Using the Unified Theory of Acceptance and Use of Technology (UTAUT) to Investigate the Intention to Use Physical Activity Apps: Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Many university students are lacking adequate physical exercise and are failing to develop physical activity (PA) behaviors in China. PA app use could improve this situation. OBJECTIVE: The aim of this study was to use the unified theory of acceptance and use of technology (UTAUT) to investigate the intention to use PA apps among university students in Guangzhou, China, and how body mass index (BMI) moderates the effects of UTAUT in explaining PA app use intention. METHODS: A cross-sectional study was conducted among 1704 university students from different universities in Guangzhou, China. The UTAUT model was used to measure the determinants of intention to use PA apps. RESULTS: Of the participants, 41.8% (611/1461) intended to use PA apps. All three UTAUT-related scales (performance expectancy, effort expectancy, and social influence) were positively associated with the intention to use PA apps after adjusting for background variables (adjusted odds ratio 1.10-1.31, P<.001). The performance expectancy scale had stronger associations with the intention to use PA apps among those whose BMI were beyond normal range compared with those whose BMI were within normal range (P<.001). CONCLUSIONS: UTAUT is useful for understanding university students' intention to use PA apps. Potential moderating effects should be kept in mind when designing UTAUT-based interventions to improve PA via app use.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".