Name: concept development and use of an automated food intake and eating behavior assessment method
Uploaded: 2021-02-19T15:00:00
Description: Watch this Scientific Journal Video about Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method at JoVE.com

We thank J. M. C. D. Meijer of theTechnical Development Studio of Wageningen University and Research for his help in the development of the mEETr tray. This research was funded by the 4 Dutch Technical Universities, 4TU- Pride and Prejudice project.

This pilot study was approved by the METC of Wageningen University prior to starting the project.
CAUTION: All the participants contributing to this project provided an informed consent, including the approval of video images showing visible and recognizable faces.
1. Sample preparation and participant consent
<ol>
	<li>Prepare a juice (glass or cup), fruit yoghurt (bowl), and fruit pieces (plate). 
	NOTE: These foods are selected for demonstration purposes o...

<ol>
	<li>Burrows, T. L., Ho, Y. Y., Rollo, M. E., Collins, C. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validity+of+dietary+assessment+methods+when+compared+to+the+method+of+doubly+labeled+water:+A+systematic+review+in+adults.">Validity of dietary assessment methods when compared to the method of doubly labeled water: A systematic review in adults.</a> Frontiers in Endocrinology. 10, 850 (2019).</li><li>Brouwer-Brolsma, E. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dietary+intake+assessment:+From+traditional+paper-pencil+questionnaires+to+technology-based+tools.+Environmental+software+systems.+Data+science+in+action.">Dietary intake assessment: From traditional paper-pencil questionnaires to technology-based tools. Environmental software systems. Data science in action.</a> Advances in Information and Communication Technology. 554, (2020).</li><li>Palese, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+nursing+home+environment+can+maximise+eating+independence+among+residents+with+cognitive+impairment?+Findings+from+a+secondary+analysis.">What nursing home environment can maximise eating independence among residents with cognitive impairment? Findings from a secondary analysis.</a> Geriatric Nursing. 41 (6), 709-716 (2020).</li><li>Krop, E. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+oral+processing+on+appetite+and+food+intake+-+A+systematic+review+and+meta-analysis.">Influence of oral processing on appetite and food intake - A systematic review and meta-analysis.</a> Appetite. 125, 253-269 (2018).</li><li>Nicolas, E., Veyrune, J. L., Lassauzay, C., Peyron, M. A., Hennequin, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validation+of+video+versus+electromyography+for+chewing+evaluation+of+the+elderly+wearing+a+complete+denture.">Validation of video versus electromyography for chewing evaluation of the elderly wearing a complete denture.</a> Journal of Oral Rehabilitation. 34 (8), 566-571 (2007).</li><li>Sabin, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+treatment+for+childhood+obesity+using+Mandometer&#174;+technology.">A novel treatment for childhood obesity using Mandometer&#174; technology.</a> International Journal of Obesity. 7, (2006).</li><li>Hermsen, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+a+Smart+fork+to+decelerate+eating+rate.">Evaluation of a Smart fork to decelerate eating rate.</a> Journal of the Academy of Nutrition and Dietetics. 116 (7), 1066-1068 (2016).</li><li>vanden Boer, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+splendid+eating+detection+sensor:+Development+and+feasibility+study.">The splendid eating detection sensor: Development and feasibility study.</a> JMIR mHealth and uHealth. 6 (9), 170 (2018).</li><li>Papapanagiotou, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+chewing+detection+system+based+on+ppg,+audio,+and+accelerometry.">A novel chewing detection system based on ppg, audio, and accelerometry.</a> IEEE Journal of Biomedical and Health Informatics. 21 (3), 607-618 (2016).</li><li>Ioakimidis, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Description+of+chewing+and+food+intake+over+the+course+of+a+meal.">Description of chewing and food intake over the course of a meal.</a> Physiology &amp; Behavior. 104 (5), 761-769 (2011).</li><li>Ruiz, L. D., Zuelch, M. L., Dimitratos, S. M., Scherr, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adolescent+obesity:+Diet+quality,+psychosocial+health,+and+cardiometabolic+risk+factors.">Adolescent obesity: Diet quality, psychosocial health, and cardiometabolic risk factors.</a> Nutrients. 12 (1), 43 (2020).</li><li>Forde, C. G., van Kuijk, N., Thaler, T., de Graaf, C., Martin, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oral+processing+characteristics+of+solid+savoury+meal+components,+and+relationship+with+food+composition,+sensory+attributes+and+expected+satiation.">Oral processing characteristics of solid savoury meal components, and relationship with food composition, sensory attributes and expected satiation.</a> Appetite. 60 (1), 208-219 (2013).</li><li>Weijzen, P. L. G., Smeets, P. A. M., de Graaf, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sip+size+of+orangeade:+effects+on+intake+and+sensory-specific+satiation.">Sip size of orangeade: effects on intake and sensory-specific satiation.</a> British Journal of Nutrition. 102 (07), 1091-1097 (2009).</li><li>Zijlstra, N., de Wijk, R. A., Mars, M., Stafleu, A., de Graaf, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+bite+size+and+oral+processing+time+of+a+semisolid+food+on+satiation.">Effect of bite size and oral processing time of a semisolid food on satiation.</a> The American Journal of Clinical Nutrition. 90 (2), 269-275 (2009).</li><li>Bolhuis, D. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Slow+food:+sustained+impact+of+harder+foods+on+the+reduction+in+energy+intake+over+the+course+of+the+day.">Slow food: sustained impact of harder foods on the reduction in energy intake over the course of the day.</a> PloS One. 9 (4), 93370 (2014).</li><li>Grinyer, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+anonymity+of+research+participants:+assumptions,+ethics+and+practicalities.">The anonymity of research participants: assumptions, ethics and practicalities.</a> Social Research Update. 36 (1), 4 (2002).</li></ol>

