Comprehensive Review of Integrated Digital Health Platforms for Life Expectancy Prediction

The rapid evolution of digital health technologies has transformed the landscape of predictive healthcare, particularly in the estimation of life expectancy. Integrated digital health platforms such as Apple HealthKit and Google Fit have emerged as pivotal tools in collecting, synthesizing, and analyzing diverse health-related data including medical records, demographic profiles, and lifestyle indicators. This article presents a comprehensive review of these platforms, focusing on their architecture, data integration capabilities, and effectiveness in supporting life expectancy prediction. Through comparative analysis, the study explores the extent to which these platforms accommodate heterogeneous data, facilitate machine learning applications, and contribute to personalized health forecasting. Key considerations such as data interoperability, privacy frameworks, and predictive accuracy are critically assessed. Additionally, real-world case studies and clinical applications are discussed to illustrate practical deployment and outcomes. The review concludes by identifying existing limitations, highlighting research gaps, and proposing future directions for the optimization of digital health ecosystems in predictive medicine.