Understanding the Behavioral Determinants of First Responder App Adoption by Integrating Perspectives From the Unified Theory of Acceptance and Use of Technology and Health Belief Model: Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Out-of-hospital cardiac arrests (OHCAs) are a leading cause of death worldwide, yet first responder apps can significantly improve outcomes by mobilizing citizens to perform cardiopulmonary resuscitation before professional help arrives. Despite their importance, limited research has examined the psychological and behavioral factors that influence individuals' willingness to adopt these apps. OBJECTIVE: Given that first responder app use involves elements of both technology adoption and preventive health behavior, it is essential to examine this behavior from multiple theoretical perspectives. Building on the unified theory of acceptance and use of technology (UTAUT) and health belief model (HBM), this study therefore developed an integrative framework to explain which behavioral determinants and demographic and health-related factors drive an individual's willingness to install a first responder app for OHCA. METHODS: We conducted a web-based cross-sectional survey (N=3660; mean age 49.95, SD 16.75 years; n=1909, 52.2% women) in June 2024 among Belgian adults. Behavioral determinants (UTAUT and HBM constructs), demographic (eg, age), and health-related (eg, cardiopulmonary resuscitation training experience) variables were measured using (multi-item) scales. Willingness to install the app served as the outcome variable. We developed a structural equation model using the Lavaan package in R and specified regression paths, on the one hand, between the behavioral determinants and willingness to install the app, and on the other hand, between the demographic and health-related factors and the behavioral determinants. Additionally, we conducted multiple group analyses to examine the moderating role of demographic and health-related factors on the relationships between the behavioral determinants and the willingness to install the app. RESULTS: Our results revealed that 2 UTAUT variables (ie, facilitating conditions: β=.07; P=.003 and social influence: β=.16; P<.001) and 3 HBM variables (ie, perceived susceptibility: β=.06; P=.003, perceived barriers: β=-.29; P<.001, and perceived benefits: β=.38; P<.001) were associated with willingness to install a first responder app for OHCA. Additionally, most demographic and health-related factors were indirectly related to willingness via behavioral determinants, with age being the sole moderator. Specifically, a negative association between perceived severity and willingness was only observed among older adults. In addition, the positive relationship between perceived benefits and willingness was stronger for older adults compared to younger ones. CONCLUSIONS: Overall, the results of this study have both theoretical and practical implications. Theoretically, this study finds its relevance in extending the UTAUT and HBM to altruistic mobile health apps and advancing our understanding of technology adoption in health contexts. Practically, the study's findings could inform real-life health campaigns aimed at enhancing citizen participation in first responder systems.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".