Abstract Background Depression is a common mental health disorder that seriously affects the quality of life and social functioning of patients. The traditional nursing model for depression mainly relies on face-to-face psychological therapy and medication treatment, facing bottlenecks such as uneven distribution of professional resources, shame barriers to seeking help, and difficulties in long-term follow-up management. In recent years, the rapid development of computer technology represented by mobile health, artificial intelligence, and remote therapy has provided a new path to break through these bottlenecks. Computer technology can not only provide convenient mental health assessment tools, but also provide personalized care plans for patients through intelligent algorithms. In this context, the aim of this study is to construct a computer technology-based psychological health care model for depression patients through a systematic approach, in order to achieve personalized intervention. Methods This study first constructed and optimized a computer-based mental health care platform for depression patients. The core functions of the platform include regular mental health assessments, mental health course push notifications, and clinical visualization dashboards. Patients are able to complete self-assessment of the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder Disorder-7 (GAD-7) on the platform. Based on the evaluation results, the platform automatically pushes personalized mental health courses and can record patient conditions, providing visual data for clinical doctors. Then, 200 adult patients with depression were recruited as research subjects and randomly divided into an intervention group (n = 100) and a control group (n = 100). The control group only received routine treatment and received standardized antidepressant medication under the guidance of a psychiatrist. The intervention group adopted a computer-based psychological health care model on the basis of conventional treatment. All patients were informed of the experiment and signed an informed consent form. The intervention lasted for 6 months, and Hamilton Depression Scale (HAMD), PHQ-9, and GAD-7 scores were collected from two groups of patients before intervention, 3 months after intervention, and 6 months after intervention. Results Before intervention, there was no significant difference in HAMD, PHQ-9, and GAD-7 scores between the two groups of patients (p.05). After 6 months of intervention, the HAMD score of the intervention group was 18.62 ± 2.33, significantly lower than the control group's 24.51 ± 3.04 (p.05). The PHQ-9 score of the intervention group was 9.36 ± 2.49, significantly lower than the control group's 15.04 ± 3.11 (p.05). The GAD-7 score of the intervention group was 9.13 ± 2.07, significantly lower than the control group's 14.51 ± 2.83 (p.05). Discussion This study developed a computer technology-based mental health care model for patients with depression, and demonstrated that the model can significantly alleviate depression and anxiety symptoms and improve patient well-being. Future research should focus on verifying its long-term effects in a wider population, combining more advanced technologies such as multimodal data analysis, virtual reality technology, etc., to better meet the nursing needs of patients with depression and promote the development of the field of mental health care. Funding No. 2025JGZ133; No. 202531719.
Li et al. (Sun,) studied this question.