Smartphone-based assessment of food environment, diet, and obesity risk

描述（由申请人提供）：我提出的论文研究的目标是整合智能手机和互联网数据技术，以开发和验证客观评估饮食和身体活动模式以及建筑环境暴露的新方法，以便在快速城市化环境中检查个人食物环境暴露，饮食和肥胖风险之间的关系在风险转变迅速发生的易感人群中。这项研究与保护公众健康高度相关，因为世界上许多地区正在迅速城市化，并面临肥胖的风险。昆明是中国一个典型的中等城市，沿着上海和北京等东部沿海城市已经出现的现代化路线，快速重建和建设新的社区。与这些环境变化同时发生的是儿童和成人肥胖率的迅速上升。这是一个很好的研究地点，因为它建立在我在过去两年中与昆明医科大学的研究人员和学生的初步研究和合作的基础上。我的研究目标是1。利用谷歌地图和野外观测评价昆明城市食品环境差异2. 基于每个学生的智能手机GPS路线，量化264名高中生的个人食品环境暴露；3. 3 .通过智能手机使用语音注释视频和体育活动记录饮食模式；通过多变量模型评估食物环境、饮食和身体活动模式和社会网络之间的关系，以及它们与BMI的关系。

DESCRIPTION (provided by applicant): The goal of my proposed dissertation research is to integrate smartphone and internet data technologies to develop and validate new methodologies in objectively assessing diet and physical activity patterns and built environment exposures in order to examine the relationships between personal food environment exposures, diets and obesity risk in a susceptible population in a rapidly urbanizing environment where risk transitions are rapidly occurring. This research is highly relevant to the protection of public health as many regions of the world are rapidly urbanizing and are at risk for obesity. Kunming is a prototypical medium-sized city in China, where there is rapid redevelopment and construction of new neighborhoods along the lines of the modernization that has already occurred in eastern coastal cities such as Shanghai and Beijing. These environmental changes are paralleled by rapidly increasing child and adult obesity rates. It is an excellent site for thi research, as it builds upon pilot studies and collaborations with researchers and students at Kunming Medical University that I have developed over the last two years. My research aims are to 1. Assess the citywide differences in food environment in Kunming using Google Maps and field observations; 2. Quantify personal food environment exposures in 264 high school students based on each student's smartphone GPS routes; 3. Record diet patterns using voice-annotated video and physical activities via smartphones, and 4. Assess the relationships between food environment, diet and physical activity patterns and social networks, and their associations with BMI via multivariate modeling.