Longitudinal relationship between social participation, depressive symptoms, and activity impairment among older patients with arthritis: a moderated mediation analysis.

BACKGROUND: Arthritis primarily affects older people and is a prominent cause of their activity impairment. This study aimed to examine the mediating role of depressive symptoms in the relationship between social participation and activity impairment, as well as to determine whether sex moderated the mediating effect. METHODS: A total of 2247 older patients with arthritis were included from the China Health and Retirement Longitudinal Study between 2015 and 2018. We first examined a simple mediation model where depressive symptoms were a mediator between social participation and activity impairment. Furthermore, sex was systematically integrated into the model as a moderator. The mediation model and moderated mediation model were analyzed using PROCESS macro. RESULTS: Mediation analysis revealed that the association between social participation and activity impairment was partially mediated by depressive symptoms (B = -0.10, 95% CI = [-0.14, -0.06]) with intermediary effect of 28.6%. Moderated mediation analysis indicated that mediation model was moderated by sex. The indirect effect of social participation on activity impairment among female patients (B = -0.15, 95% CI = [-0.21, -0.09]) was stronger than male patients (B = -0.04, 95% CI = [-0.09, -0.01]). CONCLUSION: Social participation was the key protective factor associated with depressive symptoms and activity impairment among arthritis patients. Encouraging arthritis patients to social participation and improving the depressive symptoms might avoid activity impairment, especially for female patients.

The mediation effect of social support between stigma and social alienation in patients with stroke.

Background: Social alienation is prevalent and causes adverse outcomes in stroke. Previous studies have linked stigma with social alienation. However, little is known about the mechanisms behind this relationship. This study explored the mediation effects of social support between stigma and social alienation. Methods: A cross-sectional design was used to study 248 patients with stroke admitted to a tertiary rehabilitation hospital in Beijing, China, from December 2022 to July 2023. Patients were assessed using a general information questionnaire, the Stroke Stigma Scale, the Social Support Rating Scale, and the Generalized Social Alienation Scale. The PROCESS macro in SPSS was used to examine the mediation model. Results: The results showed that stigma has a negative effect on social support (ß = -0.503, p<0.001); stigma has a positive effect on social alienation (ß = 0.768, p<0.001). Social support mediated the relationship between stigma and social alienation, with a mediation effect of 0.131 (95%CI: 0.060, 0.214), and indirect effects accounted for 17.06% of the total effect. Conclusion: Social support mediated the relationship between stigma and social alienation. These findings suggest that intervention targeting the enhancement of social support may prevent or reduce social alienation among patients with stroke.

Heterogeneous trajectories of kinesiophobia and their effects on rehabilitation outcomes after total knee arthroplasty: a prospective cohort study.

BACKGROUND: Kinesiophobia is one of the most common and aversive psychological phenomena among patients after total knee arthroplasty (TKA). This study aimed to identify trajectories of kinesiophobia, examine factors distinguishing these trajectories, and clarify the association between trajectories of kinesiophobia and rehabilitation outcomes. METHODS: In this prospective cohort study, the patients who underwent TKA were recruited between December 2021 and April 2022 from three orthopedic wards of a tertiary hospital in China. Kinesiophobia was measured using the Tampa Scale for Kinesiophobia at baseline (T0), and then at 1 month (T1) and 3 months (T2) after TKA to perform latent class growth analysis. Meanwhile, rehabilitation outcomes were assessed at 3 months after TKA, using the Kessler Psychological Distress Scale, the Hospital for Special Surgery-Knee Scale, Barthel Index, and the Impact on Participation and Autonomy questionnaire. RESULTS: The four kinesiophobia trajectories identified were as follows: low stable group (n = 120), rapid recovering group (n = 31), slow recovering group (n = 48), and stable moderate group (n = 58). Body mass index, employment status, heart disease, and pain degree significantly predicted trajectory groups (all p < 0.05). Analysis of variance revealed significant differences between the four kinesiophobia trajectories concerning all rehabilitation outcomes, except for the activities of daily living. CONCLUSION: Distinct kinesiophobia trajectories were identified, and nurses should assess the kinesiophobia of patients after TKA in the early phase. Patients in the slow recovering group are worthy of a specific focus because of their poor recovery after undergoing TKA. As important sources of psychosocial care, nurses need to customize psychological interventions for patients after TKA depending on each kinesiophobia trajectory.

Construction and Validation of Machine Learning Algorithms to Predict Chronic Post-Surgical Pain Among Patients Undergoing Total Knee Arthroplasty.

BACKGROUND: Chronic post-surgical pain (CPSP) is a common but undertreated condition with a high prevalence among patients undergoing total knee arthroplasty (TKA). An effective model for CPSP prediction has not been established yet. AIMS: To construct and validate machine learning models for the early prediction of CPSP among patients undergoing TKA. DESIGN: A prospective cohort study. PARTICIPANTS/SUBJECTS: A total of 320 patients in the modeling group and 150 patients in the validation group were recruited from two independent hospitals between December 2021 and July 2022. They were followed up for 6 months to determine the outcomes of CPSP through telephone interviews. METHODS: Four machine learning algorithms were developed through 10-fold cross-validation for five times. In the validation group, the discrimination and calibration of the machine learning algorithms were compared by the logistic regression model. The importance of the variables in the best model identified was ranked. RESULTS: The incidence of CPSP in the modeling group was 25.3%, and that in the validation group was 27.6%. Compared with other models, the random forest model achieved the best performance with the highest C-statistic of 0.897 and the lowest Brier score of 0.119 in the validation group. The top three important factors for predicting CPSP were knee joint function, fear of movement, and pain at rest in the baseline. CONCLUSIONS: The random forest model demonstrated good discrimination and calibration capacity for identifying patients undergoing TKA at high risk for CPSP. Clinical nurses would screen out high-risk patients for CPSP by using the risk factors identified in the random forest model, and efficiently distribute preventive strategy.

Longitudinal Trajectories of Depressive Symptoms Among Patients With Knee Osteoarthritis: The Role of Pain Intensity.

BACKGROUND: Pain is the primary symptom of knee osteoarthritis (KOA), significantly associated with depressive symptoms. Whether the early pain intensity could distinguish different trajectories of depressive symptoms is not well understood among patients with KOA. AIMS: To identify heterogeneous depressive symptoms trajectories among patients with KOA, and investigate the association between depressive symptoms subgroups and pain intensity. DESIGN: Secondary analysis of a cohort sample. SETTING: Three waves of data from the China Health and Retirement Longitudinal Study was collected in 28 provinces across China. PARTICIPANTS: 702 patients with KOA aged ≥ 45 years completed the Center for Epidemiologic Studies Depression Scale biennially from 2011 to 2015. METHODS: Latent class growth analysis was performed to identify the optimal trajectory shape and number of classes. Multinomial logistic regression analysis was conducted to compare pain intensity across the trajectories. RESULTS: Four distinct trajectories were identified as follows: no depressive symptoms, highly stable, decreasing, and increasing. In multivariate analysis, compared with the "no depressive symptoms" class, patients in the "highly stable" class and "increasing" class were more likely to report moderate pain (p < 0.05) and severe pain (p < 0.05). In addition, there was no significant difference in pain intensity between "no depressive symptoms" class and "decreasing" class (p > 0.05). CONCLUSIONS: These results suggest important heterogeneity in depressive symptom trajectories among patients with KOA. Pain intensity could predict different depressive symptom trajectories in patients with KOA. Efforts to improve the depressive symptoms in patients with KOA must incorporate strategies to address pain.