Young Adolescents’ Physical Activity In Five Locations As Measured Using GPS And Accelerometry
Bibliographic record
Abstract
PURPOSE: To identify youths’ physical activity (PA) in 5 locations and investigate differences by participant factors. METHODS: 616 youth (14.2±1.5 years old; 49.2% girls; 31.6% non-White; 11.2% obese) were recruited from 311 census block groups in the Baltimore, MD and Seattle, WA regions. Participants wore an accelerometer and GPS tracking device for 7±2.6 days. GIS was used to classify GPS points as (1) at home (50 meter radial buffer), (2) at school (15 meter parcel buffer), (3) near home and (4) near school (both 1 kilometer street network buffers), and (5) at all other locations. Accelerometer and GPS data were time-matched and moderate to vigorous PA (MVPA) in each location was assessed using Evenson cut points. Mixed-effects regression tested differences in MVPA minutes in each location by gender, race/ethnicity, and obesity status. RESULTS: Mean daily minutes of MVPA was 38.0±28.1 overall, 7.4±14.1 at home, 11.3±18.2 at school, 5.5±13.7 near home, 2.7±9.5 near school, and 11.7±21.6 at other locations. 5.2±11.9% of time at home, 5.3±7.4% of time at school, 8.6±15.1% of time near home, 9.8±17.7% of time near school, and 6.2±11.5% of time at other locations was spent in MVPA. Boys had 22-56% more MVPA minutes than girls in all locations except near school (Fig 1). Non-White’s had 24-42% more MVPA minutes near home and near school than White non-Hispanics. Normal/overweight youth had 40-56% more MVPA minutes near home and at other locations than obese youth. CONCLUSION: Understanding where PA occurs can provide insight into creating more supportive environments for youth PA. Schools and other locations contributed the most to youths’ overall PA, but these locations were contributors to gender disparities. The low amount of neighborhood PA suggests need and potential for increasing youths’ PA and reducing obesity through interventions to support active travel.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".