The current version of the app faced limitations in user flexibility and clarity. Users couldn’t customize nutrition sets or set specific goals like weight loss or muscle gain. The auto-generated shopping list lacked adaptability, and the History section lacked context. The graphical view only showed suggested meals, leaving users unsure about their actual consumption.

User profiles show diverse needs, from athletes wanting nutritional guidance to individuals seeking personalized health plans. They value adaptable features for efficient daily routines, quick meal options, and personalized guidance. Mealie aims to meet these needs by designing user-friendly functionalities.

The Kano Model guides Mealie’s feature development, focusing on automating processes outside of onboarding. The introduction emphasizes customizing meal plans, with algorithms generating nutrition and shopping lists. User guidance is crucial for optimization, including macro tracking and nutrition set suggestions. The unique selling point remains the stronger and more personalized integration of influencer sets.

After inputting specific dietary needs and preferences at the beginning of setup, users are provided with individual tutorials. These tutorials not only explain how to navigate the dashboard view but also demonstrate basic app functions such as the shopping list mode. During meal planning, users have the option to select dishes for the three main meals as well as snacks. These options are curated by the system based on the user’s onboarding information. The app aims to promote optimal intake of macronutrients and calories while preserving long-term goals.
After selecting meals, users can generate a personalized shopping list for the planned meals within a self-selected timeframe. 

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