Evidence map›Paper›PMID 32628122›Full record

ArticleJournal of medical Internet research2020

Technology Acceptance in Mobile Health: Scoping Review of Definitions, Models, and Measurement.

Camille Nadal, Corina Sas, Gavin Doherty

Registry-linked trialAbstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07390071 (Multimodal Telerehabilitation in Patients Undergoing CAR-T Cell Immunotherapy), which is not on this map. Cited by 146 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
146citing papers in PubMed, 11 pooled it
–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.

NCT07390071 narecruitingnot on this mapstarted 2025, after this paper: background citation

Multimodal Telerehabilitation in Patients Undergoing CAR-T Cell Immunotherapy

TypeinterventionalSponsorUniversity of UtahRan2025 to 2029Enrolled40ConditionsLymphoma, Myeloma, Cytokine Release Syndrome, Immune Effector Cell Associated Neurotoxicity SyndromeArmsHome Automated Telemanagement (HAT) Patient Unit
3 · Its place in the literature

Who cites it

146 citing papers in PubMed, 11 syntheses or guidelines pooled it.

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  10. Effect of mobile application types on stroke rehabilitation: a systematic review.Journal of neuroengineering and rehabilitation · 2023
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86 more citing papers are in PubMed but not listed here.

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

3 authors.

Camille NadalSchool of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.ORCID 0000-0002-0166-113X
Corina SasSchool of Computing and Communications, Lancaster University, Lancaster, United Kingdom.ORCID 0000-0001-9297-9612
Gavin DohertySchool of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.ORCID 0000-0002-9617-7008

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDesigning technologies that users will be interested in, start using, and keep using has long been a challenge. In the health domain, the question of technology acceptance is even more important, as the possible intrusiveness of technologies could lead to patients refusing to even try them. Developers and researchers must address this question not only in the design and evaluation of new health care technologies but also across the different stages of the user's journey. Although a range of definitions for these stages exists, many researchers conflate related terms, and the field would benefit from a coherent set of definitions and associated measurement approaches.

objectiveThis review aims to explore how technology acceptance is interpreted and measured in mobile health (mHealth) literature. We seek to compare the treatment of acceptance in mHealth research with existing definitions and models, identify potential gaps, and contribute to the clarification of the process of technology acceptance.

methodsWe searched the PubMed database for publications indexed under the Medical Subject Headings terms "Patient Acceptance of Health Care" and "Mobile Applications." We included publications that (1) contained at least one of the terms "acceptability," "acceptance," "adoption," "accept," or "adopt"; and (2) defined the term. The final corpus included 68 relevant studies.

resultsSeveral interpretations are associated with technology acceptance, few consistent with existing definitions. Although the literature has influenced the interpretation of the concept, usage is not homogeneous, and models are not adapted to populations with particular needs. The prevalence of measurement by custom surveys suggests a lack of standardized measurement tools.

conclusionsDefinitions from the literature were published separately, which may contribute to inconsistent usage. A definition framework would bring coherence to the reporting of results, facilitating the replication and comparison of studies. We propose the Technology Acceptance Lifecycle, consolidating existing definitions, articulating the different stages of technology acceptance, and providing an explicit terminology. Our findings illustrate the need for a common definition and measurement framework and the importance of viewing technology acceptance as a staged process, with adapted measurement methods for each stage.

Indexed as

Biomedical TechnologyHumansTelemedicinemHealthmobile applicationsmobile phonepatient acceptanceTechnology Acceptance Lifecycle

Identifiers

PMID32628122
PMCPMC7381045

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.