COVID-19 Pandemic and Exercise (COPE) trial: a multigroup pragmatic randomised controlled trial examining effects of app-based at-home exercise programs on depressive symptoms
Bibliographic record
Abstract
BACKGROUND: The number of adults across the globe with significant depressive symptoms has grown substantially during the COVID-19 pandemic. The extant literature supports exercise as a potent behaviour that can significantly reduce depressive symptoms in clinical and non-clinical populations. OBJECTIVE: Using a suite of mobile applications, at-home exercise, including high intensity interval training (HIIT) and/or yoga, was completed to reduce depressive symptoms in the general population in the early months of the pandemic. METHODS: A 6-week, parallel, multiarm, pragmatic randomised controlled trial was completed with four groups: (1) HIIT, (2) Yoga, (3) HIIT+yoga, and (4) waitlist control (WLC). Low active, English-speaking, non-retired Canadians aged 18-64 years were included. Depressive symptoms were measured at baseline and weekly following randomisation. RESULTS: A total of 334 participants were randomised to one of four groups. No differences in depressive symptoms were evident at baseline. The results of latent growth modelling showed significant treatment effects in depressive symptoms for each active group compared with the WLC, with small effect sizes (ESs) in the community-based sample of participants. Treatment groups were not significantly different from each other. Effect sizes were very large (eg, week 6 ES range=-2.34 to -2.52) when restricting the analysis only to participants with high depressive symptoms at baseline. CONCLUSIONS: At-home exercise is a potent behaviour to improve mental health in adults during the pandemic, especially in those with increased levels of depressive symptoms. Promotion of at-home exercise may be a global public health target with important personal, social and economic implications as the world emerges scathed by the pandemic. TRIAL REGISTRATION NUMBER: NCT04400279.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".