###### Abstract  
Type 1 Diabetes Mellitus (DM1) is a metabolic disease that is characterized by chronic hyperglycemia due to a lack of pancreatic insulin production. This forces patients to perform several blood glucose measurements per day--by means of capillary glucometers--in order to infer a trend and try to predict future values. In this way, a decision about the insulin dosage that has to be exogenously injected to maintain glycemia within the desirable levels is made. Unfortunately, this method usually suffers from relatively high imprecision. However, recent advances in information and communication technologies (ICT), along with novel biosensors that could provide a real-time comprehensive condition of the patient, offer a new perspective in DM1 management. In this sense, new disruptive technologies like Big Data, the Internet of Things (IoT), and Cloud Computing, as well as Machine Learning (ML) can play an important role in managing DM1. In this work, firstly, an analysis of previously published ICT-based methods for the management of diabetes continuous monitoring is carried out. In this way, an assessment of the possible lack of such proposals is presented, along with the challenges to be overcome in forthcoming smart DM1 management systems. Finally, an overview of a holistic ICT-based platform for DM1 management that try to solve the limitations of previous works, while at the same time, taking advantage of the abovementioned disruptive technologies is hereby proposed. diabetes mellitus management; wearable sensors; information and communication technologies _Accepted: 2018/09/2018/18/2  
either by multiple daily injections or by continuous infusion, using a pump, which permeates under the skin. In this context, new technological possibilities offer a new horizon in diabetes management. Although the first steps towards an artificial pancreas (AP) were undertaken 50 years ago [1], when the concept of a computer-supported system emulating the behavior of a pancreas by using a control algorithm was introduced, promising that technological advances achieved in recent years are supposed to bring a revolution in this field. Nowadays, an AP (which has not been completely achieved yet) is presumed to be composed of a Continuous Glucose-Monitoring (CGM) device aimed at checking the patient's glucose levels in real time (a feature that is already available) and injecting (a feature that is not yet commercially available) insulin (and glucagon, if applicable) into his or her body, along with a control system based on a closed loop, which must decide the amount of hormone (insulin or glucagon) to be injected [2; 3]. 2017 was a particularly significant year regarding the AP [4]. Clinical trials were performed and their results were published, showing a good performance under real and demanding situations. Even a commercial hybrid closed-loop system is commercially available at present, Medtronic MiniMed 670G, which automatically adjusts basal insulin every five minutes based on CGM readings and is able to stop insulin up to 30 min before reaching a preset low limit. It also automatically restarts insulin when patient's levels recover [5]. Nowadays, in this field, a total of 18 different closed-loop AP systems were identified as being in clinical phase development [6]. On the other hand, beyond the AP and CGM devices, others in the field of biometrics make it possible to maintain 24-h monitoring of the patient, thus recording important information about his or her health that could be interesting and properly used in a DM1 management context. Variables, like temperature, exercise, heart rate, and perspiration, among others, can be monitored continuously. However, the inclusion of new monitored features simultaneously generates a large amount of information and creates a new problem: the need to handle it in an appropriate information and communication technologies (ICT) framework that is designed to collect and gather data, process it, and obtain useful knowledge.