Abstract Background With the rapid development of the digital economy and fintech, supply chain finance professionals are often exposed to high-intensity decision-making pressure, uncertainty, and performance-driven work environments, resulting in a significantly higher incidence of mood disorders (such as anxiety, depression, and stress-related disorders) compared to the general workforce. Existing research indicates that mood disorders not only affect individual mental health but also weaken risk assessment and decision-making stability, thereby amplifying the operational risks of the financial system. Traditional psychological interventions often rely on offline cognitive behavioral therapy (CBT), but these methods face challenges such as high participation costs, low compliance, and insufficient intervention timeliness among supply chain finance professionals. In recent years, digital cognitive behavioral therapy (Digital CBT), leveraging mobile platforms, intelligent interaction, and data analysis technologies, has shown promising potential in the early screening and intervention of mood disorders. However, its application in specific high-risk financial professional groups still lacks systematic evaluation. Therefore, this study focuses on supply chain finance professionals, constructing and validating an early intervention model using Digital CBT to assess its comprehensive effects on alleviating mood disorders, improving psychological function, and enhancing intervention compliance. Methods This prospective controlled study included 412 employees of a regional supply chain finance institution. Participants were divided into an intervention group and a control group based on baseline mood scale results. The intervention group received 8 weeks of Digital CBT, including modules on emotion recognition, cognitive restructuring, stress management, and behavioral activation, while the control group received only routine mental health education. All participants completed standardized mood scale assessments at baseline, week 4, and week 8, and intervention usage frequency and completion rates were recorded via a platform log. The study process included participant selection, baseline assessment, implementation of the digital intervention, periodic follow-up, and outcome assessment. Results After 8 weeks, the intervention group showed a 32.6% and 28.4% decrease in anxiety and depression scale scores, respectively, compared to baseline, significantly lower than the 11.2% and 9.5% decreases in the control group (p.01). Repeated measures ANOVA showed a significant time-group interaction effect (F = 14.37, p.001). In the medium-to-high risk subgroup, the intervention efficacy rate was 68.9%, higher than the 41.7% in the control group. Platform data analysis revealed that participants with a completion rate exceeding 75% experienced greater emotional improvement (d = 0.53). Furthermore, cognitive flexibility and stress coping abilities significantly improved, showing a moderate correlation with the reduction in emotional symptoms (r = 0.41 ~ 0.47). Discussion This demonstrates that Digital CBT can achieve early, low-cost, and highly adherent intervention for emotional disorders in supply chain finance professionals, demonstrating a clear effect on alleviating negative emotions and improving psychological function. This model provides a scalable pathway for mental health management among high-stress financial professionals. Future research could combine physiological signals and work performance indicators to further assess its long-term impact on risk decision-making and occupational functioning, and explore personalized, adaptive intervention strategies.
Yue Zhu (Sun,) studied this question.