Quantifying physical activity during active commuting to school: A comparison of methodologies
Bibliographic record
Abstract
• Accurate identification of start/end times is crucial for measuring physical activity during commuting accurately. • Common interval times may overestimate sedentary time and light physical activity during commuting. • Our proposed method, based on the distance from home to school, performed as well as GPS. The current study aims to detect walking trips to/from school with different methodologies (GPS, self-reported, fixed windows [w30 and w60], and distance-based time), and to compare the start/end times for the trips, and the time accumulated in sedentary time, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). A total of 93 Spanish adolescents wore an accelerometer and GPS during school days, and the start/end times of walking trips to/from school were determined using five different methodologies. Mixed-effects limits of agreement analyses were used to determine the level of agreement between the start/end times of the walking trips identified by the five methodologies mentioned. Moreover, methodologies were determined to be equivalent if the mean difference with the GPS was within the proposed equivalence zone of ± 5.0 min. Self-reported measures showed a good level of agreement for estimating start times of walking trips to school compared to GPS, 0.0 (LoA95%:-0.3–0.2) hours. Self-reported measures were deemed equivalent to GPS for measuring sedentary time, LPA, and MVPA. W30 and distance-based time were equivalent to GPS for LPA and MVPA, but not for sedentary time. W60 was only deemed equivalent to GPS for MVPA accumulated during walking trips to and from school. Self-reported measures showed the most precise approach for estimating start times to school, as well as it deemed equivalent to GPS for quantifying sedentary time, LPA, and MVPA. Moreover, estimating the time to complete the trip based on the distance between home and school could be more appropriate than fixed windows.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".