Design of a Spoken Dialogue System to Provide Food Knowledge
to Users while Eating
Daiki Mayumi

mayumi.daiki.mb9@is.naist.jp
Nara Institute of Science andTechnology
Ikoma, Japan

Annalena Aicher

annalena.aicher@uni-ulm.de
Ulm University
Ulm, Germany

Yugo Nakamura

y-nakamura@ait.kyushu-u.ac.jp
Kyushu University
Fukuoka, Japan

Keiichi Yasumoto

yasumoto@is.naist.jp
Nara Institute of Science andTechnology
Ikoma, Japan

Yuki Matsuda

yukimat@is.naist.jp
Nara Institute of Science andTechnology
Ikoma, Japan

Wolfgang Minker

wolfgang.minker@uni-ulm.de
Ulm University
Ulm, Germany

ABSTRACT1

In this paper, we introduce the concept of a spoken dialogue system,
which aims to recognize user eating behaviors during meals andpromotes healthier eating habits by engaging users in an interactive co-dining experience. To provide an intuitive and more naturalaccess, this information is integrated into a natural conversation
with the user. Therefore, we conducted a preliminary study to explore how a human-like virtual avatar’s eating speed affects theeating pace of a co-dining user. The results indicate that dining
together with an avatar reduces the user’s feeling of lonelinessand enhances the overall satisfaction and enjoyment of the meal.
Therefore, this work represents an important step toward realizing
engaging co-dining experiences with human-like avatars and provides new insights into the future design of IoT systems to support
solitary dining and health recommendations.

Sharing meals with someone is an essential aspect of human daily
routines. Eating together, referred to as “commensality”, imbues
individuals with a sense of comfort and thereby stimulates theircommunicative capabilities. Moreover, commensality plays a pivotal role in strengthening bonds between humans and further supports the development of individual taste preferences [3]. However,
due to recent societal influences, such as those brought about by
the COVID-19 pandemic, including rising rates of late marriages
or decisions to remain unmarried, the proportion of people eating
alone (“solitary dining”) has noticeably increased. The prevalence ofsolitary dining has accelerated the diversification of dietary habits,
enticing individuals toward unhealthy food choices and eating behaviors. This skewness in dietary habits comes with various disadvantages in terms of health, nutrition, and social interactions [20].
Concerning health drawbacks, there is an increase in potential
risks for obesity and metabolic syndrome [10]. Moreover, solitaryeating is associated with a higher prevalence of unhealthy eating
behaviors [23], and a reduction in meal variety [24]. The domainof Human Food Interaction (HFI) [1, 19] specifically emphasizes
reimagining human-food relationships through the augmentationof dining experiences via technological innovations. However, current research in this field focuses on enhancing sensory food experiences through multisensory approaches. However, at current,
the majority of research is related to multisensory experience approaches that alter or enhance the sensory experience of food, andthere are very few examples of the development of systems that aim
to fundamentally change attitudes and establish a healthy diet. In
contrast, in our research, we aim to convey healthy dietary knowledge within a natural spoken interaction during a shared meal
between an avatar and a human user.
To achieve this, we will construct and evaluate a voice dialoguesystem that facilitates high-affinity interactions during meals andprovides users with valuable information on healthy eating. The
proposed system is composed of a mechanism that recognizes eating behaviors and a voice dialogue system furnished with dietaryknowledge. By engaging in shared meals through this system, we
aim to mitigate the issues associated with solitary dining and enhance users’ dietary intake behaviors and their knowledge about

CCS CONCEPTS• Human-centered computing → Natural language interfaces;
User studies.

KEYWORDS
Human Food Interaction, Eating recognition, Persuasive technology,
Intelligent Virtual Avatar
ACM Reference Format:
Daiki Mayumi, Yugo Nakamura, Yuki Matsuda, Annalena Aicher, Keiichi
Yasumoto, and Wolfgang Minker. 2023. Design of a Spoken Dialogue System
to Provide Food Knowledge to Users while Eating. In The International
Conference on the Internet of Things (IoT 2023), November 7–10, 2023, Nagoya,Japan. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3627050.
3630728

Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s).
IoT 2023, November 7–10, 2023, Nagoya, Japan© 2023 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-0854-1/23/11.
https://doi.org/10.1145/3627050.3630728

INTRODUCTION

IoT 2023, November 7–10, 2023, Nagoya, Japan

D.Mayumi et al.

meals. In this paper, we will elucidate the design and implementation of the proposed voice dialogue system and report the preliminary experimental findings regarding the influence that sharedmeals exert on individuals.
The remainder of the paper is as follows: after an overview of related work in Section 2, Section 3 gives an overview of the proposed
dialogue system. In Section 4 and Section 5 our preliminary study
and respective results are described, followed by a brief conclusion
and outlook on future work in Section 6.

