18
views
0
recommends
+1 Recommend
1 collections
    0
    shares

      Submit your digital health research with an established publisher
      - celebrating 25 years of open access

      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Mobile Digital Education for Health Professions: Systematic Review and Meta-Analysis by the Digital Health Education Collaboration

      review-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          There is a pressing need to implement efficient and cost-effective training to address the worldwide shortage of health professionals. Mobile digital education (mLearning) has been mooted as a potential solution to increase the delivery of health professions education as it offers the opportunity for wide access at low cost and flexibility with the portability of mobile devices. To better inform policy making, we need to determine the effectiveness of mLearning.

          Objective

          The primary objective of this review was to evaluate the effectiveness of mLearning interventions for delivering health professions education in terms of learners’ knowledge, skills, attitudes, and satisfaction.

          Methods

          We performed a systematic review of the effectiveness of mLearning in health professions education using standard Cochrane methodology. We searched 7 major bibliographic databases from January 1990 to August 2017 and included randomized controlled trials (RCTs) or cluster RCTs.

          Results

          A total of 29 studies, including 3175 learners, met the inclusion criteria. A total of 25 studies were RCTs and 4 were cluster RCTs. Interventions comprised tablet or smartphone apps, personal digital assistants, basic mobile phones, iPods, and Moving Picture Experts Group-1 audio layer 3 player devices to deliver learning content. A total of 20 studies assessed knowledge (n=2469) and compared mLearning or blended learning to traditional learning or another form of digital education. The pooled estimate of studies favored mLearning over traditional learning for knowledge (standardized mean difference [SMD]=0.43, 95% CI 0.05-0.80, N=11 studies, low-quality evidence). There was no difference between blended learning and traditional learning for knowledge (SMD=0.20, 95% CI –0.47 to 0.86, N=6 studies, low-quality evidence). A total of 14 studies assessed skills (n=1097) and compared mLearning or blended learning to traditional learning or another form of digital education. The pooled estimate of studies favored mLearning (SMD=1.12, 95% CI 0.56-1.69, N=5 studies, moderate quality evidence) and blended learning (SMD=1.06, 95% CI 0.09-2.03, N=7 studies, low-quality evidence) over traditional learning for skills. A total of 5 and 4 studies assessed attitudes (n=440) and satisfaction (n=327), respectively, with inconclusive findings reported for each outcome. The risk of bias was judged as high in 16 studies.

          Conclusions

          The evidence base suggests that mLearning is as effective as traditional learning or possibly more so. Although acknowledging the heterogeneity among the studies, this synthesis provides encouraging early evidence to strengthen efforts aimed at expanding health professions education using mobile devices in order to help tackle the global shortage of health professionals.

          Related collections

          Most cited references45

          • Record: found
          • Abstract: found
          • Article: not found

          Technology-enhanced simulation for health professions education: a systematic review and meta-analysis.

          Although technology-enhanced simulation has widespread appeal, its effectiveness remains uncertain. A comprehensive synthesis of evidence may inform the use of simulation in health professions education. To summarize the outcomes of technology-enhanced simulation training for health professions learners in comparison with no intervention. Systematic search of MEDLINE, EMBASE, CINAHL, ERIC, PsychINFO, Scopus, key journals, and previous review bibliographies through May 2011. Original research in any language evaluating simulation compared with no intervention for training practicing and student physicians, nurses, dentists, and other health care professionals. Reviewers working in duplicate evaluated quality and abstracted information on learners, instructional design (curricular integration, distributing training over multiple days, feedback, mastery learning, and repetitive practice), and outcomes. We coded skills (performance in a test setting) separately for time, process, and product measures, and similarly classified patient care behaviors. From a pool of 10,903 articles, we identified 609 eligible studies enrolling 35,226 trainees. Of these, 137 were randomized studies, 67 were nonrandomized studies with 2 or more groups, and 405 used a single-group pretest-posttest design. We pooled effect sizes using random effects. Heterogeneity was large (I(2)>50%) in all main analyses. In comparison with no intervention, pooled effect sizes were 1.20 (95% CI, 1.04-1.35) for knowledge outcomes (n = 118 studies), 1.14 (95% CI, 1.03-1.25) for time skills (n = 210), 1.09 (95% CI, 1.03-1.16) for process skills (n = 426), 1.18 (95% CI, 0.98-1.37) for product skills (n = 54), 0.79 (95% CI, 0.47-1.10) for time behaviors (n = 20), 0.81 (95% CI, 0.66-0.96) for other behaviors (n = 50), and 0.50 (95% CI, 0.34-0.66) for direct effects on patients (n = 32). Subgroup analyses revealed no consistent statistically significant interactions between simulation training and instructional design features or study quality. In comparison with no intervention, technology-enhanced simulation training in health professions education is consistently associated with large effects for outcomes of knowledge, skills, and behaviors and moderate effects for patient-related outcomes.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Learning anatomy via mobile augmented reality: Effects on achievement and cognitive load.

