Unique Views on Obesity-Related Behaviors and Environments: Research Using Still and Video Images.
Unique Views on Obesity-Related Behaviors and Environments: Research Using Still and Video Images.
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DOI：
10.1123/jmpb.2018-0021
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发表时间：
2018-09-01
期刊：
影响因子：
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通讯作者：
Berrigan, David
中科院分区：
文献类型：
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作者：
Carlson, Jordan A;Hipp, J Aaron;Berrigan, David
目标：通过捕捉此类研究的示例来评估与身体活动或营养相关的行为和/或环境，记录涉及使用图像的研究的挑战和益处。方法：通过本文作者的知识和网络以及文献检索来确定在研究中使用图像捕捉的研究人员（即关键信息提供者）。 29 名关键信息提供者完成了一项专为本研究开发的调查，涵盖研究类型、图像来源以及经历的挑战和收益。结果：大多数受访者在研究中使用静态图像，只有 26.7% 的受访者使用视频。图像源被分类为参与者生成（n = 13；例如，使用智能手机进行饮食评估的参与者）、研究人员生成（n = 10；例如，具有自动图像捕捉功能的可穿戴相机）或第三方策划（n = 7；例如，谷歌街景）。出现的两个主要挑战包括需要自动处理大型数据集 (58.8%) 和参与者招募/合规性 (41.2%)。与效益相关的主题包括通过增加数据覆盖率 (34.6%) 以及提高行为和环境评估的准确性 (34.6%) 对肥胖有更广泛的视角。结论：技术进步将支持在身体活动、营养行为和环境评估中更多地使用图像。为了推进这一领域的研究，健康和计算机科学家之间需要更有效的合作。特别是需要开发针对行为、环境和食品特征各个方面的自动数据提取方法。此外，解决与研究目的图像捕获相关的伦理问题的标准进展至关重要。
OBJECTIVES: To document challenges to and benefits from research involving the use of images by capturing examples of such research to assess physical activity- or nutrition-related behaviors and/or environments.METHODS: Researchers (i.e., key informants) using image capture in their research were identified through knowledge and networks of the authors of this paper and through literature search. Twenty-nine key informants completed a survey covering the type of research, source of images, and challenges and benefits experienced, developed specifically for this study.RESULTS: Most respondents used still images in their research, with only 26.7% using video. Image sources were categorized as participant generated (n = 13; e.g., participants using smartphones for dietary assessment), researcher generated (n = 10; e.g., wearable cameras with automatic image capture), or curated from third parties (n = 7; e.g., Google Street View). Two of the major challenges that emerged included the need for automated processing of large datasets (58.8%) and participant recruitment/compliance (41.2%). Benefit-related themes included greater perspectives on obesity with increased data coverage (34.6%) and improved accuracy of behavior and environment assessment (34.6%).CONCLUSIONS: Technological advances will support the increased use of images in the assessment of physical activity, nutrition behaviors, and environments. To advance this area of research, more effective collaborations are needed between health and computer scientists. In particular development of automated data extraction methods for diverse aspects of behavior, environment, and food characteristics are needed. Additionally, progress in standards for addressing ethical issues related to image capture for research purposes is critical.