Enhancing User Engagement and Behavior Change in Healthy Eating Apps: A Human-Computer Interaction Perspective

Yuxin Zhao; Jiawei Wu

doi:10.70393/6a69656173.323330

OPEN ACCESS |Research Article ||1 December 2024

Enhancing User Engagement and Behavior Change in Healthy Eating Apps: A Human-Computer Interaction Perspective

Yuxin Zhao

New York University

yz8472@nyu.edu

Applied Urban Science and Informatics, New York University, New York City, NY 10003, USA.

Jiawei Wu

Illinois Institute of Technology

jiawei.wjw.wu@gmail.com

Master of Engineering in Artificial Intelligence for Computer Vision and Control, Illinois Institute of Technology, Chicago, IL 60616, USA.

* Corresponding Author¹: Yuxin Zhao, E-Mail: yz8472@nyu.edu

Publication

Accepted Unknow ; Published 2024 December 1

Journal of Industrial Engineering and Applied Science, 2024, 2(6), 3005-6071.

Abstract

Healthy eating apps have gained popularity as tools for promoting dietary improvements and supporting behavior change. Despite their potential, maintaining user engagement and achieving sustained behavior change remain significant challenges. This paper investigates how principles of Human-Computer Interaction (HCI) can be leveraged to address these challenges and enhance the effectiveness of healthy eating apps. By conducting a comprehensive review of relevant HCI methodologies and analyzing the functionalities of existing apps, the paper identifies key factors that influence user interaction and behavioral outcomes. The research highlights that incorporating HCI principles—such as personalized feedback, gamification, and social interaction—can lead to more engaging and effective apps. Personalized feedback helps users feel more understood and supported, while gamification elements increase motivation through rewards and challenges. Social interaction features foster community support and accountability, contributing to sustained engagement. The paper concludes with practical recommendations for app developers to integrate these HCI principles and outlines future research directions to further explore and refine strategies for enhancing user engagement and behavior change in healthy eating apps.

Keywords

Human-Computer Interaction , Healthy Eating Apps , User Engagement , Behavior Change , Personalization , Gamification , Social Interaction , Usability , App Design , Dietary Habits .

Metadata

DOI:

10.70393/6a69656173.323330

ARK:

ark:/40704/JIEAS.v2n6a04

Pages: 27-34

References: 49

Disciplines: Human-Computer Interaction

Subjects: User Engagement and Behavior

Cite This Article

APA Style

Zhao, Y. & Wu, J. (2024). Enhancing user engagement and behavior change in healthy eating apps: a human-computer interaction perspective. Journal of Industrial Engineering and Applied Science, 2(6), 27-34. https://doi.org/10.70393/6a69656173.323330

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

References

1.

Sheng, Z., Wu, F., Zuo, X., Li, C., & Qiao, Y. (2024). LProtector: An LLM-driven Vulnerability Detection System. arXiv. https://arxiv.org/abs/2411.06493

2.

Yan, H., Xiao, J., Zhang, B., Yang, L., & Qu, P. (2024). The Application of Natural Language Processing Technology in the Era of Big Data. Journal of Industrial Engineering and Applied Science, 2(3), 20-27.

3.

Bonilla, M., Rasdorf, W., Liu, M., Al-Ghandour, M., & He, C. (2023). Inequity reduction in road maintenance funding for municipalities. Public Works Management & Policy, 28(3), 339-362.

4.

He, C., Yu, B., Liu, M., Guo, L., Tian, L., & Huang, J. (2024). Utilizing Large Language Models to Illustrate Constraints for Construction Planning. Buildings, 14(8), 2511.

5.

He, C., Liu, M., Wang, Z., Chen, G., Zhang, Y., & Hsiang, S. M. (2022). Facilitating smart contract in project scheduling under uncertainty—A Choquet integral approach. In Construction Research Congress 2022 (pp. 930-939).

6.

Liu, S., Li, X., & He, C. (2021). Study on dynamic influence of passenger flow on intelligent bus travel service model. Transport, 36(1), 25-37.

7.

Zhang, W., Huang, J., Wang, R., Wei, C., Huang, W., & Qiao, Y. (2024). Integration of Mamba and Transformer--MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics. arXiv preprint arXiv:2409.08530.

8.

Yi, X., & Qiao, Y. (2024). GPU-Based Parallel Computing Methods for Medical Photoacoustic Image Reconstruction. arXiv preprint arXiv:2404.10928.

9.

