Evidence map›Paper›PMID 40687274›Full record

ArticleFrontiers in psychiatry2025

Excessive or reduced rest-day sleep compensation linked to depression in Chinese adults.

Yanliqing Song, Long Wang, Yue Liu

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Yanliqing SongCollege of Sports, Nanjing Tech University, Nanjing, China.
Long WangCollege of Sports, Nanjing Tech University, Nanjing, China.
Yue LiuSchool of Athletic Performance, Shanghai University of Sport, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aims to evaluate the impact of Rest Day Catch-up Sleep (RDCS) patterns on depression among Chinese adults during the pandemic and to explore the relationship between different levels of compensation and the likelihood of depression. Methods: This study included 3,981 participants, who were divided into five groups based on changes in rest day sleep duration: no change in sleep duration (RDCS = 0 h), reduced sleep duration (RDCS < 0), moderate catch-up sleep (1 h < RDCS < 2 h), and long catch-up sleep (RDCS ≥ 2 h). A multivariable logistic regression model was used to analyze the relationship between RDCS and depression. Stratified logistic regression and interaction effect analyses were conducted to explore demographic differences in the association between RDCS and depression. Results: In the fully adjusted model, participants with reduced sleep duration had an odds ratio (OR) of 2.12 (95%CI [1.31 - 3.46]) for depression, while those with long catch-up sleep had an OR of 1.60 (95%CI [1.29 - 1.98]). Stratified logistic regression and interaction effect analyses indicated that the association between RDCS < 0 h and depression was more significant among individuals with a Body Mass Index (BMI) < 25 kg/m², while the association between RDCS ≥ 2 h and depression was more significant among individuals with a general self-rated health status. Conclusion: The results of this study indicate that both reduced sleep duration and excessive catch-up sleep during the pandemic are associated with an increased likelihood of depression. These findings highlight the importance of maintaining stable sleep patterns during special periods and provide scientific evidence for the development of targeted public health interventions.

Indexed as

depressionmental healthpandemic periodpublic health interventionsleep compensation

Identifiers

PMID40687274
PMCPMC12271152

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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.