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Browsing by Author "Zhou, Wenru"

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    Body mass index and variability in meal duration and association with rate of eating
    (Frontiers, 2022) Simon, Stacey L.; Pan, Zhaoxing; Marden, Tyson; Zhou, Wenru; Ghosh, Tonmoy; Hossain, Delwar; Thomas, J. Graham; McCrory, Megan A.; Sazonov, Edward; Higgins, Janine; University of Colorado Anschutz Medical Campus; University of Alabama Tuscaloosa; Brown University; Boston University
    BackgroundA fast rate of eating is associated with a higher risk for obesity but existing studies are limited by reliance on self-report and the consistency of eating rate has not been examined across all meals in a day. The goal of the current analysis was to examine associations between meal duration, rate of eating, and body mass index (BMI) and to assess the variance of meal duration and eating rate across different meals during the day. MethodsUsing an observational cross-sectional study design, non-smoking participants aged 18-45 years (N = 29) consumed all meals (breakfast, lunch, and dinner) on a single day in a pseudo free-living environment. Participants were allowed to choose any food and beverages from a University food court and consume their desired amount with no time restrictions. Weighed food records and a log of meal start and end times, to calculate duration, were obtained by a trained research assistant. Spearman's correlations and multiple linear regressions examined associations between BMI and meal duration and rate of eating. ResultsParticipants were 65% male and 48% white. A shorter meal duration was associated with a higher BMI at breakfast but not lunch or dinner, after adjusting for age and sex (p = 0.03). Faster rate of eating was associated with higher BMI across all meals (p = 0.04) and higher energy intake for all meals (p < 0.001). Intra-individual rates of eating were not significantly different across breakfast, lunch, and dinner (p = 0.96). ConclusionShorter beakfast and a faster rate of eating across all meals were associated with higher BMI in a pseudo free-living environment. An individual's rate of eating is constant over all meals in a day. These data support weight reduction interventions focusing on the rate of eating at all meals throughout the day and provide evidence for specifically directing attention to breakfast eating behaviors.
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    The spectrum of eating environments encountered in free living adults documented using a passive capture food intake wearable device
    (Frontiers, 2023) Breit, Matthew; Padia, Jonathan; Marden, Tyson; Forjan, Dan; Pan, Zhaoxing; Zhou, Wenru; Ghosh, Tonmoy; Thomas, Graham; McCrory, Megan A.; Sazonov, Edward; Higgins, Janine; University of Colorado Anschutz Medical Campus; University of Alabama Tuscaloosa; Brown University; Lifespan Health Rhode Island; Miriam Hospital; Boston University
    IntroductionThe aim of this feasibility and proof-of-concept study was to examine the use of a novel wearable device for automatic food intake detection to capture the full range of free-living eating environments of adults with overweight and obesity. In this paper, we document eating environments of individuals that have not been thoroughly described previously in nutrition software as current practices rely on participant self-report and methods with limited eating environment options. MethodsData from 25 participants and 116 total days (7 men, 18 women, M-age = 44 +/- 12 years, BMI 34.3 +/- 5.2 kg/mm(2)), who wore the passive capture device for at least 7 consecutive days (>= 12h waking hours/d) were analyzed. Data were analyzed at the participant level and stratified amongst meal type into breakfast, lunch, dinner, and snack categories. Out of 116 days, 68.1% included breakfast, 71.5% included lunch, 82.8% included dinner, and 86.2% included at least one snack. ResultsThe most prevalent eating environment among all eating occasions was at home and with one or more screens in use (breakfast: 48.1%, lunch: 42.2%, dinner: 50%, and snacks: 55%), eating alone (breakfast: 75.9%, lunch: 89.2%, dinner: 74.3%, snacks: 74.3%), in the dining room (breakfast: 36.7%, lunch: 30.1%, dinner: 45.8%) or living room (snacks: 28.0%), and in multiple locations (breakfast: 44.3%, lunch: 28.8%, dinner: 44.8%, snacks: 41.3%). DiscussionResults suggest a passive capture device can provide accurate detection of food intake in multiple eating environments. To our knowledge, this is the first study to classify eating occasions in multiple eating environments and may be a useful tool for future behavioral research studies to accurately codify eating environments.