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WeightNameValue
1000 Titel
  • Meal Microstructure Characterization from Sensor-Based Food Intake Detection
1000 Autor/in
  1. Doulah, Abul |
  2. Farooq, Muhammad |
  3. Yang, Xin |
  4. Parton, Jason |
  5. McCrory, Megan A. |
  6. Higgins, Janine A. |
  7. Sazonov, Edward |
1000 Erscheinungsjahr 2017
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2017-07-17
1000 Erschienen in
1000 Quellenangabe
  • 4:31
1000 Copyrightjahr
  • 2017
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.3389/fnut.2017.00031 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5512009/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Sacherschließung
lokal swallowing
lokal food intake detection
lokal meal microstructure
lokal wearable sensors
lokal chewing
lokal food diary
1000 Fachgruppe
  1. Gesundheitswesen |
  2. Ernährungswissenschaften |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/creator/RG91bGFoLCBBYnVs|https://frl.publisso.de/adhoc/creator/RmFyb29xLCBNdWhhbW1hZA==|https://frl.publisso.de/adhoc/creator/WWFuZywgWGlu|https://frl.publisso.de/adhoc/creator/UGFydG9uLCBKYXNvbg==|https://frl.publisso.de/adhoc/creator/TWNDcm9yeSwgTWVnYW4gQS4=|https://frl.publisso.de/adhoc/creator/SGlnZ2lucywgSmFuaW5lIEEu|https://frl.publisso.de/adhoc/creator/U2F6b25vdiwgRWR3YXJk
1000 Label
1000 Förderer
  1. National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health
1000 Fördernummer
  1. R01DK100796
1000 Förderprogramm
  1. -
1000 Dateien
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6409506.rdf
1000 Erstellt am 2018-08-22T09:28:00.698+0200
1000 Erstellt von 122
1000 beschreibt frl:6409506
1000 Bearbeitet von 122
1000 Zuletzt bearbeitet Thu Jan 30 21:46:31 CET 2020
1000 Objekt bearb. Wed Aug 22 09:32:12 CEST 2018
1000 Vgl. frl:6409506
1000 Oai Id
  1. oai:frl.publisso.de:frl:6409506 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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