Recognition of pace of eating
and umber of chews

Generate response statements
for dietary knowledge

Enhancing the Taste Experience

In the field of Human-Computer Interaction (HCI), various methods
for enhancing the taste and flavor perception of food have been
proposed. These include approaches such as using Galvanic Taste
Stimulation (GTS) from both inside and outside the oral cavity toenhance saltiness [15], studies demonstrating the induction of different tastes by electrically stimulating the taste receptor system on
the tongue [21], and research altering taste by presenting scents tothe olfactory senses [12, 13]. Furthermore, in existing HCI research,
the focus has primarily been on visual elements, with methods
proposed for manipulating the appearance and visual aspects offood. For instance, systems have been developed to control satiety
by altering the quantity of food consumed using Head-Mounted
Displays [17]. Similarly, Projection Mapping techniques have been
explored to change the visual aspects of food and the dining environment [22].

2.2DAvatar

GoogleSpeech-to-Text

Voice data

Dialogue evaluation

C

Emotion Recognition

B

User

RELATED WORK

Research in the domain of Human-Computer Interaction (HCI) related to dining can be categorized into different approaches [26].
The first approach focuses on leveraging various sensory experiences to alter or enhance the taste of food. The second approach
focuses on using technology to address the issue of solitary eating
among individuals. The third aims to channel individuals towards
adopting healthier eating habits.

2.1

REC

E ChatGPT

System2

A

Expanding Solitary Dining Experience

Commensality refers to the act of eating together or sharing ameal at the same table [5]. Due to recent societal changes the time
people spend dining with family or friends is notably decreasing.
Consequently, the phenomenon of solitary dining, has emerged
as an issue. Often, eating alone is perceived as not particularly
pleasurable [9]. Nonetheless, eating alone has become a familiar
routine in modern life, leading to individualized meal patterns andchanges of commensality [28]. To address the problem of solitaryeating, in the context of HCI, efforts have been made to incorporate
communication into the dining experience as a part of digital orcomputer-enhanced commensality.
For instance, systems have been proposed that bridge the timeand spatial distance between diners in remote locations [18, 25, 27].
Numerous interactive dining tables have been introduced, enabling
unique interactions with fellow diners [4, 14]. Furthermore, there
have been systems designed to create artificial dining companions
instead of human ones [7].

Figure 1: System Overview

2.3

Eating Behavior

For promoting healthier eating habits, behavioral interventions,such as slowing down the speed of eating [2, 11] and increasing
the number of chews before swallowing, have been suggested [29].
Nakamura et al. developed the eat2pic system, which leverages
visual cues by reframing the act of eating into a task of coloring a
picture, thus advocating healthier dietary practices [16]. Moreover,wearable devices like Slowee, which signals users through lightand vibrations when they eat too fast, have been proposed [8].
Sensing forks that vibrate to alert users, particularly those who
self-identify as fast eaters, aim to raise awareness and regulate
eating speed [6]. While there have been studies aimed at enhancing
taste and dining experience using information technology, there
are very few examples of systems targeting a foundational shift
in consciousness and the establishment of a consistently healthy
dietary lifestyle.

