Artificial Intelligence–Enabled Personalization of Yoga Interventions for Mental Health: A Scoping Review

Dr. Sumana Pothugunta1; Chollangi Sri Sai Ratna2; Bharath Kumar Atluri3; Dr. Rupa Selvaraj4; Swetha Mekalaturu5; P. Chaitanaya Kumar6; Pranay Vemula7

doi:10.48047/tcjh4976

Authors

Dr. Sumana Pothugunta1 Author
Chollangi Sri Sai Ratna2 Author
Bharath Kumar Atluri3 Author
Dr. Rupa Selvaraj4 Author
Swetha Mekalaturu5 Author
P. Chaitanaya Kumar6 Author
Pranay Vemula7 Author

DOI:

https://doi.org/10.48047/tcjh4976

Keywords:

Artificial Intelligence, Yoga, Mental Health, Machine Learning, Personalized Medicine; Stress; Anxiety

Abstract

Mental health disorders, particularly stress, anxiety, and depression, are growing global public health concerns. Yoga, incorporating asanas, pranayama, meditation, and mindfulness, has demonstrated potential for reducing psychological distress and improving emotional well-being, sleep, and quality of life. However, conventional yoga interventions often rely on standardized protocols that may not adequately address individual differences in physical abilities, mental health status, preferences, and treatment responses.

Artificial Intelligence (AI) offers an opportunity to develop personalized, adaptive, and data-driven yoga interventions for mental health. This scoping review aims to map current evidence on AI-enabled personalization of yoga interventions for stress, anxiety, and depression. It examines the integration of technologies including machine learning, computer vision, pose estimation, wearable sensors, conversational AI, recommendation systems, predictive analytics, and generative AI within digital yoga platforms. The review also explores personalization strategies, delivery platforms, mental health and physiological outcomes, user engagement, implementation challenges, and ethical considerations.

Guided by the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR, the review uses the Population–Concept–Context (PCC) framework and considers diverse populations and digital environments, including mobile applications, tele-yoga, virtual and augmented reality, wearables, smart mirrors, and intelligent home-based systems.

Evidence indicates that AI may enhance yoga interventions through personalized pose selection, adaptive breathing and meditation, real-time posture correction, predictive assessment, and continuous behavioural monitoring, potentially improving effectiveness, adherence, and engagement. However, challenges remain regarding clinical validation, algorithmic transparency, privacy, explainability, regulatory oversight, interoperability, and equitable access. This review identifies current evidence gaps and highlights the need for clinically validated, transparent, and ethically responsible AI-enabled yoga interventions to support precision mental health care

Downloads

Download data is not yet available.

References

Ashley, E. A. (2016). Towards precision medicine. Nature Reviews Genetics, 17(9), 507–522.

Butzer, B., LoRusso, A. M., Windsor, R., Riley, F., Frame, K., Khalsa, S. B. S., & Conboy, L. (2017). A qualitative examination of yoga for middle school adolescents. Advances in School Mental Health Promotion, 10(3), 195–219.

Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2019). Openpose: Realtime multi-person 2d pose estimation using part affinity fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172–186.

Carolan, S., Harris, P. R., & Cavanagh, K. (2017). Improving employee well-being and effectiveness: Systematic review and meta-analysis of web-based psychological interventions delivered in the workplace. Journal of Medical Internet Research, 19(7), e271.

Charow, R., Jeyakumar, T., Younus, S., Dolatabadi, E., Salhia, M., Al-Mouaswas, D., Anderson, M., Balakumar, S., Clare, M., Dhalla, A., & others. (2021). Artificial intelligence education programs for health care professionals: Scoping review. JMIR Medical Education, 7(4), e31043.

Cramer, H., Lauche, R., Anheyer, D., Pilkington, K., de Manincor, M., Dobos, G., & Ward, L. (2018a). Yoga for anxiety: A systematic review and meta-analysis of randomized controlled trials. Depression and Anxiety, 35(9), 830–843.

Cramer, H., Lauche, R., Langhorst, J., & Dobos, G. (2013). Yoga for depression: A systematic review and meta-analysis. Depression and Anxiety, 30(11), 1068–1083.

Cramer, H., Ward, L., Saper, R., Fishbein, D., Dobos, G., & Lauche, R. (2015). The safety of yoga: A systematic review and meta-analysis of randomized controlled trials. American Journal of Epidemiology, 182(4), 281–293.

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98.

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.

Ferrari, M., Allan, S., Arnold, C., Eleftheriadis, D., Alvarez-Jimenez, M., Gumley, A., & Gleeson, J. F. (2022). Digital interventions for psychological well-being in university students: Systematic review and meta-analysis. Journal of Medical Internet Research, 24(9), e39686.

Field, T. (2016). Yoga research review. Complementary Therapies in Clinical Practice, 24, 145–161.

