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Review Article

Leveraging Artificial intelligence (AI) for Stress Management in Peak Athletic Performance: An Integrative Review

Authors

  • Elizabeth Adebisi Department of Human Kinetics, Faculty of Education, University of Ibadan, Ibadan, Oyo State, Nigeria

    ajokeelizabetha@gmail.com

  • Tunmise N. Balogun Department of Human Kinetics, Faculty of Education, University of Ibadan, Ibadan, Oyo State, Nigeria
  • Solomon B. Oguntuase Department of Human Kinetics, Faculty of Education, University of Ibadan, Ibadan, Oyo State, Nigeria
  • Fatahat O. Olajide Department of Adult Education, University of Ibadan, Ibadan, Oyo State, Nigeria https://orcid.org/0009-0001-9983-5088

Abstract

Athletes often experience stressors that can impede their performance and well-being. Lack of coping resource or poorly regulated stress can undermine physical output, decision making and well-being. Effective stress management is crucial for achieving peak performance. This paper synthesize evidence on stress management strategies in sports psychology as well as AI powered stress management strategies using an integrative approach. The findings are presented narratively and organised into emerging themes. These strategies are categorized into cognitive-behavioural strategies, mindfulness-based strategies, relaxation strategies, biofeedback and technology-based and social support, and team-based strategies. AI powered stress management strategies were classified in the study as; AI powered wearable monitoring, virtual therapy and chatbot counseling, predictive analytics for stress forecasting, AI guided biofeedback training, AI enhanced injury and recovery support, AI supported cognitive behavior training, and integration of AI with human support systems. The following benefits are derived from the integration of AI in stress management for athletes; Improved performance, enhanced well-being, continuous and objective stress monitoring, all round support for athletes, Injury prevention, early detection and intervention, personalised training and recovery, confidentiality and stigma reduction. Advances in AI for stress management should focus on refining AI-powered injury prevention models, improving biometric sensing capabilities, advancing edge AI for real-time data processing, and integrating wearables sweat analysis to provide feedback, among others. This paper recommends that multimodal, specific interventions should be integrated into regular athletic training, warm-up and recovery process of athletes in order to attain peak performance and enhance overall well-being. Also that a hybrid approach adopted by sport psychologist integrating AI to support stress management among athletes will lead to a faster and positive outcome in stress management.

Keywords:

Athletes Athletic Performance Cognitive Behavioural Strategies Machine Learning Mindfulness-Based Strategies Smart Sport Psychology Stress Management

Article information

Journal

Scientific Journal of Engineering, and Technology

Volume (Issue)

2(2), (2025)

Pages

94-106

Published

09-09-2025

How to Cite

Adebisi, E., Balogun, T. N., Oguntuase, S. B., & Olajide, F. O. (2025). Leveraging Artificial intelligence (AI) for Stress Management in Peak Athletic Performance: An Integrative Review. Scientific Journal of Engineering, and Technology, 2(2), 94-106. https://doi.org/10.69739/sjet.v2i2.999

References

Addleman, J. S., Lackey, N. S., DeBlauw, J. A., & Hajduczok, A. G. (2024). Heart rate variability applications in strength and conditioning: A narrative review. Journal of Functional Morphology and Kinesiology, 9(2), 93. DOI: https://doi.org/10.3390/jfmk9020093

Adelusi, J. O., Oguntuase, S. B., & Jaiyeoba, O. M. (2023). Basic psychological needs predicting physical activity participation among young adults. Athens Journal of Sport, 10(4), 261-274. DOI: https://doi.org/10.30958/ajspo.10-4-4

Ajiye, O. T., & Omokhabi, A. A. (2025). The Potential and Ethical Issues of Artificial Intelligence in Improving Academic Writing. ShodhAI: Journal of Artificial Intelligence, 2(1), 1-9. https://doi.org/10.29121/ShodhAI.v2.i1.2025.241 DOI: https://doi.org/10.29121/shodhai.v2.i1.2025.24

Ajiye, O. T., & Ukpabi, I. D. (2025). Supporting athlete well-being: The role of artificial intelligence in stress management and mental health. Unizik Journal of Educational Research, Science and Vocational Studies, 2(1), May–August.

Arnold, R., & Fletcher, D. (2021). Stressors, hassles and adversities in stress, well-being and performance in sport (pp. 31-62), R. Arnold and D. Fletcher (Eds.). New York: Routledge. DOI: https://doi.org/10.4324/9780429295874-3

Baltzell, A., & Aktar, V. L.V. (2014). Mindfulness meditation training for sport (MMTS) intervention: Impact of MMTS with division I female athletes. The Journal of Happiness & Well-being, 2(2), 160-173.

