Study on sedentary behavior and its influencing factors in elderly ovarian cancer patients during home confinement

Sedentary behavior is prevalent among the elderly and has become an increasingly serious public health issue. Although extensive research has been conducted on the status and risk factors of sedentary behavior in the general elderly population, the current situation and related influencing factors of sedentary behavior in elderly patients with ovarian cancer, a disease-specific group, have not been fully explored. In this study, a total of 206 elderly ovarian cancer (EOC) patients who received treatment at the First Affiliated Hospital of Zhejiang University School of Medicine from September 1, 2022, to February 28, 2025, were selected as the research subjects by convenience sampling. A cross-sectional survey was conducted using the General Information Questionnaire, Chinese Adult Sedentary Behavior Questionnaire, Social Support Rating Scale, Nutritional Risk Screening 2002, and Edmonton Symptom Assessment Scale. The influencing factors of sedentary behavior in EOC patients were analyzed by the logistic regression model and CHAID decision tree model. Among the 206 EOC patients, the average sedentary time was 7.4±3.0 h/d, and 161 patients (78.2%) had sedentary behavior (sedentary time ≥5 h/d). Logistic regression analysis and CHAID decision tree algorithm both demonstrated that social support and cancer symptom burden were the influencing factors of sedentary behavior in EOC patients (P<0.05). Moreover, the Chi-square Automatic Interaction Detection (CHAID) algorithm further revealed an interaction between the two factors, indicating that the social support level was the most crucial determinant. Our study reveals that sedentary behavior among EOC patients is alarmingly prevalent, necessitating urgent attention from medical professionals. Given the significant impact of social support and cancer symptom burden on this sedentary behavior, healthcare providers should proactively assess and intervene to address these influential factors, thereby mitigating the adverse consequences of excessive sedentary time for EOC patients. Decision tree and logistic regression models effectively identify sedentary behavior determinants with good predictive power. A combined approach is recommended to leverage their complementary strengths, providing a robust basis for reducing sedentary behavior in EOC patients.