Evaluating actual use of outdoor exercise equipment in a community park in Southern California through video-captured behavioral assessment
Bibliographic record
Abstract
Aim: Test feasibility of measuring physical activity levels and actual use of outdoor exercise equipment by park users through a human-centered virtual audit in an observational study in southern California. Methods: Approximately 3,000 h of continuous video footage were collected using a stationary, customized, wireless data-transmission-enabled outdoor camera in Eastgate Park, Garden Grove, California. The camera captured images of the outdoor exercise equipment every 30 s for 14 h a day over seven months. A virtual audit was conducted on 300 h of footage by a team of researchers who tracked weather conditions, equipment usage, duration of use, and observed the age, gender, and activity levels (sedentary, moderate, or vigorous) of park users. Equipment use was categorized as intended (correct use) or unintended (e.g., resting on equipment). Results: Pooling observations from all eight exercise machines, adults used the equipment as intended an estimated 77% of the time (95% CI: 76%-79%). Youth used the equipment as intended an estimated 39% of the time (95% CI: 37%-40%). Overall, park users used the equipment as intended an estimated 60% of the time (95% CI: 59%-62%). Conclusion: Virtual audits can effectively differentiate between intended and actual use of outdoor exercise equipment in recreational parks. This method can complement field observation methods, such as SOPARC, and aid in planning public health promotion and education programs to increase proper use of exercise equipment, however it is time intensive. With the advent of AI, automated visual audits can provide timely analysis of park improvement impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".