Influencing factors of kinesophobia in middle-aged and elderly patients with chronic musculoskeletal pain
Objective To investigate current status of kinesophobia in middle-aged and elderly inpatients with chronic musculoskeletal pain(CMP),and explore the influencing factors.Methods A purposive sampling method was used to select middle-aged and elderly CMP patients hospitalised in a Grade IIIA orthopaedic hospital in Urumqi,Xinjiang between January and June 2023 as the study subjects.General questionnaire,Tampa scale for Kinesiophobia(TSK),general self-efficacy scale,medical coping models questionnaire and social support rating scale were used in the investigation.Logistic regression analysis and decision tree model were employed to analyse the factors that influenced kinesiophobia in the patients,and the predictive effects of the two models were evaluated.Results It was found that 67.3%of the 352 included inpatients had kinesiophobia.Self-efficacy and social support were found as the influential factors for kinesiophobia by both of logistic regression analysis and decision tree model.Moreover,both Logistic regression analysis and the decision tree model showed high value in prediction of kinesophobia in middle-aged and elderly patients with CMP with AUC at[0.971(95%CI:0.952-0.990)and 0.948(95%CI:0.921～0.974),respectively]with standard error at 0.010 and 0.013,respectively.However,the logistic regression analysis exhibited higher sensitivity and specificity in comparison with the decision tree model(99.2%vs.96.6%;89.5%vs.87.8%,respectively).Conclusions Middle-aged and elderly CMP inpatients are highly susceptible to kinesiophobia.A logistic regression analysis combined with the decision tree model can fully spot the influencing factors of kinesophobia in middle-aged and elderly patients with CMP.Therefore,it is suggested to combine the two methods in the assessment and management of kinesiophobia among middle-aged and elderly CMP patients.
musculoskeletal painkinesiophobiamiddle-aged and elderly peopleLogistic regressiondecision tree modelcross-sectional study
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