Angeles David, Kurtek Sebastian, Klein Elizabeth, Brinkman Marielle, Ferketich Amy
College of Public Health, The Ohio State University, Columbus, OH, USA.
Department of Statistics, The Ohio State University, Columbus, OH, USA.
J Appl Stat. 2023 Jul 12;51(6):1191-1209. doi: 10.1080/02664763.2023.2233143. eCollection 2024.
Health warning labels have been found to increase awareness of the harmful effects of tobacco products. An eye tracking study was conducted to determine the optimal placement and type of a health warning label on tobacco waterpipes. Participants viewed images that contained one of (1) four waterpipes, (2) three different types of warning labels, (3) placed in three locations. Typically, statistical analysis of eye tracking data is conducted based on summary statistics such as total dwell time, duration score, and number of visits to an area of interest. However, these summary statistics fail to capture the complete variability in a participant's eye movement. Instead, we propose to estimate heat maps defined on the entire image domain using the raw two-dimensional coordinates of eye movement via kernel density estimation. For statistical analysis of heat maps, we adopt the Fisher-Rao Riemannian geometric framework, which enables computationally efficient comparisons of heat maps, statistical summarization and exploration of variability in a sample of heat maps, and metric-based hierarchical clustering. We apply this framework to eye tracking data from the tobacco waterpipe study and comment on the results in the context of the optimal placement and type of health warning labels on tobacco waterpipes.
健康警示标签已被发现可提高人们对烟草制品有害影响的认识。开展了一项眼动追踪研究，以确定烟草水烟管上健康警示标签的最佳位置和类型。参与者观看了包含以下内容之一的图像：(1) 四种水烟管；(2) 三种不同类型的警示标签；(3) 放置在三个位置。通常，眼动追踪数据的统计分析是基于诸如总注视时间、持续时间得分和对感兴趣区域的访问次数等汇总统计数据进行的。然而，这些汇总统计数据未能捕捉到参与者眼动的完整变异性。相反，我们建议通过核密度估计，使用眼动的原始二维坐标来估计在整个图像域上定义的热图。对于热图的统计分析，我们采用费希尔 - 拉奥黎曼几何框架，该框架能够对热图进行高效的计算比较、对热图样本中的变异性进行统计汇总和探索，以及基于度量的层次聚类。我们将此框架应用于烟草水烟管研究的眼动追踪数据，并在烟草水烟管上健康警示标签的最佳位置和类型的背景下对结果进行评论。