3

PROPOSED SYSTEM

In order to be able to detect unhealthy eating habits, the systemneeds to be equipped with the respective sensors and processing
capabilities to analyse the user behavior in real-time. Therefore,
we aim for a robust design that enhances the accuracy of sensor
recognition during food intake, which enables the system to beeasy to use in everyday life. Additionally, to provide valid andwell-considered health and food information, an integration ofan appropriate knowledge source is necessary. In a first step thesystem shall guide users towards acquiring correct food knowledge,
especially to avoid unhealthy food choices and rapid consumption.
In more detail, the proposed system consists of five components:
PC, a monitor, a webcam, a depth camera 1 and a microphone. The
first, using the depth camera positioned at the top of the monitor,
the system captures the user’s eating behaviors. This technology
allows for the detection of the user’s skeletal movements, enabling
the system to recognize the meal’s pace and chewing frequency
without the need for sensors on the tableware (Figure 1, A). The
second, voice inputs from the user are captured by a microphone
and processed through Google’s speech-to-text API (Figure 1, B).
The third, facial recognition is performed using a webcam to recognize the user’s emotions, the outcomes of which determine whether
to maintain the dialogue and if the user acknowledges the content
1 https://store.intelrealsense.com/buy-intel-realsense-depth-camera-d457.html?1504367728.1664317294

Design of a Dialogue System

IoT 2023, November 7–10, 2023, Nagoya, Japan

Experiment Result
Solitary Dining
With Avatar

7

Evaluation scale

6
5
4
3
2
1

Enjoyable

Loneliness

Tasteness

Satisfaction

Figure 3: Result of Experiment
Figure 2: Experiment Sceneof the conversation (Figure 1, C). The fourth, on the screen, ananthropomorphic avatar joins the user in dining. Based on the dataobtained from the user’s eating speed in part A, the avatar’s eating
pace is adjusted, effectively guiding the user towards a healthier eating rhythm (Figure 1, D). Finally, the system then crafts a response
for the user. By using the recognition outcomes from parts A,B andC, the system, through ChatGPT, generates feedback concerning
healthy eating habits (Figure 1, E). This feedback is presented to theuser via the avatar shown in Figure 1, D. Furthermore, to optimize
the voice recognition rate from Figure 1, B, the system is designed toencourage the user to speak at moments when they aren’t chewing,based on the data (Figure 1, A). This approach minimizes potential
recognition errors, paving the way for a robust system design. To
investigate the impact of the proposed system on dietary behavior
and the acquisition of healthy eating knowledge, we conductedpre- and post-experiment surveys on health literacy. Furthermore,
we examined the influence of users’ nationality and culture andevaluated how information technology interventions may lead tovarying outcomes based on user attributes.

4

PRELIMINARY STUDY

To examine whether sharing a meal with an avatar alleviates the
feeling of loneliness during solitary dining, we conducted a preliminary experiment. The study involved ten male participants in their
twenties (average age = 22.7). A lunch box was provided, and allparticipants consumed the same meal during each experiment. The
experiment consisted of two sessions. In the first session, participants ate alone, engaging in activities such as using their mobile
phones or watching videos, simulating their typical dining experience. In the second session, participants shared the meal withan avatar (Figure 2). The avatar engaged in casual conversation,
discussing everyday topics, hobbies, and making statements like ahuman would. Throughout the experiment, the participants’ mealtime interactions were recorded with cameras. After the meal theparticipants were asked to complete a questionnaire. The questionnaire utilized a 7-point Likert scale and consisted of four items (Q1:
How enjoyable was your mealtime experience? Q2: How lonely did
you feel during the meal? Q3: Were you able to savor the taste of
the meal? Q4: How satisfied were you with the meal?).

5

RESULTSThe results are illustrated in Figure Figure 3. The results indicate
that, compared to solitary dining, sharing a meal with an avatar
tends to reduce feelings of loneliness and increase satisfaction during the mealtime. However, it was observed that dining alone received higher ratings for the enjoyment of mealtime and the perception of taste. In other words, the practice of dining with someone, even if they are not human, may be an effective method for
mitigating feelings of loneliness. Additionally, the decrease in mealtime satisfaction can be attributed to respondents who reported
feeling better mentally when eating while conversing with others.
Conversely, some users expressed feelings of tension during avatarmediated dining, stating uncertainty about who was behind theavatar. Others mentioned that they found dining alone to be morementally enjoyable compared to dining with an avatar.
This experiment has revealed several expectations that users
have towards avatars. It was found to be challenging for avatars to
evoke the same level of familiarity as humans. On the other hand,users expressed a desire for avatars that can provide new insights
into topics related to their interests and hobbies. In this experiment, we employed a format where avatars were questioned about
mealtime and hobbies. However, there is a potential need for future
experiments to investigate the receptiveness of avatars in providing
insights into these topics. Furthermore, since the participants inthis study consisted only of males in their twenties, it is essential tobroaden the participant pool to include females and individuals ofvarying age groups in future experiments. Additionally, exploring
the impact on individuals who have limited opportunities for daily
conversation, such as elderly persons, would be a valuable avenue
for further research.