Fiske, A., Henningsen, P., & Buyx, A. (2019). Your robot therapist will see you now: Ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. Journal of Medical Internet Research, 21(5), e13216.

Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Machine Learning and the City: Applications in Architecture and Urban Design, 535–545.

Fusar-Poli, P., Correll, C. U., Arango, C., Berk, M., Patel, V., & Ioannidis, J. P. (2021). Preventive psychiatry: A blueprint for improving the mental health of young people. World Psychiatry, 20(2), 200–221.

Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial intelligence in healthcare (pp. 295–336). Elsevier.

Goldberg, S. B., Tucker, R. P., Greene, P. A., Davidson, R. J., Wampold, B. E., Kearney, D. J., & Simpson, T. L. (2018). Mindfulness-based interventions for psychiatric disorders: A systematic review and meta-analysis. Clinical Psychology Review, 59, 52–60.

Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., Hinder, S., Fahy, N., Procter, R., Shaw, S., & others. (2017). Beyond adoption: A new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of Medical Internet Research, 19(11), e8775.

Harvey, S. B., Modini, M., Joyce, S., Milligan-Saville, J. S., Tan, L., Mykletun, A., Bryant, R. A., Christensen, H., & Mitchell, P. B. (2017a). Can work make you mentally ill? A systematic meta-review of work-related risk factors for common mental health problems. Occupational and Environmental Medicine, 74(4), 301–310. https://doi.org/10.1136/oemed-2016-104015

Keesara, S., Jonas, A., & Schulman, K. (2020). Covid-19 and health care’s digital revolution. New England Journal of Medicine, 382(23), e82.

Kiseleva, A., Kotzinos, D., & De Hert, P. (2022). Transparency of AI in healthcare as a multilayered system of accountabilities: Between legal requirements and technical limitations. Frontiers in Artificial Intelligence, 5, 879603.

Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022). From promise to practice: Towards the realisation of AI-informed mental health care. The Lancet Digital Health, 4(11), e829–e840.

Kruse, C. S., Krowski, N., Rodriguez, B., Tran, L., Vela, J., & Brooks, M. (2017). Telehealth and patient satisfaction: A systematic review and narrative analysis. BMJ Open, 7(8), e016242.

Kush, R. D., Warzel, D., Kush, M. A., Sherman, A., Navarro, E. A., Fitzmartin, R., Pétavy, F., Galvez, J., Becnel, L. B., Zhou, F., & others. (2020). FAIR data sharing: The roles of common data elements and harmonization. Journal of Biomedical Informatics, 107, 103421.

Kvedar, J. C., Fogel, A. L., Elenko, E., & Zohar, D. (2016). Digital medicine’s march on chronic disease. Nature Biotechnology, 34(3), 239–246.

Lattie, E. G., Stiles-Shields, C., & Graham, A. K. (2022). An overview of and recommendations for more accessible digital mental health services. Nature Reviews Psychology, 1(2), 87–100.

Leslie, D. (2019). Understanding artificial intelligence ethics and safety. arXiv Preprint arXiv:1906.05684.

Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., Beam, A. L., Chan, A.-W., Collins, G. S., Deeks, A. D. J., & others. (2020a). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. The Lancet Digital Health, 2(10), e537–e548.

Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., Beam, A. L., Chan, A.-W., Collins, G. S., Deeks, A. D. J., & others. (2020b). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. The Lancet Digital Health, 2(10), e537–e548.

Manyazewal, T., Woldeamanuel, Y., Blumberg, H. M., Fekadu, A., & Marconi, V. C. (2021a). The potential use of digital health technologies in the African context: A systematic review of evidence from Ethiopia. NPJ Digital Medicine, 4(1), 125.

Masters, K. (2019). Artificial intelligence in medical education. Medical Teacher, 41(9), 976–980.

Meskó, B., Drobni, Z., Bényei, É., Gergely, B., & GyHorffy, Z. (2017). Digital health is a cultural transformation of traditional healthcare. Mhealth, 3, 38.

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.

Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13, 23–47.

Morley, J., Machado, C. C., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2021a). The ethics of AI in health care: A mapping review. Ethics, Governance, and Policies in Artificial Intelligence, 313–346.

Morley, J., Murphy, L., Mishra, A., Joshi, I., & Karpathakis, K. (2022). Governing data and artificial intelligence for health care: Developing an international understanding. JMIR Formative Research, 6(1), e31623.

Munn, Z., Peters, M. D., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018a). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18(1), 143.

Organization, W. H. & others. (2019a). WHO guideline: Recommendations on digital interventions for health system strengthening. World Health Organization.

Organization, W. H. (2022). World mental health report: Transforming mental health for all. Executive summary. World Health Organization.

Pascoe, M. C., Hetrick, S. E., & Parker, A. G. (2020). The impact of stress on students in secondary school and higher education. International Journal of Adolescence and Youth, 25(1), 104–112.

