Abstract
BACKGROUND: The goal of treating chronic diseases, including hematologic malignancies, is to increase patients' life span and to improve their capabilities as much as possible; so that patients could maintain an appropriate level of quality of life (QoL) and continue their lives. Most studies performed to evaluate the treatment of various diseases were mostly focused on the increase of life expectancy regardless of the QoL and treatment issues. Furthermore, fatigue is one of the most common and distressing side effects of cancer and treatments related to it, which can affect a patient's QoL, and be followed by many problems. This study was designed and implemented with the aim to determine the factors affecting the QoL and fatigue in patients with leukemia undergoing chemotherapy. MATERIALS AND METHODS: This was a cross-sectional correlation descriptive-analytical study. One hundred and fifteen patients with leukemia referred to Sayyed-Al-Shohada Hospital were enrolled in the study through convenient sampling method. To collect data, a three-part questionnaire was used: The first part was related to demographic characteristics and disease-related data and the second part was the fatigue check list and the third part was the Short-Form 36 Health Survey (SF-36) related to QoL. The data were analyzed after collection and coding through Software SPSS version 18 and descriptive and analytical statistics (analysis of variance test, independent t test, Pearson's and Spearman's correlation coefficient) with 95% confidence interval. RESULTS: The results showed that there was a significant correlation between QoL in Physical Component Summary with gender (P = 0.03), educational level (P = 0.09), and marital status (P = 0.004), Also there was a significant correlation between QoL in Mental Component Summary with educational level (P = 0.01) and economic status (P = 0.02). Findings showed that there was a significant correlation between fatigue and marital status (P = 0.005). But statistically there was no significant correlation between demographic variables such as age, educational level, and employment status with patients' fatigue. Spearman correlation coefficient showed that there was a significant correlation between fatigues with pain (P = 0.005). Also findings revealed the statistically significant correlation between fatigues with economic status (P = 0.003). CONCLUSION: According to the present results, it seems that some demographic factors affect QoL and significant relationship exists between them. Fatigue in patients with leukemia dramatically affects their QoL. Therefore, it is important for medical staff to pay attention to demographics and fatigue in leukemic patients in order to improve their QoL and help them to organize their activities to promote health and QoL.