Enhancing Neighborhood-Specific Mobility Self-Efficacy In Older Adults: A Wearable-Based Urban Crowdsensing Approach
Bibliographic record
Abstract
The interaction between urban infrastructure design and older adults' mobility remains a critical challenge.Frequent encounters with stressors in built environments, such as steep stairs, uneven sidewalks, or complex traffic signage, reduce older adults' mobility self-efficacy (MSE), ultimately impairing their ability to navigate urban spaces independently.While substantial research has focused on enhancing physical accessibility, limited attention has been given to psychological accessibility, particularly neighborhood-specific MSE, which directly correlates with older adults' likelihood of engaging in outdoor activities.This study explores the potential of wearable-based urban crowdsensing to enhance older adults' neighborhood-specific MSE by leveraging physiological and geolocation data to identify neighborhood stressors.An age-friendly mobile application, SafeCommute, was developed to provide older adults with visual previews of stressors and practical navigation tips, empowering them to anticipate and address challenges in their neighborhoods.A nine-week randomized controlled trial was conducted with 16 older adults, divided into an intervention group using the app and a control group receiving standard mobility support.Changes in Neighborhood-specific MSE and Mobility Intention levels were assessed pre-and postintervention using mixed-model ANOVA.The results demonstrated significant improvements in the intervention group, with Neighborhood-specific MSE increasing by 21% compared to minimal changes in the control group.The significant group-by-time interaction for neighborhood-specific MSE [F (1,14) = 13.264,p < 0.01, η² = 0.487] highlights the intervention's targeted impact.These findings highlight the potential of wearable-based urban crowdsensing to address neighborhood-specific stressors and improve older adults' confidence in navigating their environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".