Investigating the Effects of Physical Activity Counselling on Depressive Symptoms, Affect and Physical Activity in Female Undergraduate Students with Depression: A Multiple Baseline Single-Subject Design
Bibliographic record
Abstract
Background: In Canada, women aged 15-24 report the highest rate of depression, an age group which represents a significant proportion of undergraduate students (Hanlon, 2012). Although pharmacology remains the primary treatment for depression, it may not be the most sufficient (Stanton et al., 2014). Physical activity has been demonstrated to have a large and significant antidepressant effect in individuals with depression (Schuch et al., 2016), though what remains challenging is identifying the most effective way to activate this population. Physical Activity Counselling (PAC) has been shown in research to effectively increase levels of physical activity (Fortier et al., 2011). However, the effects of PAC have not been considered in a population of female students with depression specifically. Purpose: To investigate the effects of PAC on depressive symptoms, affect and physical activity in female undergraduate students with depression. Methods: Five female undergraduate students with depression received two months of PAC from a registered Kinesiologist. The study followed a multiple baseline, single-subject design in which measures were taken during four study phases: baseline, intervention, end point and follow-up. Data was collected, including daily objective measures of physical activity, using accelerometers, and self-reported measures of depressive symptoms, positive affect, negative affect and physical activity, using online surveys administered every second day. Results: Visual analyses revealed that depressive symptoms decreased and self-reported physical activity increased from baseline throughout subsequent study phases in all five participants, as hypothesized. Statistical analyses supported these results. Estimated effect sizes of grouped averages indicated that decreases in depressive symptoms from baseline throughout each study phase ranged from small to large, while increases in self-reported physical activity were in the medium to large range. Conclusions: Findings of this study provide initial support for Physical Activity Counselling as a potential strategy to increase physical activity levels and reduce depression among female undergraduate students with depression. Future research is recommended on this important topic.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".