6

CONCLUSION

In this work, we proposed a spoken dialogue system designed tofoster high-affinity interactions during meals, particularly during
shared dining experiences. One of its main purposes is to address
the issue of solitary dining, improve the eating behaviors of users,
and provide them with knowledge about healthy eating habits. In
an initial experiment we investigated the impact of dining alone
versus dining with an avatar. The results indicated that sharing
meals reduced feelings of loneliness and increased satisfaction withmealtime. However, the relationship between avatar and human is
crucial, as dining with an avatar, who is not perceived as a friend,
induced feelings of tension and thus, less pleasure. This valuable

IoT 2023, November 7–10, 2023, Nagoya, Japan

insight into the user expectations concerning co-dining avatars willserve as a basis for designing the system proposed in this work andthus, contribute to the development of future IoT technologies in
the context of Human Food Interaction.

ACKNOWLEDGMENT
This study was supported in part by JST PRESTO under Grant No.
JPMJPR2039.

REFERENCES[1] Ferran Altarriba Bertran, Samvid Jhaveri, Rosa Lutz, Katherine Isbister, andDanielle Wilde. 2019. Making Sense of Human-Food Interaction. In Proceedingsof the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow,
Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY,
USA, 1–13. https://doi.org/10.1145/3290605.3300908[2] Dieuwerke P. Bolhuis and Russell S.J. Keast. 2016. Assessment of eating rate andfood intake in spoon versus fork users in a laboratory setting. Food Quality andPreference 49 (2016), 66–69. https://doi.org/10.1016/j.foodqual.2015.11.015[3] Carole Counihan. 2018. Commensality and taste. International Journal of Food
Design 3, 2 (2018), 143–146. https://doi.org/10.1386/ijfd.3.2.143_3[4] Roelof A. J. de Vries, Gijs H. J. Keizers, Sterre R. van Arum, Juliet A. M. Haarman,
Randy Klaassen, Robby W. van Delden, Bert-Jan F. van Beijnum, and Janet H. W.
van den Boer. 2021. Multimodal Interactive Dining with the Sensory Interactive Table: Two Use Cases. In Companion Publication of the 2020 International
Conference on Multimodal Interaction (Virtual Event, Netherlands) (ICMI ’20 Companion). Association for Computing Machinery, New York, NY, USA, 332–340.
https://doi.org/10.1145/3395035.3425654[5] Håkan Jönsson, Maxime Michaud, and Nicklas Neuman. 2021. What Is Commensality? A Critical Discussion of an Expanding Research Field. IJERPH 18, 12(June 2021), 1–17.
[6] Azusa Kadomura, Cheng-Yuan Li, Yen-Chang Chen, Koji Tsukada, Itiro Siio, andHao-hua Chu. 2013. Sensing Fork: Eating Behavior Detection Utensil and Mobile
Persuasive Game. In CHI ’13 Extended Abstracts on Human Factors in Computing
Systems (Paris, France) (CHI EA ’13). Association for Computing Machinery, New
