Baumgartner Michelle, Kuhn Christian, Nakas Christos T, Herzig David, Bally Lia
Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zurich, Zurich, Switzerland.
J Diabetes Sci Technol. 2024 Jul 26:19322968241264744. doi: 10.1177/19322968241264744.
Despite remarkable progress in diabetes technology, most systems still require estimating meal carbohydrate (CHO) content for meal-time insulin delivery. Emerging smartphone applications may obviate this need, but performance data in relation to patient estimates remain scarce.
The objective is to assess the accuracy of two commercial CHO estimation applications, SNAQ and Calorie Mama, and compare their performance with the estimation accuracy of people with type 1 diabetes (T1D).
Carbohydrate estimates of 53 individuals with T1D (aged ≥16 years) were compared with those of SNAQ (food recognition + quantification) and Calorie Mama (food recognition + adjustable standard portion size). Twenty-six cooked meals were prepared at the hospital kitchen. Each participant estimated the CHO content of two meals in three different sizes without assistance. Participants then used SNAQ for CHO quantification in one meal and Calorie Mama for the other (all three sizes). Accuracy was the estimate's deviation from ground-truth CHO content (weight multiplied by nutritional facts from recipe database). Furthermore, the applications were rated using the Mars-G questionnaire.
Participants' mean ± standard deviation (SD) absolute error was 21 ± 21.5 g (71 ± 72.7%). Calorie Mama had a mean absolute error of 24 ± 36.5 g (81.2 ± 123.4%). With a mean absolute error of 13.1 ± 11.3 g (44.3 ± 38.2%), SNAQ outperformed the estimation accuracy of patients and Calorie Mama (both > .05). Error consistency (quantified by the within-participant SD) did not significantly differ between the methods.
SNAQ may provide effective CHO estimation support for people with T1D, particularly those with large or inconsistent CHO estimation errors. Its impact on glucose control remains to be evaluated.
尽管糖尿病技术取得了显著进展，但大多数系统仍需要估算餐食中的碳水化合物（CHO）含量，以便在进餐时注射胰岛素。新兴的智能手机应用程序可能会消除这一需求，但与患者估算相关的性能数据仍然很少。
评估两款商业CHO估算应用程序SNAQ和卡路里妈妈的准确性，并将它们的性能与1型糖尿病（T1D）患者的估算准确性进行比较。
将53名年龄≥16岁的T1D患者的碳水化合物估算结果与SNAQ（食物识别+量化）和卡路里妈妈（食物识别+可调整标准份量）的估算结果进行比较。在医院厨房准备了26份熟食。每位参与者在没有帮助的情况下估算三种不同份量的两份餐食的CHO含量。然后，参与者使用SNAQ对一份餐食进行CHO量化，使用卡路里妈妈对另一份餐食进行CHO量化（所有三种份量）。准确性是估算值与实际CHO含量（重量乘以食谱数据库中的营养成分）的偏差。此外，使用Mars-G问卷对应用程序进行评分。
参与者的平均±标准差（SD）绝对误差为21±21.5克（71±72.7%）。卡路里妈妈的平均绝对误差为24±36.5克（81.2±123.4%）。SNAQ的平均绝对误差为13.1±11.3克（44.3±38.2%），其估算准确性优于患者和卡路里妈妈（两者均P>.05）。各方法之间的误差一致性（通过参与者内标准差量化）没有显著差异。
SNAQ可能为T1D患者提供有效的CHO估算支持，特别是那些CHO估算误差较大或不一致的患者。其对血糖控制的影响仍有待评估。
