Abstract
This research examines an artificial intelligence-driven personalized intervention system that integrates physical exercise and mindfulness practices to support academic performance and psychological wellbeing among university students in eastern China. A 16-week controlled intervention study enrolled 328 undergraduate students from three comprehensive universities, comparing three conditions: AI-personalized interventions (n = 110), standardized interventions (n = 108), and controls (n = 110). The AI system employed machine learning algorithms to analyze multidimensional student data and generate tailored recommendations. Results indicated that the AI-personalized group was associated with larger improvements across academic metrics (10.28% GPA increase, 95% CI [8.94, 11.62], d = 0.89, p < 0.001), psychological parameters (36.7% stress reduction, 95% CI [33.2, 40.1], d = 1.42, p < 0.001), and physiological indicators (28.4% HRV improvement, 95% CI [24.8, 32.0], d = 1.13, p < 0.001) compared to standardized interventions and controls. Regression analysis identified intervention adherence, sleep quality improvement, and stress reduction as factors associated with outcomes. The hybrid neural network architecture combining student feature analysis, exercise matching, and mindfulness adaptation offers a framework for personalized health interventions in academic settings. These findings, while promising, are specific to Chinese university contexts with particular cultural and technological characteristics, and cross-cultural validation remains necessary before broader generalization.
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Data availability
The datasets generated and analyzed during the current study, including group-level summary statistics, algorithm specifications, and complete analysis code, are provided in Supplementary Information S1. Raw physiological data containing personally identifiable information are not publicly available due to institutional ethics requirements (HEBU-REC-2024-031) but aggregated datasets are available from the corresponding author upon reasonable request through formal data-sharing agreements.
References
Chen, X. et al. Academic stress and mental health among college students: A systematic review and meta-analysis. J. Am. Coll. Health. 70 (3), 392–406 (2022).
Storrie, K., Ahern, K. & Tuckett, A. A systematic review: students with mental health problems—a growing problem. Int. J. Nurs. Pract. 16 (1), 1–6 (2010).
Mandolesi, L. et al. Effects of physical exercise on cognitive functioning and wellbeing: biological and psychological benefits. Front. Psychol. 9, 509 (2018).
Conley, C. S., Durlak, J. A. & Kirsch, A. C. A meta-analysis of universal mental health prevention programs for higher education students. Prev. Sci. 16 (4), 487–507 (2015).
Chung, K., Park, J. Y., Joung, D. & Jhung, K. Application of artificial intelligence in mental health: current landscape and future directions. Front. Psychiatry. 12, 673825 (2021).
Cai, H. et al. Individualized data-driven digital phenotyping and AI-based interventions for stress management in college students. J. Med. Internet. Res. 25 (4), e42857 (2023).
Nahum-Shani, I. et al. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann. Behav. Med. 52 (6), 446–462 (2018).
Dunton, G. F., Rothman, A. J., Leventhal, A. M. & Intille, S. S. How intensive longitudinal data can stimulate advances in health behavior maintenance theories and interventions. Translational Behav. Med. 11 (1), 281–286 (2021).
Husky, M. M., Kovess-Masfety, V. & Swendsen, J. D. Stress and anxiety among university students in France during Covid-19 mandatory confinement. Compr. Psychiatr. 102, 152191 (2020).
Auerbach, R. P. et al. Prevalence and distribution of mental disorders. J. Abnorm. Psychol. 127 (7), 623–638 (2018).
Lyzwinski, L. N., Caffery, L., Bambling, M. & Edirippulige, S. The relationship between stress and maladaptive weight-related behaviors in college students: A review of the literature. Am. J. Health Educ. 49 (3), 166–178 (2018).
Denovan, A. & Macaskill, A. Stress and subjective well-being among first year UK undergraduate students. J. Happiness Stud. 18 (2), 505–525 (2017).
Bruffaerts, R. et al. Mental health problems in college freshmen: prevalence and academic functioning. J. Affect. Disord. 225, 97–103 (2018).
Lattie, E. G. et al. Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: systematic review. J. Med. Internet. Res. 21 (7), e12869 (2019).
Eisenberg, D., Hunt, J. & Speer, N. Mental health in American colleges and universities: variation across student subgroups and across campuses. J. Nerv. Mental Disease. 201 (1), 60–67 (2013).
