Personality-Driven Variations in Fitness App Affordance Actualization Among Adults: Quantitative Survey Study
Bibliographic record
Abstract
Background: Fitness apps aim to advance individuals' health and wellness by encouraging consistent healthy habits. Despite their widespread use, sustaining user engagement remains a challenge. Research studies on fitness apps have identified app affordances as one of the key factors that influence user engagement. Some affordances, such as exercise guidance and activity status updates, are shown to support users in achieving their health goals if the users actualize them. However, these affordances need to be actualized by the users to seize these benefits. While identifying these app affordances can deepen our insight into user-app interactions, the impact of personality traits on the actualization of these affordances remains underexplored. Objective: This study aims to examine the influence of personality traits on the actualization of fitness app affordances. Methods: Building on affordance actualization theory and the Big Five personality framework, we hypothesized about certain personality traits influencing the actualization of certain app affordances. We tested these hypotheses using a survey of adult Fitbit app (Google LLC) users (N=442). We used validated measures from the literature to assess these variables. We analyzed the survey data using covariance-based structural equation modeling. Results: Our findings reveal distinct affordance actualization patterns based on users' personality traits. Users with the conscientious personality trait primarily actualize the updating affordance (β=0.136, P=.01), while the influence of the conscientious trait on actualization of rewards (β=-0.154, P=.06), competing (β=-0.118, P=.18), comparing (β=-0.084, P=.33), reminding (β=-0.060, P=.44), or guidance (β=-0.006, P=.95) affordances was not significant. The openness to experience trait showed a significant positive effect on actualization of updating affordances (β=0.227, P=.001), but did not significantly influence actualization of searching (β=-0.172, P=.11), watching others (β=-0.077, P=.50), or guidance (β=-0.005, P=.96) affordances. Users with the agreeableness trait actualized comparison (β=0.213, P=.02), guidance (β=0.259, P=.003), and encouragement (β=0.244, P=.01) affordances, while the effect of the agreeableness trait on actualization of watching others was not significant (β=0.143, P=.13). Extravert users actualized recognition (β=0.191, P<.001), self-presentation (β=0.165, P=.002), and watching others (β=0.167, P=.003) affordances, but did not actualize updating affordances (β=0.001, P=.98). Finally, a lower emotional stability trait did not significantly influence any of the hypothesized affordances, with nonsignificant effects on guidance (β=-0.083, P=.30), reminding (β=-0.093, P=.21), and updates (β=-0.036, P=.49). Conclusions: Our study shows that certain personality traits are associated with the actualization of specific affordances. These findings underscore the need to tailor fitness app affordances to individual differences, rather than relying on a one-size-fits-all approach. Designing fitness app functionality that aligns with various personality traits may promote deeper and more sustained user engagement. Further research is needed to investigate the relationship between personality traits and app affordance actualization.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".