Evidence map›Paper›PMID 39228960›Full record

ArticleNeuropsychiatric disease and treatment2024

Unveiling the Interplay Between Depressive Symptoms' Alleviation and Quality of Life Improvement in Major Depressive Disorder: A Network Analysis Based on Longitudinal Data.

Tong Guo, Yuan Feng, Jingjing Zhou, Linghui Meng, Xuequan Zhu, Xu Chen, Le Xiao, Lei Feng, Ling Zhang, Yu-Tao Xiang and 2 more

Abstract read
In one paragraph

Article in Neuropsychiatric disease and treatment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

12 authors.

Tong GuoBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People's Republic of China.
Yuan FengBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People's Republic of China.
Jingjing ZhouBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People's Republic of China.
Linghui MengBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People's Republic of China.
Xuequan ZhuBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People's Republic of China.
Xu ChenBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People's Republic of China.
Le XiaoBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People's Republic of China.ORCID 0000-0003-4323-036X
Lei FengBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People's Republic of China.ORCID 0000-0001-7810-5070
Ling ZhangBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People's Republic of China.
Yu-Tao XiangUnit of Psychiatry, Department of Public Health and Medicinal Administration, Faculty of Health Sciences, Institute of Translational Medicine, University of Macau, Taipa, Macao SAR, People's Republic of China.
Yan-Jie ZhaoBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People's Republic of China.ORCID 0000-0003-3989-9386
Gang WangBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Understanding the dynamic relationship between depressive symptoms and quality of life (QOL) is essential in improving long-term outcomes for patients with Major Depressive Disorder (MDD). While previous studies often relied on cross-sectional data, there is a pressing need for stronger evidence based on longitudinal data to better inform the development of effective clinical interventions. By focusing on key depressive symptoms, such interventions have the potential to ultimately enhance QOL in individuals with MDD. Methods: This multi-center prospective study, conducted between 2016 and 2020, enrolled outpatients and inpatients diagnosed with MDD across twelve psychiatric hospitals in China. Longitudinal data on Patient Health Questionnaire - 9 (PHQ-9) and Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form (Q-LES-Q-SF) was analyzed using an Extended Bayesian Information Criterion (EBIC) graphical least absolute shrinkage and selection operator (gLASSO) network model to explore the connections between depressive symptom changes and QOL changes. Flow network was applied to investigate relationships between individual symptom changes and overall QOL score change, as well as daily functional independence. Results: This study included 818 participants with complete data after 8-week antidepressant treatment. Apart from the overlapping items from PHQ-9 and Q-LES-Q-SF, the three edges between "mood" (delta-QLES2) and "anhedonia" (delta-DEP1), between "physical health" (delta-QLES1) and "sleep problems" (delta-DEP3), and between "physical health" (delta-QLES1) and "sad mood" (delta-DEP2) were the most strong bridges between the cluster of depressive symptoms alleviation and the cluster of QOL change. "Anhedonia" (delta-DEP1), "sad mood" (delta-DEP2) and "loss of energy" (delta-DEP4) had the highest bridge strength between the alleviations of depressive symptoms and the total score change of Q-LES-Q-SF. Anhedonia had the greatest connection with participants' satisfaction with function in daily life. Conclusion: This study highlighted the potential for developing highly effective interventions by targeting on central symptoms, thereby to ultimately improve QOL for patients with MDD.

Indexed as

acute phase treatmentdepressive symptomsnetwork analysispatients with MDDquality of life

Identifiers

PMID39228960
PMCPMC11370766

What Socratic holds

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LicenceCC BY-NC
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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 Socratic graph.