Beal, R., Norman, T. J., & Ramchurn, S. D. (2019). Artificial Intelligence for team sports: a survey. The Knowledge Engineering Review, 34, e28. https://doi.org/10.1017/S0269888919000225. DOI: https://doi.org/10.1017/S0269888919000225

Blascovich, J., & Mendes, W. B. (2000). Challenge and threat appraisals: The role of affective cues. In J.P. Forgas (Ed.). Feeling and thinking: The role of affect in social cognition (pp. 59-82). Paris: Cambridge University Press.

Blumenstein, B., Eli, B. M., & Teneubaum, G. (2002). Brain and body in sport exercise: biofeedback applications in performance enhancement. West Suxxes: John Wiley and Son.

Bodemer, O. (2023). Enhancing Individual Sports Training through Artificial Intelligence: A Comprehensive Review. Eng OA, 1(2), 111-119. DOI: https://doi.org/10.33140/EOA.01.02.09

Carron, A. V., Hausenblas, H. A., & Eys, M. A. (2020). Group dynamics in sport. Fitness Information Technology.

Chenyi, Z., Weijian, Z., Bo, L., Zhiyong, Z. & Feng, Z. (2024). A Review on the Application of Artificial Intelligence in Basketball Sports. International Journal of Computer Science in Sport, Sciendo, 23(2), 62-90. https://doi.org/10.2478/ijcss-2024-0013. DOI: https://doi.org/10.2478/ijcss-2024-0013

Corrado, S., Tosti, B., Mancone, S., Di Libero, T., Rodio, A., Andrade, A., & Diotaiuti, P. (2024). Improving mental skills in precision sports by using neurofeedback training: a narrative review. Sports, 12(3), 70. https://doi.org/10.3390/sports12030070. DOI: https://doi.org/10.3390/sports12030070

Cox, R. H., Martens, M. P., & Russell, W. D. (2003). Measuring anxiety in athletics: The revised competitive state anxiety inventory-2. Journal of Sport and Exercise Psychology, 25(4), 519-533. DOI: https://doi.org/10.1123/jsep.25.4.519

Currie, A. (2021). Athlete mental health: future directions. British Journal of Sports Medicine, 55(3), 123–124. https://doi.org/10.1136/bjsports-2020-103464. DOI: https://doi.org/10.1136/bjsports-2021-104443

Dadı, M. M., & Yıldız, O. (2022). Opinions of football referees on the VAR system and VAR training. Turkish Journal of Sport And Exercise, 24(1), 52-65. https://dergipark.org.tr/en/pub/tsed/issue/69792/1072304.

Dozois, D. J. A., & Beck, A. T. (2023). Negative thinking: Cognitive products and schema structures. In D.J.A Dozois & K.S. Dobson (Eds.), Treatment of psychosocial risk factors in depression (pp. 207-232). American Psychological Association. Htttps://doi.org/10.1037/0000332-010. DOI: https://doi.org/10.1037/0000332-010

Dudek, S., Koziak, W., Makieła, M., Bętkowska, A., Kornacka, A., Dudek, W., Szostak, K., Tomaka, R., & Byra, A. (2025). Revolutionizing Sports: The Role of Wearable Technology and AI in Training and Performing Analysis. Quality in Sport, 39, 58456. https://dx.doi.org/10.12775/QS.2025.39.58456 DOI: https://doi.org/10.12775/QS.2025.39.58456

Dwyer, D. B., Kempe, M., & Knobbe, A. (2022). Using Artificial Intelligence to Enhance Sport Performance. Frontiers in Sports and Active Living, 4, 886730. https://doi.org/10.3389/FSPOR.2022.886730/BIBTEX DOI: https://doi.org/10.3389/fspor.2022.886730

Engel, G. L. (1977). The need for a new medical model: a challenge for biomedicine. Science, 196(4286), 129-36. https://doi.org/10.1126/science.847460. DOI: https://doi.org/10.1126/science.847460

Farzan, M., Ebrahimi, H., Pourali, M., & Sabeti, F. (2025). Artificial intelligence-powered cognitive behavioral therapy chatbots, a systematic review. Iranian journal of psychiatry, 20(1), 102. DOI: https://doi.org/10.18502/ijps.v20i1.17395

Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., & Sarris, J. (2020). The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis. World Psychiatry, 16(3), 287-298. DOI: https://doi.org/10.1002/wps.20472

