Abstract
Systems that aim to maintain and improve the health of citizens are steadily gaining importance. Digital transformation is having a positive impact on healthcare. Gamification motivates individuals to maintain and improve their physical and mental well-being. In the era of artificial intelligence and big data, healthcare is not only digital, but also predictive, proactive, and preventive. Big data and artificial intelligence techniques are called to play an essential role in gamified eHealth services and devices allowing to offer personalized care. This chapter aims to explore the possibilities of artificial intelligence and big data techniques to support and improve gamified eHealth services and devices, including wearable technology, which are essential for digital natives but also increasingly important for digital immigrants. These services and devices can play an important role in the prevention and diagnosis of diseases, in the treatment of illnesses, and in the promotion of healthy lifestyle habits.
TopGamification Is All Around
Gamification refers to the use of game design elements and procedures within non-game contexts (Deterding et al., 2011). Gamification involves applying game design techniques, game mechanics, and/or game style to non-game situations to engage users and to facilitate them the solving of problems in a fun way.
Many experts think that the philosophy of gamification can be successfully applied in very different domains, allowing offering enhanced and motivating applications and services to the user (Werbach & Hunter, 2012; Zichermann & Cunningham, 2011; Zichermann & Linder, 2013).
Gamification is increasingly and better accepted by society. Today's children and young people are digital natives who have grown up in homes and schools where technological devices are common and are the door to access a myriad of services, products and experiences.
In addition, many schools are carrying out digitization projects in their classrooms. Children use applications such as Kahoot, Classcraft or Educaplay, which makes them feel like absolute protagonists of a wonderful game that is the game of learning. As protagonists of their favourite videogame, students accumulate points and level up, can choose which armour to wear every day, and they even have numerous powers that allow them, for example, to save a classmate who did a math operation wrong, or to challenge their classmates with a pass word on the analysis of adjectives, or with a questionnaire on the capitals of the European countries.
The potential of these tools have rapidly attracted the interest of researchers, both in the field of technology and in the field of education. Authors like Bawa (2019) or Wang & Tahir (2020) have researched about how Kahoot! enhances learners’ performance and engagement levels more than traditional teaching methods. Other researchers have focused on the potential of Educaplay to improve education (Charrupi et al., 2019), and to help students with special needs (Sánchez-Salazar et al., 2019). Bonvin et al. (2017) tried to characterize the social component of players’ engagement while using the role-playing game for classroom management Classcraft, and Sanchez et al. (2017) used Classcraft to emphasize that transforming a situation into a game does not consist of using elements that have a game-like aspect, but rather of a non-essentialistic vision of play, generating a metaphor around the situation to build a reflexive space where the nature and meaning of interactions are modified.
Key Terms in this Chapter
Healthcare Wearable Devices: Portable devices designed to collect the data of individuals' personal health and activity.
Health Monitoring: It is the tracking of any aspect of an individual's health. It is very important for illness prevention.
Artificial Intelligence: A wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.
PLB (Point Leaderboards Badges) Model: A model based on precise mechanics powered by points, objectives, levels, rankings, progress, missions, challenges, badges, notifications, and obstacles, structured to engage users in a real experience, in which motivation, participation and the amusement factor are essential.
Machine Learning: It refers to the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence.
Deep Learning: It refers to Artificial Neural Networks and related machine learning algorithms that uses using multiple layers of neurons. It is seen as a subset of machine learning in artificial intelligence.
Health Literacy: It refers to the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.
Big Data: It refers to the possibility of analyzing and systematically extracting information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.
TAM (Technology Acceptance Model): A theory that models how users come to accept and use a technology. It has been one of the most influential models of technology acceptance, according to what there are two primary factors influencing an individual’s intention to use new technology: perceived ease of use and perceived usefulness.