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Register a new userUnpacking Adherence and Engagement in Pervasive Health Games
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Added by
Magy Seif El-Nasr
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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No Arabic abstract
Pervasive health games have a potential to impact health-related behaviors. And, similar to other types of interventions, engagement and adherence in health games is the keystone for examining their short- and long-term effects. Many health-based applications have turned to gamification principles specifically to. enhance their engagement. However, according to many reports, only 41% of participants are retained in single player games and 29% in social games after 90 days. These statistics raise multiple questions about factors influencing adherence and engagement. This paper presents an in-depth mixed-methods investigation of game design factors affecting engagement with and adherence to a pervasive commercial health game, called SpaPlay. We analyzed interview and game behavior log data using theoretical constructs of sustained engagement to identify design elements affecting engagement and adherence. Our findings indicate that design elements associated with autonomy. and relatedness from the Self-Determination Theory and integrability, a measure of how well activities align with a persons life style, are important factors affecting engagement and adherence.
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Outcome-driven studies designed to evaluate potential effects of games and apps designed to promote healthy eating and exercising remain limited either targeting design or usability factors while omitting out health-based outcomes altogether, or tend to be too narrowly focuses on behavioral outcomes within a short periods of time thereby less likely to influence longitudinal factors that can help sustain healthy habits. In this paper we argue for a unified approach to tackle behavioral change through focusing on both health outcomes and cognitive precursors, such as players attitudes and behaviors around healthy eating and exercising, motivation stage and knowledge and awareness about nutrition or physical activity. Key findings from a 3-month long game play study, with 47 female participants indicate that there are clear shifts in players perceptions about health and knowledge about eating. This paper extends our current understandings about approaches for evaluating health games and presents a unified approach to assess effectiveness of game-based health interventions through combining health-based outcomes and shifts in players cognitive precursors.
Applications for learning and training have been developed and highlighted as important tools in health education. Despite the several approaches and initiatives, these tools have not been used in an integrated way. The specific skills approached by each application, the absence of a consensus about how to integrate them in the curricula, and the necessity of evaluation tools that standardize their utilization are the main difficulties. Considering these issues, Portal of Games and Environments Management for Designing Activities in Health (Pegadas) was designed and developed as a web portal that offers the services of organizing and sequencing serious games and virtual environments and evaluating the performance of the user in these activities. This article presents the structure of Pegadas, including the proposal of an evaluation model based on learning objectives. The results indicate its potential to collaborate with human resources training from the proposal of the sequencing, allowing a linked composition of activities and providing the reinforcement or complement of tasks and contents in a progressive scale with planned educational objective-based evaluation. These results can contribute to expand the discussions about ways to integrate the use of these applications in health curricula.
This paper summarizes current notes about a new mixed-reality paradigm that we named as pervasive virtuality. This paradigm has emerged recently in industry and academia through different initiatives. In this paper we intend to explore this new area by proposing a set of features that we identified as important or helpful to realize pervasive virtuality in games and entertainment applications.
Medication adherence is a problem of widespread concern in clinical care. Poor adherence is a particular problem for patients with chronic diseases requiring long-term medication because poor adherence can result in less successful treatment outcomes and even preventable deaths. Existing methods to collect information about patient adherence are resource-intensive or do not successfully detect low-adherers with high accuracy. Acknowledging that health measures recorded at clinic visits are more reliably recorded than a patients adherence, we have developed an approach to infer medication adherence rates based on longitudinally recorded health measures that are likely impacted by time-varying adherence behaviors. Our framework permits the inclusion of baseline health characteristics and socio-demographic data. We employ a modular inferential approach. First, we fit a two-component model on a training set of patients who have detailed adherence data obtained from electronic medication monitoring. One model component predicts adherence behaviors only from baseline health and socio-demographic information, and the other predicts longitudinal health measures given the adherence and baseline health measures. Posterior draws of relevant model parameters are simulated from this model using Markov chain Monte Carlo methods. Second, we develop an approach to infer medication adherence from the time-varying health measures using a Sequential Monte Carlo algorithm applied to a new set of patients for whom no adherence data are available. We apply and evaluate the method on a cohort of hypertensive patients, using baseline health comorbidities, socio-demographic measures, and blood pressure measured over time to infer patients adherence to antihypertensive medication.
Problem-Based Learning (PBL) is a popular approach to instruction that supports students to get hands-on training by solving problems. Question Pool websites (QPs) such as LeetCode, Code Chef, and Math Playground help PBL by supplying authentic, diverse, and contextualized questions to students. Nonetheless, empirical findings suggest that 40% to 80% of students registered in QPs drop out in less than two months. This research is the first attempt to understand and predict student dropouts from QPs via exploiting students engagement moods. Adopting a data-driven approach, we identify five different engagement moods for QP students, which are namely challenge-seeker, subject-seeker, interest-seeker, joy-seeker, and non-seeker. We find that students have collective preferences for answering questions in each engagement mood, and deviation from those preferences increases their probability of dropping out significantly. Last but not least, this paper contributes by introducing a new hybrid machine learning model (we call Dropout-Plus) for predicting student dropouts in QPs. The test results on a popular QP in China, with nearly 10K students, show that Dropout-Plus can exceed the rival algorithms dropout prediction performance in terms of accuracy, F1-measure, and AUC. We wrap up our work by giving some design suggestions to QP managers and online learning professionals to reduce their student dropouts.
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