Gender Preference and Difference in Behavior Modeling in Fitness Applications: A Mixed-Method Approach
Bibliographic record
Abstract
In recent years, the employment of behavior models to motivate behavior change has become a global trend in fitness application design. However, there is hardly any large-scale study of these applications to understand users’ exercise-type preferences, their drivers and barriers, and the potential of employing them for gender-based tailoring. To bridge this gap, we conducted a mixed-method study among 669 participants to investigate users’ exercise-type preferences (their drivers and barriers) and how they and gender can impact users’ social-cognitive beliefs and projected performance of bodyweight exercises. Firstly, we presented to the study participants a behavior model performing push-up or squat bodyweight exercise in a fitness application and asked them to rate their perceived self-efficacy, self-regulation, outcome expectation, and projected (exercise) performance level as observers of the behavior model. Secondly, we presented the study participants with a preselected list of commonly employed exercise types in fitness applications and requested them to identify their most/least preferred, and the reasons behind their choices. Our results showed that there were differences between both genders in their exercise-type preferences, perceived self-efficacy and projected exercise performance level. Males prefer push-up, squat, crunch, plank, and chair dip the most, with effectiveness being the most important driver, followed by ease of performance and improvement of the physique, look, and appearance. On the other hand, females prefer squat, crunch, jumping jack, step up, and plank the most, with ease of performance being the most important driver, followed by improvement of the physique, look, appearance, and effectiveness. Moreover, males prefer running in place the least, while females prefer push-up the least, with perceived difficulty being the greatest barrier for both genders. Moreover, our analysis of variance supported the female’s least preference for a push-up. Females have a lower perceived self-efficacy and projected performance level for push-up than males. We discussed the implications of our findings and provided guidelines for tailoring fitness applications on the market to users’ preferences and gender.
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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 | 0.033 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".