Franklin Nina C, Arena Ross
Department of Physical Therapy, University of Illinois at Chicago, Chicago, IL; Integrative Physiology Laboratory, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL.
Department of Physical Therapy, University of Illinois at Chicago, Chicago, IL; Integrative Physiology Laboratory, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL.
Prog Cardiovasc Dis. 2016 May-Jun;58(6):595-604. doi: 10.1016/j.pcad.2016.02.003. Epub 2016 Feb 18.
Obesity is an independent contributor to cardiovascular disease (CVD) and a major driving force behind racial/ethnic and gender disparities in risk. Due to a multitude of interrelating factors (i.e., personal, social, cultural, economic and environmental), African-American (AA) women are disproportionately obese and twice as likely to succumb to CVD, yet they are significantly underrepresented in behavioral weight management interventions. In this selective review we highlight components of the limited interventions shown to enhance weight loss outcomes in this population and make a case for leveraging Web-based technology and artificial intelligence techniques to deliver personalized programs aimed at obesity treatment and CVD risk reduction. Although many of the approaches discussed are generally applicable across populations burdened by disparate rates of obesity and CVD, we specifically focus on AA women due to the disproportionate impact of these non-communicable diseases and the general paucity of interventions targeted to this high-risk group.
肥胖是心血管疾病(CVD)的一个独立促成因素,也是风险方面种族/族裔和性别差异背后的主要驱动力。由于众多相互关联的因素(即个人、社会、文化、经济和环境因素),非裔美国(AA)女性肥胖比例过高,患心血管疾病的可能性是其他人的两倍,但在行为体重管理干预措施中,她们的代表性却明显不足。在这篇选择性综述中,我们强调了有限干预措施的组成部分,这些措施已被证明能提高该人群的减肥效果,并主张利用基于网络的技术和人工智能技术来提供旨在治疗肥胖和降低心血管疾病风险的个性化项目。尽管所讨论的许多方法通常适用于受不同肥胖率和心血管疾病负担影响的人群,但由于这些非传染性疾病的影响不成比例,且针对这一高危群体的干预措施普遍匮乏,我们特别关注非裔美国女性。