A stroke is a life-changing event that may end up as a disability, with repercussions on the patient’s quality of life. Stroke rehabilitation therapies are helpful to regain some of the patient’s lost functionality. However, in practice stroke patients may suffer from a gradual loss of motivation. Gamified systems are used to increase user motivation, hence, gamified elements have been implemented into stroke rehabilitation therapies in order to improve patients’ engagement and adherence. This review work focuses on selecting and analyzing developed and validated gamified stroke rehabilitation systems published between 2009 and 2017 to identify the most important features of these systems. After extensive research, 32 articles have met the selection criteria, resulting in a total of 28 unique works. The works were analyzed and a total of 20 features were identified. The features are explained, making emphasis on the works that implement them extensively. Finally, a classification of features based on objectives is proposed, which was used to identify the relationships between features and implementation gaps. It was found that there is a tendency to develop low-cost solutions as in-home therapy systems and provide a variety of games. This review allowed the definition of the opportunities for future research direction such as systems addressing the three rehabilitation areas; data analytics to make decisions; motivational content identification based on automatic engagement detection and emotion recognition; and alert systems for patient´s safety.

Training design for automatic skills has a vast domain of application, such as education, physical and cognitive rehabilitation, as well as sports, arts and professional training. Gamification concept used in technology‐assisted training has the potential to increase motivation, engagement and adherence to the training programme. Currently, the general gamification models of learning, did not take into account the temporal specificity of the game elements for automaticity acquisition training. In order to address this problem, an extensive overview of the key training attributes that impact automaticity acquisition was carried out. Then, based on this review, the three steps of a proposed model were presented. The first step of this model, named Task Analytics, helps with task‐specific training decisions. The second step provides descriptive and prescriptive approaches for the three phases of automaticity acquisition (fast learning, slow learning and automatization). The descriptive part characterizes each phase using psychological and performance‐related qualities, while the prescriptive part recommends the appropriate training elements for each phase. Based on the prescriptive part, a game‐design model is proposed in the third step, which classifies the game mechanics and maps them onto each phase of automaticity acquisition. Finally, to validate this approach, a mobile game was designed based on the proposed gamification model, and it was compared to control design. The two approaches are tested with 49 participants. The results showed that the experimental group had a significantly better engagement and higher performance. Furthermore, the experimental group showed significantly better performance in a multitasking challenge designed to evaluate the automaticity. The main contribution of this article is the proposed game design model that takes into account the temporal specificity of game elements during the acquisition of automaticity.

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