Evidence map›Paper›PMID 35368510›Full record

ArticleFrontiers in public health2021

To Create a Safe and Healthy Place for Children: The Associations of Green Open Space Characteristics With Children's Use.

Ming Ma, Michael Adeney, Wei Chen, Darong Deng, Shaohua Tan

Abstract read
In one paragraph

Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. 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.

Ming MaCollege of Architecture and Urban Planning, Yunnan University, Yunnan, China.
Michael AdeneySchool of Public Health, University of Washington, Seattle, WA, United States.
Wei ChenDepartment of Bone and Joint Surgery, Sanya Central Hospital, Sanya, China.
Darong DengThe First Studio, Chongqing Planning and Design Institute, Chongqing, China.
Shaohua TanFaculty of Architecture and Urban Planning, Chongqing University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Green open space (GOS) is an important outdoor resource for the well-being of children by providing places for physical activity (PA), especially in the highly urbanized environment. The COVID-19 lockdowns have made children have more sedentary time than before due to less access to public places. This article aims to examine the associations of GOS characteristics (environmental and surrounding) and children's use (visitation and PA pattern) to provide evidence for promoting their PA during the pandemic. This study employed the method of GPS positioner, accelerometer, and survey to measure the children's actual use in GOS. A total of 179 children participated in the study and 10 GOSs were selected. The children were provided with the accelerometers and GPS positioners to track their walking steps, duration, and locations. The environmental characteristics and 1 km buffer of the selected GOSs were explored as extended study area. Results showed that 49.16% of children reported more visitations than before the pandemic, and 48.60% of them preferred to go on weekdays during the pandemic. Both environmental and surrounding characteristics could affect the visitation pattern. The size (

Indexed as

COVID-19Environment DesignAccelerometryChildCommunicable Disease ControlExerciseHumanschildrendesignenvironmental characteristicsgreen open spacelandscapephysical activityplanning

Identifiers

PMID35368510
PMCPMC8966046

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.