Reduced Physical Activity During COVID-19 in Children With Congenital Heart Disease: A Longitudinal Analysis
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) was associated with a reduction in physical activity in children with congenital heart disease (CHD) in early 2020. Given the increased cardiovascular risk of this population, optimizing cardiovascular health behaviour is important. The aim of the study is to determine how the ongoing COVID-19 pandemic has impacted longitudinal physical activity measures in children with CHD. Methods: As part of a prospective cohort study, children and adolescents aged 9-16 years old with moderate-to-complex CHD were recruited from British Columbia Children's Hospital and partnership clinics across British Columbia and the Yukon territory. Daily step counts were measured continuously over 3 years (2018-2021) with Fitbit Charge 2. School status during the COVID-19 pandemic was assessed with parent- or self-report survey. Results: < 0.001). There was a loss of seasonal variation in physical activity, and reduced levels of physical activity persisted when most children resumed in-person schooling in September 2020. Conclusions: We demonstrated a significant decrease in physical activity and loss of seasonal patterns in children with CHD during 2020. These findings represent a worsening of the cardiovascular risk profile in children with CHD, who are already at an increased risk of adverse cardiovascular outcomes. Mitigation strategies are needed to optimize the cardiovascular health status of children with CHD as the pandemic persists.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".