Integrated Nutrition and Fitness Tracking: A Narrative Review of Technological Capabilities, Behavioral Outcomes, and Ethical Considerations

Suman Yadav; Ekta Soni

doi:10.55524/ijirem.2025.12.6.7

Abstract

Health management technologies including nutritional monitoring and physical activity tracking have emerged as an avenue for health management. This article details how the convergence of nutritional and physical activity tracking can enhance user engagement, data validity, and sustain long-term behaviour change. Evidence suggests that tracking nutrition and activity together can enhance awareness of energy balance, and through ongoing feedback with personalized guidance may alter health behaviours. The science behind wearables, mobile apps, and artificial intelligence is partially to blame, but there are systemic barriers (e.g., interoperability, privacy, user drop-off) to overcome before this can be implemented and sustained at the system-level, and ultimately healthy governance, standards, and equitable access for the future will need to be developed to inspire trust in digital health discourse. Ultimately, the ability to use tracking may represent an important link between personalized, preventative, data-informed health care.

Keywords

Digital Health, Fitness Tracking, Nutrition Tracking, Wearable Technology

References

M. Schuld, I. Sinayskiy, and F. Petruccione, “An introduction to quantum machine learning,” Contemporary Physics, vol. 56, no. 2, pp. 172–185, 2015. Available from: https://doi.org/10.1080/00107514.2014.964942
J. Biamonte, et al., “Quantum machine learning,” Nature, vol. 549, no. 7671, pp. 195–202, 2017. Available from: https://www.nature.com/articles/nature23474
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. Available from: https://ieeexplore.ieee.org/abstract/document/6795963
T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” European Journal of Operational Research, vol. 270, no. 2, pp. 654–669, 2018. Available from: https://doi.org/10.1016/j.ejor.2017.11.054
P. Rebentrost, M. Mohseni, and S. Lloyd, “Quantum support vector machine for big data classification,” Physical Review Letters, vol. 113, no. 13, p. 130503, 2014. Available from: https://doi.org/10.1103/PhysRevLett.113.130503
Havlí?ek, et al., “Supervised learning with quantum-enhanced feature spaces,” Nature, vol. 567, no. 7747, pp. 209–212, 2019. Available from: https://www.nature.com/articles/s41586-019-0980-2
K. Bharti, et al., “Noisy intermediate-scale quantum (NISQ) algorithms,” Reviews of Modern Physics, vol. 94, no. 1, p. 015004, 2022. Available from: https://doi.org/10.1103/RevModPhys.94.015004
M. Cerezo, et al., “Variational quantum algorithms,” Nature Reviews Physics, vol. 3, no. 9, pp. 625–644, 2021. Available from: https://www.nature.com/articles/s42254-021-00348-9
J. Preskill, “Quantum Computing in the NISQ era and beyond,” Quantum, vol. 2, p. 79, 2018. Available from: https://doi.org/10.22331/q-2018-08-06-79
N. C. Thompson, K. Greenewald, K. Lee, and G. F. Manso, “The Computational Limits of Deep Learning,” MIT Initiative on the Digital Economy Research Brief, 2020. Available from: https://doi.org/10.48550/arXiv.2007.05558
R. Prater, T. Hanne, and R. Dornberger, “Generalized Performance of LSTM in Time-Series Forecasting,” Applied Artificial Intelligence, 2024. Available from: https://doi.org/10.1080/08839514.2024.2377510
S. Siami-Namini, N. Tavakoli, and A. Siami-Namini, “The Performance of LSTM and BiLSTM in Forecasting Time Series,” in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018. Available from: https://ieeexplore.ieee.org/abstract/document/9005997
M. Schuld and N. Killoran, “Quantum machine learning in feature Hilbert spaces,” Physical Review Letters, vol. 122, no. 4, p. 040504, 2019. Available from: https://doi.org/10.1103/PhysRevLett.122.040504
Z. S. A. H. K. S. Z. N. M. F. A. K. K. B. H. M. Y. Khan, et al., “A Hybrid Quantum-Classical Model for Stock Price Prediction Using Quantum-Enhanced Long Short-Term Memory,” Entropy, vol. 26, no. 11, p. 954, 2024. Available from: https://doi.org/10.3390/e26110954
J. R. McClean, et al., “Barren plateaus in quantum neural network training landscapes,” Nature Communications, vol. 9, no. 1, p. 4812, 2018. Available from: https://www.nature.com/articles/s41467-018-07090-4
Google, “Quantum error correction using neural networks,” U.S. Patent Application, referenced in related news, 2025.
Cong, S. Choi, and M. D. Lukin, “Quantum convolutional neural network,” Nature Physics, vol. 15, pp. 1273–178, 2019. Available from: https://www.nature.com/articles/s41567-019-0648-8
H. Y. Huang, et al., “Generalization in Quantum Machine Learning from Few Training Data,” Nature Communications, vol. 13, no. 1, p. 4919, 2022. Available from: https://www.nature.com/articles/s41467-022-32550-3
Z. Wang and H. Tang, “Artificial Intelligence for Quantum Error Correction: A Comprehensive Review,” arXiv:2412.20380, 2024. Available from: https://doi.org/10.48550/arXiv.2412.20380
Qiskit contributors, “Qiskit: An Open-source Framework for Quantum computing,” 2023.

Cites this article as

S. Yadav, E. Soni, "Integrated Nutrition and Fitness Tracking: A Narrative Review of Technological Capabilities, Behavioral Outcomes, and Ethical Considerations", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-12, Issue-6, Page No-38-43, 2025. Available from: https://doi.org/10.55524/ijirem.2025.12.6.7