Evidence map›Paper›PMID 36302820›Full record

ArticleScientific reports2022

Actual conditions of person-to-object contact and a proposal for prevention measures during the COVID-19 pandemic.

Teruaki Hayashi, Daisuke Hase, Hikaru Suenaga, Yukio Ohsawa

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Teruaki HayashiDepartment of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. hayashi@sys.t.u-tokyo.ac.jp.
Daisuke Hase *Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.
Hikaru Suenaga *Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.
Yukio Ohsawa *Department of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study focused on human contact behavior with objects and discussed countermeasures during the COVID-19 pandemic across 15 location types. Reducing contact with objects and disinfecting items can be implemented at a relatively low cost. We created a protocol for organizing the objects, and 1260 subjects who went outside during a day between December 3-7, 2020 in Tokyo and Kanagawa, Japan were surveyed. The participants touched 7317 objects in total; the most common objects were doors, chairs, baskets, elevator equipment, and cash. One-way analysis of variance and Scheffé's multiple comparison test showed that supermarkets had the lowest mean and median values despite having the highest number of users, contact objects, and object types. Conversely, the values for hotels were the highest, significantly higher than that for other places, excluding amusement parks, workplaces, and schools and universities. Furthermore, the long-tailed frequency distribution of the number of objects suggests that the objects touched by many individuals are limited; thus, it is important to determine the objects to be prioritized for disinfection at each location. The data and protocol could inform infection countermeasures that properly address the contact realities as they pertain to people's behavior and objects.

Indexed as

COVID-19HumansJapanPandemicsTokyoTouch

Identifiers

PMID36302820
PMCPMC9610312

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.