Fletcher, D., & Sarkar, M. (2012). A grounded theory of psychological resilience in Olympic champions. Psychology of Sports and Exercise, 13, 669-678. Doi: 10.1016/j.psychsport.2012.04.007 DOI: https://doi.org/10.1016/j.psychsport.2012.04.007

Freeman, P., & Rees, T. (2021). The effects of perceived and received support on objective performance outcome. European Journal of Sport Science, 9(6), 359-368. DOI: https://doi.org/10.1080/17461390802261439

Gaffney, H., Mansell, W., & Tai, S. (2019). Conversational agents in the treatment of mental health problems: Systematic review. JMIR Mental Health, 6(10), e14166. https://doi.org/10.2196/14166. DOI: https://doi.org/10.2196/14166

Gardner, F. L., & Moore, Z. E. (2007). The psychology of enhancing human performance: The mindfulness acceptance commitment (MAC) approach. New York, NY, US: Springer Publishing Co. DOI: https://doi.org/10.1891/9780826103369

Gascón, A., Marco, Á., Buldain, D., Alfaro-Santafé, J., Alfaro-Santafé, J. V., Gómez-Bernal, A., & Casas, R. (2025). A Diagnostic and Performance System for Soccer: Technical Design and Development. Sports, 13(1), 10. https://doi.org/10.3390/SPORTS13010010 DOI: https://doi.org/10.3390/sports13010010

Gaume, A., Vialatte, A., Mora-Sanchez, A., Ramdani, C., & Vialatte, F. B. (2016). A psycho-engineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback. Neuroscience, Bio-behavioural Review, 68, 891-910. DOI: https://doi.org/10.1016/j.neubiorev.2016.06.012

Giggins, O. M., Persson, U. M., & Caulfield, B. (2020). Biofeedback in sport and exercise: The past, present and future. International Journal of Sports Science and Coaching, 15(3), 355–366.

Gong, A., Gu, F., Nan, W., Qu, Y., Jiang, C., & Fu, Y. (2021). A review of neurofeedback training for improving sport performance from the perspective of user experience. Frontiers in Neuroscience, 15, 638369. https://doi.org/10.3389/fnins.2021.638369 DOI: https://doi.org/10.3389/fnins.2021.638369

Gruzelier, J. H. (2019). EEG-neurofeedback for optimising performance. Neuroscience & Biobehavioral Reviews, 44, 124-141. DOI: https://doi.org/10.1016/j.neubiorev.2013.09.015

Gustafsson, H., Sagar, S. S., & Stenling, A. (2017). Fear of failure, psychological stress, and burnout among adolescent athletes competing in high level sport. Scandinavian journal of medicine & science in sports, 27(12), 2091-2102. https://doi.org/10.1111/sms.12797. DOI: https://doi.org/10.1111/sms.12797

Haase, L., Kentta, G., Hickman, S., Baltzell, A. L., & Paulus, M. (2016). Mindful training in elite athletes: mPEAK with BMX cyclists. In a Baltzell (Ed.), Mindfulness and performance (pp. 186-207). New York, NY: Cambridge University Press. DOI: https://doi.org/10.1017/CBO9781139871310.010

Haney, C. J. (2004). Stress management interventions for female athletes: Relaxation and cognitive restructuring. International Journal of Sport Psychology, 35(2), 109-118

Hardy, J., & Olivier, E. J. (2014). Self-talk, positive thinking and thought stopping, in Encylopedia of sport and exercise psychology. Thousand Oaks, California: Sage.

Hurley, O. A. (2021). Sport cyberpsychology in action during the COVID-19 pandemic (opportunities, challenges, and future possibilities): A narrative review. Frontiers in Psychology, 12, 621283. DOI: https://doi.org/10.3389/fpsyg.2021.621283

Inkster, B., Sarda, S., & Subramanian, V. (2018). An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation. JMIR mHealth and uHealth, 6(11), e12106. DOI: https://doi.org/10.2196/12106

Islam, T., & Washington, P. (2023). Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data. arXiv preprint.

Jacobson, E., & McGuigan, F. J. (2020). Progressive relaxation: A physiological and clinical investigation of muscular states and their significance in psychology and medical practice. University of Chicago Press.

Jaiyeoba, O. M. (2016). Determinants of dietary habits and nutrient intake among athletes in Oyo State. Nigeria Journal of Sports Psychology Association of Nigeria 11, 31-40

Jaiyeoba, O. M. (2017). Influence of recreational activities as work stress coping strategies among female academic staff in University of Lagos. West African Journal of Physical and Health Education, 21, 100-110.

