Personalized Machine Learning Intervention to Improve Sleep Quality Using Wearable Technology in Healthy Middle-Aged Adults From Mexico City: Protocol for a Pilot Randomized Controlled Trial

This pilot study establishes the feasibility and preliminary effect size for ML-personalized sleep interventions using consumer wearables within a manufacturer-independent personalization framework. The approach combines objective device monitoring with a subjective measure (PSQI) to test whether precision targeting of individual sleep parameters outperforms generic recommendations. If validated, this methodology could advance sleep interventions from universal protocols toward individualized behavioral targeting. The resulting dataset will enable model refinement and provide preliminary evidence for scaling personalized sleep health interventions in healthy populations.