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      Objective Determination of Eating Occasion Timing (OREO): Combining Self-Report, Wrist Motion, and Continuous Glucose Monitoring to Detect Eating Occasions in Adults With Pre-Diabetes and Obesity

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          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 ( n = 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 2. 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.

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          Most cited references29

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          Is Open Access

          Epidemiology of Type 2 Diabetes – Global Burden of Disease and Forecasted Trends

          The rising burden of type 2 diabetes is a major concern in healthcare worldwide. This research aimed to analyze the global epidemiology of type 2 diabetes. We analyzed the incidence, prevalence, and burden of suffering of diabetes mellitus based on epidemiological data from the Global Burden of Disease (GBD) current dataset from the Institute of Health Metrics, Seattle. Global and regional trends from 1990 to 2017 of type 2 diabetes for all ages were compiled. Forecast estimates were obtained using the SPSS Time Series Modeler. In 2017, approximately 462 million individuals were affected by type 2 diabetes corresponding to 6.28% of the world’s population (4.4% of those aged 15–49 years, 15% of those aged 50–69, and 22% of those aged 70+), or a prevalence rate of 6059 cases per 100,000. Over 1 million deaths per year can be attributed to diabetes alone, making it the ninth leading cause of mortality. The burden of diabetes mellitus is rising globally, and at a much faster rate in developed regions, such as Western Europe. The gender distribution is equal, and the incidence peaks at around 55 years of age. Global prevalence of type 2 diabetes is projected to increase to 7079 individuals per 100,000 by 2030, reflecting a continued rise across all regions of the world. There are concerning trends of rising prevalence in lower-income countries. Urgent public health and clinical preventive measures are warranted.
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            Trends in Obesity and Severe Obesity Prevalence in US Youth and Adults by Sex and Age, 2007-2008 to 2015-2016

            This study uses National Health and Nutrition Examination Survey data to characterize trends in obesity prevalence among US youth and adults between 2007-2008 and 2015-2016.
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              • Article: not found

              The Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute.

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                Author and article information

                Contributors
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                Journal
                Journal of Diabetes Science and Technology
                J Diabetes Sci Technol
                SAGE Publications
                1932-2968
                1932-2968
                September 25 2023
                Affiliations
                [1 ]Department of Population Health, Institute for Excellence in Health Equity, NYU Langone Health, New York, NY, USA
                [2 ]Division of Biostatistics, Department of Population Health, NYU Langone Health, New York, NY, USA
                [3 ]Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA
                [4 ]Department of Computer Science, Stevens Institute of Technology, Hoboken, NJ, USA
                [5 ]Department of Nutrition, University of Nevada, Reno, NV, USA
                [6 ]Department of Medicine, NYU Langone Health, New York, NY, USA
                [7 ]Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, NY, USA
                Article
                10.1177/19322968231197205
                37747075
                cce397fa-b36a-490b-903e-2d759eae5457
                © 2023

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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