This article presents the development of a database and an initial analysis aimed at detecting potential correlations between mobile phone usage and frailty levels among elderly individuals. The primary objective is to conduct exhaustive monitoring of all information provided by smartphones, focusing on both the use of common applications and sensor data related to mobility during activities of daily living (ADLs), and to contrast these findings with assessed frailty levels. This work forms part of a broader study involving elderly participants aged between 73 and 96 years, based in the Barcelona area. The database constructed for this study is based on two main strategies. First, to gather comprehensive data on smartphone usage, a group of 10 elderly individuals was observed under normal daily living conditions over a period of up to four weeks. For this initial group, key geriatric indices—Barthel Index, Lawton–Brody Instrumental Activities of Daily Living Scale, and Frail-VIG Index—were previously determined through ambulatory medical evaluations. Second, the database was expanded by generating synthetic data using Generative Adversarial Networks (GANs) and Gaussian Copula models, incorporating an additional group of real patients for whom frailty indices were already known. For those patients, we do not have any data from their own smartphones. The combined real and synthetic dataset was used to identify the most significant features and to support feature selection for classification. Generalized Linear Models (GLMs) were subsequently applied to develop decision trees, with the goal of establishing correlations between smartphone usage patterns and different frailty indices. Finally, the model outputs were analysed to extract meaningful insights that could inform future clinical applications.