Bibliometric Analysis of Trends, Innovations, and the Future of CBT-Based Mobile Interventions for Depression

Zhe Gao¹Tingzhou Zhao¹Yuhang Li¹Wenhao Huang¹Junxi Tang¹Xujun Yu¹Yulin Li^1*Tangming PENG^2*

• ¹School of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China
• ²The Second Clinical Medical School,, Chengdu University of Traditional Chinese Medicine, Chengdu, China

Background: Depression is a major global health challenge, and traditional cognitive behavioral therapy (CBT) is constrained by therapist shortages and economic barriers. CBT-based mobile applications offer a scalable and accessible alternative, yet a comprehensive overview of their global research landscape remains limited. Objective: To map global research trends, clinical progress, and emerging frontiers in CBT-based mobile applications for depression using bibliometric methods. Methods: Relevant studies were systematically retrieved from the Web of Science Core Collection, PubMed, and PsycINFO from inception to June 25, 2025. CiteSpace 6.4 R1, Microsoft Excel 2019, and Python were used for visualization and data analysis, including temporal publication trends, co-authorship, co-citation, keyword analyses, and citation burst detection. Results: The WoSCC analysis identified 350 articles published between 2013 and 2025, showing a marked growth trajectory with leading contributions from the United States and major academic centers. Dominant themes included smartphone interventions, blended treatments, CBT for insomnia, and adolescent depression, while citation bursts indicated recent shifts toward prevention, technology integration, and standardized outcome measures. The PubMed analysis included 72 clinical trial articles, highlighting randomized controlled trials as the predominant design and revealing growing interest in integrating interpersonal therapy and mindfulness within broader, interdisciplinary treatment frameworks. The PsycINFO analysis comprised 20 articles and provided a complementary behavioral science perspective, emphasizing mobile phone–delivered CBT for major depression, digital interventions targeting comorbid social anxiety, culturally adapted applications for Chinese cultural groups, and emerging work linking mobile health and virtual reality. Conclusions: Research on CBT-based mobile applications for depression is rapidly advancing toward more personalized, adaptive, and preventive digital interventions grounded in robust clinical and behavioral evidence. Strengthening global, interdisciplinary collaboration and leveraging innovative technologies will be critical for translating these tools into effective, scalable services. Over the next five to ten years, key research streams are likely to include the integration of Artificial Intelligence (AI) and large language models (LLMs) into mobile CBT platforms and the convergence of app-based interventions with sensor-based digital phenotyping, wearable devices, and immersive technologies to enhance real-time monitoring, user engagement, and long-term outcomes, with the potential to narrow treatment gaps across diverse populations.