Dynamic analysis of the relationship between systemic lupus erythematosus disease activity and psychosocial support

Abstract

Background: Systemic lupus erythematosus (SLE) is a multi-system autoimmune disease that significantly affects both the physical and mental health of patients. Psychosocial support systems play a crucial role in managing chronic diseases, yet their specific impact on the disease activity of SLE patients remains unclear. This study aims to explore the dynamic relationship between disease activity in SLE patients and various types of psychosocial support systems.

Methods: We conducted a retrospective longitudinal observational study, including 150 SLE patients who received treatment at our hospital from January 2022 to January 2023. Emotional support, tangible support, social interaction support, and informational support were assessed using the revised Social Support Rating Scale. Disease activity was quantified using the European Consensus Lupus Activity Measurement. The relationship between psychosocial support and disease activity was analyzed using Spearman's rank correlation coefficient and multiple linear regression models, with Bootstrap resampling employed to test the robustness of the results.

Results: We found a significant negative correlation between psychosocial support and SLE disease activity, with emotional support, social interaction support, and informational support showing stronger negative correlations. Multiple regression analysis revealed that the inhibitory effects of emotional support, social interaction support, and informational support on disease activity increased over time. Although the impact of tangible support was not statistically significant, it gradually became more apparent over time.

Conclusion: Our findings indicate a significant negative correlation between psychosocial support and SLE disease activity, particularly with emotional support, social interaction support, and informational support. Over time, the impact of tangible support also becomes evident. These findings provide important references for the comprehensive treatment and management of SLE patients. However, due to the observational nature of the study, the causality of this relationship requires further exploration.

Keywords: disease activity; longitudinal studies; psychosocial support systems; retrospective studies; social support; systemic lupus erythematosus.

Figure 1

Correlation analysis of different types of psychosocial support and SLE disease activity. Higher scores in emotional support, social interaction support, and informational support are generally associated with lower disease activity, reflected in the downward trend of the box plot. The relationship between tangible support and SLE activity is less clear, with a more uniform distribution, suggesting that tangible support may have a minimal impact on SLE activity.

Figure 2

Residual plot and QQ plot of the multivariable regression analysis. The residual plot shows the differences between actual and predicted values and their relationship with fitted values. Observing the residual distribution, we find that data points are randomly distributed around the zero line, with no obvious systematic bias, indicating a good model fit. The QQ plot shows that most data points closely follow the reference line, suggesting that the residuals are approximately normally distributed, which is a good sign for the reliability of the statistical inference in this linear regression model.

Figure 3

Sensitivity distribution of Bootstrap regression coefficients. The regression coefficients for emotional support, social interaction support, and informational support are relatively stable, showing strong negative correlations and low uncertainty, whereas the impact of tangible support scores is less evident and more variable.

Figure 4

Heatmap of regression coefficients for four types of psychosocial support. The heatmap shows the regression coefficients for four types of psychosocial support at 1, 4, 8, and 12 months. Color changes represent the magnitude of the regression coefficients: the closer the color is to red, the larger the regression coefficient; the closer the color is to blue, the smaller the regression coefficient. “−” only indicates a negative correlation and does not represent the magnitude.