Determinants of Nurses’ Continuance Intention to Use Mobile Health Apps in Clinical Nursing Practice: Structural Equation Modeling to Extend the Expectation-Confirmation Model
Notice bibliographique
Résumé
Background: Mobile health (mHealth) apps enhance clinical nursing by improving access to resources and patient care. Further benefits include reduced errors, time savings, better communication, cost reduction, and training. Understanding factors driving nurses' continued mHealth adoption is key to its sustained success. Objective: This study extends the expectation-confirmation model (ECM) to explore the determinants of Iranian nurses' continuance intention to use mHealth apps in their daily clinical routines. Methods: A cross-sectional, descriptive-analytical study was conducted among 315 nurses from hospitals affiliated with Kashan University of Medical Sciences. The Nurses' Mobile Health Device Acceptance Scale (NMHDA-Scale) was developed by the authors in 2022. The Intention to Continue Using Mobile Health Applications for Nurses questionnaire assesses nurses' future willingness to use mHealth apps in their practice. This questionnaire was designed based on the ECM and the approach by Waltz et al. Its primary aim is to identify the factors that influence mHealth device acceptance, specifically among clinical nurses, as previous studies have not focused on this group and have shown inconsistent relationships between various factors. Participants completed structured questionnaires measuring perceived usefulness, perceived ease of use, social influence, habits, and technology anxiety. Data were analyzed using structural equation modeling in AMOS (version 26). The model tested relationships among confirmation, perceived usefulness, social influence, technology anxiety, and mHealth continuance behavior. Results: The analyzed sample (n=315) consisted primarily of female (252/315, 80%) and married (243/315, 77.1%) nurses, with a mean age of 35.67 (SD 1.24) years. The analysis revealed that perceived usefulness was significantly influenced by both confirmation (P<.001) and social influence (P<.001). Perceived ease of use was negatively impacted by new technology anxiety (P<.001), indicating that higher anxiety levels reduced perceived ease of use. Additionally, mHealth continuance behavior was positively associated with habits (P=.002), social influence (P<.001), and perceived security risks (P=.008). Contrary to expectations, perceived usefulness did not directly influence mHealth continuance (P=.15), suggesting that other factors, such as habits and social influence, play a more significant role in long-term use. Conclusions: Sustained mHealth app use by nurses hinges more on social influence and confirmed expectations than perceived usefulness. Although new technology anxiety remains a barrier, habits and social influence are key to long-term adoption. Hospital leaders should prioritize strategies that foster positive social reinforcement, minimize security concerns, and reduce anxiety through training and support when integrating mHealth into nursing workflows. These findings offer critical insights for improving digital health implementation, ultimately enhancing patient care and clinical efficiency.
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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 | 0,011 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».