Evidence map›Paper›PMID 42238682›Full record

ArticleDigital health

PEPRKD-depression: A knowledge database supporting evidence-based personalized exercise prescription recommendations in depression.

Danting Li, Jiale Du, Ting Bao, Yingbo Zhang, Jinhua Feng, Xingyun Liu, Rongrong Wu, Cheng Bi, Amin Ullah, Bairong Shen

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

10 authors.

Danting LiHealth Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0003-4084-1579
Jiale DuTranslational Informatics Center, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Ting BaoHealth Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China.
Yingbo ZhangTranslational Informatics Center, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Jinhua FengTranslational Informatics Center, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Xingyun LiuTranslational Informatics Center, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Rongrong WuTranslational Informatics Center, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Cheng BiTranslational Informatics Center, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Amin UllahTranslational Informatics Center, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Bairong ShenTranslational Informatics Center, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0003-2899-1531

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Depression is a prevalent mental disorder associated with substantial social and familial burdens, and exercise is increasingly recognized as a promising non-pharmacological intervention; however, research is hindered by heterogeneous approaches, safety concerns, and individual variability. A structured knowledge framework may support the development of more personalized exercise prescription. This study aimed to construct PEPRKD-Depression (Personalized Exercise Prescription Recommendation Knowledge Database for Depression), a structured knowledge database of exercise interventions for depression, to systematically integrate existing literature and provide a knowledge foundation for personalized exercise prescription. Methods: Data were sourced from PubMed and included original studies on exercise interventions for depression published between 1960 and 2023. Relevant data including structured exercise programs, patient information, fitness and risk assessments, adverse events, and outcomes, were extracted, standardized, and organized. The knowledge database was developed with Vue3, hosted on Nginx, and uses MySQL for data storage. Results: PEPRKD-Depression includes 567 studies with 769 exercise intervention protocols involving 100,794 subjects across 49 countries. It extracted 662 depression-related items (e.g., symptoms, disorders, perinatal and subthreshold depression). Exercise regimens followed the "FITT-VPP" principle: Frequency, Intensity, Time, Type, Volume, Progression, Periods of time. The knowledge database is publicly available at: https://dpa.bioinf.org.cn/. Conclusion: PEPRKD-Depression is a comprehensive exercise therapy knowledge database for depression that provides a structured repository of exercise intervention information that may serve as a preliminary knowledge resource for future personalized exercise recommendation research and decision-support applications.

Indexed as

decision support tooldepressionexercise prescriptionknowledge databasepersonalized recommendation system

Identifiers

PMID42238682
PMCPMC13227018

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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.