Lipson, S. K., Lattie, E. G. & Eisenberg, D. Increased rates of mental health service utilization by U.S. College students: 10-year population-level trends (2007–2017). Psychiatric Serv. 70 (1), 60–63 (2019).
Regehr, C., Glancy, D. & Pitts, A. Interventions to reduce stress in university students: A review and meta-analysis. J. Affect. Disord. 148 (1), 1–11 (2013).
Flett, J. A. M. et al. Mobile mindfulness meditation: A randomised controlled trial of the effect of two popular apps on mental health. Mindfulness 10 (5), 863–876 (2019).
He, J. et al. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 25 (1), 30–36 (2019).
Graham, S. et al. Artificial intelligence for mental health and mental illnesses: an overview. Curr. Psychiatry Rep. 21 (11), 116 (2019).
Wang, L. et al. Personalized exercise recommendation using wearable device data: A comprehensive review. IEEE J. Biomedical Health Inf. 26 (2), 874–889 (2022).
Zhu, J., Pande, A., Mohapatra, P. & Han, J. J. Using deep learning for energy expenditure Estimation with wearable sensors. IEEE J. Biomedical Health Inf. 23 (4), 1499–1511 (2019).
Shatte, A. B. R., Hutchinson, D. M. & Teague, S. J. Machine learning in mental health: A scoping review of methods and applications. Psychol. Med. 49 (9), 1426–1448 (2019).
Torous, J. et al. 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 (2021).
Liao, P., Klasnja, P., Tewari, A. & Murphy, S. A. Sample size calculations for micro-randomized trials in mHealth. Stat. Med. 35 (12), 1944–1971 (2016).
Aguilera, A. et al. mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE study. BMJ Open. 10 (3), e034723 (2020).
Mohr, D. C., Zhang, M. & Schueller, S. M. Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Ann. Rev. Clin. Psychol. 13, 23–47 (2017).
Opoku Asare, K. et al. Predicting depression from smartphone behavioral markers using machine learning methods, hyperparameter optimization, and feature importance analysis: exploratory study. JMIR mHealth uHealth. 9 (7), e26540 (2021).
Doryab, A. et al. Identifying behavioral phenotypes of loneliness and social isolation with passive sensing: statistical analysis, data mining and machine learning of smartphone and Fitbit data. JMIR mHealth uHealth. 7 (7), e13209 (2019).
Chen, I. Y., Joshi, S. & Ghassemi, M. Treating health disparities with artificial intelligence. Nat. Med. 26 (1), 16–17 (2020).
McMorris, T. & Hale, B. J. Differential effects of differing intensities of acute exercise on speed and accuracy of cognition: A meta-analytical investigation. Brain Cogn. 80 (3), 338–351 (2012).
Erickson, K. I. et al. Physical activity, cognition, and brain outcomes: A review of the 2018 physical activity guidelines. Med. Sci. Sports. Exerc. 51 (6), 1242–1251 (2019).
Anderson, E. & Shivakumar, G. Effects of exercise and physical activity on anxiety. Front. Psychiatry. 4, 27 (2013).
Tang, Y. Y., Hölzel, B. K. & Posner, M. I. The neuroscience of mindfulness meditation. Nat. Rev. Neurosci. 16 (4), 213–225 (2015).
Pascoe, M. C., Thompson, D. R., Jenkins, Z. M. & Ski, C. F. Mindfulness mediates the physiological markers of stress: systematic review and meta-analysis. J. Psychiatr. Res. 95, 156–178 (2017).
Edwards, M. K. & Loprinzi, P. D. Comparative effects of meditation and exercise on physical and psychosocial health outcomes: A review of randomized controlled trials. Postgrad. Med. 130 (2), 222–228 (2018).
Vu, M. A. T. et al. A shared vision for machine learning in neuroscience. J. Neurosci. 38 (7), 1601–1607 (2018).
Rajendran, N. et al. Adaptive just-in-time health interventions using automated sensor data: systematic review. J. Med. Internet. Res. 24 (5), e33509 (2022).
Bauer, M. et al. Ethical perspectives on recommending digital technology for patients with mental illness. Int. J. Bipolar Disorders. 5 (1), 6 (2017).
Chen, M., Mao, S. & Liu, Y. Big data: A survey. Mob. Networks Appl. 19 (2), 171–209 (2014).
Guo, H. et al. DeepFM: A factorization-machine based neural network for CTR prediction. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. ;1725–1731. (2017).
