Exploring ethical monitoring of physical activity behaviors among adults: a Smart Platform study operationalizing digital citizen science
Bibliographic record
Abstract
Background According to the World Health Organization, 27% of adults do not meet the recommended daily levels of physical activity (PA), making accurate PA measurement essential for informing evidence-based policies. This study explores ethical engagement with citizens through their ubiquitous digital tools (i.e., smartphones) to examine variations between retrospectively and prospectively reported PA behaviors within the same cohort. Methods This study is part of the Smart Platform, a digital citizen science initiative for ethical monitoring and real-time intervention. Data were collected from 118 adults who participated over eight consecutive days, including both weekdays and weekends. Prospective PA was assessed using time-triggered ecological assessments, while retrospective PA was measured using a modified, time-triggered, smartphone-based validated tool. Paired sample t-tests were used to compare retrospective and prospective PA. Linear regression models examined associations between socio-demographic and contextual factors and both types of PA reporting. Analyses were conducted for the overall sample and by gender (male vs. female). Results Participants consistently reported higher PA through retrospective measures compared to prospective ones (p < 0.001). In the overall sample, one significant association was found in the retrospective model, while three were identified in the prospective model. Among males, those who engaged in PA for fun or to maintain physical health reported higher retrospective PA, though this was not significant in the prospective model. In contrast, female participants who engaged in PA for fun reported higher PA in both retrospective and prospective models. Conclusions Although exploratory, early findings suggest that repeated, prospective assessments via ubiquitous digital devices may enhance the validity and reliability of PA measurement. As citizen-owned digital tools become increasingly widespread, ethically leveraging big data through digital citizen science offers a promising approach to improve PA monitoring and support public health efforts.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".