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Record W4407292891 · doi:10.12775/jehs.2025.78.57749

Physical activity and depression

2025· article· en· W4407292891 on OpenAlexaff
Monika Wojtasik, Katarzyna Żak, Adam Załóg, Maria Nowak, Edyta Pietryszak, Wojciech Kulej, Paulina Szołtek, Rafał Pardela, Marcel Paruzel, Martyna Łęcka

Bibliographic record

VenueJournal of Education Health and Sport · 2025
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsDepression (economics)MedicinePsychologyEconomics

Abstract

fetched live from OpenAlex

This article aims to present the significance of physical activity as a key element in the prevention and therapy of depressive disorders. Through a review of the latest scientific research findings, the article focuses on the role of regular physical activity in reducing the risk of depressive disorders and its effectiveness in the therapeutic process. Various aspects of physical activity are analyzed, such as the type and intensity of training, their impact on neurobiological and psychosocial functions. The article discusses the benefits of physical activity, including mood improvement, reduction of depressive symptoms, and overall well-being enhancement. Furthermore, attention is drawn to the biological mechanisms that may underlie the positive impact of physical activity on mental health. In a therapeutic context, the article examines how physical activity can be an effective complement to traditional methods of treating depression, both pharmacological and psychotherapeutic. The importance of adapting training programs to individual needs and preferences of patients is also emphasized. As a result, the article provides a comprehensive overview of current knowledge regarding the role of physical activity in the prevention and therapy of depressive disorders, with the hope of inspiring further research and refining intervention strategies in the field of mental health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.423
Teacher spread0.392 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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