PERSPECTIVES АND POSSIBILITIES ОF USING ARTIFICIAL INTELLIGENCE DURING AUTOGENIC TRAINING FOR PSYCHOPHYSIOLOGICAL STATE CORRECTION

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published: Apr 12, 2024

  Anna Rode

  Yulia Rode

Abstract

The article explores the development of autogenic training for the correction of psychophysical states using artificial intelligence tools. The research aims to organise the application areas of artificial intelligence for diagnosis and correction of psychophysical states through autogenic training. The results indicate that autogenic training is an important approach in the spectrum of treatment methods for psychophysical disorders, with its main advantages being the flexibility of the technique and its ability to induce relaxation and psychophysiological self-regulation through passive concentration and repetition of specific phrases. The analysis shows that while the practice is stable and consists of sequential procedures, it can be adapted to different techniques and needs. Here, the use of artificial intelligence (AI) can significantly improve the personalisation of the treatment process and its effectiveness. The application of AI in the context of autogenic training opens up new perspectives for the diagnosis and treatment of psychophysical disorders. AI can optimise psychotherapeutic interventions by adapting training sessions to the individual needs of the user, thereby achieving better results in relaxation and psychophysical recovery. A distinctive feature of AI is also its ability to provide detailed feedback and track user progress, contributing to more effective adjustment and improvement of the training process. The integration of AI with virtual and augmented reality technologies can further enhance the autogenic training experience, creating a more immersive and controlled environment for relaxation. The development of digital tools and mobile applications based on AI has already demonstrated its positive impact on the psychophysical health of users, paving the way for more innovative and effective solutions in the future. Thus, the use of AI in autogenic training for the correction of psychophysiological states promises significant prospects for improving the quality of life and well-being of individuals.

How to Cite

Rode, A., & Rode, Y. (2024). PERSPECTIVES АND POSSIBILITIES ОF USING ARTIFICIAL INTELLIGENCE DURING AUTOGENIC TRAINING FOR PSYCHOPHYSIOLOGICAL STATE CORRECTION. Economics & Education, 9(1), 6-11. https://doi.org/10.30525/2500-946X/2024-1-1
Article views: 86 | PDF Downloads: 62

##plugins.themes.bootstrap3.article.details##

Keywords

artificial intelligence, autogenic training, psychophysical disorders, psycho-correction

References

Alvares, G. A., Quintana, D. S., Hickie, I. B., & Guastella, A. J. (2016). Autonomic nervous system dysfunction in psychiatric disorders and the impact of psychotropic medications: A systematic review and meta-analysis. Journal of Psychiatry & Neuroscience, 41, 89–104. E-source: https://pubmed.ncbi.nlm.nih.gov/26447819/

Arango, C., Dragioti, E., Solmi, M., Cortese, S., Domschke, K., Murray, R. M., Jones, P. B., Uher, R., Carvalho, A. F., & Reichenberg, A. et al. (2021). Risk and protective factors for mental disorders beyond genetics: An evidence-based atlas. World Psychiatry Official Journal of the World Psychiatric Association, 20, 417–436. E-source: https://pubmed.ncbi.nlm.nih.gov/34505386/

Ernst, E., & Kanji, N. (2000). Autogenic training for stress and anxiety: A systematic review. Complementary Therapies in Medicine, 8, 106–110. E-source: https://pubmed.ncbi.nlm.nih.gov/10859603/

Kanji, N. (1997). Autogenic training. Complementary Therapies in Medicine, 5, 162–167. DOI: https://doi.org/10.1016/S0965-2299(97)80060-X

Kessler, R. C., Stein, M. B., Petukhova, M. V., Bliese, P., Bossarte, R. M., Bromet, E. J., Fullerton, C. S., Gilman, S. E., Ivany, C., Lewandowski-Romps, L., et al. (2017). Predicting suicides after outpatient mental health visits in the Army study to assess risk and resilience in servicemembers (Army STARRS). Molecular Psychiatry, 22, 544–551. E-source: https://pubmed.ncbi.nlm.nih.gov/27431294/

Leung, J. Y., Barr, A. M., Procyshyn, R. M., Honer, W. G., & Pang, C. C. (2012). Cardiovascular side-effects of antipsychotic drugs: The role of the autonomic nervous system. Pharmacology & Therapeutics, 135, 113–122. DOI: https://doi.org/10.1016/j.pharmthera.2012.04.003

Luthe, W. (1979). About the Methods of Autogenic Therapy. In Mind/Body Integration; Peper, E., Ancoli, S., Quinn, M. (Eds.); Springer: Boston, MA, USA. E-source: https://link.springer.com/chapter/10.1007/978-1-4613-2898-8_12

Pamer, C., Serpi, T., & Finkelstein, J. (2008). Analysis of Maryland poisoning deaths using classification and regression tree (CART) analysis. AMIA Annual Symposium Proceedings, 550–554. E-source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2656088/

Poulin, C., Shiner, B., Thompson, P., Vepstas, L., Young-Xu, Y., Goertzel, B., Watts, B., Flashman, L., & McAllister, T. (2014). Predicting the risk of suicide by analyzing the text of clinical notes. PLoS ONE, 9, e85733. E-source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0085733

Romans Ch. 9 Ways Tech Will Help You Learn and Practice Autogenic Training. MUO, 2023. E-source: https://www.makeuseof.com/tech-learn-practice-autogenic-training/

Schultz, J. H., & Luthe, W. (1969). Autogenic Therapy Volume 1 Autogenic Methods, 1st edition. Grune & Stratton, Inc.: New York, NY, USA. E-source: https://archive.org/details/autogenictherapy0001unse

Yurdakul, L., Holttum, S., & Bowden, A. (2009). Perceived changes associated with autogenic training for anxiety: A grounded theory study. Psychology and Psychotherapy, 82(Pt 4), 403–419. DOI: https://doi.org/10.1348/147608309X444749