A Structural Equation Model of Health-Related Quality of Life in Chinese Patients With Rheumatoid Arthritis

Abstract

Background: The aim of this study was to examine how body image, Disease Activity Score in 28 joints, the feeling of being anxious, depression, fatigue, quality of sleep, and pain influence the quality of life (QoL) in patients with rheumatoid arthritis (RA). Methods: A multicenter cross-sectional survey with convenience sampling was conducted from March 2019 and December 2019, 603 patients with RA from five hospitals were evaluated using the Body Image Disturbance Questionnaire, Disease Activity Score in 28 joints, Hospital Anxiety and Depression Scale, Fatigue Severity Scale, Pittsburgh Sleep Quality Index, Short Form 36 Health Survey, and Global Pain Scale. The relationship between quality of life and other variables was evaluated by using the structural equation model (SEM). Results: A total of 580 patients were recruited. SEM fitted the data very well with a root mean square error of approximation (RMSEA) of 0.072. Comparative fit index of 0.966, and Tucker-Lewis index of 0.936. The symptoms and the normalized factor load of six variables showed that the normalized factor load of pain was 0.99. Conclusions: The QoL model was used to fit an SEM to systematically verify and analyze the population disease data, biological factors, and the direct and indirect effects of the symptom group on the QoL, and the interactions between the symptoms. Therefore, the diagnosis, treatment and rehabilitation of RA is a long-term, dynamic, and complex practical process. Patients' personal symptoms, needs, and experiences also vary greatly. Comprehensive assessment of patients' symptoms, needs, and experiences, as well as the role of social support cannot be ignored, which can help to meet patients' nursing needs, improve their mood and pain-based symptom management, and ultimately improve patients' QoL.

Keywords: body image; depression; pain; quality of life; rheumatoid arthritis.

Figure 1

Revised Wilson and Cleary model of health-related quality of life. Adapted from “Linking Clinical Variables with Health-Related Quality of Life: A Conceptual Model of Patient Outcomes,” by Wilson and Cleary (26). Copyright by JAMA. Used with permission.

Figure 2

Goodness of fit of structural equation models of variables in a cross-sectional study of health-related quality of life. (1) Latent variable: biological and physiological status; measured variables: number of risk factors (e.g., hyperlipidemia, diabetes). (2) Latent variable: symptom status; measured variable: BIDQ, DAS28, HADS, FSS, PSQI, and GPS. BIDQ, Body Image Disturbance Questionnaire; DAS28, Disease Activity Score in 28 joints; HADS, Hospital Anxiety and Depression Scale; FSS, Fatigue Severity Scale questionnaire; PSQI, Pittsburgh Sleep Quality Index; GPS, Global Pain Scale. (3) Latent variable: physical functional status; measured variable: physical function status was based on the total score of “go to the store,” “do chores in my home,” “enjoy my friends and family,” “exercise,” and “participate in my favorite hobbies” in GPS. GPS, Global Pain Scale. (4) Latent variable: QoL; measured variable: SF-36 total score. QoL, quality of life; SF-36, Short Form 36 Health Survey. (5) Latent variable: characteristics of the environment; measured variable: social support. (6) Latent variable: characteristics of the individual measured variable: demographic characteristics (age, education, income, etc).