Adaptive TDEE Calculator

Research-grounded energy expenditure tracking

8 followers

Research-grounded energy expenditure tracking

8 followers

An adaptive TDEE calculator that replaces vague 4-option activity checkboxes with an integrated AI parsing layer. The AI evaluates granular user data to determine precise activity inputs, which are then processed by exact mathematical formulas rather than static population averages. The calculation logic dynamically corrects for lean body mass, NEAT variance, metabolic adaptation, and the acute energetic cost of regular plasma donation, mapping all outputs directly to peer-reviewed research.

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Launch tags:Health & Fitness•Artificial Intelligence•GitHub

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Maker

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I built this adaptive TDEE calculator to solve a specific frustration with mainstream fitness apps: their reliance on static, one-size-fits-all equations and vague activity multipliers. Traditional models force you to pick between four arbitrary checkboxes (like "Lightly Active" or "Moderately Active") which completely ignores how your body actually expends energy. This calculator replaces those generic checkboxes entirely by integrating an AI engine as the frontend input processor. Instead of forcing your lifestyle into a rigid category, the AI analyzes your granular descriptions of daily living, training volume, and lifestyle factors to map out an accurate activity profile. Once the AI defines those precise inputs, they are fed into exact, deterministic mathematical formulas under the hood. The core math shifts the priority to lean body mass rather than just total scale weight, offering a more accurate foundational baseline. From there, the formulas layer on the parsed activity tracking and model dynamic changes like Non-Exercise Activity Thermogenesis (NEAT) and metabolic adaptation over time. It even accounts for less common but highly impactful biological variables, such as the direct caloric and metabolic cost of regular plasma donation. Every single calculation metric, modifier, and formula used under the hood is mapped directly to published, peer-reviewed nutritional science and metabolic research, all of which are cited transparently directly in the web application's footer. The backend is built to be as minimal and fast as possible, using a clean Node.js configuration to serve the single-page application without unnecessary script overhead or tracking bloat. I’m launching this today to get direct feedback from the community on the calculation logic, the usability of the UI layout, and any additional metabolic variables or tracking integrations you think would be valuable to add next.

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3mo ago

Mailwarm

What inputs are you asking users for to estimate activity, like wearables data or just a text description?

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3mo ago

Maker

@karimbenkeroum Right now, it is entirely text-based. You just type in a description of your daily routine.

Instead of making you connect a wearable or deal with a glitchy API sync, you describe your day in plain English (e.g., your job, your workout structure, sets, cardio, general daily steps, etc.). The AI reads that text, pulls out the specific activity patterns, and maps them to precise numbers.

Those numbers then feed directly into the actual mathematical formulas to calculate your TDEE

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3mo ago

Forum Threads

p/adaptive-tdee-calculator

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3mo ago

The new best TDEE calculator in the game.

Most online TDEE calculators suffer from the exact same user-input bottleneck: they force you to pick between four vague, highly subjective activity checkboxes (e.g., "Lightly Active" vs. "Moderately Active"). This forces everyone to guess their baseline, which fundamentally corrupts the downstream calculations before they even begin.

To fix this, I built an adaptive TDEE calculator that replaces those generic checkboxes entirely with an integrated AI engine. Instead of picking a static multiplier, you describe your regular daily movement, training protocols, and lifestyle factors in natural language. The AI parses this qualitative description to map out a precise, granular activity profile.

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