Smart AI Chatbots for Tailored Nutrition and Fitness Guidance
Bibliographic record
Abstract
The global surge in obesity and overweight cases has led to an increased risk of conditions such as cardiovascular diseases, diabetes, and other health complications. With the growing popularity of mobile health (mHealth) platforms, Artificial Intelligence (AI) has become an essential tool for developing personalized weight management solutions. This paper presents an AI-powered chatbot embedded within a mobile health application, designed to deliver real-time, personalized recommendations for diet, cooking guidance, and workout routines. Utilizing natural language processing (NLP) and fitness data analysis, the chatbot provides tailored health metrics, including body mass index (BMI), fat percentage, and obesity levels, while offering motivational support to encourage users to stick to their plans. With a user-friendly interface, the application presents personalized advice in an organized and engaging format. The system is further enhanced by real-time feedback mechanisms and progress tracking, which allow for continuous adaptation to the user’s individual journey.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".