Evidence map›Paper›PMID 38687589›Full record

ArticleJMIR human factors2024

Identifying Factors of User Acceptance of a Drone-Based Medication Delivery: User-Centered Design Approach.

Franziska Fink, Ivonne Kalter, Jenny-Victoria Steindorff, Hans Konrad Helmbold, Denny Paulicke, Patrick Jahn

Abstract read
In one paragraph

Article in JMIR human factors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Franziska Fink *Translation Region for Digitalised Healthcare, Department of Internal Medicine, Faculty of Medicine, University Medicine, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.ORCID 0000-0001-5143-8844
Ivonne Kalter *Translation Region for Digitalised Healthcare, Department of Internal Medicine, Faculty of Medicine, University Medicine, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.ORCID 0009-0001-3362-8318
Jenny-Victoria SteindorffTranslation Region for Digitalised Healthcare, Department of Internal Medicine, Faculty of Medicine, University Medicine, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.ORCID 0009-0003-9464-8528
Hans Konrad HelmboldDepartment of Economics, Anhalt University of Applied Sciences, Bernburg, Germany.ORCID 0009-0003-8254-086X
Denny PaulickeTranslation Region for Digitalised Healthcare, Department of Internal Medicine, Faculty of Medicine, University Medicine, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.ORCID 0000-0002-9808-215X
Patrick JahnTranslation Region for Digitalised Healthcare, Department of Internal Medicine, Faculty of Medicine, University Medicine, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.ORCID 0000-0002-1533-6717

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

objectiveThis 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).

methodsA 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.

resultsThe 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 (F

conclusionsWith 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.

Indexed as

AircraftMedication SystemsMobile ApplicationsUser-Centered DesignAdultFemaleHumansMaleMiddle AgedSurveys and Questionnaireshuman-drone interactionmedical suppliesparticipative researchtechnology acceptanceuser-centered design

Identifiers

PMID38687589
PMCPMC11094598

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.