Bedtime Smartphone Use and Sleep: Exploring Scalable Solutions for Long-Term Well-Being

Abstract

Smartphone usage is widespread among young adults, yet its excessive use at bedtime raises concerns about its impact on sleep quality. This study investigates the effects of bedtime smartphone use and proposes an AI-driven system to mitigate its negative influence. The system integrates machine learning models and personalized intervention techniques to enhance sleep hygiene. Data collection involved young adults (20-30 years old) analyzing sleep patterns, common disruptors, and user preferences for interaction, including camera-based emotion detection and chatbot-driven guidance. The proposed model leverages multi-task learning for sleep pattern prediction, real-time app usage monitoring, and emotion recognition to provide adaptive recommendations. Additionally, gamification strategies, such as progress tracking and reward-based engagement, encourage adherence to healthier sleep routines. The findings demonstrate the potential of technology-driven interventions in addressing digital-age sleep challenges. By integrating AI-powered insights, personalized strategies, and interactive engagement, this research contributes to developing scalable solutions that promote long-term improvements in sleep quality and overall well-being