Analysis of the intrinsic biophysical and molecular process correlations between music activity and biosensor-monitored mental health status
Abstract
Music activities, such as listening or performing music, have been linked to various mental health benefits, including stress reduction, emotional regulation, and overall well-being. However, from the perspective of cellular and molecular biomechanics, the underlying mechanisms linking music activity and mental health status, especially as monitored through biosensors, require further exploration. With advancements in biosensor technology, it is possible to observe physiological indicators such as heart rate, galvanic skin response, and brainwave activity that reflect mental health in real time during music-related activities. Changes in heart rate might be associated with the modulation of autonomic nervous system activity, which in turn can affect the release of neurotransmitters and intracellular signaling pathways at the cellular and molecular level. Brainwave activity alterations could reflect changes in neural cell excitability and synaptic transmission, involving complex molecular cascades. The study examines the relationship between music activities and mental health status utilizing biosensor data to measure physiological responses associated with mental health indicators. Data preprocessing included normalization to standardize physiological measurements and noise reduction to enhance signal quality. Feature extraction utilized Scale-Invariant Feature Transform (SIFT) to identify key features associated with physiological changes during music engagement. The study proposed a Golden Jackal Optimized Intelligent Extreme Gradient Boosting (GJO-IXGBoost) method, which was then applied to analyze the processed data, providing robust insights into the correlations between music activities and mental health. Statistical techniques, including correlation analysis and regression modeling, were used in this study. The proposed method is the performance of various evaluation metrics such as MSE (9.8%), RMSE (21.3%), MAE (13.2%), accuracy (92%), F1-score (90.2%), sensitivity (91%), MCC (85.6%) and specificity (93%). The results suggesting a positive impact of music activities, especially active participation, on mental health as monitored by biosensor data could potentially be due to the modulation of cellular and molecular processes. Applying the GJO-IXGBoost method helps in deciphering these complex cellular and molecular biomechanical correlates, contributing to the evidence base for the therapeutic potential of music in mental health interventions at the cellular and molecular level.
References
1. Alam A, and Mohanty A. Music and Its Effect on Mathematical and Reading Abilities of Students: Pedagogy for Twenty-First Century Schools. In Interdisciplinary Perspectives on Sustainable Development (pp. 342-346). 2023; CRC Press.
2. Koirala NR, Paudel A, Upadhyay S, and Koirala A. Music and mental health. Journal of Psychiatrists’ Association of Nepal. 2024; 13(1), pp.37-43.
3. Ghanai K. The Neuroscience of Music: An Interdisciplinary Study of the Effects of Music on the Brain. 2023.
4. Tervaniemi M. The neuroscience of music–towards ecological validity. Trends in Neurosciences. 2023; 46(5), pp.355-364.
5. Bonde LO, Stensæth K, and Ruud E. Music and Health. A Comprehensive Model. Department of Communication and Psychology, Aalborg University, Denmark. 2023.
6. Poli A. Measurement and processing of multimodal physiological signals in response to external stimuli by wearable devices and evaluation of parameters influencing data acquisition. 2023.
7. Ma C. The Influence of College Physical Education Teaching on Students’ Mental Health and Skill Improvement under the embodied cognition Theory. Revista de Psicología del Deporte (Journal of Sport Psychology). 2024; 33(2), pp.366-375.
8. Сулаймонова Д. An analysis of the importance of music therapy in an inclusive education. Зарубежнаялингвистика и лингводидактика. 2024; 2(1/S), pp.318-326.
9. Jianxin Z, Wenhong L, Yuli L, and Ruihong X. Understanding Aesthetic Principles in Music and its Effect on Contemporary Music Composition: An Educational Psychology Perspective. Journal of Psychology and Behavior Studies. 2024; 4(2), pp.08-30.
10. Cordero Jr DA. Music for Healing: A Careful Application of Music Therapy for the Sick. Journal of Pain & Palliative Care Pharmacotherapy. 2024; pp.1-2.
11. Trost W, Trevor C, Fernandez N, Steiner F, and Frühholz S. Live music stimulates the affective brain and emotionally entrains listeners in real time. Proceedings of the National Academy of Sciences. 2024; 121(10), p.e2316306121.
12. Smalley AJ, White MP, Sandiford R, Desai N, Watson C, Smalley N, Tuppen J, Sakka L, and Fleming LE. Soundscapes, music, and memories: Exploring the factors that influence emotional responses to virtual nature content. Journal of Environmental Psychology. 2023; 89, p.102060.
13. Musgrave G. Music and wellbeing vs. musicians’ wellbeing: examining the paradox of music-making positively impacting wellbeing, but musicians suffering from poor mental health. Cultural Trends. 2023; 32(3), pp.280-295.
14. Chen S, Zheng L, and Chen Y. Mental Health and Therapeutic Music: An Interdisciplinary Exploration. In Interdisciplinary Research on Healthcare and Social Service: Chinese and Cross-Cultural Perspectives (pp. 221-231). Cham: Springer Nature Switzerland. 2024.
15. Wang L, Hu Y, Jiang N., and Yetisen AK. Biosensors for psychiatric biomarkers in mental health monitoring. Biosensors and Bioelectronics. 2024; p.116242.
16. Bhave A, Renold FK, and Gloor PA. Using plants as biosensors to measure the emotions of jazz musicians. In Handbook of Social Computing (pp. 173-188). Edward Elgar Publishing. 2024.
17. Smith AA, Li R, and Tse ZTH. Reshaping healthcare with wearable biosensors. Scientific Reports. 2023; 13(1), p.4998.
18. Fareesha F, Chandanashree YK, Gowthami V, Jayachandran R, and Kalathil S. February. Real-Time Artificial Mood-tracking and Health-monitoring System (RAMAHS) for people with mental illness and their Caregivers. In 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC) (pp. 1-6). IEEE. 2023.
19. Hunt AM, and Sims F. Integrating Physiological Measures within a Music Therapy Research Course: Program Description and Initial Evaluation. Dialogues in Music Therapy Education. 2024.
20. Erdem A, Eksin E, Senturk H, Yildiz E, and Maral M. Recent developments in wearable biosensors for healthcare and biomedical applications. TrAC Trends in Analytical Chemistry. 2023; p.117510.
21. Xu C, Song Y, Sempionatto JR, Solomon SA, Yu Y, Nyein HY, Tay RY, Li J, Heng W, Min J, and Lao A. A physicochemical-sensing electronic skin for stress response monitoring. Nature Electronics. 2024; 7(2), pp.168-179.
22. Khan A, DeVoe E, and Andreescu S. Carbon-based electrochemical biosensors as diagnostic platforms for connected decentralized healthcare. Sensors & Diagnostics. 2023; 2(3), pp.529-558.
23. Wasilewski T, Kamysz W, and Gębicki J. AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring. Biosensors. 2024; 14(7).
24. Jia Y. Impact of Music Teaching on Student Mental Health Using IoT, Recurrent Neural Networks, and Big Data Analytics. Mobile Networks and Applications. 2024; pp.1-20.
Copyright (c) 2025 Lisha He
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
