Exploring the Links between Physical Activity, Emotional Regulation, and Mental Well-Being in Jordanian University Students
Bibliographic record
Abstract
Background: University students face multiple stressors that negatively impact their mental well-being. Effective emotional regulation and physical activity are crucial for mood management and overall health. This study explored the connection between physical activity, emotional regulation, and mental health symptoms (depression, anxiety, and stress) in Jordanian university students. Methods: A cross-sectional online survey involved 416 students (146 male and 270 female) from Jordanian universities. The survey covered demographics, physical activity (International Physical Activity Questionnaire), emotional regulation strategies (Emotion Regulation Questionnaire), and mental health symptoms (Depression Anxiety Stress Scales). Pearson’s correlations examined relationships, and ANOVA compared differences in ‘low’, ‘medium’, and ‘high’ physical activity groups. Results: Greater use of expressive suppression was correlated with increased anxiety symptom severity (p = 0.029). Although physical activity levels were not significantly related to emotional regulation, the ‘high’ physical activity group reported lower depression (p < 0.001) and anxiety symptom severity (p < 0.001) than the ‘low’ and ‘medium’ groups. Conclusions: Increased physical activity and emotional expression suppression are independently associated with improved mental well-being in Jordanian university students. This study underscores the importance of integrating physical activity and emotional expression strategies to support student well-being.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".