York, NY, USA, 1551–1556. https://doi.org/10.1145/2468356.2468634[7] Rohit Ashok Khot, Eshita Sri Arza, Harshitha Kurra, and Yan Wang. 2019. FoBo:
Towards Designing a Robotic Companion for Solo Dining. In Extended Abstractsof the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow,
Scotland Uk) (CHI EA ’19). Association for Computing Machinery, New York, NY,
USA, 1–6. https://doi.org/10.1145/3290607.3313069[8] Joohee Kim, Kwang-Jae Lee, Mankyung Lee, Nahyeon Lee, Byung-Chull Bae,
Genehee Lee, Juhee Cho, Young Mog Shim, and Jun-Dong Cho. 2016. Slowee:
A Smart Eating-Speed Guide System with Light and Vibration Feedback. In
Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in
Computing Systems (San Jose, California, USA) (CHI EA ’16). Association for
Computing Machinery, New York, NY, USA, 2563–2569. https://doi.org/10.1145/
2851581.2892323[9] Dannie Korsgaard, Thomas Bjøner, and Niels Christian Nilsson. 2019. Where
would you like to eat? A formative evaluation of mixed-reality solitary meals in
virtual environments for older adults with mobility impairments who live alone.
Food Research International 117 (2019), 30–39. https://doi.org/10.1016/j.foodres.
2018.02.051 Special issue on "Virtual reality and food: Applications in sensory
and consumer science”.
[10] A Rom Kwon, Yeong Sook Yoon, Kyong Pil Min, Yoon Kyung Lee, and Ji Ho Jeon.
2018. Eating alone and metabolic syndrome: A population-based Korean National
Health and Nutrition Examination Survey 2013–2014. Obesity Research & Clinical
Practice 12, 2 (2018), 146–157. https://doi.org/10.1016/j.orcp.2017.09.002[11] Sook Ling Leong, Clara Madden, Andrew Gray, Debra Waters, and Caroline
Horwath. 2011. Faster Self-Reported Speed of Eating Is Related to Higher Body
Mass Index in a Nationwide Survey of Middle-Aged Women. Journal of theAmerican Dietetic Association 111, 8 (2011), 1192–1197. https://doi.org/10.1016/j.
jada.2011.05.012[12] Daiki Mayumi, Yugo Nakamura, Yuki Matsuda, Kentaro Ueda, Shinya Misaki, andKeiichi Yasumoto. 2023. Kaolid: Lid-type Olfactory Interface to Improve Taste ofBeverages with Ortho-Retronasal Smell. In 2023 IEEE International Conference on
Pervasive Computing and Communications Workshops and other Affiliated Events(PerCom Workshops). IEEE Computer Society, Los Alamitos, CA, USA, 303–305.
https://doi.org/10.1109/PerComWorkshops56833.2023.10150412[13] Daiki Mayumi, Yugo Nakamura, Shinya Misaki, Yuki Matsuda, and Keiichi Yasumoto. 2022. Aromug: Mug-Type Olfactory Interface to Assist in Reducing Sugar
Intake. In Adjunct Proceedings of the 2022 ACM International Joint Conference
on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers (Cambridge, United Kingdom) (UbiComp/ISWC ’22
Adjunct). Association for Computing Machinery, 183–187.