Sun, Y., & Ortiz, J. (2024). Machine Learning-Driven Pedestrian Recognition and Behavior Prediction for Enhancing Public Safety in Smart Cities. Journal of Artificial Intelligence and Information, 1, 51-57.

10.

Song, C., Wu, B., & Zhao, G. (2024). Applications of Novel Semiconductor Materials in Chip Design. Journal of Industrial Engineering and Applied Science, 2(4), 81–89.

11.

Li, W. (2024). Transforming Logistics with Innovative Interaction Design and Digital UX Solutions. Journal of Computer Technology and Applied Mathematics, 1(3), 91-96.

12.

Zhong, Y. N. (2024). Optimizing the Structural Design of Computing Units in Autonomous Driving Systems and Electric Vehicles to Enhance Overall Performance Stability. International Journal of Advance in Applied Science Research, 3, 93-98.

13.

Zhong, Y. (2024). Enhancing the Heat Dissipation Efficiency of Computing Units Within Autonomous Driving Systems and Electric Vehicles.

14.

Yan, Y., Guo, F., Mo, H., & Huang, X. (2024, March). Hierarchical Tracking Control for a Composite Mobile Robot Considering System Uncertainties. In 2024 16th International Conference on Computer and Automation Engineering (ICCAE) (pp. 512-517). IEEE.

15.

Guo, F., Mo, H., Wu, J., Pan, L., Zhou, H., Zhang, Z., ... & Huang, F. (2024). A hybrid stacking model for enhanced short-term load forecasting. Electronics, 13(14), 2719.

16.

Zhao, G., Li, P., Zhang, Z., Guo, F., Huang, X., Xu, W., ... & Chen, J. (2024). Towards sar automatic target recognition multicategory sar image classification based on light weight vision transformer. arXiv preprint arXiv:2407.06128.

17.

Qiao, Y., Li, K., Lin, J., Wei, R., Jiang, C., Luo, Y., & Yang, H. (2024, June). Robust domain generalization for multi-modal object recognition. In 2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) (pp. 392-397). IEEE.

18.

Sheng, Z., Li, Y., Li, Z., & Liu, Z. (2019, August). Displacement Measurement Based on Computer Vision. In 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) (pp. 448-453). IEEE.

19.

Wang, H., Wang, G., Sheng, Z., & Zhang, S. (2019). Automated segmentation of skin lesion based on pyramid attention network. In Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10 (pp. 435-443). Springer International Publishing.

20.

Xu, C., Yu, J., Chen, W., & Xiong, J. (2024, January). Deep learning in photovoltaic power generation forecasting: Cnn-lstm hybrid neural network exploration and research. In The 3rd International Scientific and Practical Conference (Vol. 363, p. 295).

21.

Zhu, M., Zhang, Y., Gong, Y., Xu, C., & Xiang, Y. (2024). Enhancing Credit Card Fraud Detection A Neural Network and SMOTE Integrated Approach. arXiv preprint arXiv:2405.00026.

22.

Sun, Y., & Ortiz, J. (2024). Data Fusion and Optimization Techniques for Enhancing Autonomous Vehicle Performance in Smart Cities. Journal of Artificial Intelligence and Information, 1, 42-50.

23.

Sokolov, A., Sabelli, F., Li, W., & Seco, L. A. (2023). Towards Automating Causal Discovery in Financial Markets and Beyond. Behzad and Li, Wuding and Seco, Luis A., Towards Automating Causal Discovery in Financial Markets and Beyond (December 27, 2023).

24.

Wu, J., & Xiao, J. (2024). Application of Natural Language Processing in Network Security Log Analysis. Journal of Computer Technology and Applied Mathematics, 1(3), 39-47.

25.

Xiao, J., & Wu, J. (2024). Transfer Learning for Cross-Language Natural Language Processing Models. Journal of Computer Technology and Applied Mathematics, 1(3), 30-38.

26.

Wu, B., Song, C., & Zhao, G. (2024). Applications of Heterogeneous Integration Technology in Chip Design. Journal of Industrial Engineering and Applied Science, 2(4), 66–72.

27.

Song, C., Wu, B., & Zhao, G. (2024). Optimization of Semiconductor Chip Design Using Artificial Intelligence. Journal of Industrial Engineering and Applied Science, 2(4), 73–80.

28.

Li, W. (2024). User-Centered Design for Diversity: Human-Computer Interaction (HCI) Approaches to Serve Vulnerable Communities. Journal of Computer Technology and Applied Mathematics, 1(3), 85-90.

29.

Xiao, J., Zhang, B., Zhao, Y., Wu, J., & Qu, P. (2024). Application of Large Language Models in Personalized Advertising Recommendation Systems. Journal of Industrial Engineering and Applied Science, 2(4), 132-142.

