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Meal Microstructure Characterization from Sensor-Based Food Intake Detection.

ABSTRACT: To avoid the pitfalls of self-reported dietary intake, wearable sensors can be used. Many food ingestion sensors offer the ability to automatically detect food intake using time resolutions that range from 23 ms to 8 min. There is no defined standard time resolution to accurately measure ingestive behavior or a meal microstructure. This paper aims to estimate the time resolution needed to accurately represent the microstructure of meals such as duration of eating episode, the duration of actual ingestion, and number of eating events. Twelve participants wore the automatic ingestion monitor (AIM) and kept a standard diet diary to report their food intake in free-living conditions for 24 h. As a reference, participants were also asked to mark food intake with a push button sampled every 0.1 s. The duration of eating episodes, duration of ingestion, and number of eating events were computed from the food diary, AIM, and the push button resampled at different time resolutions (0.1-30s). ANOVA and multiple comparison tests showed that the duration of eating episodes estimated from the diary differed significantly from that estimated by the AIM and the push button (p-value <0.001). There were no significant differences in the number of eating events for push button resolutions of 0.1, 1, and 5 s, but there were significant differences in resolutions of 10-30s (p-value <0.05). The results suggest that the desired time resolution of sensor-based food intake detection should be ≤5 s to accurately detect meal microstructure. Furthermore, the AIM provides more accurate measurement of the eating episode duration than the diet diary.

SUBMITTER: Doulah A

PROVIDER: S-EPMC5512009 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:BackgroundFood insecurity, a state of not being able to consistently access nutritious food due to financial constraints, has been associated with poor dietary intake among college students. The extent to which campus food resources contribute to this association is unknown.ObjectivesThis study examined the association between food insecurity and dietary intake in a sample of undergraduate students with unlimited meal plans and dining hall access at a large, public Midwestern university.DesignThe study design is cross-sectional. The data used are baseline data from a broader sugar-sweetened beverage intervention study that were collected using a Qualtrics survey prior to the intervention.Participants/settingThe sample consisted of 1033 undergraduate students recruited from 3 dining halls. The data were collected in November 2018.Main outcome measuresFood security was assessed using the 6-item Short Form Food Security Survey Module. Dietary intake was assessed using the National Cancer Institute 26-item Dietary Screener Questionnaire and the Beverage Intake Questionnaire-15.Statistical analyses performedGeneralized linear regression models were used to examine differences in dietary intake by students' food security status, adjusting for students' sociodemographic characteristics.ResultsIn the sample, 14% of students were food-insecure. After adjusting for sociodemographic variables, food-insecure students reported 9% lower intake of fruits (P = 0.02), 9% lower intake of vegetables (P < 0.001), 10% higher intake of dairy (P = 0.002), 6% higher intake of total added sugars (P = 0.01), 10% higher intake of added sugars from sugar-sweetened beverages (P = 0.01), 4% higher intake of calcium (P = 0.01), and 4% lower intake of fiber (P = 0.01) compared with food-secure students. With respect to beverage intake, food-insecure students had 56% higher intake of total sugar-sweetened beverages (P = 0.002), which was driven by 185% higher intake of energy and sports drinks (P = 0.001), and 121% higher intake of sweetened teas (P = 0.001).ConclusionsDespite having identical food resources within campus dining halls, there were significant differences in the diets of college students by food security status.

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Meal Microstructure Characterization from Sensor-Based Food Intake Detection.

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