Evidence map›Paper›PMID 28417951›Full record

ArticleInternational journal of environmental research and public health2017

Association of Motorcycle Use with Risk of Overweight in Taiwanese Urban Adults.

Chien-Yu Lin, Yung Liao, Jong-Hwan Park

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Walk ScoreInternational journal of environmental research and public health · 2019
    Article
  2. 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

3 authors.

Chien-Yu LinInstitute of Health Behaviors and Community Sciences, National Taiwan University, 17, Xuzhou Road, Taipei 100, Taiwan. r05850001@ntu.edu.tw.
Yung LiaoDepartment of Health Promotion and Health Education, National Taiwan Normal University, 162, Heping East Road Section 1, Taipei 106, Taiwan. liaoyung@ntnu.edu.tw.
Jong-Hwan ParkInstitute of Convergence Bio-Health, Dong-A University, 32, Daeshingongwon-Ro, Seo-Gu, Busan 49201, Korea. prof.parkjh@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sedentary transport is known to adversely affect health. Few studies have focused on motorcycle use. This study examines the association of motorcycle use with overweight in urban adults in Taiwan. Cross-sectional data from 1069 Taiwanese adults aged 20-64 years in three urban cities were collected in 2015. Data on self-reported body mass index, time spent in motorcycle use, lifestyle behavioral factors, and sociodemographic variables were obtained. Unadjusted and adjusted logistic regression models were applied. In Model 1, adults who spent more time using a motorcycle (third quartile, odds ratio (OR) = 1.17; fourth quartile, OR = 1.60) were more likely to be overweight compared with the first quartile. In Model 2, after adjusting for the covariates, only the fourth quartile of motorcycle use (OR = 1.50) was associated with a higher risk of overweight. Higher time spent in motorcycle use is related to higher risk of being overweight, even after adjustment for potential demographic and behavioral confounders. Intervention and behavioral change strategies targeting motorcycle use should be considered.

Indexed as

AdultBody Mass IndexCross-Sectional StudiesFemaleHealth BehaviorHumansLife StyleLogistic ModelsMaleMiddle AgedMotorcyclesOdds RatioOverweightRisk FactorsSelf ReportSocioeconomic Factorsmotorcycle useoverweightsedentary behaviortransportationurban adults

Identifiers

PMID28417951
PMCPMC5409611

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.