A healthy diet and a healthy eating behavior have shown to play a key role in the prevention of and solution to overweight and obesity11. However, many of the methods used to measure the dietary intake and the eating behavior are burdensome for users, researchers, and health-care professionals and may be biased as they are dependent on memory and portion size estimations. Using the mEETr, independently or alongside conventional video and dietary assessment methods, would decrease the effort and th...

An erratum was issued for:&#160;<a href="https://www.jove.com/t/62144">Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method</a>. An author name was updated.
The name was updated from:
Cees de Graaf
to:
Kees de Graaf

An erratum was issued for:&#160;<a href="https://www.jove.com/t/62144">Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method</a>. An author name was updated.

Erratum: Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

In nutrition research and dietary practice, it is key to have good measures of what, how much, and how people eat, to find solutions to the overweight and obesity problems. To assess dietary intake, often conventional self-report questionnaires are used such as food diaries, 24 h recalls or food frequency questionnaires1. These methods rely on self-report and are therefore time-consuming and prone to bias due to social-desirable answers, memory inadequacy, and difficulties in estimating portion sizes2,3. In addition to measures of the diet quality (food type and amount eaten), it is also important to know how the food is eaten, as eating behaviors that slow down food intake have been shown to prevent overconsumption within a meal4. To assess eating behavior the golden standard is to have two observers annotate video recordings of people eating a meal5. This method is rather labor intensive and time consuming and does not allow for immediate feedback on the behavior.
Recent technological advances now provide the opportunity to combine automated measures of food intake with automated measures of the eating behavior over the course of a meal. In response to these developments, a new sensor-based dietary assessment method was developed, called the mEETr, the acronym of the two Dutch words &#39;Meter&#39; (translated: measuring device), and &#39;eet&#39; (translated: to eat). The mEETr is a regular dining tray with three built-in weighing stations (Figure 1 demonstrates the design of the tray and the sensor plates) and a camera holder. Each weighing station consists of three triangularly positioned measurement points to distribute the weight. The weighing stations measure the weight of the bowl, plate, and drinking cup or glass continuously over the meal. The mEETr also includes a video-camera holder. Currently, the camera holder is separate from the tray, but for standardization purposes an integrated camera after the next upgrade of mEETr (a folding video camera stick) would be ideal. The camera facilitates automated real-time analysis of the number of bites and chews, and eating duration, which allows for the generation of information on the eating rate and the bite size. Automated analysis of eating behavior is done with the use of a newly developed algorithm. Various research groups have developed devices to provide people real-time feedback on the acceleration of eating and the quantity people eat6. Also, augmented forks have been developed to provide real-time feedback on the number of bites and their frequency within a meal7. Additionally, an ear sensor was developed to measure the microstructure of eating in free living conditions8,9. Similar to this device is the set-up used by Ioakimidis et al.10, where video measures were combined with a weighing plate to determine the food intake, number of bites, and chewing behavior.
Compared to these devices the novelty of the mEETr is that it combines automated measures of food intake of two plates and a drinking cup (n = 3) and eating behavior (e.g., eating rate, number of bites, bite size, and chewing behavior) in one device. The mEETr, as demonstrated, is suited for within meal measures of food intake and eating behavior within a controlled (eating lab) environment, but eventually the aim is to use the mEETr in less controlled environments where re-occurring meal plans are used such as daycares, elderly-homes, and hospitals.
Ultimately, the mEETr will provide a more objective, and as such, more accurate and precise measure of food intake and eating behavior than conventional dietary assessment methods and manual coding of videos. Better measures of the food intake would benefit nutrition and health research, but also the health professionals in their challenge to combat the increase in food-related non-communicable diseases11. Ultimately the mEETr can be used in research and health-care settings as well as by health-conscious users at home by linking the mEETr to existing technologies and software, such as other health apps or smart watches. Overall, these health measures provide the user or the health-care professional with a rather diverse and complete overview of a variety of health-behavior patterns (e.g., food intake, eating behavior, energy expenditure based on real-life measures, sleep, stress) enabling the user to optimize their diet and create a healthy lifestyle.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Battery</td>
			<td>na</td>
			<td>na</td>
			<td>Battery pack (LiPo) and charge electronics via an USB port connector. No data from this port.</td>
		</tr>
		<tr>
			<td>Connector program</td>
			<td>Noldus</td>
			<td></td>
			<td>Noldus Information technology software dashboard nview</td>
		</tr>
		<tr>
			<td>Dinner tray</td>
			<td>na</td>
			<td>na</td>
			<td>Standard dinner tray from glass inforced epoxy</td>
		</tr>
		<tr>
			<td>Larger scale</td>
			<td>na</td>
			<td>na</td>
			<td>One high range custom made scale based on a triple force sensor method.</td>
		</tr>
		<tr>
			<td>Mainboard</td>
			<td>na</td>
			<td>na</td>
			<td>A mainboard converting the three scale measurements to calibrated weight numbers. This board also contains the low power short range RF transmitter.</td>
		</tr>
		<tr>
			<td>OS Windows</td>
			<td>Microsoft</td>
			<td></td>
			<td>windows 10 Pro 64 bit</td>
		</tr>
		<tr>
			<td>Processor program</td>
			<td>Noldus</td>
			<td></td>
			<td>Noldus Information technology software FaceReader</td>
		</tr>
		<tr>
			<td>Receiver program</td>
			<td>Noldus</td>
			<td></td>
			<td>Noldus Information technology software Observer</td>
		</tr>
		<tr>
			<td>RF receiver</td>
			<td>na</td>
			<td>na</td>
			<td>Custom build USB converter connected to a RF receiver. This receiver has a squelch setting for making it low range sensitive.</td>
		</tr>
		<tr>
			<td>Small scales</td>
			<td>na</td>
			<td>na</td>
			<td>Two low range custom made scales based on a triple force sensor method.</td>
		</tr>
	</tbody>
</table>

