通过移动生酮饮食计划简化癫痫治疗。

Hanzhou Li, Jeffrey L Jauregui, Cagla Fenton, Claire M Chee, A G Christina Bergqvist
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引用次数: 5

摘要

背景:生酮饮食(KD)是治疗难治性癫痫的一种有效的替代疗法。这种高脂肪、低蛋白质和碳水化合物的饮食模仿了与禁食相关的代谢和激素变化。目的:为了最大限度地提高KD的有效性,在平均三年的治疗期间,每顿饭都被精确地计划、计算和称重到0.1克以内。管理KD是耗时的,可能会阻止护理人员和患者追求或继续这种治疗。因此,我们研究了更快地规划KD的方法,并通过移动应用程序使该过程更加便携。方法:营养数据来自美国农业部(USDA)营养数据库。用户选择的食物被转换成具有n个变量和三个约束条件的线性方程:规定的脂肪含量、规定的蛋白质含量和规定的碳水化合物含量。根据所选食物的数量,应用技术推导出欠定系统的解。结果:该方法在iOS设备上实现,并进行了多种食物和不同食物选择数量的测试。在每种情况下,应用程序构建的膳食计划都在95%的KD要求精度内。结论:在本研究中,我们试图通过线性代数模型自动计算KD来减少计算一顿饭所需的时间。我们通过提供最佳建议和纳入USDA数据库来改进以前的KD计算器。我们相信这个移动应用程序将帮助KD和其他膳食治疗制剂更省时,更方便。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Epilepsy Treatment Simplified through Mobile Ketogenic Diet Planning.

Epilepsy Treatment Simplified through Mobile Ketogenic Diet Planning.

Epilepsy Treatment Simplified through Mobile Ketogenic Diet Planning.

Epilepsy Treatment Simplified through Mobile Ketogenic Diet Planning.

Background: The Ketogenic Diet (KD) is an effective, alternative treatment for refractory epilepsy. This high fat, low protein and carbohydrate diet mimics the metabolic and hormonal changes that are associated with fasting.

Aims: To maximize the effectiveness of the KD, each meal is precisely planned, calculated, and weighed to within 0.1 gram for the average three-year duration of treatment. Managing the KD is time-consuming and may deter caretakers and patients from pursuing or continuing this treatment. Thus, we investigated methods of planning KD faster and making the process more portable through mobile applications.

Methods: Nutritional data was gathered from the United States Department of Agriculture (USDA) Nutrient Database. User selected foods are converted into linear equations with n variables and three constraints: prescribed fat content, prescribed protein content, and prescribed carbohydrate content. Techniques are applied to derive the solutions to the underdetermined system depending on the number of foods chosen.

Results: The method was implemented on an iOS device and tested with varieties of foods and different number of foods selected. With each case, the application's constructed meal plan was within 95% precision of the KD requirements.

Conclusion: In this study, we attempt to reduce the time needed to calculate a meal by automating the computation of the KD via a linear algebra model. We improve upon previous KD calculators by offering optimal suggestions and incorporating the USDA database. We believe this mobile application will help make the KD and other dietary treatment preparations less time consuming and more convenient.

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