Investigating the Key Persuasive Features for Fitness App Design and Extending the Persuasive System Design Model: A Qualitative Approach
Bibliographic record
Abstract
Physical inactivity has been recognized as one of the leading risk factors that account for many non-communicable diseases, with the World Health Organization labeling it as the fourth leading risk factor for global mortality. This has led researchers and developers to create fitness apps to support and motivate people to engage in physical activity more regularly. However, there is limited research on how collectivist and individualist users from different social and cultural backgrounds differ in terms of the persuasive features they care about in fitness apps. Having knowledge of the cultural differences will help designers and developers create better fitness apps tailored to the two main types of culture. Hence, we conducted an empirical study to uncover how both cultures differ and the possibility of extending the Persuasive System Design (PSD) model. We found that Primary Task Support (Self-Monitoring and Goal-Setting) is requested more by the individualist group than the collectivist group. On the other hand, Dialog Support (Reminder and Suggestion) is requested more by the collectivist group than the individualist group. Finally, we found that the PSD model can be extended with Goal-Setting and Verbal Persuasion for fitness app design.
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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 | 0.038 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".