Physical activity evaluation in children with congenital heart disease
Bibliographic record
Abstract
Significant advances in the management of children with congenital heart disease (CHD) have resulted in marked improvements in survival and life expectancy. Thus, there is an increased emphasis on promoting physical activity to optimise healthy development and long-term cardiovascular health. Evaluation of physical activity levels as part of ongoing clinical care is recommended to facilitate physical activity counselling and/or exercise prescription. Physical activity is a complex health behaviour that is challenging to evaluate. We provide an overview of techniques for measuring physical activity in children with CHD with a focus on how to do this in the clinical context. Accelerometers are devices that objectively assess intensity and duration of physical activity under free living conditions. They enable evaluation against physical activity guidelines, but are costly and require advanced technical expertise. Pedometers are a simple-to-use and cost-effective alternative, but an outcome metric of daily step count limits classification against guidelines. Commercial wearable activity trackers offer an appealing user experience and can provide valid estimates in children. Furthermore, activity trackers enable remote monitoring of physical activity levels, which may facilitate exercise prescription and activity counselling. Questionnaires are the most cost-effective and time-effective method, but recall error in younger children is a consideration. Routine exercise testing in children with CHD provides important insight into functional status but should not be viewed as a proxy measure of habitual physical activity. Understanding the spectrum and role of physical activity measurement tools is important for clinicians focused on optimising cardiovascular health in children with CHD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".