@Article{信息:doi 10.2196 / /移动医疗。5341，作者=“Koldijk, Saskia and Kraaij, Wessel and Neerincx, Mark A”，标题=“从工作压力和干预理论中得出普遍幸福技术的要求:框架和案例研究”，期刊=“JMIR Mhealth Uhealth”，年=“2016”，月=“7”，日=“05”，卷=“4”，数=“3”，页=“e79”，关键词=“心理压力”;专业的输家;行为症状;自我管理;卫生技术;摘要:背景:办公室环境中的压力是一个大问题，经常导致倦怠。新技术正在兴起，比如容易获得的传感器、上下文推理和电子教练应用程序。在家庭和工作幸福感的智能推理(SWELL)项目中，我们探索了使用这种新的普及技术为幸福感的自我管理提供支持的潜力，重点关注个人的压力应对。理想情况下，这些新的普及系统应该以现有的工作压力和干预理论为基础。 However, there is a large diversity of theories and they hardly provide explicit directions for technology design. Objective: The aim of this paper is to present a comprehensive and concise framework that can be used to design pervasive technologies that support knowledge workers to decrease stress. Methods: Based on a literature study we identify concepts relevant to well-being at work and select different work stress models to find causes of work stress that can be addressed. From a technical perspective, we then describe how sensors can be used to infer stress and the context in which it appears, and use intervention theory to further specify interventions that can be provided by means of pervasive technology. Results: The resulting general framework relates several relevant theories: we relate ``engagement and burn-out'' to ``stress'', and describe how relevant aspects can be quantified by means of sensors. We also outline underlying causes of work stress and how these can be addressed with interventions, in particular utilizing new technologies integrating behavioral change theory. Based upon this framework we were able to derive requirements for our case study, the pervasive SWELL system, and we implemented two prototypes. Small-scale user studies proved the value of the derived technology-supported interventions. Conclusions: The presented framework can be used to systematically develop theory-based technology-supported interventions to address work stress. In the area of pervasive systems for well-being, we identified the following six key research challenges and opportunities: (1) performing multi-disciplinary research, (2) interpreting personal sensor data, (3) relating measurable aspects to burn-out, (4) combining strengths of human and technology, (5) privacy, and (6) ethics. ", issn="2291-5222", doi="10.2196/mhealth.5341", url="http://mhealth.www.mybigtv.com/2016/3/e79/", url="https://doi.org/10.2196/mhealth.5341", url="http://www.ncbi.nlm.nih.gov/pubmed/27380749" }