D.Mayumi et al.

[14] Yash Dhanpal Mehta, Rohit Ashok Khot, Rakesh Patibanda, and Florian ’Floyd’
Mueller. 2018. Arm-A-Dine: Towards Understanding the Design of Playful Embodied Eating Experiences. In Proceedings of the 2018 Annual Symposium on
Computer-Human Interaction in Play (Melbourne, VIC, Australia) (CHI PLAY
’18). Association for Computing Machinery, New York, NY, USA, 299–313.
https://doi.org/10.1145/3242671.3242710[15] Hiromi Nakamura, Tomohiro Amemiya, Jun Rekimoto, Hideyuki Ando, andKazuma Aoyama. 2021. Anodal Galvanic Taste Stimulation to the Chin Enhances
Salty Taste of NaCl Water Solution. Journal of Robotics and Mechatronics 33, 5(10 2021), 1128–1134. https://doi.org/10.20965/jrm.2021.p1128[16] Yugo Nakamura, Rei Nakaoka, Yuki Matsuda, and Keiichi Yasumoto. 2023. Eat2pic:
An Eating-Painting Interactive System to Nudge Users into Making HealthierDiet Choices. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 7, 1, Article
24 (mar 2023), 23 pages. https://doi.org/10.1145/3580784[17] Takuji Narumi, Yuki Ban, Takashi Kajinami, Tomohiro Tanikawa, and Michitaka
Hirose. 2012. Augmented Perception of Satiety: Controlling Food Consumption
by Changing Apparent Size of Food with Augmented Reality. In Proceedings of the
SIGCHI Conference on Human Factors in Computing Systems (Austin, Texas, USA)(CHI ’12). Association for Computing Machinery, New York, NY, USA, 109–118.
https://doi.org/10.1145/2207676.2207693[18] Mamoun Nawahdah and Tomoo Inoue. 2013. Virtually Dining Together in TimeShifted Environment: KIZUNA Design. In Proceedings of the 2013 Conference on
Computer Supported Cooperative Work (San Antonio, Texas, USA) (CSCW ’13).
Association for Computing Machinery, New York, NY, USA, 779–788. https://doi.org/10.1145/2441776.2441863[19] Anton Nijholt, Carlos Velasco, Marianna Obrist, Katsunori Okajima, and Charles
Spence. 2018. 3rd International Workshop on Multisensory Approaches to
Human-Food Interaction. In Proceedings of the 20th ACM International Conference on Multimodal Interaction (Boulder, CO, USA) (ICMI ’18). Association for
Computing Machinery, New York, NY, USA, 657–659. https://doi.org/10.1145/
3242969.3265860[20] Barry M Popkin, Linda S Adair, and Shu Wen Ng. 2012.
Globalnutrition transition and the pandemic of obesity in developing countries. Nutrition Reviews 70, 1 (01 2012), 3–21. https://doi.org/10.1111/j.
1753-4887.2011.00456.x arXiv:https://academic.oup.com/nutritionreviews/articlepdf/70/1/3/24094379/nutritionreviews70-0003.pdf[21] Nimesha Ranasinghe, Meetha Nesam James, Michael Gecawicz, Jonathan Bland,and David Smith. 2020. Influence of Electric Taste, Smell, Color, and Thermal
Sensory Modalities on the Liking and Mediated Emotions of Virtual Flavor
Perception. In Proceedings of the 2020 International Conference on MultimodalInteraction. 296–304.
[22] Sho Sakurai, Takuji Narumi, Yuki Ban, Tomohiro Tanikawa, and Michitaka
Hirose. 2015. CalibraTable: Tabletop System for Influencing Eating Behavior. In SIGGRAPH Asia 2015 Emerging Technologies (Kobe, Japan) (SA ’15). Association for Computing Machinery, New York, NY, USA, Article 4, 3 pages.
https://doi.org/10.1145/2818466.2818483[23] Charles Spence. 2017. Comfort food: A review. International Journal of Gastronomy and Food Science 9 (2017), 105–109. https://doi.org/10.1016/j.ijgfs.2017.07.001[24] Wakako Takeda and Melissa K. Melby. 2017. Spatial, temporal, and health associations of eating alone: A cross-cultural analysis of young adults in urban Australia
and Japan. Appetite 118 (2017), 149–160. https://doi.org/10.1016/j.appet.2017.08.
013[25] Hitomi Tsujita, Svetlana Yarosh, and Gregory D. Abowd. 2010. CU-Later: A
Communication System Considering Time Difference. In Proceedings of the 12th
ACM International Conference Adjunct Papers on Ubiquitous Computing - Adjunct(Copenhagen, Denmark) (UbiComp ’10 Adjunct). Association for Computing Machinery, New York, NY, USA, 435–436. https://doi.org/10.1145/1864431.1864474[26] Carlos Velasco, Qian J Wang, Marianna Obrist, and Anton Nijholt. 2023. A
reflection on the state of multisensory human–food interaction research. (62023). https://sussex.figshare.com/articles/journal_contribution/A_reflection_
on_the_state_of_multisensory_human_food_interaction_research/23495525[27] Jun Wei, Xuan Wang, Roshan Lalintha Peiris, Yongsoon Choi, Xavier Roman
Martinez, Remi Tache, Jeffrey Tzu Kwan Valino Koh, Veronica Halupka, andAdrian David Cheok. 2011. CoDine: An Interactive Multi-Sensory System for
Remote Dining. In Proceedings of the 13th International Conference on Ubiquitous
Computing (Beijing, China) (UbiComp ’11). Association for Computing Machinery,
New York, NY, USA, 21–30. https://doi.org/10.1145/2030112.2030116[28] Luke Yates and Alan Warde. 2016. Eating together and eating alone: Meal
arrangements in British households. The British journal of sociology 68 (11 2016).
https://doi.org/10.1111/1468-4446.12231[29] Yong Zhu and James H. Hollis. 2014. Increasing the Number of Chews before
Swallowing Reduces Meal Size in Normal-Weight, Overweight, and Obese Adults.
Journal of the Academy of Nutrition and Dietetics 114, 6 (2014), 926–931. https://doi.org/10.1016/j.jand.2013.08.020