioma should consider symptoms and resilience, along with demographic and clinical factors. Interventions developed to improve quality of life in glioma patients must also consider these factors.
PURPOSE: The aims of this study were to explore the relationship among symptoms, resilience, post-traumatic growth, and quality of life, and to identify the influence of these variables on quality of life in patients with glioma. METHODS: A correlational, cross-sectional research design was used. A convenience sample of 120 patients was recruited from an outpatient neurosurgery clinic. Data analyses included descriptive statistics, independent t-test, one-way ANOVA, Pearson's correlation coefficient, and hierarchical regression analysis and were performed with the SPSS WIN 25.0 program. RESULTS: Quality of life positively correlated with the duration of disease diagnosis and resilience and negatively correlated with age, age at onset, severity of symptoms, and interference in symptoms. Resilience was negatively correlated with severity of symptoms and interference with symptoms, and was positively correlated with post-traumatic growth. Hierarchical regression analysis showed that demographic and clinical factors explained 39.3% of the variance in quality of life in gliomapatients. The explanatory power increased by 22.1% and 15.1%, respectively, when interference in symptoms and resilience were considered. CONCLUSIONS: Assessment of quality of life in patients with glioma should consider symptoms and resilience, along with demographic and clinical factors. Interventions developed to improve quality of life in gliomapatients must also consider these factors.
Authors: Ben Rimmer; Iakov Bolnykh; Lizzie Dutton; Joanne Lewis; Richéal Burns; Pamela Gallagher; Sophie Williams; Vera Araújo-Soares; Fiona Menger; Linda Sharp Journal: Qual Life Res Date: 2022-08-06 Impact factor: 3.440