Jaiyeoba, O. M. (2018). Cognitive behavioural therapy and exercise adherence among older adults. Nigeria Society for Sports Management Journal, 6, 57-65.

Jaiyeoba, O. M. (2018). Unpleasant life occurrences as determinants of mental health status among undergraduates of the University of Ibadan. Ibadan Journal of Education Studies, 15(2), 33-42.

Jaiyeoba, O. M. (2021). Psychological consequences of sport injury on mental well-being of elite athletes in Ibadan Nigeria. Journal of Positive Psychology and Counselling 8, 166-180.

Jaiyeoba, O. M. (2022). Biofeedback technology for stress management and self-regulation of athletes. Journal of Human Kinetics and Health Education Pedagogy, 4(1).

Jaiyeoba, O. M., & Ogunsanya, J. O. (2021). Psychological characteristics of health-related quality of life among retired elite athletes in Nigeria. European Journal of Physical Education and Sport Science, 7(3), 164-174. https://oapub.org/edu/index.php/ejep/article/view/3986/6622. DOI: https://doi.org/10.46827/ejpe.v7i3.3986

Jaiyeoba, O. M., & Oguntuase S. B. (2019). Re-engineering grassroot sports for improved social Inclusion and transformational development. Journal of International Council for Health, Physical Education, Recreation, Sport and Dance, 5(3), 188-203.

Jerath, R., Crawford, M. W., & Barnes, V. A. (2023). Integration of Smartwatches and HRV Technology: Enhancing Stress Management, Exercise Recovery, and Sleep Health. Cureus, 15(7), e42283. https://doi.org/10.7759/cureus.42283. DOI: https://doi.org/10.7759/cureus.42283

Jones, B. J., Kaur, S., Miller, M., & Spencer, R. M. (2020). Mindfulness-based stress reduction benefits psychological well-being, sleep quality, and athletic performance in female collegiate rowers. Frontiers in psychology, 11, 572980. https://doi.org/10.3389/fpsyg.2020.572980 DOI: https://doi.org/10.3389/fpsyg.2020.572980

Jones, M., Meijen, C., McCarthy, P. J., & Sheffield, D. (2009). A theory of challenge and threat states in athletes. International review of sport and exercise psychology, 2(2), 161-180. https://doi.org/10.1080/17509840902829331 DOI: https://doi.org/10.1080/17509840902829331

Kabat-Zinn, J. (1982). An outpatient program in behavioral medicine for chronic pain patients based on the practice of mindfulness meditation: Theoretical considerations and preliminary results. General hospital psychiatry, 4(1), 33-47. https://doi.org/10.1016/0163-8343(82)90026-3 DOI: https://doi.org/10.1016/0163-8343(82)90026-3

Kaufman, K. A., Glass, C. R., & Arnkoff, D. B. (2009). Evaluation of mindful sport performance enhancement (MSPE): A new approach to promote flow in athletes. Journal of Clinical Sports Psychology, 4, 334-356 DOI: https://doi.org/10.1123/jcsp.3.4.334

Kudieka, B. M., & Wust, S. (2010). Human models in acute and chronic stress: assessing determinants of individual hypothalamus-pituitary adrenal axis activity and reactivity. Stress, 13(1), 1-14. https://doi.org/10.3109/10253890902874913. DOI: https://doi.org/10.3109/10253890902874913

Kudlackova, K., Eccles, D. W., & Dieffenbach, K. (2013). Use of relaxation skills in differentially skilled athletes. Psychology of Sport and Exercise, 14, 468-475. DOI: https://doi.org/10.1016/j.psychsport.2013.01.007

Li, R. T., Kling, S. R., Salata, M. J., Cupp, S. A., Sheehan, J., & Voos, J. E. (2016). Wearable performance devices in sports Medicine. Sports health, 8(1), 74-78. https://doi.org/10.1177/1941738115616917 DOI: https://doi.org/10.1177/1941738115616917

Li, X., Liu, X., & Zhao, Y. (2020). Real-time performance analysis using wearable sensor and AI Technology in sports. Sensors, 20(21), 5984.

McEwen, B. S., & Stellar, E. (2021). Stress and the individual: Mechanisms leading to disease. Archives of Internal Medicine, 153(18), 2093-2101. DOI: https://doi.org/10.1001/archinte.1993.00410180039004

Morris, T., Spittle, M., & Watt, A. P. (2020). Imagery in sport. Human Kinetics.

