Move on Bikes Program: A Community-Based Physical Activity Strategy in Mexico City
Bibliographic record
Abstract
Open streets programs are free and multisectoral programs in which streets are temporally closed allowing access to walkers, runners, rollerbladers, and cyclists. The Move on Bikes program (by its name in Spanish Muévete en Bici) (MEB) consists of 55 km of interconnected streets in middle-high income areas of Mexico City. There is scarce evidence on the evaluation of this program in Mexico. The purposes of this study were to estimate the participation, physical activity levels among the MEB participants, and the association of the frequency of participation with sociodemographic, physical, and program characteristics. METHODS: From October 2017 to July 2018, six hundred seventy-nine MEB participants were surveyed using a questionnaire that contains sociodemographic, physical, and program characteristics. A wide-angle video camera was used to estimate the average speed of each activity per event per participant. Based on the information collected by the program authorities and survey interviews, we estimated the number of participants per event. RESULTS: On a typical MEB program day, 21,812 people participated. MEB program users accumulated an average of 221 min of moderate-to-vigorous physical activity (MVPA) per typical Sunday and 88.4% accumulated at least 150 min of MVPA. In total, 29.6% of users attended the program every Sunday. Those who were more likely to attend the program frequently included: men, those aged 41 to 64 years old, users classified as very and sufficiently active, those that used active transportation to travel to the program, and participants that came alone. CONCLUSIONS: This study provides evidence that the MEB program adds an extra 71 min/week of MVPA to more than 20,000 users.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".