Patel, V., Saxena, S., Lund, C., Thornicroft, G., Baingana, F., Bolton, P., Chisholm, D., Collins, P. Y., Cooper, J. L., Eaton, J., Herrman, H., Herzallah, M. M., Huang, Y., Jordans, M. J. D., Kleinman, A., Medina-Mora, M. E., Morgan, E., Niaz, U., Omigbodun, O., … UnÜtzer, Jü. (2018). The Lancet Commission on global mental health and sustainable development. The Lancet, 392(10157), 1553–1598. https://doi.org/10.1016/S0140-6736(18)31612-X

Patel, V., Saxena, S., Lund, C., Thornicroft, G., Baingana, F., Bolton, P., Chisholm, D., Collins, P. Y., Cooper, J. L., Eaton, J., & others. (2018a). The Lancet Commission on global mental health and sustainable development. The Lancet, 392(10157), 1553–1598.

Peters, M. D., Godfrey, C. M., Khalil, H., McInerney, P., Parker, D., & Soares, C. B. (2015). Guidance for conducting systematic scoping reviews. JBI Evidence Implementation, 13(3), 141–146.

Peters, M. D., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2021a). Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Implementation, 19(1), 3–10.

Prince, M., Patel, V., Saxena, S., Maj, M., Maselko, J., Phillips, M. R., & Rahman, A. (2007). No health without mental health. The Lancet, 370(9590), 859–877. https://doi.org/10.1016/S0140-6736(07)61238-0

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358.

Ratna, C. S. S., & Dwarapudi, B. (2023). IMPACT OF YOGA PRACTICE ON WORK-LIFE BALANCE OF WOMEN MANAGERS–A STUDY IN SELECTIVE ORGANISATIONS. International Journal of Management (IJM), 14(7), 37-51.

Ratna, C. S. S., Kumari, S., & Majumdar, V. (2023). Impact of SMET yoga module on work-life balance among women managers—A pilot study. Journal of Research Administration, 5(2), 1029–1043. https://doi.org/10.13140/RG.2.2.29527.02720

Richardson, S., Lawrence, K., Schoenthaler, A. M., & Mann, D. (2022). A framework for digital health equity. NPJ Digital Medicine, 5(1), 119.

Riva, G., Wiederhold, B. K., & Mantovani, F. (2019). Neuroscience of virtual reality: From virtual exposure to embodied medicine. Cyberpsychology, Behavior, and Social Networking, 22(1), 82–96.

Rivera, S. C., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., Ashrafian, H., Beam, A. L., Collins, G. S., Darzi, A., Deeks, J. J., & others. (2020). Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. The Lancet Digital Health, 2(10), e549–e560.

Rudin, C. (2019a). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.

Santana, M. J., Manalili, K., Jolley, R. J., Zelinsky, S., Quan, H., & Lu, M. (2018). How to practice person-centred care: A conceptual framework. Health Expectations, 21(2), 429–440.

Spanhel, K., Balci, S., Feldhahn, F., Bengel, J., Baumeister, H., & Sander, L. B. (2021). Cultural adaptation of internet-and mobile-based interventions for mental disorders: A systematic review. NPJ Digital Medicine, 4(1), 128.

Thornicroft, G., Chatterji, S., Evans-Lacko, S., Gruber, M., Sampson, N., Aguilar-Gaxiola, S., Al-Hamzawi, A., Alonso, J., Andrade, L., Borges, G., & others. (2017). Undertreatment of people with major depressive disorder in 21 countries. The British Journal of Psychiatry, 210(2), 119–124.

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.

Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt-Jepsen, M., Whelan, P., Carvalho, A. F., Keshavan, M., Linardon, J., & Firth, J. (2021). The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry, 20(3), 318–335.

Torous, J., Lipschitz, J., Ng, M., & Firth, J. (2020). Dropout rates in clinical trials of smartphone apps for depressive symptoms: A systematic review and meta-analysis. Journal of Affective Disorders, 263, 413–419.

Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D., Horsley, T., Weeks, L., & others. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473.

Tyagi, A., & Cohen, M. (2016). Yoga and heart rate variability: A comprehensive review of the literature. International Journal of Yoga, 9(2), 97.

Vigo, D., Thornicroft, G., & Atun, R. (2016). Estimating the true global burden of mental illness. The Lancet Psychiatry, 3(2), 171–178. https://doi.org/10.1016/S2215-0366(15)00505-2

Wind, T. R., Rijkeboer, M., Andersson, G., & Riper, H. (2020). The COVID-19 pandemic: The ‘black swan’for mental health care and a turning point for e-health. Internet Interventions, 20, 100317.

Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D. H., Curtis, K., Merchant, G. C., Naughton, F., & Blandford, A. (2016). Understanding and promoting effective engagement with digital behavior change interventions. American Journal of Preventive Medicine, 51(5), 833–842.