Hyper Personalization Using AI: Elevate Fitness

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]29/09/2025 at 11:12 AM[/if 2224] | [if 1553 equals=””] Volume : 12 [else] Volume : 12[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 10 21

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    Nikhil Nishad, Ritika, Tanisha, Saransh Prakash,

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  1. Student, Student, Student, Department of Computer Applications, Echelon Institute of Technology Faridabad, Department of Computer Applications, Echelon Institute of Technology Faridabad, Department of Computer Applications, Echelon Institute of Technology Faridabad, Haryana, Haryana, Haryana, India, India, India

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Abstract

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nIn the modern era, many individuals find it challenging to prioritize their health and well-being due to busy lifestyles filled with work, academic, and personal obligations. Conventional fitness and nutrition programs often apply uniform strategies that overlook individual differences in body type, preferences, and goals. This lack of real-time feedback and contextual customization often leads to poor user engagement and unsatisfactory results over time. Elevate is a comprehensive, AI-powered fitness and wellness platform created to overcome these limitations by delivering highly personalized digital health solutions. Users benefit from an interactive, full-body interface showcasing over 1,500 exercises, along with built-in wellness utilities such as BMI, body fat percentage, and macronutrient calculators. By integrating detailed user context, the platform generates dietary recommendations that are accurate, relevant, and aligned with individual needs. Built on Next.js 15, Elevate delivers smooth and efficient frontend interactions, while Tailwind CSS v4 helps craft a layout that automatically adapts to different screen sizes, offering a user-friendly experience on both mobile and desktop devices. The platform also uses Zustand for efficient state management and Supabase to handle real-time data updates and secure user sessions. At its core is an intelligent personalization engine that adapts workouts and nutrition plans to each user based on ongoing feedback and health data. Leveraging Groq APIs and OpenAI’s advanced language models via edge functions, the system delivers fast, context-aware results. Additional tools include a full-body interactive map, nutrition calculators, and integrations with Google OAuth, analytics, and n8n-based automation. This study focuses on enhancing Elevate’s AI nutrition capabilities through advanced prompt engineering to improve the precision and personalization of its dietary guidance.nn

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Keywords: Digital health, personalized fitness, personalized nutrition, AI in health, machine learning, large language models, recommendation systems, user modeling

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Mobile Computing, Communications & Mobile Networks ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Mobile Computing, Communications & Mobile Networks (jomccmn)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nNikhil Nishad, Ritika, Tanisha, Saransh Prakash. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Hyper Personalization Using AI: Elevate Fitness[/if 2584]. Journal of Mobile Computing, Communications & Mobile Networks. 29/09/2025; 12(03):10-21.

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nNikhil Nishad, Ritika, Tanisha, Saransh Prakash. [if 2584 equals=”][226 striphtml=1][else]Hyper Personalization Using AI: Elevate Fitness[/if 2584]. Journal of Mobile Computing, Communications & Mobile Networks. 29/09/2025; 12(03):10-21. Available from: https://journals.stmjournals.com/jomccmn/article=29/09/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Volume 12
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 28/06/2025
Accepted 20/07/2025
Published 29/09/2025
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Publication Time 93 Days

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