            Augmented reality (AR), a new generation of technology, has attracted the attention of educators in recent years. In this study, a MagicBook was developed for a neuroanatomy topic by using mobile augmented reality (mAR) technology. This technology integrates virtual learning objects into the real world and allow users to interact with the environment using mobile devices. The purpose of this study was to determine the effects of learning anatomy via mAR on medical students' academic achievement and cognitive load. The mixed method was applied in the study. The random sample consisted of 70 second-year undergraduate medical students: 34 in an experimental group and 36 in a control group. Academic achievement test and cognitive load scale were used as data collection tool. A one-way MANOVA test was used for analysis. The experimental group, which used mAR applications, reported higher achievement and lower cognitive load. The use of mAR applications in anatomy education contributed to the formation of an effective and productive learning environment. Student cognitive load decreased as abstract information became concrete in printed books via multimedia materials in mAR applications. Additionally, students were able to access the materials in the MagicBook anytime and anywhere they wanted. The mobile learning approach helped students learn better by exerting less cognitive effort. Moreover, the sensory experience and real time interaction with environment may provide learning satisfaction and enable students to structure their knowledge to complete the learning tasks. Anat Sci Educ 9: 411-421. © 2016 American Association of Anatomists.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Building an inclusive definition of e-learning: An approach to the conceptual framework

              E-learning is part of the new dynamic that characterises educational systems at the start of the 21st century. Like society, the concept of e-learning is subject to constant change. In addition, it is difficult to come up with a single definition of e-learning that would be accepted by the majority of the scientific community. The different understandings of e-learning are conditioned by particular professional approaches and interests. An international project, based on the participation of experts around the world, was undertaken to agree on a definition of e-learning. To this end, two main research activities were carried out. First, an extensive review was conducted of the literature on the concept of e-learning, drawing from peer-reviewed journals, specialised web pages, and books. Second, a Delphi survey was sent out to gather the opinions of recognised experts in the field of education and technology regarding the concept of e-learning with a view to reaching a final consensus. This paper presents the outcomes of the project, which has resulted in an inclusive definition of e-learning subject to a high degree of consensus that will provide a useful conceptual framework to further identify the different models in which e-learning is developed and practiced.
                Bookmark

                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                February 2019
                12 February 2019
                : 21
                : 2
                : e12937
                Affiliations
                [1 ] Centre for Population Health Sciences Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore Singapore
                [2 ] Graduate School of Medicine The University of Tokyo Tokyo Japan
                [3 ] Health Informatics Centre Karolinska Institutet Stockholm Sweden
                [4 ] Harvard TH Chan School of Public Health Harvard University Boston, MA United States
                [5 ] Harvard Medical School Harvard University Boston, MA United States
                [6 ] Family Medicine and Primary Care Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore Singapore
                [7 ] Department of Primary Care and Public Health School of Public Health Imperial College London London United Kingdom
                Author notes
                Corresponding Author: Lorainne Tudor Car lorainne.tudor.car@ 123456ntu.edu.sg
                Author information
                http://orcid.org/0000-0002-8356-3444
                http://orcid.org/0000-0001-6519-4174
                http://orcid.org/0000-0003-2985-8169
                http://orcid.org/0000-0002-1531-5983
                http://orcid.org/0000-0001-6531-2863
                http://orcid.org/0000-0001-8414-7664
                Article
                v21i2e12937
                10.2196/12937
                6390189
                30747711
                78d1c4c6-2421-4d46-976f-dd664973bfcb
                ©Gerard Dunleavy, Charoula Konstantia Nikolaou, Sokratis Nifakos, Rifat Atun, Gloria Chun Yi Law, Lorainne Tudor Car. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 12.02.2019.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/.as well as this copyright and license information must be included.

                History
                : 29 November 2018
                : 20 December 2018
                : 15 January 2019
                : 17 January 2019
                Categories
                Review
                Review

                Medicine
                mlearning,digital education,health workforce,systematic review,meta-analysis
                Medicine
                mlearning, digital education, health workforce, systematic review, meta-analysis

                Comments

                Comment on this article