30.

Wu, J., Qu, P., Zhang, B., & Zhou, Z. (2024). Sentiment Analysis in Social Media: Leveraging BERT for Enhanced Accuracy. Journal of Industrial Engineering and Applied Science, 2(4), 143-149.

31.

Zhao, Y., Qu, P., Xiao, J., Wu, J., & Zhang, B. (2024). Optimizing Telehealth Services with LILM-Driven Conversational Agents: An HCI Evaluation. Journal of Industrial Engineering and Applied Science, 2(4), 122-131.

32.

Zhang, B., Yan, H., Wu, J., & Qu, P. (2024). Application of Semantic Analysis Technology in Natural Language Processing. Journal of Computer Technology and Applied Mathematics, 1(2), 27-34.

33.

Zhao, Y., Wu, J., Qu, P., Zhang, B., & Yan, H. (2024). Assessing User Trust in LLM-based Mental Health Applications: Perceptions of Reliability and Effectiveness. Journal of Computer Technology and Applied Mathematics, 1(2), 19-26.

34.

Qu, P., Zhang, B., Wu, J., & Yan, H. (2024). Comparison of Text Classification Algorithms based on Deep Learning. Journal of Computer Technology and Applied Mathematics, 1(2), 35-42.

35.

Zhang, B., Xiao, J., Yan, H., Yang, L., & Qu, P. (2024). Review of NLP Applications in the Field of Text Sentiment Analysis. Journal of Industrial Engineering and Applied Science, 2(3), 28-34.

36.

Dang, B., Ma, D., Li, S., Qi, Z., & Zhu, E. (07 2024). Deep learning-based snore sound analysis for the detection of night-time breathing disorders. Applied and Computational Engineering, 76, 109–114. doi:10.54254/2755-2721/76/20240574

37.

Chen, Q., & Wang, L. (2024). Social Response and Management of Cybersecurity Incidents. Academic Journal of Sociology and Management, 2(4), 49-56.

38.

Song, C. (2024). Optimizing Management Strategies for Enhanced Performance and Energy Efficiency in Modern Computing Systems. Academic Journal of Sociology and Management, 2(4), 57-64.

39.

Chen, Q., Li, D., & Wang, L. (2024). Blockchain Technology for Enhancing Network Security. Journal of Industrial Engineering and Applied Science, 2(4), 22-28.

40.

Chen, Q., Li, D., & Wang, L. (2024). The Role of Artificial Intelligence in Predicting and Preventing Cyber Attacks. Journal of Industrial Engineering and Applied Science, 2(4), 29-35.

41.

Chen, Q., Li, D., & Wang, L. (2024). Network Security in the Internet of Things (IoT) Era. Journal of Industrial Engineering and Applied Science, 2(4), 36-41.

42.

Li, D., Chen, Q., & Wang, L. (2024). Cloud Security: Challenges and Solutions. Journal of Industrial Engineering and Applied Science, 2(4), 42-47.

43.

Li, D., Chen, Q., & Wang, L. (2024). Phishing Attacks: Detection and Prevention Techniques. Journal of Industrial Engineering and Applied Science, 2(4), 48-53.

44.

Song, C., Zhao, G., & Wu, B. (2024). Applications of Low-Power Design in Semiconductor Chips. Journal of Industrial Engineering and Applied Science, 2(4), 54–59.

45.

Zhao, G., Song, C., & Wu, B. (2024). 3D Integrated Circuit (3D IC) Technology and Its Applications. Journal of Industrial Engineering and Applied Science, 2(4), 60–65.2

46.

Kholmatov, S. (2024). Multimodal Sentiment Analysis: A Study on Emotion Understanding and Classification by Integrating Text and Images. Academic Journal of Natural Science, 1(1), 51-56.

47.

Lin, W., Xiao, J., & Cen, Z. (2024). Exploring Bias in NLP Models: Analyzing the Impact of Training Data on Fairness and Equity. Journal of Industrial Engineering and Applied Science, 2(5), 24-28.

48.

Dang, B., Zhao, W., Li, Y., Ma, D., Yu, Q., & Zhu, E. Y. (2024). Real-Time Pill Identification for the Visually Impaired Using Deep Learning. 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE), 552–555. doi:10.1109/CISCE62493.2024.10653353

49.

Wang, L., Xu, Z., Stone, P., & Xiao, X. (2024). Grounded curriculum learning. arXiv preprint arXiv:2409.19816.

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