Zang, Y. et al. A hybrid deep learning approach for exercise recognition and quantification from wearable devices. Inform. Fusion. 76, 375–390 (2021).
Guo, F. et al. Graph neural networks for health applications: Challenges, methods, and directions. IEEE/ACM Trans. Comput. Biol. Bioinf. 20 (2), 2021–2040 (2023).
Chikersal, P. et al. Detecting depression and predicting its onset using longitudinal symptoms captured by passive sensing. ACM Trans. Computer-Human Interact. 28 (1), 1–41 (2021).
Ebert, D. D., Cuijpers, P., Muñoz, R. F. & Baumeister, H. Prevention of mental health disorders using internet-and mobile-based interventions: A narrative review and recommendations for future research. Front. Psychiatry. 8, 116 (2017).
Kim, D. et al. Development of machine learning model for diagnostic disease prediction based on laboratory tests. Sci. Rep. 11 (1), 7303 (2021).
Sander, L., Rausch, L. & Baumeister, H. Effectiveness of internet-based interventions for the prevention of mental disorders: A systematic review and meta-analysis. JMIR Mental Health. 3 (3), e38 (2016).
Masi, C. M., Chen, H. Y., Hawkley, L. C. & Cacioppo, J. T. A meta-analysis of interventions to reduce loneliness. Personality Social Psychol. Rev. 15 (3), 219–266 (2011).
Feter, N. et al. Physical activity and mental health during the COVID-19 pandemic: A systematic review. Int. J. Environ. Res. Public Health. 18 (12), 6266 (2021).
Winter, S. J., Sheats, J. L. & King, A. C. The use of behavior change techniques and theory in technologies for cardiovascular disease prevention and treatment in adults: A comprehensive review. Prog. Cardiovasc. Dis. 58 (6), 605–612 (2016).
Ramos-Lima, L. F. et al. The use of machine learning techniques in trauma-related disorders: A systematic review. J. Psychiatr. Res. 121, 159–172 (2020).
Yasin, S. et al. EEG based major depressive disorder and bipolar disorder detection using neural networks: A review. Comput. Methods Programs Biomed. 202, 106007. https://doi.org/10.1016/j.cmpb.2021.106007 (2021).
Irfan, M. et al. Role of hybrid deep neural networks (HDNNs), computed Tomography, and chest X-rays for the detection of COVID-19. Int. J. Environ. Res. Public Health. 18 (6), 3056. https://doi.org/10.3390/ijerph18063056 (2021).
Yasin, S., Othmani, A., Raza, I. & Hussain, S. A. Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review. Comput. Biol. Med. 159, 106741. https://doi.org/10.1016/j.compbiomed.2023.106741 (2023).
Yasin, S., Hussain, S. A., Khan, S., Raza, I. & Muzammel, M. AI-Enabled Electroencephalogram (EEG) Analysis for Depression Relapse Detection in Quadriplegic Patients. 2024 IEEE International Conference on Computer and Information Management Systems (ICCIMS). :1–6. (2024). https://doi.org/10.1109/ICCIMS62600.2024.10690640
Liao, P., Greenewald, K., Klasnja, P. & Murphy, S. Personalized heartsteps: A reinforcement learning algorithm for optimizing physical activity. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4 (1), 1–22. https://doi.org/10.1145/3381007 (2020).
Mair, J. L. et al. Effective behavior change techniques in digital health interventions for the prevention or management of noncommunicable diseases: an umbrella review. Ann. Behav. Med. 57 (10), 817–835. https://doi.org/10.1093/abm/kaad041 (2023).
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2022). https://www.R-project.org/
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67 (1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).
Rosseel, Y. & lavaan An R package for structural equation modeling. J. Stat. Softw. 48 (2), 1–36. https://doi.org/10.18637/jss.v048.i02 (2012).
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Ke Zhang: Conceptualization, Methodology, Software architecture design and AI algorithm development, Formal analysis, Investigation, Writing - original draft, Supervision, Project administration, Resource coordination.Meng Yang: Validation, Resources, Data curation, Writing - review & editing, Visualization.Liying Li: Data collection platform development and user interface implementation, Investigation, Data collection, Formal analysis.
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Zhang, K., Yang, M. & Li, L. Optimization of academic performance and mental health in college students through an AI-driven personalized physical exercise and mindfulness intervention system. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37028-6
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DOI: https://doi.org/10.1038/s41598-026-37028-6
