Impact of the COVID-19 Lockdown on Physical Activity Levels and Health Parameters in Young Adults with Cancer
Bibliographic record
Abstract
The lockdown of the COVID-19 pandemic impacted physical activity (PA) levels around the world, affecting health parameters in young adults with cancer (YAC). To our knowledge, there is no evidence of the impact of the lockdown on the Spanish YAC. To analyse the changes in PA levels before, during, and after the lockdown of the YAC and its impact on health metrics in Spain, in this study, we utilized a self-reported web survey. PA levels decreased during the lockdown, and a significant increase in PA was observed after the lockdown. Moderate PA had the largest reduction (49%). Significant increases in moderate PA were noted after the lockdown (85.2%). Participants self-reported more than 9 h of sitting per day. HQoL and fatigue levels were significantly worse during the lockdown. The impact of the COVID-19 pandemic in this cohort of Spanish YAC showed a decrease in PA levels during the lockdown, affecting sedentarism, fatigue and HQoL. After lockdown, PA levels partially recovered, while HQoL and fatigue levels remained altered. This may have long-term physical effects such as cardiovascular comorbidities associated with sedentarism and psychosocial effects. It is necessary to implement strategies such as cardio-oncology rehabilitation (CORE), an intervention that can be delivered online, potentially improving participants' health behaviours and outcomes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".