concept development and use of an automated food intake and eating behavior assessment method

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. Recent technological innovations allow for the development of more accurate and precise dietary and eating behavior assessment tools that require less effort for both the user and the researcher. Therefore, a new sensor-based device to assess food intake and eating behavior was developed. The device is a regular dining tray equipped with a video camera and three separate built-in weighing stations. The weighing stations measure the weight of the bowl, plate, and drinking cup continuously over the course of a meal. The video camera positioned to the face records eating behavior characteristics (chews, bites), which are analyzed using artificial intelligence (AI)-based automatic facial expression software. The tray weight and the video data are transported at real-time to a personal computer (PC) using a wireless receiver. The outcomes of interest, such as the amount eaten, eating rate and bite size, can be calculated by subtracting the data of these measures at the timepoints of interest. The information obtained by the current version of the tray can be used for research purposes, an upgraded version of the device would also facilitate the provision of more personalized advice on dietary intake and eating behavior. Contrary to the conventional dietary assessment methods, this dietary assessment device measures food intake directly within a meal and is not dependent on memory or the portion size estimation. Ultimately, this device is therefore suited for daily main meal food intake and eating behavior measures. In the future, this technology based dietary assessment method can be linked to health applications or smart watches to obtain a complete overview of exercise, energy intake, and eating behavior.

A slower ingestion rate (Figure 7), smaller sip/bite sizes (Figure 8), and more chews (Figure 9) led to lower intake of the salad compared to the yoghurt and juice (Figure 6) as measured by the mEETr tray. The participants ate 17% less of the fruit salad compared to the fruit juice. All the eating behavior characteristics differed between the juice, yoghurt, and salad (Figure 7</str...

Watch this Scientific Journal Video about Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method at JoVE.com

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

This protocol shows and explains a new technology-based dietary assessment method. The method consists of a dining tray with multiple built-in weighing scales and a video camera. The device is unique in the sense that it incorporates automated measures of food and drink intake and eating behavior over the course of a meal.

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. ...

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