Maramis Christos, Moulos Ioannis, Ioakimidis Ioannis, Papapanagiotou Vasileios, Langlet Billy, Lekka Irini, Bergh Cecilia, Maglaveras Nicos
Department of Medicine, School of Life Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Department of Medicine, School of Life Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Comput Methods Programs Biomed. 2020 Oct;194:105485. doi: 10.1016/j.cmpb.2020.105485. Epub 2020 May 1.
BACKGROUND & OBJECTIVE: The study of eating behavior has made significant progress towards understanding the association of specific eating behavioral patterns with medical problems, such as obesity and eating disorders. Smartphones have shown promise in monitoring and modifying unhealthy eating behavior patterns, often with the help of sensors for behavior data recording. However, when it comes to semi-controlled deployment settings, smartphone apps that facilitate eating behavior data collection are missing. To fill this gap, the present work introduces ASApp, one of the first smartphone apps to support researchers in the collection of heterogeneous objective (sensor-acquired) and subjective (self-reported) eating behavior data in an integrated manner from large-scale, naturalistic human subject research (HSR) studies.
This work presents the overarching and deployment-specific requirements that have driven the design of ASApp, followed by the heterogeneous eating behavior dataset that is collected and the employed data collection protocol. The collected dataset combines objective and subjective behavior information, namely (a) dietary self-assessment information, (b) the food weight timeseries throughout an entire meal (using a portable weight scale connected wirelessly), (c) a photograph of the meal, and (d) a series of quantitative eating behavior indicators, mainly calculated from the food weight timeseries. The designed data collection protocol is quick, straightforward, robust and capable of satisfying the requirement of semi-controlled HSR deployment.
The implemented functionalities of ASApp for research assistants and study participants are presented in detail along with the corresponding user interfaces. ASApp has been successfully deployed for data collection in an in-house testing study and the SPLENDID study, i.e., a real-life semi-controlled HSR study conducted in the cafeteria of a Swedish high-school in the context of an EC-funded research project. The two deployment studies are described and the promising results from the evaluation of the app with respect to attractiveness, usability, and technical soundness are discussed. Access details for ASApp are also provided.
This work presents the requirement elucidation, design, implementation and evaluation of a novel smartphone application that supports researchers in the integrated collection of a concise yet rich set of heterogeneous eating behavior data for semi-controlled HSR.
在理解特定饮食行为模式与肥胖和饮食失调等医学问题之间的关联方面，饮食行为研究已取得显著进展。智能手机在监测和改变不健康饮食行为模式方面展现出了潜力，通常借助行为数据记录传感器来实现。然而，在半控制式部署环境中，有助于收集饮食行为数据的智能手机应用却十分匮乏。为填补这一空白，本研究推出了ASApp，这是首批能以集成方式支持研究人员从大规模自然主义人体研究（HSR）中收集异质客观（传感器获取）和主观（自我报告）饮食行为数据的智能手机应用之一。
本文介绍了驱动ASApp设计的总体及特定部署要求，随后阐述了所收集的异质饮食行为数据集以及采用的数据收集协议。所收集的数据集结合了客观和主观行为信息，即（a）饮食自我评估信息，（b）整餐过程中的食物重量时间序列（使用无线连接的便携式体重秤），（c）餐食照片，以及（d）一系列主要根据食物重量时间序列计算得出的定量饮食行为指标。所设计的数据收集协议快速、直接、稳健，能够满足半控制式HSR部署的要求。
详细介绍了ASApp为研究助手和研究参与者实现的功能以及相应的用户界面。ASApp已成功部署用于内部测试研究和SPLENDID研究中的数据收集，SPLENDID研究是在一个由欧盟资助的研究项目背景下，于瑞典一所高中食堂进行的现实生活半控制式HSR研究。文中描述了这两项部署研究，并讨论了该应用在吸引力、可用性和技术合理性评估方面取得的良好结果。还提供了ASApp的访问细节。
本研究介绍了一款新型智能手机应用的需求阐明、设计、实现和评估，该应用支持研究人员在半控制式HSR中集成收集一组简洁而丰富的异质饮食行为数据。
