Design and Evaluation of a Digital Health App (SingaporeWALK) for Active Aging: Pre-Post Intervention Study
Bibliographic record
Abstract
Background: The global trend toward population aging poses significant challenges for maintaining older adults' health and well-being, particularly in multicultural urban environments like Singapore. Despite the potential of digital health interventions, older adults face substantial barriers to technology adoption, including complex interfaces and culturally inappropriate content. Existing mobile health apps often fail to integrate physical, nutritional, and mental health components or accommodate the needs of multicultural older adult populations. Objective: To address gaps in mobile app design for older adults and bridge the digital divide, this research aimed to create and evaluate the SingaporeWALK (SGWALK) app, a culturally inclusive digital health solution promoting active aging through community-based interventions among Singapore's older adults. Methods: The SGWALK app was developed using participatory design methodology involving iterative testing with older adults to ensure appropriateness and usability. The app integrates 3 core components: exergames (Fruit Ninja, Piano Step, and Arctic Punch) aligned with Singapore's exercise guidelines for older adults, nutrition tracking based on local dietary recommendations, and mental well-being assessment using the Mental Health Continuum-Short Form. Following development, a 4-week pre-post intervention study was conducted with 48 participants (aged 60-85 y) randomly allocated to 4 conditions: conventional exercise, exergames only, exergames with health coach support, or exergames with peer support. In total, 5 wearable inertial measurement unit sensors captured movement data during weekly 30-minute supervised sessions at community centers. Primary outcomes included changes in physical activity metrics, technology acceptance, and mental well-being measured through pre- and postintervention assessments. Results: The 4-week intervention demonstrated significant improvements across multiple health domains. Physical activity measures showed a 6.5% increase in maximum acceleration (t47=3.82, P<.001), while nutritional tracking revealed steady improvements in healthy eating patterns throughout the intervention period. Mental health assessments indicated that participants classified as "mentally well" consistently outperformed the "moderate" group across physical activity measures. Technology acceptance showed substantial enhancement, with willingness to use health apps increasing from mean 3.18 (SD .79) to mean 3.95 (SD .82; t29=-3.63, P<.001), and perceived ease of use improving from mean 3.01 (SD .70) to mean 3.76 (SD .68; t29=-4.08, P<.001). Additionally, participants developed more efficient movement patterns over time and formed supportive social relationships during the community-based implementation. Conclusions: The SGWALK app shows promise for promoting active aging and reducing technology barriers among Singapore's older adults. The community-based implementation model, bilingual interface, integrated health monitoring approach, and sensor-based movement tracking offer potential advantages over existing solutions. These findings provide useful insights for researchers and practitioners developing digital health interventions for older adult populations in multicultural urban settings.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".