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本文引用的文献

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Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies.数字化生物标志物在个性化营养中的应用:利用非侵入性可穿戴技术预测用餐时刻和间质葡萄糖。
Nutrients. 2022 Oct 24;14(21):4465. doi: 10.3390/nu14214465.
2
Effect of a Personalized Diet to Reduce Postprandial Glycemic Response vs a Low-fat Diet on Weight Loss in Adults With Abnormal Glucose Metabolism and Obesity: A Randomized Clinical Trial.个性化饮食降低餐后血糖反应与低脂饮食对葡萄糖代谢异常合并肥胖成年人减肥效果的随机临床试验
JAMA Netw Open. 2022 Sep 1;5(9):e2233760. doi: 10.1001/jamanetworkopen.2022.33760.
3
Detection of Meals and Physical Activity Events From Free-Living Data of People With Diabetes.从糖尿病患者的自由生活数据中检测进餐和身体活动事件。
J Diabetes Sci Technol. 2023 Nov;17(6):1482-1492. doi: 10.1177/19322968221102183. Epub 2022 Jun 15.
4
Top-Down Detection of Eating Episodes by Analyzing Large Windows of Wrist Motion Using a Convolutional Neural Network.通过使用卷积神经网络分析大窗口的手腕运动来进行进食事件的自上而下检测。
Bioengineering (Basel). 2022 Feb 11;9(2):70. doi: 10.3390/bioengineering9020070.
5
Temporal Eating Patterns and Eating Windows among Adults with Overweight or Obesity.成年人超重或肥胖与时间性进食模式和进食窗口。
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Automatic and Controlled Processing: Implications for Eating Behavior.自动加工和控制加工:对进食行为的影响。
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Obesity, unfavourable lifestyle and genetic risk of type 2 diabetes: a case-cohort study.肥胖、不良生活方式和 2 型糖尿病的遗传风险:病例-队列研究。
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Automated Estimation of Food Type from Body-worn Audio and Motion Sensors in Free-Living Environments.在自由生活环境中通过佩戴在身体上的音频和运动传感器自动估计食物类型
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Epidemiology of Type 2 Diabetes - Global Burden of Disease and Forecasted Trends.2 型糖尿病的流行病学——全球疾病负担和预测趋势。
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Automated meal detection from continuous glucose monitor data through simulation and explanation.通过模拟和解释实现连续血糖监测数据的自动膳食检测。
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目的:确定进食时机:结合自我报告、腕部运动和连续血糖监测来检测糖尿病前期和肥胖成年人的进食时机。

Objective Determination of Eating Occasion Timing: Combining Self-Report, Wrist Motion, and Continuous Glucose Monitoring to Detect Eating Occasions in Adults With Prediabetes and Obesity.

机构信息

Department of Population Health, Institute for Excellence in Health Equity, NYU Langone Health, New York, NY, USA.

Division of Biostatistics, Department of Population Health, NYU Langone Health, New York, NY, USA.

出版信息

J Diabetes Sci Technol. 2024 Mar;18(2):266-272. doi: 10.1177/19322968231197205. Epub 2023 Sep 25.

DOI:10.1177/19322968231197205
PMID:37747075
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10973869/
Abstract

BACKGROUND

Accurately identifying eating patterns, specifically the timing, frequency, and distribution of eating occasions (EOs), is important for assessing eating behaviors, especially for preventing and managing obesity and type 2 diabetes (T2D). However, existing methods to study EOs rely on self-report, which may be prone to misreporting and bias and has a high user burden. Therefore, objective methods are needed.

METHODS

We aim to compare EO timing using objective and subjective methods. Participants self-reported EO with a smartphone app (self-report [SR]), wore the ActiGraph GT9X on their dominant wrist, and wore a continuous glucose monitor (CGM, Abbott Libre Pro) for 10 days. EOs were detected from wrist motion (WM) using a motion-based classifier and from CGM using a simulation-based system. We described EO timing and explored how timing identified with WM and CGM compares with SR.

RESULTS

Participants ( = 39) were 59 ± 11 years old, mostly female (62%) and White (51%) with a body mass index (BMI) of 34.2 ± 4.7 kg/m. All had prediabetes or moderately controlled T2D. The median time-of-day first EO (and interquartile range) for SR, WM, and CGM were 08:24 (07:00-09:59), 9:42 (07:46-12:26), and 06:55 (04:23-10:03), respectively. The median last EO for SR, WM, and CGM were 20:20 (16:50-21:42), 20:12 (18:30-21:41), and 21:43 (20:35-22:16), respectively. The overlap between SR and CGM was 55% to 80% of EO detected with tolerance periods of ±30, 60, and 120 minutes. The overlap between SR and WM was 52% to 65% EO detected with tolerance periods of ±30, 60, and 120 minutes.

CONCLUSION

The continuous glucose monitor and WM detected overlapping but not identical meals and may provide complementary information to self-reported EO.

摘要

背景

准确识别进食模式,特别是进食时间、频率和进食时间的分布(EOs),对于评估进食行为很重要,特别是对于预防和管理肥胖症和 2 型糖尿病(T2D)。然而,现有的研究 EO 的方法依赖于自我报告,这可能容易出现报告错误和偏差,并且用户负担很高。因此,需要客观的方法。

方法

我们旨在比较使用客观和主观方法的 EO 时间。参与者使用智能手机应用程序(自我报告 [SR])自我报告 EO,并在优势手腕上佩戴 ActiGraph GT9X,同时佩戴 Abbott Libre Pro 连续血糖监测仪 10 天。通过基于运动的分类器从腕部运动(WM)和基于模拟的系统从 CGM 中检测 EO。我们描述了 EO 时间,并探讨了 WM 和 CGM 识别的时间与 SR 相比如何。

结果

参与者(n=39)年龄为 59±11 岁,主要为女性(62%)和白人(51%),体重指数(BMI)为 34.2±4.7kg/m²。所有人均患有前驱糖尿病或 2 型糖尿病得到了适度控制。SR、WM 和 CGM 的每日首次 EO 时间(中位数和四分位距)分别为 08:24(07:00-09:59)、09:42(07:46-12:26)和 06:55(04:23-10:03)。SR、WM 和 CGM 的最后一次 EO 时间中位数分别为 20:20(16:50-21:42)、20:12(18:30-21:41)和 21:43(20:35-22:16)。SR 和 CGM 之间的重叠率为 55%至 80%,EO 的检测容忍期为±30、60 和 120 分钟。SR 和 WM 之间的重叠率为 52%至 65%,EO 的检测容忍期为±30、60 和 120 分钟。

结论

连续血糖监测仪和 WM 检测到重叠但不相同的膳食,可能为自我报告的 EO 提供补充信息。