No abstract

The management of cancer patients’ symptoms in doctor consultations is a cornerstone in clinical care, this process being fundamental for the follow-up of the evolution of these. This article presents an application that allows collecting periodically and systematically the data of cancer patients and their visualization by the medical team. In this article, we made the analysis, design, implementation, and final evaluation by analyzing the correlation of this data collection with interaction patterns to determine how the user information can be enriched with information from the interaction patterns. We have followed an agile methodology based on the iterative and incremental development of successive prototypes with increased fidelity, where the requirements and solutions have evolved over time according to the need and assessments made. The comprehensive analysis of the patient’s condition allowed us to perform a first analysis of the correlation of the states of patients concerning mood, sleeping quality, and pain with the interaction patterns. A future goal of this project is to optimize the process of data collection and the analysis of information. Another future goal is to reduce the time dedicated to reporting the evolution of symptoms in face-to-face consultations and to help professionals in analyzing the patient’s evolution even in the period that has not been attended in person.

IoT provides applications and possibilities to improve people’s daily lives and business environments. However, most of these technologies have not been exploited in the field of emotions. With the amount of data that can be collected through IoT, emotions could be detected and anticipated. Since the study of related works indicates a lack of methodological approaches in designing IoT systems from the perspective of emotions and smart adaption rules, we introduce a methodology that can help design IoT systems quickly in this scenario, where the detection of users is valuable. In order to test the methodology presented, we apply the proposed stages to design an IoT smart recommender system named EmotIoT. The system allows anticipating and predicting future users’ emotions using parameters collected from IoT devices. It recommends new activities for the user in order to obtain a final state. Test results validate our recommender system as it has obtained more than 80% accuracy in predicting future user emotions.

Nowadays, gamification offers several advantages in order to motivate a change in the behavior towards health and wellness. Although it is a relatively new trend, many fields have already realized its potential, and those related to health have also begun to make use of it. This paper introduces an application developed to improve patient monitoring and motivation through the use of gamification. We have applied the mechanics and dynamics of games in a non-game context, such as the introduction of data for health monitoring, in order to attract the patient. With the use of gamification, we make the introduction of data less tedious and, in addition, increase levels of motivation, as a further benefit. In this work we have conducted a user study aimed at evaluating the usability of gamification. We also studied the resources that encourage patients to use the application and how to increase their motivation and satisfaction. The results show that the app is easy to use. Second, they show that we implemented a scalable and self-recursive system. Finally, these results indicate that our system for resources sharing is a system in which patients feel comfortable when sharing and receiving those resources and they encourage us for further developments and studies based on the feedback received.