Persuasive Features that Drive the Adoption of a Fitness Application and the Moderating Effect of Age and Gender
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Bibliographic record
Abstract
Fitness apps equipped with various persuasive features have become popular worldwide due to the physical inactivity crisis. However, there is a limited understanding of the most important persuasive features that drive their adoption and the moderating effect of age and gender. To bridge this gap, we designed storyboards illustrating six of the commonly employed persuasive strategies in persuasive health applications: Goal-Setting/Self-Monitoring, Reward, Social Learning, Social Comparison, Competition and Cooperation. We conducted an empirical study in which we asked the participants to evaluate their receptiveness to the six persuasive features and their intention to use a fitness app that features them. The result of our Partial Least Square Path Modeling (PLSPM) shows that, overall, Goal-Setting/Self-Monitoring is the strongest predictor of the intention to use a fitness app, followed by Reward and Competition, both of which are in second place. However, Social Learning and Social Comparison turn out to be non-predictors of intention to use. Based on these findings, we recommend that a minimally viable (one-size-fits-all) fitness app, in a personal setting, should support a Goal-Setting/Self-Monitoring feature, coupled with a Reward feature, to increase its appeal to a wide audience. Moreover, in a social setting, it should support a Competition feature to increase its appeal to a wide audience. We discuss these findings and the gender and age differences in the relationships between users’ receptiveness to the six persuasive features and their intention to use a fitness app that support them.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it