Effects of a 6-week at-home exercise intervention on psychological and physical indicators of well-being during the COVID-19 pandemic: Secondary analysis of a pragmatic randomized trial
Bibliographic record
Abstract
To examine whether a 6-week at-home exercise intervention, delivered via mobile applications (apps), improves psychological and physical well-being and alleviates ill-being symptoms in adults during the early months of the COVID-19 pandemic. Low active Canadian adults (n = 334) were recruited and randomized to one of three app conditions [Yoga, n = 86; high-intensity interval training (HIIT), n = 82, HIIT + Yoga, n = 83] or a waitlist control condition (n = 83). Those in the exercise conditions (HIIT, Yoga, HIIT + Yoga) were asked to use their respective modules in the apps to complete four 20-min sessions per week for six weeks. Indicators of well-being (flourishing, general mental health, life satisfaction, positive affect, resilience, and perceived physical health) and ill-being (burden of psychosocial problems, negative affect, psychological distress, psychological stress, and physical symptoms) were measured every week over the intervention. After six weeks, compared to the waitlist control condition, those in the HIIT + Yoga condition improved in all well-being outcomes, those in the Yoga condition showed improvements in flourishing, positive affect, and perceived physical health, and those in the HIIT condition showed no improvements in well-being indicators. None of the three exercise conditions demonstrated any improvement in ill-being indicators compared to the waitlist control condition. Engagement with at-home exercise apps over 6 weeks can improve multiple facets of well-being (e.g., flourishing, positive affect), especially when a variety of modalities of exercise are presented. • App-based exercise interventions represent a low-cost, low-barrier pathway to physical activity. • In this trial, app-based at-home exercise did not significantly improve ill-being factors (e.g., distress, negative affect). • However, the apps did improve participants' well-being (e.g., flourishing, resilience). • These effects were most prominent when participants had access to both yoga and high-intensity interval training applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 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".