Objective:To evaluate the effects of different psychological interventions on depression and pain in patients with arthritis using the method of network Meta-analysis.Methods:Randomized controlled trials (RCTs) on psychological interventions for arthritis patients were systematically searched in PubMed, Embase, Cochrane Library, CINAHL, Web of Science, PsycINFO, WanFang and CNKI databases. The retrieval time limit was from the establishment of databases to September 30, 2021. The literature was screened according to inclusion and exclusion criteria, and quality was assessed using the Cochrane Manual recommended RCT bias risk assessment tool. STATA 15.0 software was used for network Meta-analysis according to frequency framework.Results:A total of 59 RCTS were included, involving 8 psychological interventions and 5 726 patients. For the primary outcome of depression, at post-intervention, the ranking results showed that relaxation was the most likely to be the best intervention. However, in the comparison of different intervention effects, only cognitive behavioral therapy showed statistically significant difference compared with the control group ( P<0.05) . At follow-up, the ranking results showed that cognitive behavioral therapy was the most likely to be the best, and the difference was statistically significant compared with the control group ( P < 0.05) . After intervention, for the secondary index pain, the ranking results showed that hypnosis intervention was the most likely to be the best, but the intervention effect of relaxation intervention and cognitive behavioral therapy was statistically significant compared with the control group ( P< 0.05) . At follow-up, the ranking results showed that the best possibility of receiving commitment therapy was the highest, and the difference was statistically significant compared with the control group ( P < 0.05) . Conclusions:Cognitive behavioral therapy has the best effect on depression indicators in arthritis patients, and relaxation intervention and acceptance commitment therapy have the best effects on pain indicators after intervention and at follow-up, respectively. The potential interventions include relaxation intervention and hypnosis intervention, which are worthy of further study.

Objective:To construct the prediction model of knee degeneration in patients with knee osteoarthritis based on bioelectrical impedance index, and evaluate the prediction performance and application efficiency of the model.Methods:This was a cross-sectional study. From May to July 2021, 248 knee joints of 124 patients with knee osteoarthritis at home from Shijiazhuang Yuqiang Community Health Service Center who participated in physical examination were selected by convenience sampling to establish the model. According to Kellgren-Lawrence (K-L) grading system, the knee joints were divided into four groups, namely K-L1 ( n=19) , K-L2 ( n=103) , K-L3 ( n=96) , and K-L4 ( n=30) . The indicators included in the model were selected through analysis of variance or Kruskal-Wallis test, and a prediction model of knee degeneration was established using support vector machine, and the model was optimized using grid parameter optimization method. The prediction performance of the model was evaluated by the area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, accuracy, positive predictive value and negative predictive value. Results:The indicators in the model included age, complications, lumbar/back/hip pain, high-risk occupation, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) -pain, WOMAC-function, capacitive reactance and phase angle. The area under the ROC curve of the training set model was 0.999, the prediction accuracy was 0.920, and the 95% confidence interval was 0.868 to 0.957. The area under the ROC curve of the test set model was 0.833, the prediction accuracy was 0.682, and the 95% confidence interval was 0.572 to 0.780.Conclusions:The prediction model of knee degeneration has good prediction performance and is easy to use, which can be used as a screening tool for knee degeneration in patients with knee osteoarthritis.