发展文化相关的设计指南,鼓励体育活动:一个社会认知理论的观点

IF 5.9 Q1 Computer Science Journal of Healthcare Informatics Research Pub Date : 2018-05-31 eCollection Date: 2018-12-01 DOI:10.1007/s41666-018-0026-9
Kiemute Oyibo, Rita Orji, Julita Vassileva
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引用次数: 0

摘要

在全球范围内,缺乏运动和非传染性疾病的发病率呈上升趋势。这就需要采取系统的方法来解决这个几乎已成为全球流行病的问题。研究表明,理论驱动的干预措施比不知情的干预措施更有可能取得成效。然而,有关体育锻炼的决定因素和文化调节作用的研究却很少。为了弥补这一差距,我们对来自个人主义文化和集体主义文化的 633 名参与者进行了一项关于体育锻炼决定因素的大规模比较研究。社会认知理论是一种广泛应用于健康干预的行为理论,我们利用该理论为每种文化的体育锻炼决定因素建模,并将其映射到应用领域的可实施策略中。然而,在集体主义文化中,社会支持(βT = 0.42,p 结果期望(βT = 0.11,p 体育锻炼)和自我调节(βT = 0.33,p 体育锻炼)在个体主义文化中的作用是不同的。我们讨论了这些发现,将各自的行为决定因素与健康领域中相应的说服策略进行了映射,并提供了一套通用的设计指南,以便根据各自的文化定制策略。
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Developing Culturally Relevant Design Guidelines for Encouraging Physical Activity: a Social Cognitive Theory Perspective.

The prevalence of physical inactivity and non-communicable diseases is on the rise worldwide. This calls for a systematic approach in addressing the problem, which is almost becoming a global epidemic. Research has shown that theory-driven interventions are more likely to be effective than uninformed interventions. However, research on the determinants of physical activity and the moderating effect of culture is scarce. To bridge this gap, we conducted a large-scale comparative study of the determinants of physical activity among 633 participants from individualist and collectivist cultures. Using the Social Cognitive Theory, a widely applied behavioral theory in health interventions, we modeled the determinants of physical activity for each culture and mapped them to implementable strategies in the application domain. Our structural equation model shows that, in the individualist culture, Self-EfficacyT = 0.55, p < 0.001) and Self-RegulationT = 0.33, p < 0.001) are the strongest determinants of Physical Activity. However, in the collectivist culture, Social SupportT = 0.42, p < 0.001) and Outcome ExpectationT = 0.11, p < 0.01) are the strongest determinants of Physical Activity. We discussed these findings, mapped the respective behavioral determinants to the corresponding persuasive strategies in the health domain and provided a set of general design guidelines for tailoring the strategies to the respective cultures.

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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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