Literature DB >> 33438494

Proposing a mobile apps acceptance model for users in the health area: A systematic literature review and meta-analysis.

Sami S Binyamin1, Bassam A Zafar1.   

Abstract

Due to rapid advancements in the field of information and communication technologies, mobile health (mHealth) has become a significant topic in the delivery of healthcare. Despite the perceived advantages and the large number of mHealth initiatives, the success of mHealth ultimately relies on whether these initiatives are used; their benefits will be diminished should people not use them. Previous literature has found that the adoption of mHealth by users is not yet widespread, and little research has been conducted on this problem. Therefore, this study identifies the antecedents of the intention to use mHealth and proposes a general model that might prove beneficial in explaining the acceptance of mHealth. The authors performed a quantitative meta-analysis of 49 journal papers published over the past 10 years and systematically reviewed the evidence regarding the most commonly identified factors that may affect the acceptance of mHealth. The findings indicate that the proposed model includes the seven most commonly used relationships in the selected articles. More specifically, the model assumes that perceived usefulness positively affects perceived ease of use and user behavioral intention to use mHealth is commonly influenced by five factors: perceived usefulness, perceived ease of use, attitude toward behavior, subjective norms, and facilitating conditions. The results of this work provide important insights into the predictors of mHealth acceptance for future researchers and practitioners.

Entities:  

Keywords:  behavioral intention; mHealth; mobile health; technology acceptance; technology adoption

Year:  2021        PMID: 33438494     DOI: 10.1177/1460458220976737

Source DB:  PubMed          Journal:  Health Informatics J        ISSN: 1460-4582            Impact factor:   2.681


  8 in total

1.  Exposure Detection Applications Acceptance: The Case of COVID-19.

Authors:  Adi Alsyouf; Abdalwali Lutfi; Mohammad Al-Bsheish; Mu'taman Jarrar; Khalid Al-Mugheed; Mohammed Amin Almaiah; Fahad Nasser Alhazmi; Ra'ed Masa'deh; Rami J Anshasi; Abdallah Ashour
Journal:  Int J Environ Res Public Health       Date:  2022-06-14       Impact factor: 4.614

2.  Spanish adaptation and validation of the User Version of the Mobile Application Rating Scale (uMARS).

Authors:  Ruben Martin-Payo; Sergio Carrasco-Santos; Marcelino Cuesta; Stoyan Stoyan; Xana Gonzalez-Mendez; María Del Mar Fernandez-Alvarez
Journal:  J Am Med Inform Assoc       Date:  2021-11-25       Impact factor: 7.942

3.  Protocol of a Single-Blind Two-Arm (Waitlist Control) Parallel-Group Randomised Controlled Pilot Feasibility Study for mHealth App among Incontinent Pregnant Women.

Authors:  Aida Jaffar; Sherina Mohd Sidik; Chai Nien Foo; Noor Azimah Muhammad; Rosliza Abdul Manaf; Siti Irma Fadhilah Ismail; Nazhatussima Suhaili
Journal:  Int J Environ Res Public Health       Date:  2021-04-30       Impact factor: 3.390

4.  Computer-Delivered Cognitive Training and Transcranial Direct Current Stimulation in Patients With HIV-Associated Neurocognitive Disorder: A Randomized Trial.

Authors:  Raymond L Ownby; Jae Kim
Journal:  Front Aging Neurosci       Date:  2021-11-15       Impact factor: 5.750

5.  Usability Testing and Technology Acceptance of an mHealth App at the Point of Care During Simulated Pediatric In- and Out-of-Hospital Cardiopulmonary Resuscitations: Study Nested Within 2 Multicenter Randomized Controlled Trials.

Authors:  Laëtitia Gosetto; Manon Sauvage; Johan N Siebert; Laurie Bloudeau; Laurent Suppan; Frédérique Rodieux; Kevin Haddad; Florence Hugon; Alain Gervaix; Christian Lovis; Christophe Combescure; Sergio Manzano; Frederic Ehrler
Journal:  JMIR Hum Factors       Date:  2022-03-01

6.  Indonesian hospital telemedicine acceptance model: the influence of user behavior and technological dimensions.

Authors:  Steffi Alexandra; Putu Wuri Handayani; Fatimah Azzahro
Journal:  Heliyon       Date:  2021-12-14

7.  Predictors of the Acceptance of an Electronic Coach Targeting Self-management of Patients With Type 2 Diabetes: Web-Based Survey.

Authors:  Zeena Harakeh; Hilde Van Keulen; Koen Hogenelst; Wilma Otten; Iris M De Hoogh; Pepijn Van Empelen
Journal:  JMIR Form Res       Date:  2022-08-16

8.  The development of an instrument to predict patients' adoption of mHealth in the developing world.

Authors:  Michael Addotey-Delove; Richard E Scott; Maurice Mars
Journal:  Inform Med Unlocked       Date:  2022-03-05
  8 in total

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