Bienestar digital con IA: predecir el exceso de uso antes de que ocurra

The article argues that common screen-time apps like Screen Time, Digital Wellbeing, Freedom and Opal rely on static limits, reactive alerts and easily disabled blocks. It proposes a system built on an XGBoost forecaster that predicts next-day app usage and estimates when a user will hit a limit, along with a binge-risk score explained via SHAP. A transparent controller would gradually reduce usage toward a goal without cutting more than 10% per week, and an LLM coach would intervene at critical moments. The author reports a usage forecast R² of about 0.77 and a binge-risk AUC of about 0.79, which he deliberately left below higher benchmarks rather than manipulating the data.