A Sustainable mHealth Intervention to Promote Physical Activity for Healthy Aging: A Pilot Study of the “Every Walk You Take” Citizen Science Initiative
Bibliographic record
Abstract
The objective of the Every Walk You Take initiative was to co-design and test, in a pilot study, a sustainable mHealth intervention prototype (mobile app) to promote physical activity. This prototype would help to identify the barriers to and facilitators of active living in individuals older than 55 years. A prototype of the intervention was co-designed by a community of stakeholders in Barcelona who were familiar with the social and economic burden of aging in high-income societies. The app’s functionalities included recommendations for healthy routes in the city (parks, pedestrian lanes, and streets) according to environmental variables (air quality and climate) and personal preferences (route difficulty, distance, and geolocation), and ecological momentary assessments (pictures and voice notes) were collected to identify the barriers to and facilitators of performing these routes. To test the app, a pilot study was conducted over two 7-day cycles with citizen scientists recruited at the life-long learning centers of two deprived neighborhoods in Barcelona. A total of 21 citizen scientists (mean age = 67 (standard deviation = 7)), 86% of them female, collected 112 comments and 48 pictures describing their perceived barriers to and facilitators of active living. Every Walk You Take is a new, validated, and sustainable mHealth intervention that is directly involved in health promotion, as it empowers the citizens of Barcelona to play an active role in their own healthcare. This intervention has the potential to be implemented in different cities around the world to collect information on the community determinants of health and health assets.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".