اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدInvestigating behavior change indicators and cognitive measures in persuasive health games
82
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
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.
قيم البحث
اقرأ أيضاً
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 app
lications 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.
In cognitive psychology, automatic and self-reinforcing irrational thought patterns are known as cognitive distortions. Left unchecked, patients exhibiting these types of thoughts can become stuck in negative feedback loops of unhealthy thinking, lea
ding to inaccurate perceptions of reality commonly associated with anxiety and depression. In this paper, we present a machine learning framework for the automatic detection and classification of 15 common cognitive distortions in two novel mental health free text datasets collected from both crowdsourcing and a real-world online therapy program. When differentiating between distorted and non-distorted passages, our model achieved a weighted F1 score of 0.88. For classifying distorted passages into one of 15 distortion categories, our model yielded weighted F1 scores of 0.68 in the larger crowdsourced dataset and 0.45 in the smaller online counseling dataset, both of which outperformed random baseline metrics by a large margin. For both tasks, we also identified the most discriminative words and phrases between classes to highlight common thematic elements for improving targeted and therapist-guided mental health treatment. Furthermore, we performed an exploratory analysis using unsupervised content-based clustering and topic modeling algorithms as first efforts towards a data-driven perspective on the thematic relationship between similar cognitive distortions traditionally deemed unique. Finally, we highlight the difficulties in applying mental health-based machine learning in a real-world setting and comment on the implications and benefits of our framework for improving automated delivery of therapeutic treatment in conjunction with traditional cognitive-behavioral therapy.
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.
Intelligent conversational agents, or chatbots, can take on various identities and are increasingly engaging in more human-centered conversations with persuasive goals. However, little is known about how identities and inquiry strategies influence th
e conversations effectiveness. We conducted an online study involving 790 participants to be persuaded by a chatbot for charity donation. We designed a two by four factorial experiment (two chatbot identities and four inquiry strategies) where participants were randomly assigned to different conditions. Findings showed that the perceived identity of the chatbot had significant effects on the persuasion outcome (i.e., donation) and interpersonal perceptions (i.e., competence, confidence, warmth, and sincerity). Further, we identified interaction effects among perceived identities and inquiry strategies. We discuss the findings for theoretical and practical implications for developing ethical and effective persuasive chatbots. Our published data, codes, and analyses serve as the first step towards building competent ethical persuasive chatbots.
Social comparison-based features are widely used in social computing apps. However, most existing apps are not grounded in social comparison theories and do not consider individual differences in social comparison preferences and reactions. This pape
r is among the first to automatically personalize social comparison targets. In the context of an m-health app for physical activity, we use artificial intelligence (AI) techniques of multi-armed bandits. Results from our user study (n=53) indicate that there is some evidence that motivation can be increased using the AI-based personalization of social comparison. The detected effects achieved small-to-moderate effect sizes, illustrating the real-world implications of the intervention for enhancing motivation and physical activity. In addition to design implications for social comparison features in social apps, this paper identified the personalization paradox, the conflict between user modeling and adaptation, as a key design challenge of personalized applications for behavior change. Additionally, we propose research directions to mitigate this Personalization Paradox.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
