Comparison Of Accelerometer And Pedometer Measured Physical Activity In Rural Elementary Schools
Bibliographic record
Abstract
Schools are an ideal setting for physical activity (PA) promotion efforts. Objective methods to monitor moderate-to-vigorous PA (MVPA) have emphasized accelerometers, which can be cost prohibitive. To monitor the success of promotion efforts, schools need access to low-cost, valid and reliable tools. Using pedometers to count accumulated steps above 120 steps/min has been suggested as an alternative MVPA measure. PURPOSE: To determine if using the 120 steps/min threshold with pedometers to measure children’s MVPA at school provides equivalent MVPA estimates compared to research-grade accelerometers. METHODS: Children (n = 316, 52.8% boys) from six rural elementary schools (grades 1, 3, and 5) had their PA monitored at school over 4 consecutive days. Two PA monitors were placed on an elastic belt and positioned over each child’s right hip. Pedometer data were downloaded daily and accelerometer data were processed using the Evenson cutpoints in 15 s epochs. MVPA estimates from the monitors were compared with: 1) t-tests, 2) Pearson correlations, and 3) Bland-Altman plots. RESULTS: Pedometers measured (M ± SD) 17.5 ± 6.4 min of MVPA during the school day, while accelerometers measured 24.0 ± 9.0 min of MVPA (p < 0.001; Table 1). The correlation between pedometer- and accelerometer-determined MVPA was 0.64. Correlations for boys and girls were 0.63 and 0.67, respectively. Grade-level correlations ranged from 0.54 to 0.66. Bland-Altman plots indicated the limits of agreement ranged from -7.4 to 23.5 min of MVPA. CONCLUSION: Although pedometer-determined MVPA was moderately correlated with accelerometer-determined MVPA, the two measures do not appear to be equivalent. Further research should explore the potential to correct this discrepancy between devices.Table 1: School-based MVPA MinutesSupported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, grant award number 2011-68001-30020.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".