Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/765
Title: Systematic Review of Persuasive Health Technology Design and Evaluation Models
Authors: Kasali, F. A
Awodele, O
Kuyoro, A
Keywords: Behavioral change models, Persuasive health applications, Persuasive systems, PSD model
Issue Date: Jan-2017
Publisher: International Journal of Computer Science and Business Informatics
Citation: Kasali, F. A., Awodele, O. & Kuyoro, S.(2017). Systematic Review of Persuasive Health Technology Design and Evaluation Models. International Journal of Computer Science and Business Informatics. ISSN: 1694-2108 | Vol. 17, No. 1
Series/Report no.: 17;1
Abstract: Persuasive technologies for promoting physical fitness, good nutrition and other healthy behaviors have been growing in popularity. Despite their appeal, the design and evaluation of these technologies remains a challenge and usually require a fully functional prototype and long term deployment just like any other information system. Hence, the focus of this paper is to review some persuasive and behavioral change models used in designing and evaluating persuasive technologies and identify their inherent limitations. To achieve the stated objectives, the systematic review method of research was done to understand the various persuasive system models and relevant information was extracted using the Inductive approach. Currently, the Persuasive System Design (PSD) framework is considered to be one of the commonly and most comprehensive framework for designing and evaluation of persuasive systems. However, some of its design features overlap and are difficult to analyze. This review research has brought to light the need to extend the PSD theoretical model with a measurable and integrated usability model which can adequately measure the efficiency and effectiveness of persuasive design outputs at the early phase of persuasive system development in future studies.
URI: http://localhost:8080/xmlui/handle/123456789/765
Appears in Collections:Computer Science

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