## 2 Related Work
In [33], a very detailed and extensive categorization of mobile apps on the market has been proposed, grouping them into 18 clusters. Among them, two categories concern data management and communication, nine on monitoring and tracking a patient's health conditions, five on the treatment of specific diseases (including compliance), and two on the support of doctors in analyzing data and reasoning. Many WES-like applications have been recently developed that can be classified accordingly. In [34], an expert system based on the _wireless sensor networks_ paradigm is presented for supporting aged people at home, customized to the specific needs of the elderly. In [35], a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson's disease and atypical parkinsonism. In [36], a web-based remote human pulse monitoring application with intelligent data analysis is presented, based on physiological sensors, wireless communication, and intelligent data analysis technologies, for home health care in daily lives. A pulmonary disease management system with on-body and near-body sensors is described in [37]; fall detection is the main goal of applications in [38; 39; 40], where different kinds of reasoning techniques are combined with data detection from sensors to obtain personalized alarms about fall risk for elderly people. Successful use of mHealth apps can potentially provide information and support diagnosis. Such tools can increase communication levels among the roles involved in disease management, i.e., patients, professionals, and caregivers, as highlighted in [41]. In [42], correspondence, competence, and relevance are indicated as the most valuable characteristics of an mHealth service to be fully accepted. Correspondence concerns the trade-off between the functionalities of the service and the needs perceived by the user. Competence regards the psychological effort the user requires during the interaction with the service. Relevance is a measure of the user's personal understanding of a service. Some researchers [43] focus on trust dimensions too, like privacy and security. Indeed, it is necessary to keep the user involvement high on a long-term horizon [44], developing apps that consider user experience to promote their long term use to reach effective behavior change of the users, with benefits from the reduction of health risks perspective [45]. User experience depends on the emotional state of the user, that could change during and after the interaction with a product or service [46]. According to [47], UK aspects having influence on the relationship between the user and a product are privacy, context, emotional state, functionality, infrastructure, service response time, and visual attractiveness. Given that user experience is context-dependent, mHealth apps should export different characteristics of user experience, like ubiquity, interactivity, personalization, and context sensitivity [48]. Moreover, patient history, clear instructions, and feedback are key features that mHealth apps should include based on users' needs [49].