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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64835
Volume:
10 |
6 |
Jun 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Department of computer science
Galgotias university
Department of computer science
Galgotias university
Department of computer science
Galgotias university
We can see a pattern of growth in Fitness technology over recent years, but most available applications still seem to fall short because they treat everyone the same. In almost every context like food they are supposed to eat or the training regime they are supposed to follow there geographical vicinity doesn't account their general diet doesn't matter their progress is also neglected and their goals are not taken in consideration.the solution we came up with GUTS BARBELL is a proposed mobile fitness application. It is organized around three modules that work together the first one is a meal planning engine that draws from the user's nationality and regional food culture to make daily meal plans suitable for their body and goal.second a hypertrophy program generator that based on the users preferences makes optimal training programs considering how long they have been working out. Third A blog system that generates fitness content based on the users interests. This paper Shows the foundation, system architecture, and technical approach for making GUTS BARBELL, based on established research from exercise science. The aim is to make a science based lifting application that can provide credible research based information to everyone
KEYWORDS
This paper has outlined the design, theoretical underpinnings and anticipated benefits of GUTS BARBELL, a mobile fitness app based on three distinct modules: a culturally sensitive meal planner, an evidence-based muscle-building program generator and culturally sensitive AI-based blogging content generator. The justification for this project is simple - the market is large and growing, and failing many of its customers by attempting to personalize as an aftermarket service. The distinction of GUTS BARBELL is not in any particular technology decision, but in the integration of three areas of personalization - nutrition, exercise and educational content - into a unified system, and all centred on the person. The meal planning engine's integration of locally relevant food databases with evidence-based macro-nutritional targets is a practical innovation that has implications for adherence. The program generator's use of volume landmarks and dynamic auto-regulation brings the expertise of a coach to a mobile device. The smart blogging system using LLMs offers a way to scale fitness education, at a scale impossible for human content teams. The future work planned includes: First, a full prototype will be developed and tested with a range of users to confirm the design ideas proposed in this paper. Second, the culturally indexed food database will be extended to include foods from the Southeast Asian, Middle Eastern, African and Latin American cultures for wider use. Third, data from wearable devices, especially heart rate variability and sleep data, will be used in the auto-regulation module, enabling recovery management to react to more objective data than just self-reported measurements. Fourth, longitudinal studies will be conducted to examine the effectiveness of hypertrophy, dietary adherence and fitness literacy improvement over 12 and 24 weeks. Finally, we will investigate the blog generation system as a federated learning system where personalization models are improved by collective patterns in user engagement but individual data is not shared. GUTS BARBELL is really not a fitness app, but an attempt to incorporate evidence-based fitness knowledge into a market that has excluded historically marginalised groups. This is the work this paper starts.
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Publication Date
6/11/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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