The Effects of a Guided Mindful Walk on Mental Health in University Students
Bibliographic record
Abstract
International Journal of Exercise Science 17(5): 590-601, 2024. College campuses in the United States are experiencing high levels of mental distress without adequate psychological resources to address the need. In addition, the majority of university students do not meet the physical activity guidelines for mental and physical health. Effective and time efficient resources are needed to address poor mental health and low physical activity among university students on college campuses. Mindful walking may be a promising solution. The purpose was to 1) measure change in mental health and 2) estimate physical activity from participation in a guided mindful walk in a diverse student sample. Students participated in a mindful walking route which included seven stops (0.85 miles) during the Spring 2022 semester. Undergraduate students (n = 44) were mean ± SD age 20.9 ± 3.8 years and 68% female. Validated surveys were given pre- and post-participation measuring mental health constructs of state mindfulness (Toronto Mindfulness Scale; TMS), state anxiety (visual analogue scale), and state stress (Short Stress State Questionnaire; SSSQ). Physical activity was estimated via steps on a Yamax pedometer worn at the hip. After the guided mindful walk, total state mindfulness score significantly improved (mean ± SD) (pre: 27.5 ± 8.2, post: 32.8 ± 9.5; p < 0.001); state anxiety significantly decreased (pre: 3.7 ± 2.4, post: 2.4 ± 2; p < 0.0001) and total state stress score was reduced (pre: 66.1 ± 10.7, post: 63.4 ± 8.3; p = 0.03). Physical activity averaged 1,726 ± 159 steps. Completion of a guided mindful walk can reduce anxiety and stress, while increasing mindfulness among university students.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".