Evidence map›Paper›PMID 33501134›Full record

ReviewFrontiers in robotics and AI2019

Computational Commensality: From Theories to Computational Models for Social Food Preparation and Consumption in HCI.

Radoslaw Niewiadomski, Eleonora Ceccaldi, Gijs Huisman, Gualtiero Volpe, Maurizio Mancini

Abstract readReview
In one paragraph

Review in Frontiers in robotics and AI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Digital Forms of Commensality in the 21st Century: A Scoping Review.International journal of environmental research and public health · 2022
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Radoslaw NiewiadomskiCONTACT Unit, Istituto Italiano di Tecnologia, Genoa, Italy.
Eleonora CeccaldiInfoMus Lab, DIBRIS, University of Genoa, Genoa, Italy.
Gijs HuismanDigital Society School, Amsterdam University of Applied Sciences, Amsterdam, Netherlands.
Gualtiero VolpeInfoMus Lab, DIBRIS, University of Genoa, Genoa, Italy.
Maurizio ManciniSchool of Computer Science and Information Technology, University College Cork, Cork, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Food and eating are inherently social activities taking place, for example, around the dining table at home, in restaurants, or in public spaces. Enjoying eating with others, often referred to as "commensality," positively affects mealtime in terms of, among other factors, food intake, food choice, and food satisfaction. In this paper we discuss the concept of "Computational Commensality," that is, technology which computationally addresses various social aspects of food and eating. In the past few years, Human-Computer Interaction started to address how interactive technologies can improve mealtimes. However, the main focus has been made so far on improving the individual's experience, rather than considering the inherently social nature of food consumption. In this survey, we first present research from the field of social psychology on the social relevance of Food- and Eating-related Activities (F&EA). Then, we review existing computational models and technologies that can contribute, in the near future, to achieving Computational Commensality. We also discuss the related research challenges and indicate future applications of such new technology that can potentially improve F&EA from the commensality perspective.

Indexed as

augmented experiencecommensalityembodied interfacesfoodfood recognitionHCIsocial robotssocial signal processing

Identifiers

PMID33501134
PMCPMC7805905

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.