Identifying Factors of User Acceptance of a Drone-Based Medication Delivery: User-Centered Design Approach
Notice bibliographique
Résumé
Background The use of drones in the health care sector is increasingly being discussed against the background of the aging population and the growing shortage of skilled workers. In particular, the use of drones to provide medication in rural areas could bring advantages for the care of people with and without a need for care. However, there are hardly any data available that focus on the interaction between humans and drones. Objective This study aims to disclose and analyze factors associated with user acceptance of drone-based medication delivery to derive practice-relevant guidance points for participatory technology development (for apps and drones). Methods A controlled mixed methods study was conducted that supports the technical development process of an app design for drone-assisted drug delivery based on a participatory research design. For the quantitative analysis, established and standardized survey instruments to capture technology acceptance, such as the System Usability Scale; Technology Usage Inventory (TUI); and the Motivation, Engagement, and Thriving in User Experience model, were used. To avoid possible biasing effects from a continuous user development (eg, response shifts and learning effects), an ad hoc group was formed at each of the 3 iterative development steps and was subsequently compared with the consisting core group, which went through all 3 iterations. Results The study found a positive correlation between the usability of a pharmacy drone app and participants’ willingness to use it (r=0.833). Participants’ perception of usefulness positively influenced their willingness to use the app (r=0.487; TUI). Skepticism had a negative impact on perceived usability and willingness to use it (r=−0.542; System Usability Scale and r=−0.446; TUI). The study found that usefulness, skepticism, and curiosity explained most of the intention to use the app (F3,17=21.12; P<.001; R2=0.788; adjusted R2=0.751). The core group showed higher ratings on the intention to use the pharmacy drone app than the ad hoc groups. Results of the 2-tailed t tests showed a higher rating on usability for the third iteration of the core group compared with the first iteration. Conclusions With the help of the participatory design, important aspects of acceptance could be revealed by the people involved in relation to drone-assisted drug delivery. For example, the length of time spent using the technology is an important factor for the intention to use the app. Technology-specific factors such as user-friendliness or curiosity are directly related to the use acceptance of the drone app. Results of this study showed that the more participants perceived their own competence in handling the app, the more they were willing to use the technology and the more they rated the app as usable.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».