Winter Sandra J, Sheats Jylana L, King Abby C
Stanford Prevention Research Center, Stanford University School of Medicine, Palo Alto, CA.
Stanford Prevention Research Center, Stanford University School of Medicine, Palo Alto, CA.
Prog Cardiovasc Dis. 2016 May-Jun;58(6):605-12. doi: 10.1016/j.pcad.2016.02.005. Epub 2016 Feb 20.
This review examined the use of health behavior change techniques and theory in technology-enabled interventions targeting risk factors and indicators for cardiovascular disease (CVD) prevention and treatment. Articles targeting physical activity, weight loss, smoking cessation and management of hypertension, lipids and blood glucose were sourced from PubMed (November 2010-2015) and coded for use of 1) technology, 2) health behavior change techniques (using the CALO-RE taxonomy), and 3) health behavior theories. Of the 984 articles reviewed, 304 were relevant (240=intervention, 64=review). Twenty-two different technologies were used (M=1.45, SD=+/-0.719). The most frequently used behavior change techniques were self-monitoring and feedback on performance (M=5.4, SD=+/-2.9). Half (52%) of the intervention studies named a theory/model - most frequently Social Cognitive Theory, the Trans-theoretical Model, and the Theory of Planned Behavior/Reasoned Action. To optimize technology-enabled interventions targeting CVD risk factors, integrated behavior change theories that incorporate a variety of evidence-based health behavior change techniques are needed.
本综述考察了健康行为改变技术和理论在以技术为支撑的干预措施中的应用,这些干预措施旨在针对心血管疾病(CVD)预防和治疗的风险因素及指标。从PubMed(2010年11月 - 2015年)获取了针对身体活动、体重减轻、戒烟以及高血压、血脂和血糖管理的文章,并对其进行编码,以确定是否使用了1)技术,2)健康行为改变技术(使用CALO - RE分类法),以及3)健康行为理论。在审查的984篇文章中,304篇相关(240篇为干预研究,64篇为综述)。使用了22种不同的技术(均值 = 1.45,标准差 = ±0.719)。最常使用的行为改变技术是自我监测和绩效反馈(均值 = 5.4,标准差 = ±2.9)。一半(52%)的干预研究提到了一种理论/模型——最常见的是社会认知理论、跨理论模型以及计划行为理论/理性行动理论。为了优化以技术为支撑的针对CVD风险因素的干预措施,需要整合行为改变理论,这些理论应纳入各种基于证据的健康行为改变技术。