Academic Journal of Computing & Information Science, 2025, 8(9); doi: 10.25236/AJCIS.2025.080911.
Zhou Zhou, Jian Li
College of Physical Education and Health, Guangxi Normal University, Guilin, 541000, Guangxi, China
With the increasing prevalence of overweight and obesity among college students, effective intervention through intelligent means has become a critical issue that urgently needs to be addressed. This paper implements comprehensive health intervention and management through the collaborative work of multiple modules, including real-time data collection, user profile analysis, personalized recommendations, and feedback optimization. The system constructs a personalized recommendation module using an algorithmic model that combines collaborative filtering and content-based filtering. Furthermore, dynamic adjustments are made based on user feedback. The effectiveness of this system is verified in a three-month randomized controlled trial. The experimental results show that participants in the experimental group show significant improvements in both blood glucose and blood lipid levels. All five participants in the experimental group have negative blood glucose levels, ranging from -0.3 mmol/L to -0.4 mmol/L, with an average blood glucose change of -0.33 mmol/L, demonstrating excellent control results.
Intelligent Perception and Control Technology; Artificial Intelligence; Internet of Things; Deep Learning; Edge Computing
Zhou Zhou, Jian Li. Design of Personalized Diet and Exercise Intervention System for Overweight College Students Supported by Intelligent Algorithms. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 9: 74-81. https://doi.org/10.25236/AJCIS.2025.080911.
[1] Bermingham, K. M., Linenberg, I., Polidori, L., et al. (2024). Effects of a personalized nutrition program on cardiometabolic health: A randomized controlled trial. Nature Medicine, 30(7), 1888-1897.
[2] Noone, J., Mucinski, J. M., DeLany, J. P., et al. (2024). Understanding the variation in exercise responses to guide personalized physical activity prescriptions. Cell Metabolism, 36(4), 702-724.
[3] Pranoto, N. W., Fauziah, V., Anugrah, S., et al. (2024). The effectiveness of diet and exercise in the management of obesity. Retos: Nuevas tendencias en educación física, deporte y recreación, 58, 951-959.
[4] Yaffe, K., Vittinghoff, E., Dublin, S., et al. (2024). Effect of personalized risk-reduction strategies on cognition and dementia risk profile among older adults: The SMARRT randomized clinical trial. JAMA Internal Medicine, 184(1), 54-62.
[5] Dergaa, I., Saad, H. B., El Omri, A., et al. (2024). Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI’s GPT-4 model. Biology of Sport, 41(2), 221-241.
[6] Saxena, R., Sharma, V., Saxena, A. R., et al. (2024). Harnessing AI and gut microbiome research for precision health. Journal of Artificial Intelligence General Science (JAIGS), 3(1), 74-88.
[7] Kothinti, R. R. (2024). Deep learning in healthcare: Transforming disease diagnosis, personalized treatment, and clinical decision-making through AI-driven innovations. World Journal of Advanced Research and Reviews, 24(2), 2841-2856.
[8] Sampogna, G., Toni, C., Catapano, P., et al. (2024). New trends in personalized treatment of depression. Current Opinion in Psychiatry, 37(1), 3-8. [9] Yurkovich, J. T., Evans, S. J., Rappaport, N., et al. (2024). The transition from genomics to phenomics in personalized population health. Nature Reviews Genetics, 25(4), 286-302.
[10] Zhou, T., Wang, Y., Yan, L., et al. (2023). Spoiled for choice? Personalized recommendation for healthcare decisions: A multiarmed bandit approach. Information Systems Research, 34(4), 1493-1512.
[11] Ratiner, K., Ciocan, D., Abdeen, S. K., et al. (2024). Utilization of the microbiome in personalized medicine. Nature Reviews Microbiology, 22(5), 291-308.