Investigating the effects of physical activity counselling (pac) on physical activity levels and depressive symptoms in female undergraduate students suffering from depression
Bibliographic record
Abstract
Depression is a serious health concern among university students (Ibrahim et al., 2013). Although pharmacotherapy remains the primary treatment for depression, it may not be the most sufficient treatment (Stanton et al., 2014). Recent reviews and meta-analyses support that physical activity has a considerable influence on reducing depressive symptoms (Schuch et al., 2016; Wegner et al., 2014); however there are obvious challenges in getting people moving. One potential strategy, which has received support in helping people become more active, is Physical Activity Counselling (Fortier et al., 2011). Physical Activity Counselling (PAC) focuses on motivating individuals to be more physically active for personally derived reasons. The purpose of this study was to investigate the effects of a two-month PAC intervention on physical activity levels and depressive symptoms in female undergraduate students suffering from depression. The hypotheses were: (1) PAC will increase physical activity levels (2) Increased physical activity levels will reduce depressive symptoms. Physical activity and depressive symptoms were assessed via self-reported questionnaires (Godin Leisure-Time Exercise Questionnaire and Patient Health Questionnaire) administered every second day through FluidSurveys, an online platform. Results from visual analysis supported our hypotheses. Statistical analysis, using paired-samples t-tests, revealed an increase in self-reported physical activity from baseline (M=11.10, SD= 8.46) to endpoint (M=21.60, SD= 13.46) and a decrease in depressive symptoms from baseline (M=14.60, SD=6.69) to endpoint (M= 11.00, SD= 3.80). The eta squared statistics (0.60 and 0.44) indicated large effects. These results support the role of PAC as an approach to increase physical activity levels for the improvement of depressive symptoms.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".