Evidence map›Paper›PMID 39157302›Full record

ArticlePCN reports : psychiatry and clinical neurosciences2024

Online peer support for mental health in individuals with post-acute sequelae of COVID-19: A pre-post pilot study with mixed methods.

Megumi Hazumi, Michi Miyake, Mayumi Kataoka, Kentaro Usuda, Daisuke Nishi

Abstract read
In one paragraph

Article in PCN reports : psychiatry and clinical neurosciences, 2024. 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

5 authors.

Megumi HazumiDepartment of Public Mental Health Research, National Institute of Mental Health National Center of Neurology and Psychiatry Kodaira Tokyo Japan.ORCID 0000-0002-1625-6962
Michi MiyakeDepartment of Public Mental Health Research, National Institute of Mental Health National Center of Neurology and Psychiatry Kodaira Tokyo Japan.
Mayumi KataokaDepartment of Public Mental Health Research, National Institute of Mental Health National Center of Neurology and Psychiatry Kodaira Tokyo Japan.
Kentaro UsudaDepartment of Public Mental Health Research, National Institute of Mental Health National Center of Neurology and Psychiatry Kodaira Tokyo Japan.
Daisuke NishiDepartment of Public Mental Health Research, National Institute of Mental Health National Center of Neurology and Psychiatry Kodaira Tokyo Japan.ORCID 0000-0001-9349-3294

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: This pilot examined the effect of online peer support on mental health problems among individuals with post-acute sequelae of COVID-2019 (PASC). Methods: A single-arm pre-post design of online peer-support design consisting of eight sessions of 1 h per week with three to six participants and two facilitators per group was performed. Participants were recruited from online communities, social media, and medical clinics for the PASC between May and August 2023. The degrees of depression, anxiety, loneliness, social withdrawal, and self-esteem were measured pre- and post-intervention. Participants' statements during the sessions were analyzed using thematic analyses. Results: Of the 18 participants, three dropped out of the interventions, and 17 (including two participants who dropped out) completed the pre- and post-intervention questionnaires. Depression severity significantly decreased in the paired Conclusion: Online peer support may be helpful in treating depression in individuals with PASC.

Indexed as

depressionmixed methodpeer supportpost‐acute sequelae of COVID‐19pre‐post intervention

Identifiers

PMID39157302
PMCPMC11330590

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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.