Evidence map›Paper›PMID 35596218›Full record

ArticleBMC psychiatry2022

Association between sedentary behavior and depression among Japanese medical students during the COVID-19 pandemic: a cross-sectional online survey.

Tsubasa Tashiro, Noriaki Maeda, Shogo Tsutsumi, Makoto Komiya, Satoshi Arima, Rami Mizuta, Kazuki Fukui, Yuichi Nishikawa, Yukio Urabe

Open access · goldAbstract read
In one paragraph

Article in BMC psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
3.6field-weighted citation impact, top 7% of its field
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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Tsubasa TashiroGraduate School of Biomedical and Health Sciences Division of Integrated Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan.
Noriaki MaedaGraduate School of Biomedical and Health Sciences Division of Integrated Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan.
Shogo TsutsumiGraduate School of Biomedical and Health Sciences Division of Integrated Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan.
Makoto KomiyaGraduate School of Biomedical and Health Sciences Division of Integrated Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan.
Satoshi ArimaGraduate School of Biomedical and Health Sciences Division of Integrated Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan.
Rami MizutaGraduate School of Biomedical and Health Sciences Division of Integrated Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan.
Kazuki FukuiGraduate School of Biomedical and Health Sciences Division of Integrated Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan.
Yuichi NishikawaFaculty of Frontier Engineering, Institute of Science & Engineering, Kanazawa University, Kanazawa, 920-1192, Japan.
Yukio UrabeGraduate School of Biomedical and Health Sciences Division of Integrated Health Sciences, Hiroshima University, Hiroshima, 734-8553, Japan. yurabe@hiroshima-u.ac.jp.
Hiroshima University · JPKanazawa University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSedentary behavior, particularly prevalent during the 2019 coronavirus disease (COVID-19), has been shown to lead to depression. In medical students, this could negatively affect the provision of healthcare. Thus, this study aimed to investigate the association between sedentary behavior and depression in Japanese medical students during the COVID-19 pandemic.

methodsAn online cross-sectional survey was conducted from July 30 to August 30, 2021, using anonymous questionnaire system to assess respondents' sociodemographic characteristics, physical activity, sedentary behavior among 1000 Japanese university students. Depression was assessed using the Patient Health Questionnaire-2 (PHQ-2). The data of 484 respondents (48.4%) were included in a stepwise analysis, where we set the difference between medical and non-medical students as Model 1 and that between medical students with and without depression as Model 2. For group comparisons of both models, the chi-square test was used for sociodemographic characteristics, and the Mann-Whitney U-test was used for physical activity and sedentary behavior. In Model 2, factors associated with depression among medical students were analyzed by logistic regression analysis.

resultsIn Model 1, medical students were less physically active (p < 0.001), had longer sedentary time (p < 0.001), and had higher PHQ-2 scores (p = 0.048) than non-medical students. In Model 2, medical students with depression had longer sedentary time (p = 0.004) and longer leisure screen time than those without depression (p = 0.007). Moreover, logistic regression analysis adjusted for potential confounders showed that sedentary time (OR = 1.001, p = 0.048) and leisure screen time (OR = 1.003, p = 0.003) were significantly associated with depression among medical students.

conclusionsBased on these results, it is evident that reducing Japanese medical students' sedentary time and leisure screen time can help combat depression during the COVID-19 pandemic; thus, these results can guide the development of appropriate interventions to prevent and treat depression.

Indexed as

COVID-19Students, MedicalCross-Sectional StudiesDepressionHumansJapanPandemicsSedentary BehaviorCOVID-19DepressionJapanMedical studentsOnline surveyPandemicScreen timeSedentary behavior

Identifiers

PMID35596218
PMCPMC9121084
OpenAlexW4280626935

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.