The Effects of a Self-Management Treatment Package on Physical Activity in University Students with Depressive Symptoms
Bibliographic record
Abstract
Research demonstrates that exercise interventions are effective in decreasing depressive symptoms; however, these treatments are infrequently implemented in clinical practice. Self-management techniques offer an effective, cost-efficient approach to teaching individuals with depression to engage in increased physical activity. This study evaluated a treatment package including goal setting, self-monitoring, and feedback for increasing participants’ daily steps. Secondary measures included depressive symptoms, sleep quality and duration. A changing-criterion design within a concurrent multiple baseline design across two participant dyads was used. Results demonstrated that the treatment was efficacious for increasing walking in participants, with varying degrees of consistency. Additionally, increased walking may improve sleep duration. Mid-treatment scores on the University Student Depression Inventory showed decreases in some symptoms (i.e., lower total and, or subscale[s] scores) suggesting walking may be associated with a decrease in some symptoms. Clinician ratings on the Clinical Global Impression Scale indicated that the change in symptoms were significant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".