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Meal Microstructure Characterization from Sensor-Based Food Intake Detection
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 b...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5512009/ https://www.ncbi.nlm.nih.gov/pubmed/28770206 http://dx.doi.org/10.3389/fnut.2017.00031 |
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author | Doulah, Abul Farooq, Muhammad Yang, Xin Parton, Jason McCrory, Megan A. Higgins, Janine A. Sazonov, Edward |
author_facet | Doulah, Abul Farooq, Muhammad Yang, Xin Parton, Jason McCrory, Megan A. Higgins, Janine A. Sazonov, Edward |
author_sort | Doulah, Abul |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5512009 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-55120092017-08-02 Meal Microstructure Characterization from Sensor-Based Food Intake Detection Doulah, Abul Farooq, Muhammad Yang, Xin Parton, Jason McCrory, Megan A. Higgins, Janine A. Sazonov, Edward Front Nutr Nutrition 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. Frontiers Media S.A. 2017-07-17 /pmc/articles/PMC5512009/ /pubmed/28770206 http://dx.doi.org/10.3389/fnut.2017.00031 Text en Copyright © 2017 Doulah, Farooq, Yang, Parton, McCrory, Higgins and Sazonov. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Nutrition Doulah, Abul Farooq, Muhammad Yang, Xin Parton, Jason McCrory, Megan A. Higgins, Janine A. Sazonov, Edward Meal Microstructure Characterization from Sensor-Based Food Intake Detection |
title | Meal Microstructure Characterization from Sensor-Based Food Intake Detection |
title_full | Meal Microstructure Characterization from Sensor-Based Food Intake Detection |
title_fullStr | Meal Microstructure Characterization from Sensor-Based Food Intake Detection |
title_full_unstemmed | Meal Microstructure Characterization from Sensor-Based Food Intake Detection |
title_short | Meal Microstructure Characterization from Sensor-Based Food Intake Detection |
title_sort | meal microstructure characterization from sensor-based food intake detection |
topic | Nutrition |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5512009/ https://www.ncbi.nlm.nih.gov/pubmed/28770206 http://dx.doi.org/10.3389/fnut.2017.00031 |
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