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

Abstract Dietary intake, eating behaviors, and context are important in chronic disease development, yet our ability to accurately assess these in research settings can be limited by biased traditional self-reporting tools. Objective measurement tools, specifically, wearable sensors, present the opp...

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Autores principales:	Brooke M. Bell, Ridwan Alam, Nabil Alshurafa, Edison Thomaz, Abu S. Mondol, Kayla de la Haye, John A. Stankovic, John Lach, Donna Spruijt-Metz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/2b171b7cb6834771be33d05b97f4406e
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spelling	oai:doaj.org-article:2b171b7cb6834771be33d05b97f4406e2021-12-02T11:36:25ZAutomatic, wearable-based, in-field eating detection approaches for public health research: a scoping review10.1038/s41746-020-0246-22398-6352https://doaj.org/article/2b171b7cb6834771be33d05b97f4406e2020-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0246-2https://doaj.org/toc/2398-6352Abstract Dietary intake, eating behaviors, and context are important in chronic disease development, yet our ability to accurately assess these in research settings can be limited by biased traditional self-reporting tools. Objective measurement tools, specifically, wearable sensors, present the opportunity to minimize the major limitations of self-reported eating measures by generating supplementary sensor data that can improve the validity of self-report data in naturalistic settings. This scoping review summarizes the current use of wearable devices/sensors that automatically detect eating-related activity in naturalistic research settings. Five databases were searched in December 2019, and 618 records were retrieved from the literature search. This scoping review included N = 40 studies (from 33 articles) that reported on one or more wearable sensors used to automatically detect eating activity in the field. The majority of studies (N = 26, 65%) used multi-sensor systems (incorporating > 1 wearable sensors), and accelerometers were the most commonly utilized sensor (N = 25, 62.5%). All studies (N = 40, 100.0%) used either self-report or objective ground-truth methods to validate the inferred eating activity detected by the sensor(s). The most frequently reported evaluation metrics were Accuracy (N = 12) and F1-score (N = 10). This scoping review highlights the current state of wearable sensors’ ability to improve upon traditional eating assessment methods by passively detecting eating activity in naturalistic settings, over long periods of time, and with minimal user interaction. A key challenge in this field, wide variation in eating outcome measures and evaluation metrics, demonstrates the need for the development of a standardized form of comparability among sensors/multi-sensor systems and multidisciplinary collaboration.Brooke M. BellRidwan AlamNabil AlshurafaEdison ThomazAbu S. MondolKayla de la HayeJohn A. StankovicJohn LachDonna Spruijt-MetzNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-14 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Brooke M. Bell Ridwan Alam Nabil Alshurafa Edison Thomaz Abu S. Mondol Kayla de la Haye John A. Stankovic John Lach Donna Spruijt-Metz Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review
description	Abstract Dietary intake, eating behaviors, and context are important in chronic disease development, yet our ability to accurately assess these in research settings can be limited by biased traditional self-reporting tools. Objective measurement tools, specifically, wearable sensors, present the opportunity to minimize the major limitations of self-reported eating measures by generating supplementary sensor data that can improve the validity of self-report data in naturalistic settings. This scoping review summarizes the current use of wearable devices/sensors that automatically detect eating-related activity in naturalistic research settings. Five databases were searched in December 2019, and 618 records were retrieved from the literature search. This scoping review included N = 40 studies (from 33 articles) that reported on one or more wearable sensors used to automatically detect eating activity in the field. The majority of studies (N = 26, 65%) used multi-sensor systems (incorporating > 1 wearable sensors), and accelerometers were the most commonly utilized sensor (N = 25, 62.5%). All studies (N = 40, 100.0%) used either self-report or objective ground-truth methods to validate the inferred eating activity detected by the sensor(s). The most frequently reported evaluation metrics were Accuracy (N = 12) and F1-score (N = 10). This scoping review highlights the current state of wearable sensors’ ability to improve upon traditional eating assessment methods by passively detecting eating activity in naturalistic settings, over long periods of time, and with minimal user interaction. A key challenge in this field, wide variation in eating outcome measures and evaluation metrics, demonstrates the need for the development of a standardized form of comparability among sensors/multi-sensor systems and multidisciplinary collaboration.
format	article
author	Brooke M. Bell Ridwan Alam Nabil Alshurafa Edison Thomaz Abu S. Mondol Kayla de la Haye John A. Stankovic John Lach Donna Spruijt-Metz
author_facet	Brooke M. Bell Ridwan Alam Nabil Alshurafa Edison Thomaz Abu S. Mondol Kayla de la Haye John A. Stankovic John Lach Donna Spruijt-Metz
author_sort	Brooke M. Bell
title	Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review
title_short	Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review
title_full	Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review
title_fullStr	Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review
title_full_unstemmed	Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review
title_sort	automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/2b171b7cb6834771be33d05b97f4406e
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Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review

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