Digital citizen science for ethical monitoring of youth physical activity frequency: Comparing mobile ecological prospective assessments and retrospective recall
Bibliographic record
Abstract
Physical inactivity is a leading risk factor for mortality worldwide. Understanding youth patterns of moderate-to-vigorous physical activity (MVPA) is essential for addressing non-communicable diseases. Digital citizen science approaches, using citizen-owned smartphones for data collection, offer an ethical and innovative method for monitoring MVPA. This study compares the frequency of MVPA reported by youth using retrospective surveys and mobile ecological prospective momentary assessments (mEPAs) to explore the potential of digital citizen science for physical activity (PA) surveillance. Youth (N = 808) were recruited from Saskatchewan, Canada, between August and December 2018. Sixty-eight participants (ages 13-21) provided complete data on retrospective surveys (International Physical Activity Questionnaire, Simple Physical Activity Questionnaire, Global Physical Activity Questionnaire) and prospective mEPAs. Wilcoxon signed-rank tests compared retrospective and prospective MVPA frequencies, while negative binomial regression analysis examined associations between contextual factors and MVPA. Significant differences were found in the frequency of MVPA reported via retrospective surveys versus mEPAs (p < 0.000). Prospective MVPA was associated with family and friend support, having drug-free friends, part-time employment, and school distance, while retrospective MVPA frequency was associated with school and strength training. Digital citizen science, utilizing mEPAs, can provide more accurate and timely data on youth MVPA. With increasing smartphone access and digital literacy, mEPAs represent a promising method for developing effective and personalized MVPA recommendations for youth. However, these findings should be interpreted with caution, as the sample represents a small subset of youth, limiting generalizability to other youth populations.
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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 | 0.037 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".