Literature DB >> 30441644

The importance of field experiments in testing of sensors for dietary assessment and eating behavior monitoring.

Abul Doulah, Xin Yang, Jason Parton, Janine A Higgins, Megan A McCrory, Edward Sazonov.   

Abstract

The field of sensor-based dietary assessment and behavioral monitoring is rapidly expanding. New devices and methods for detection for food intake and characterization of ingestive behavior, energy intake and nutrition have been introduced. Quite often the testing of new devices is limited to restricted meals in laboratory setting, which has the advantage of being controlled, but may not be representative of real life conditions. To illustrate the importance of field testing, we performed a statistical comparison of meal microstructure metrics acquired in laboratory versus a field-like study. In the laboratory study, individual participants ate a self-selected meal in isolation. In the field-like study, participants consumed selfselected meals in a social setting. In both studies, the participants were monitored by both video observation and wearable food intake sensors. Statistically significant differences were observed in the duration of the meals, duration of ingestion, number of bouts of ingestion, duration of pauses between ingestive bouts, number of bites and other metrics. These results suggest that field testing presents a far different picture of ingestion process and therefore is needed for any realistic assessment of the monitoring devices.

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Year:  2018        PMID: 30441644     DOI: 10.1109/EMBC.2018.8513623

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  6 in total

1.  Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment.

Authors:  Muhammad Farooq; Abul Doulah; Jason Parton; Megan A McCrory; Janine A Higgins; Edward Sazonov
Journal:  Nutrients       Date:  2019-03-13       Impact factor: 5.717

2.  A Questionnaire-Based Assessment of Hunger, Speed of Eating and Food Intake in Children with Obesity.

Authors:  Arnold Slyper; Joelle Shenker; Ariel Israel
Journal:  Diabetes Metab Syndr Obes       Date:  2021-01-08       Impact factor: 3.168

3.  Dietary Nutritional Information Autonomous Perception Method Based on Machine Vision in Smart Homes.

Authors:  Hongyang Li; Guanci Yang
Journal:  Entropy (Basel)       Date:  2022-06-24       Impact factor: 2.738

Review 4.  Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review.

Authors:  Brooke M Bell; Ridwan Alam; Nabil Alshurafa; Edison Thomaz; Abu S Mondol; Kayla de la Haye; John A Stankovic; John Lach; Donna Spruijt-Metz
Journal:  NPJ Digit Med       Date:  2020-03-13

5.  Validation of a Deep Learning System for the Full Automation of Bite and Meal Duration Analysis of Experimental Meal Videos.

Authors:  Dimitrios Konstantinidis; Kosmas Dimitropoulos; Billy Langlet; Petros Daras; Ioannis Ioakimidis
Journal:  Nutrients       Date:  2020-01-13       Impact factor: 5.717

Review 6.  Oral Processing, Satiation and Obesity: Overview and Hypotheses.

Authors:  Arnold Slyper
Journal:  Diabetes Metab Syndr Obes       Date:  2021-07-26       Impact factor: 3.168

  6 in total

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