Mulgan, T. (2016). Superintelligence: Paths, dangers, strategies. In: Oxford University Press. DOI: https://doi.org/10.1093/pq/pqv034

Murray, A., & Turner, A. (2022). AI in athlete support: Enhancing cognitive behavioural therapy through technology. Psychology of Sport and Exercise, 59, 102137.

Oguntuase, S. B., & Sun, Y. (2022). Effects of mindfulness training and locus of control on self-efficacy of professional football players in Nigeria. Journal of Physical Education and Sport, 22(5), 1112-1119. https://doi.org/10.7752/jpes.2022.05140. DOI: https://doi.org/10.7752/jpes.2022.05140

Ojokheta, K., & Omokhabi, A. A. (2023). Project initiatives on inclusive and Equitable Use of Artificial Intelligence in Education Lessons Derivable for Policy Direction in Nigeria. COUNS-EDU: The International Journal of Counselling and Education, 8(3). https://doi.org/10.23916/002030845430 DOI: https://doi.org/10.23916/0020230845430

Omokhabi, A. A. (2021) Technology and parenting in the digital age: Opportunities and risks. African Journal of Adult Learning, 1(1), 37–56.

Omokhabi, A. A. (2023). Using digital technology to enhance adolescent and young adult Development: An examination of implications for child welfare in Nigeria. Simulacra, 6(1), 1–16. https://doi.org/10.21107/sml. V6i1.18239 DOI: https://doi.org/10.21107/sml.v6i1.18239

Omokhabi, A. A., Omokhabi, U. S., & Oloyede, T. O. (2025). Social media impact on marital Conflict among married couples in three Southwestern Nigerian states. Simulacra, 8(1), 1–16. https://doi.org/10.21107/sml.v8i1.27215 DOI: https://doi.org/10.21107/sml.v8i1.27215

Omokhabi, U. S. (2021). Alternative Approaches to Mental Health delivery in the New Normal Nigeria. In Ojokheta, K. O.and Kester, K. O. (Eds.) Education Reengineering in the New Normal (pp. 613-627). Department of Adult Education, University of Ibadan.

Omokhabi, U. S. (2025). Adult learners ‘Mental Health and Academic Outcomes at the Federal University Oye –Ekiti Sandwich Programme Nigeria, Fuwukari Journal of Social Sciences, 4(1), 120-142.

Omokhabi, U. S., Erumi, B. S. U., Omilani, M. A., & Omokhabi, A. A. (2025). Empowering Women with disabilities: AI-driven reproductive health solutions. ShodhAI: Journal of Artificial Intelligence, 2(1), 40–48. https://doi.org/10.29121/ShodhAI.v2.i1.2025.30 DOI: https://doi.org/10.29121/shodhai.v2.i1.2025.30

Owen, T., Mellalieau, S. D., & Hanton, S. (2009). Stress management in applied sport psychology, In S.D. Mellalieau & S. Hanton (Eds.), Advances in Applied Sport Psychology. New York: Routledge DOI: https://doi.org/10.4324/9780203887073

Pagaduan, J. C., Chen, Y.-S., Fell, J. W., & Wu, S. X. (2020). Can Heart Rate Variability Biofeedback Improve Athletic Performance? A Systematic Review. Journal of Human Kinetics, 72, 15–25. https://doi.org/10.2478/hukin-2019-0101 DOI: https://doi.org/10.2478/hukin-2019-0101

Rahmani, M., Majedi, N., Hemmatinejad, M., & Jamshidi, A. (2024). Artificial Intelligence in the Sports Industry. Application of Artificial Review Article. AI and Tech in Behavioral and Social Sciences, 2(2), 20-27. https://doi.org/10.61838/kman.aitech.2.2.4 DOI: https://doi.org/10.61838/kman.aitech.2.2.4

Rees, T., & Hardy, L. (2004). Matching social support with stressors: effect on factors underlying performance in tennis. Psychology of Sport and Exercise, 5(3), 319-337. DOI: https://doi.org/10.1016/S1469-0292(03)00018-9

Reis, F. J., Alaiti, R. K., Vallio, C. S., & Hespanhol, L. (2024). Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives. Brazilian journal of physical therapy, 28(3), 101083. DOI: https://doi.org/10.1016/j.bjpt.2024.101083

Rossi, A., Pappalardo, L., Cintia, P., & Iaia, F. M. (2018). Effective injury forecasting in soccer with GPS training data and machine learning. PLOS ONE, 13(7), e0201264. DOI: https://doi.org/10.1371/journal.pone.0201264

