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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».