StepsBooster-S: A Culturally Tailored Step-Based Persuasive Application for Promoting Physical Activity
Bibliographic record
Abstract
An inactive lifestyle is associated with an increased risk of health problems. The combination of mobile step-trackers and persuasive strategies can be considered useful tools for encouraging physical activity. This paper presents the design, development, and evaluation of a culturally tailored persuasive app to motivate physical activity. For this research, we developed a step-tracking app, StepsBooster-S, that is tailored to be culturally appropriate for Saudi adults using the user-centred design approach. A 10-day in-the-wild study was conducted with 30 participants to evaluate the usability and effectiveness of the app using a mixed-methods approach. Results showed that StepsBooster-S is generally effective; however, it led to a highly significant increase in physical activity among the Saudis compared to Canadians. Our results also showed that the Saudi audience engaged more with the app, reported more positive experience from using the app, and enjoyed the collectivists-oriented features such as cooperation more than the Canadian audience. We conclude that persuasive health apps, especially those that are targeted at physical activity, are more effective if they are tailored to be culturally appropriate for the target audience. These findings reinforce the importance of cultural factors for designing technologies that motivate behaviour change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".