TRAINER – A Content-Based Recommender System of Training Workouts and Nutritional Diets for Fitness Enthusiasts

Abstract : The TRAINER system is a personalized health and fitness solution developed for the Fitness Zone Fitness Center in Antipolo, Philippines, to address the shortcomings of generic, “one-size-fits-all” training programs. By utilizing a content-based recommender system driven by machine learning, the platform integrates individual user profiles—including fitness levels, personal goals, and dietary preferences—with a curated repository of workouts and nutritional plans. The system features a web-based interface for real-time data collection and progress tracking, employing a continuous feedback loop to ensure recommendations remain dynamic and adaptive. Ultimately, TRAINER seeks to enhance client adherence and health outcomes by providing data-driven, context-aware guidance that bridges the gap between limited instructor availability and the diverse needs of fitness enthusiasts.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-16
DOI
https://doi.org/10.5281/zenodo.19639793
Primary Topic
Mobile Health and mHealth Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

TRAINER – A Content-Based Recommender System of Training Workouts and Nutritional Diets for Fitness Enthusiasts

Tucay Edmundo C., Ricafort Reagan B.
Zenodo (CERN European Organization for Nuclear Research)
Mobile Health and mHealth Applications
article

TRAINER – A Content-Based Recommender System of Training Workouts and Nutritional Diets for Fitness Enthusiasts

Tucay Edmundo C., Ricafort Reagan B.
article en

Abstract

Abstract : The TRAINER system is a personalized health and fitness solution developed for the Fitness Zone Fitness Center in Antipolo, Philippines, to address the shortcomings of generic, “one-size-fits-all” training programs. By utilizing a content-based recommender system driven by machine learning, the platform integrates individual user profiles—including fitness levels, personal goals, and dietary preferences—with a curated repository of workouts and nutritional plans. The system features a web-based interface for real-time data collection and progress tracking, employing a continuous feedback loop to ensure recommendations remain dynamic and adaptive. Ultimately, TRAINER seeks to enhance client adherence and health outcomes by providing data-driven, context-aware guidance that bridges the gap between limited instructor availability and the diverse needs of fitness enthusiasts.

Zenodo (CERN European Organization for Nuclear Research)
AMA Computer University (PH)
Zero hunger
Openalex Percentile: Top 4%
Mobile Health and mHealth Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

TRAINER – A Content-Based Recommender System of Training Workouts and Nutritional Diets for Fitness Enthusiasts — Tucay Edmundo C., Ricafort Reagan B. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS