Email this article AN AI-POWERED SELF-ACCESS AND SELF-MONITORING APPLICATION FOR DEPRESSION AND ANXIETY
About this article
Received: 21/02/25 Revised: 11/06/25 Published: 27/06/25Authors
1. Dang Nguyen Nam Anh, 1) School of Computer Science and Engineering, International University - Vietnam National University, Ho Chi Minh City; 2) Vietnam National University, Ho Chi Minh City2. Nguyen Hoang Luan, 1) School of Computer Science and Engineering, International University - Vietnam National University, Ho Chi Minh City; 2) Vietnam National University, Ho Chi Minh City
3. Le Nguyen Binh Nguyen, School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
4. Le Duy Tan
, 1) School of Computer Science and Engineering, International University - Vietnam National University, Ho Chi Minh City; 2) Vietnam National University, Ho Chi Minh City
Abstract
Mental health, particularly anxiety disorders, has been recognized as a significant global challenge, profoundly impacting the quality of life for individuals worldwide. Traditional barriers, such as geographical constraints and the inflexibility of mental health care, have hindered efforts to address these issues effectively. Therefore, in this study, an advanced mental health monitoring system incorporating artificial intelligence (AI) technology has been proposed to overcome these limitations. Specifically, this system integrates smart wristbands to collect sleep data from patients. The collected data is then visualized on both web and mobile platforms in real-time, enabling therapists to monitor progress and design appropriate treatment plans. Additionally, AI technology is utilized to analyze historical data, providing personalized recommendations for sleep schedules and predicting sleep quality for the upcoming week. This system offers tailored interventions for individual patients. It empowers them to actively participate in their mental health treatment actively, improving their overall well-being and fostering a healthier mindset.
Keywords
Full Text:
PDFReferences
[1] World Health Organization, “What we do,” 2025. [Online]. Available: https://www.who.int/about/what-we-do. [Accessed January 10, 2025].
[2] UNICEF, “The State of the World’s Children 2021: On My Mind – Promoting, protecting and caring for children’s mental health,” New York: UNICEF, October 5, 2021. [Online]. Available: https://www.unicef.org/reports/state-worlds-children-2021. [Accessed January 10, 2025].
[3] G. M. Sansbury et al., “Improving integrated depression and non-communicable disease care in Malawi through engaged leadership and supportive implementation climate,” BMC Health Serv. Res., vol. 23, no. 1, 2023, doi: 10.1186/s12913-023-10344-7.
[4] C. E. Okechukwu, “A call for improved mental health workforce in low-income countries,” International Journal of Social Psychiatry, vol. 68, no. 2, pp. 465–467, 2022, doi: 10.1177/00207640211039255
[5] E. Girma et al., “Understanding mental health stigma and discrimination in Ethiopia: A qualitative study,” Cambridge Prisms: Global Mental Health, vol. 11, 2024, doi: 10.1017/gmh.2024.55.
[6] M. Cleary, “The challenges of mental health care reform for contemporary mental health nursing practice: relationships, power and control,” Int. J. Ment. Health Nurs., vol. 12, no. 2, 2003, doi: 10.1046/j.1440-0979.2003.00280.x.
[7] A. Vaidyam, J. Halamka, and J. Torous, “Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study,” JMIR Mhealth Uhealth, vol. 10, no. 1, 2022, doi: 10.2196/30557.
[8] F. Lin et al., “SleepSense: A Noncontact and Cost-Effective Sleep Monitoring System,” IEEE Trans Biomed Circuits Syst, vol. 11, no. 1, 2017, doi: 10.1109/TBCAS.2016.2541680.
[9] G. Matar, M. Haddad, M. El Falou, and C. H. L. Li, “Unobtrusive sleep monitoring using cardiac, breathing and movements activities: An exhaustive review,” IEEE Access, vol. 6, pp. 45129–45144, 2018, doi: 10.1109/ACCESS.2018.2865487.
[10] F. Lin, Y. Yang, M. T. Pourhomayoun, and M. Sarrafzadeh, “SleepSense: A noncontact and cost-effective sleep monitoring system,” IEEE Trans. Biomed. Circuits Syst., vol. 11, no. 1, pp. 189–202, Feb. 2017, doi: 10.1109/TBCAS.2016.2541680.
[11] N. Surantha, O. K. Utomo, E. M. Lionel, I. D. Gozali, and S. M. Isa, “Intelligent Sleep Monitoring System Based on Microservices and Event-Driven Architecture,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3167637.
[12] N. Surantha, C. Adiwiputra, O. K. Utomo, S. M. Isa, and B. Soewito, “IoT System for Sleep Quality Monitoring using Ballistocardiography Sensor,” International Journal of Advanced Computer Science and Applications(IJACSA), vol. 11, no. 1, 2020, doi: 10.14569/IJACSA.2020.0110126.
[13] D. Diotte, “Sleep Dataset,” Kaggle, 2022. [Online]. Available: https://www.kaggle.com/datasets/ danagerous/sleep-data. [Accessed January 10, 2025].
[14] B. Sousa and C. Gonçalves, “FedAAA-SDN: Federated Authentication, Authorization and Accounting in SDN controllers,” Computer Networks, vol. 239, 2024, doi: 10.1016/j.comnet.2023.110130.
[15] W. Moore and S. Frye, “Review of HIPAA, Part 1: History, protected health information, and privacy and security rules,” J. Nucl. Med. Technol., vol. 47, no. 4, 2019, doi: 10.2967/JNMT.119.227819.
[16] H. Li, L. Yu, and W. He, “The Impact of GDPR on Global Technology Development,” Journal of Global Information Technology Management, vol. 22, no. 01, pp. 1–6, 2019, doi: 10.1080/1097198X.2019.1569186.
DOI: https://doi.org/10.34238/tnu-jst.12111
Refbacks
- There are currently no refbacks.