Pedometer Efficacy for Clinical Care in Pediatric Cardiology
Bibliographic record
Abstract
BACKGROUND: Physical activity is essential to the long-term health of children living with cardiac disease. The simplicity and cost of pedometers make them an attractive alternative to accelerometers for monitoring the physical activity behaviors of these children. This study compared measures obtained from commercial-grade pedometers and accelerometers. METHODS: Pediatric cardiology outpatients (n = 41, mean age = 8.4 [3.7] y, 61% female) wore a pedometer and accelerometer daily for 1 week. Step counts and minutes of moderate to vigorous physical activity were compared between devices, accounting for age group, sex, and diagnostic severity, using univariate analysis of variance. RESULTS: While pedometer data were significantly correlated with accelerometers (r > .74, P < .001), measurements obtained were significantly different between devices. Overall, pedometers overestimated physical activity data. The overestimation of moderate to vigorous physical activity was significantly less among adolescents than younger age groups (P < .01, ηp2=.38). For step counts, there was a significant age by sex interaction observed where preschool and adolescent males tended to have greater differences between accelerometer and step count data than females (P < .01, ηp2=.33). Differences between devices were not associated with severity of diagnosis. CONCLUSIONS: The distribution of pedometers in a pediatric outpatient clinic was feasible, yet the data collected significantly overestimated physical activity, especially among younger children. Practitioners who want to introduce objective measurements as part of their physical activity counseling practice should use pedometers to monitor individual changes in physical activity and consider patient age before administering these devices for clinical care.
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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 | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".