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. ...

Nutrition researchers and dieticians use indirect measures to measure food intake, such as food recalls. With a meter, we can measure actual food intake during the meal and also people's eating behavior. The main advantage of the meter is that we can measure more precise how much people eat with little effort from consumers or patients.This technique can be used to get better insight in what an individual eats. This can be especially important for those who suffer from diabetes or those who are malnourished, such as the elderly. The procedure will be demonstrated by Els Siebelink, who is head of the Dietician Department and a trained research dietician.Participants are Femke de Gooijer and Michele Tufano, both PhD students, and all work at the Human Nutrition and Health Department of Wageningen University. Begin by preparing a juice, fruit yogurt, and fruit pieces. Recruit volunteers who agree to participate in the study.Exclude participants wearing glasses who cannot wear lenses or those who have facial hair to avoid measurement errors. Inform the participants about the study and data collection, and obtain their signature on the informed consent before collecting data. After ensuring that the light in the room is evenly distributed and that there is no background noise on the video recordings, seat the participant before the table with the tabletop located just below their chest.The entire torso should be visible, including the arms. Connect the wireless receiver of the tray and the webcam to a laptop. Switch on the tray, and start the laptop, making sure that the tray is charged, which is indicated by a green light.Open the connector program, the receiver, and the processor software program together with the dashboard, respectively. Make sure the image frame is correct to prevent poor image quality and that the participant's head and chest, including arms and shoulders, are clearly visible. Keep the tray dry, including a 50-millimeter thin base panel with a central circuit board below the tray.Transfer the weighing data at a one-second interval via a short-range radio signal of about one-meter distance. Connect the receiver to a personal computer via a USB port. Make sure that the plate, the cup, and the bowl are not resting on the platform or the surrounding tray.Use the center ring to avoid this. Place the meter in front of the participant, and instruct the participant to eat as much or as little as he or she wants while looking straight into the webcam. Remind them not to put their hands in front of their face while eating.Start a new observation in the receiver software. Log the date, participant number, participant's gender, age, and anthropometric data, such as weight and height. Include additional information such as the study condition and the study visit in the observation name.Press record in the receiver software to record the observation and activate the dashboard in order to check the video recordings and the incoming data during data collection. Prior to the recording, ask the participant to raise the card with the participant number and raise their hand at the start and end of the meal. End the observation when the participant finishes eating.It takes two minutes to transfer all the data to a spreadsheet. Disconnect the webcam and the tray receiver from the laptop, and clean it with a cleaning tissue or cleaning spray. Automated measures of eating behavior are stored under the heading Data.Click on Export Data to extract the raw data. The output file contains data on the participant number, real time, relative starting time, and timestamped eating behavior variables, such as number of bites, number of chews, and chewing duration. Summarize and visualize the results in different bar charts within the program.Export the log files to a spreadsheet, and perform the data analysis using the statistical program of preference. The meter tray measured lower intake of the salad compared to the yogurt and juice. The participants ate 17%less of the fruit salad compared to the fruit juice, a slower ingestion rate, smaller sip or bite sizes, and more chews led to lower intake of the salad compared to the yogurt and juice, as measured by the meter tray.The participants chewed significantly more on the fruit salad compared to the yogurt and juice. The observed number of chews differed by a factor of three between the yogurt and fruit salad. The bite size was smallest for the salad, 6.5 grams per bite compared to eight grams per sip of juice.Overall, the number of chews, the bite size, and the eating rate seemed to affect the amount that was eaten during the meal in an eating lab setting. In future versions of the meter, we would like to build in a camera that comes from the side or from the lower corner level, such that it is less intrusive for people than a camera that comes straight in front of them. Future versions of the system will allow us to study food intake behavior in real life.This also allows us to give feedback to the consumer directly on how much they consume, what they consume, and also how fast they are eating.

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