Formative evaluation of a mobile liquid portion size estimation interface for people with varying literacy skills.
Formative evaluation of a mobile liquid portion size estimation interface for people with varying literacy skills.
复制标题
针对具有不同读写能力的人的移动液体份量估计界面的形成性评估。
DOI：
10.1007/s12652-012-0152-9
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发表时间：
2013
影响因子：
--
通讯作者：
Welch,JanetL
中科院分区：
文献类型：
--
作者：
Chaudry,BeenishMoalla;Connelly,Kay;Siek,KatieA;Welch,JanetL
慢性病患者，尤其是文化水平较低的患者，通常很难估计液体的份量以帮助他们保持在建议的液体限量内。有大量的移动应用程序可以帮助人们监测他们的营养摄入量，但不幸的是，这些应用程序要求用户具有较高的读写能力和计算能力来记录份量大小。在本文中，我们提出了两项研究，其中使用成年人在饮食回忆研究期间用于份量估计的认知策略设计了份量估计界面的低保真度和高保真度版本，并由具有不同读写能力的慢性病人群进行了评估。低保真度界面由 10 名患者进行了评估，他们都能够通过该界面准确估计各种液体的份量。十八名参与者对饮食和液体监测移动应用程序中包含的高保真版本进行了为期 6 周的现场评估。尽管第二项研究无法证实估计的准确性，但积极与界面互动的参与者在研究结束时表现出更好的健康结果。基于这些发现，我们为设计下一代准确且低文化水平的液体部分大小估计移动界面提供了建议。
Chronically ill people, especially those with low literacy skills, often have difficulty estimating portion sizes of liquids to help them stay within their recommended fluid limits. There is a plethora of mobile applications that can help people monitor their nutritional intake but unfortunately these applications require the user to have high-literacy and numeracy skills for portion size recording. In this paper, we present two studies in which the low- and the high-fidelity versions of a portion size estimation interface, designed using the cognitive strategies adults employ for portion size estimation during diet recall studies, was evaluated by a chronically ill population with varying literacy skills. The low fidelity interface was evaluated by ten patients who were all able to accurately estimate portion sizes of various liquids with the interface. Eighteen participants did an in situ evaluation of the high-fidelity version incorporated in a diet and fluid monitoring mobile application for 6 weeks. Although the accuracy of the estimation cannot be confirmed in the second study but the participants who actively interacted with the interface showed better health outcomes by the end of the study. Based on these findings, we provide recommendations for designing the next iteration of an accurate and low literacy-accessible liquid portion size estimation mobile interface.