Abstract P648: Flattening the Curve: A Behavioural Economics Approach to Increase Activity in Summer Months
Bibliographic record
Abstract
Objective: The purpose of this study was to determine if the behavioural economics principle “Fresh Start Effect” was evident within population-based patterns of cycling-specific active transportation (AT). The primary hypothesis was that cycling traffic along AT trails would not exhibit a fresh start effect, compared to cycling traffic along leisure trails. Design and Methods: We triangulated data from three data sources (n=3 million individual data counts) to test this hypothesis. First, the main outcome measure, cycling traffic was captured during summer months (May to September) from 2014 to 2019 with electromagnetic EcoCounters embedded in 5 paved multi-use trails in Winnipeg, Canada. Trails were categorized as AT (bi-modal peaks in traffic in the morning and afternoon) or leisure based on daily patterns of cycling traffic (single peaks of traffic in the afternoon). To confirm trends in AT-based cycling, we used daily bicycle parking data from 2012 to 2019 provided by one of the cities largest companies. To replicate the leisure-based Fresh Start effect trend we used data for fitness centre visits from the largest University in the city between 2017 and 2019. To estimate the weekly trends in occupational attendance we captured sales data from a local coffee shop from 2013 to 2019. Generalized linear mixed effects models compared counts between Mondays and other weekdays for all 4 datasets. Results: Cycling traffic was ~22% lower on Fridays relative to Mondays for both AT trails (-89 counts; 95% CI: -33 to -145counts for AT trails vs -86 counts). Nearly identical trends were observed for leisure type multi-use trails (-86 counts; 95% CI: -38 to -135 counts for leisure trails). Daily rates of occupational bicycle parking (i.e. cycling to work) were 14% lower on Fridays compared the beginning of the week (-7 counts; 95% CI: -4 to -10 counts). Daily rates of fitness centre attendance (i.e. leisure physical activity) were 18% lower on Fridays relative to the beginning of the week (-519 counts; 95% CI: -408 to -630 counts). In contrast to cycling and fitness centre data, daily coffee sales increased 15% on Fridays relative to the beginning of the week, (+42.8 counts; 95% CI: +34.1 - +51.5) suggesting that declines in cycling and leisure physical activity were not related to fewer people working on Fridays. Conclusions: Population-based AT and leisure-based cycling patterns are sensitive to the Fresh Start Effect. These data provide data to guide targeted public health strategies to increase AT and leisure-based cycling in urban areas.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".