Dietary assessment can be based on pattern recognition rather than recall
Dietary assessment can be based on pattern recognition rather than recall
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DOI：
10.1016/j.mehy.2020.109644
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发表时间：
2020-07-01
影响因子：
4.7
通讯作者：
Dansinger, M. L.
中科院分区：
文献类型：
--
作者：
Katz, D. L.;Rhee, L. Q.;Dansinger, M. L.
饮食是现代世界健康状况的主要预测指标，包括各种原因造成的死亡率，但很少被测量；而在发达国家，几乎每个成年人都知道自己的大致血压，但几乎没有人知道他们的客观饮食质量。主要当局呼吁将营养纳入每个电子健康记录，作为给予饮食质量应有的常规关注所需的许多补救措施之一。现有的捕捉饮食摄入量的工具要么是基于实时日志记录，要么是基于回忆。日志记录，或称日志记录，是时间和劳动密集型的。众所周知，回忆是不可靠的，因为人类在记住细节方面非常糟糕。即使考虑到召回的挑战，这些饮食摄取方法也是劳动和时间密集型的，需要在n-of-1水平上进行分析。我们假设，膳食摄入量评估可以是“逆向工程”的--基于对完全形成的饮食模式的认识来预测评估--而不是努力一次组合这样一个代表食物、膳食、菜肴或一天。这种基于模式识别的方法与现有方法相比具有潜在的优势，包括速度、效率、成本和适用性。我们已经开发并临时测试了这样一个系统，到目前为止的结果支持了我们的假设。我们相信，利用模式识别使饮食评估变得快速、用户友好、经济和可扩展，可以将饮食质量转换为普遍测量和常规管理的生命体征。在这篇文章中，我们提出了支持案例。
Diet is the leading predictor of health status, including all-cause mortality, in the modern world, yet is rarely measured; whereas virtually every adult in a developed country knows their approximate blood pressure, hardly any knows their objective diet quality. Leading authorities have called for the inclusion of nutrition in every electronic health record as one of the many remedial steps required to give dietary quality the routine attention it warrants. Existing tools to capture dietary intake are based on either real-time journaling or recall. Journaling, or logging, is time and labor intensive. Recall is notoriously unreliable, as humans are notably bad at remembering detail. Even allowing for the challenge of recall, these dietary intake methods are labor and time intensive, and require analysis at the n-of-1 level. We hypothesize that dietary intake assessment can be "reverse engineered"-predicating assessment on the recognition of fully formed dietary patterns-rather than endeavoring to assemble such a representation one food, meal, dish, or day at a time. This pattern recognition-based method offers potential advantages over existing methods, including speed, efficiency, cost, and applicability. We have developed and provisionally tested such a system, and the results thus far support our hypothesis. We are convinced that leveraging pattern recognition to make dietary assessment quick, user-friendly, economical, and scalable can allow for the conversion of dietary quality into a universally measured and routinely managed vital sign. In this paper, we present the supporting case.