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.
J Diabetes Sci Technol. 2024 Mar;18(2):266-272. doi: 10.1177/19322968231197205. Epub 2023 Sep 25.
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.
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.
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.
The continuous glucose monitor and WM detected overlapping but not identical meals and may provide complementary information to self-reported EO.
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。
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。
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。
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。