A Comparative Study of the Efficacy of Cognitive Group Therapy and Aerobic Exercise in the Treatment of Depression among the Students
Bibliographic record
Abstract
BACKGROUND: Depression is one of the most common mental disorders. Finding effective treatments for such a disorder with higher efficiency lower side effects and affordability is an active area of research in psychiatry. This study aimed to comparatively analyze the effects of the cognitive group therapy and aerobic exercises on depression, automatic negative thoughts and dysfunctional attitudes of students at Kermanshah University of Medical Science. METHODS: In this clinical trial, 46 associate and undergraduate students at Kermanshah University of Medical Science were randomly divided into three groups: cognitive therapy, aerobic exercise, and control. The data was gathered both before and 8 weeks after the intervention. Beck Depression Inventory (BDI-II), automatic negative thoughts (ATQ), and the Dysfunctional Attitude Scale (DAS) were used as the data collection instruments. The data were analyzed with SPSS version 15 using paired samples T-test, chi-square test, Kruskal-Wallis test, and analysis of variance (ANOVA). RESULTS: Cognitive therapy caused a significant decrease in depression, belief in automatic negative thoughts, and dysfunctional attitudes in comparison to the control group (p<0.05). Although aerobics compared to the control group causes more reductions in the variables, however, It was only meaningful for the depression variable (p=0.049). Cognitive therapy also reduced the variables more than the aerobic exercise, but the decrease was not statistically significant. CONCLUSIONS: Cognitive group therapy and aerobic exercise are effective in treating depression. For treating depression, aerobic exercise can be used as a therapy itself or along with cognitive-behavioral therapy and pharmacotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".