IntroductionThe recent years have witnessed a continuous increase in lifestyle related health challenges around the world. As a result, researchers and health practitioners have focused on promoting healthy behavior using various behavior change interventions. The designs of most of these interventions are informed by health behavior models and theories adapted from various disciplines. Several health behavior theories have been used to inform health intervention designs, such as the Theory of Planned Behavior, the Transtheoretical Model, and the Health Belief Model (HBM). However, the Health Belief Model (HBM), developed in the 1950s to investigate why people fail to undertake preventive health measures, remains one of the most widely employed theories of health behavior. However, the effectiveness of this model is limited. The first limitation is the low predictive capacity (R2 < 0.21 on average) of existing HBM’s variables coupled with the small effect size of individual variables. The second is lack of clear rules of combination and relationship between the individual variables. In this paper, we propose a solution that aims at addressing these limitations as follows: (1) we extended the Health Belief Model by introducing four new variables: Self-identity, Perceived Importance, Consideration of Future Consequences, and Concern for Appearance as possible determinants of healthy behavior. (2) We exhaustively explored the relationships/interactions between the HBM variables and their effect size. (3) We tested the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model.Methods:To achieve the objective of this paper, we conducted a quantitative study of 576 participants’ eating behavior. Data for this study were collected over a period of one year (from August 2011 to August 2012). The questionnaire consisted of validated scales assessing the HBM determinants – perceived benefit, barrier, susceptibility, severity, cue to action, and self-efficacy – using 7-point Likert scale. We also assessed other health determinants such as consideration of future consequences, self-identity, concern for appearance and perceived importance. To analyses our data, we employed factor analysis and Partial Least Square Structural Equation Model (PLS-SEM) to exhaustively explore the interaction/relationship between the determinants and healthy eating behavior. We tested for the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model and investigated possible mediating effects.Results:The results show that the three newly added determinants are better predictors of healthy behavior. Our extended HBM model lead to approximately 78% increase (from 40 to 71%) in predictive capacity compared to the old model. This shows the suitability of our extended HBM for use in predicting he...
Abstract:We argue that traditional sequencing technology developed in the field of intelligent tutoring systems could find an immediate place in largescale web-based education. This paper discusses two models that have been explored by the authors -the dynamic course generation system DCG and the concept-based course maintenance system CoCoA. DCG includes components for domain authoring and for automatic generation of adaptive courses on the WWW. It allows automatic generation of individualised courses according to the learner's goal and previous knowledge, and can dynamically adapt the course according to the learner's success in acquiring knowledge. . CoCoA can check the consistency and quality of a course at any moment of its life and also assists course developers in some routine operations.
The development of user-adaptive systems is of increasing importance for industrial applications. User modeling emerged from the need to represent in the system knowledge about the user in order to allow informed decisions on how to adapt to match the user's needs. Most of the research in this field, however, has been theoretical, "top-down." Our approach, in contrast, was driven by the needs of the application and shows features of bottom-up, user-centered design.We have implemented a user modeling component supporting a task-based interface to a hypermedia information system for hospitals and tested it under realistic conditions. A new architecture for user modeling has been developed which focuses on the tasks performed by users. It allows adaptive browsing support for users with different level of experience, and a level of adaptability. The requirements analysis shows that the differences in the information needs of users with different levels of experience are not only quantitative, but qualitative. Experienced users are not only able to cope with a wider browsing space, but sometimes prefer to organize their search in a different way. That is why the user model and the interface of the system are designed to support a smooth transition in the access options provided to novice users and to expert users. Keywords:adaptation, adaptive interfaces, hypermedia and hypertext navigation, intelligent information retrieval, office / hospital documentation systems, task-based context for information retrieval, task-structures. Modeling and User Adapted Interaction, vol. 6, Nos. 2-3, 1996 . 2 Published in User IntroductionBrowsing is a useful technique for retrieving documents from data-bases (Thompson & Croft, 1989).It has been widely applied recently as hypertext and hypermedia systems have become increasingly popular (Begoray, 1990). The main cognitive advantage of this technique is that users in general are better able to recognize the information they want than to characterize it in advance. The disadvantages of browsing are that it is easy to get lost in a complex network of nodes representing documents and concepts and that there is no guarantee that browsing will be as effective as a more conventional search. If it offers a rich set of links, the system is responsible for helping the users understand what the links mean, how they might be used, and how to find their way in the network. Without this kind of help, browsing can take on the aspect of the user finding her way in a maze, where she can become hopelessly "lost in (hyper)space" (Conklin, 1987). User modeling can help in supporting the user's navigation and information retrieval. A user model is an explicitly represented collection of data about the user which allows the system to adapt its options to the needs of the user. The intensive development in the field of user modeling during the last decade (Kobsa & Pohl, 1994a) makes it possible to consider it as a practical approach for ensuring user-adapted information access in a hypermedia in...
We have developed a tool for the authoring of adaptive CAL courses, called "Dynamic Courseware Generator" (DCG). It generates an individual course according to the learner's goals and previous knowledge and dynamically adapts the course according to the learner's success in acquiring knowledge. The DCG runs on a WWW server. The learner receives from this server an individualized course targeted to a specified goal. Afterwards, s/he is adaptively guided by the course through a space of teaching materials on the WWW. Unlike other CAL courses on the WWW, a course produced by the DCG is interactive, it tests the learner's knowledge and dynamically adapts to the student's progress. The authoring tool can be used also for collaborative authoring and learning.
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