Ruiz-Vanoye, J. A, Parra, O. D., Penna, A. F., & Barrera-Cámara, R.A. (2025). Enhancing Performance and Well-being in the Sports Industry through Smart Sport Psychology. International Journal of Combinatorial Optimization Problems and Informatics, 16(2), 1-16. DOI: https://doi.org/10.61467/2007.1558.2025.v16i2.1059

Rumbold, J. L., Fletcher, D., & Daniels, K. (2012). A systematic review of stress management interventions with sport performers. Sport, Exercise and Performance Psychology, 1(3), 173-193. DOI: https://doi.org/10.1037/a0026628

Russo, M. A., Santarelli, D. M., & O’Rourke, D. (2017). The physiological effects of slow breathing in the healthy human. Breathe, 13(4), 298-309. DOI: https://doi.org/10.1183/20734735.009817

Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Front. Public Health, 5, 258. https://doi.org/10.33899/fpubh.2017.00258. DOI: https://doi.org/10.3389/fpubh.2017.00258

Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A Scoping review of methods and applications. Psychological Medicine, 49(9), 1426–1448. DOI: https://doi.org/10.1017/S0033291719000151

Slater, M., & Sanchez-Vives, M. V. (2021). Enhancing our lives with immersive virtual reality. Frontiers in Robotics and AI, 3, 74. DOI: https://doi.org/10.3389/frobt.2016.00074

Stoican, K., & Oeschger, R. (2025). 600 meters to VO2max: Predicting Cardiorespiratory Fitness with an Uphill Run. Current Issues in Sport Science (CISS), 10(2), 024–024. https://doi.org/10.36950/2025.2CISS024. DOI: https://doi.org/10.36950/2025.2ciss024

Te’eni, D., Yahav, I., Zagalsky, A., Schwartz, D. G., & Silverman, G. (2023). Reciprocal Human-Machine Learning: A Theory and an Instantiation for Message Classification. Management Science. DOI: https://doi.org/10.1287/mnsc.2022.03518

Thompson, R. A., & Sherman, R. (2014). Reflection on athletes and eating disorders. Psychology of Sport and Exercise, 15(6), 729-734. https://doi.org/10.1016/j.psychsport.2014.06.005 DOI: https://doi.org/10.1016/j.psychsport.2014.06.005

Tod, D., Hardy, J., & Oliver, E. (2011). Effects of self-talk: A systematic review. Journal of Sport & Exercise Psychology, 33(5), 666–687. https://doi.org/10.1123/jsep.33.5.666 . DOI: https://doi.org/10.1123/jsep.33.5.666

Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019). Chatbots and conversational agents in mental health: A review of the psychiatric landscape. The Canadian Journal of Psychiatry, 64(7), 456–464. https://doi.org/10.1177/0706743719828977 DOI: https://doi.org/10.1177/0706743719828977

Vanhée, L., Andersson, G., Garcia, D., & Sikström, S. (2025). The rise of artificial intelligence for cognitive behavioral therapy: A bibliometric overview. Applied Psychology: Health and Well‐Being, 17(2), e70033. DOI: https://doi.org/10.1111/aphw.70033

Wei, W. (2019). Research progress on virtual reality (VR) and augmented reality (AR) in tourism and hospitality: A critical review of publications from 2000 to 2018. Journal of Hospitality and Tourism Technology, 10(4), 539-570. https://doi.org/10.1108/JHTT-04-2018-0030 DOI: https://doi.org/10.1108/JHTT-04-2018-0030

Williams, J. M., & Krane, V. (2021). Applied sport psychology: Personal growth to peak performance (8th ed.). McGraw-Hill Education.

Zhou, D. (2025). Artificial Intelligence in Sport: A Narrative Review (Applications, Challenges, Future Directions). Journal of Sports Sciences, 43(2), 123–140. https://doi.org/10.1080/02640414.2024.1234567.

Zhou, K. (2024). Design of Training Load Monitoring and Adjustment Algorithm for Athletes: Based on Heart Rate Variability and Body Index Data. Journal of Electrical System, 20, 1600-1611. DOI: https://doi.org/10.52783/jes.3080

Zhu, Z. (2025). Design and implementation of an intelligent sports management system (ISMS) Using wireless sensor networks. PeerJ Computer Science, 11, e2637. https://doi.org/10.7717/PEERJ-CS.2637 DOI: https://doi.org/10.7717/peerj-cs.2637

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