Effective Factors in Adoption of Mobile Health Applications between Medical Sciences Students Using the UTAUT Model

Abstract

Background Students with complex health care services process face constant challenges with regard to health education. The mobile devices are an important tool that can install various applications for using information such as clinical guidelines, drug resources, clinical calculations, and the latest scientific evidence without any time and place limitations. And this happens only when students accept and use it. Objective The purpose of this article is to identify the factors influencing students in their intention to use mobile health (mHealth) by using Unified Theory of Acceptance and Use of Technology (UTAUT) model. Methods A standard questionnaire was used to collect the data from nearly 302 Lorestan University of medical science students including nutrition and public health, paramedicine, nursing and midwifery, pharmacy, dentistry, and medical schools. The data were processed using LISREL (Scientific Software International, Inc., Lincolnwood, Illinois) and SPSS (IBM Corp., Armonk, New York) softwares and the statistical analysis technique was based on structural equation modeling (SEM). Result A total of 300 questionnaires including valid responses were used in this study. The results showed that mediator of age did not affect the predictors of intention to use mHealth, and the level of education and gender directly affected the intention to use. In addition, effort expectancy, facilitating condition, and behavioral intention directly and indirectly have effect on use, whereas the result revealed no significant relationship between two important processes of performance expectancy and social influence with students' behavioral intention to use the mHealth. Conclusions The present study provides valuable information on mobile health acceptance factors for widespread use of this device among students of universities of medical sciences as a base infrastructure for a variety of information about health services and learning. Review and comparison of results with other studies showed that mHealth acceptance factors were different from other end users (elderly, patients, and health professionals).

Examining consumer use of mobile health applications by the extended UTAUT model

“…According to the study's findings, the most significant factor affecting using mHealth applications may be facilitating conditions. This finding was parallel with mHealth and e-health studies in the literature (Boontarig et al, 2012;Alam et al, 2018;Garavand, Samadbeik, Nadri, Rahimi and Asadi, 2019;Lestari and Rofianto, 2020). In this context, it may be stated that consumers intend to use mHealth applications due to the advantages these applications provide for their lives.…”

“…Previous studies on mHealth and eHealth have also found the effects of this factor on intentions to use (Hoque and Sorwar, 2017;Bawack and Kamdjoug, 2018;Alam et al, 2018;Alam et al, 2020). Effort expectancy and social influence were also factors affecting intention to use mHealth and eHealth applications in many studies (Dünnebeil, Sunyaev, Blohm, Leimeister and Krcmar, 2012;Martinez-Caro, Cegarra-Navarro and Solano-Lorente, 2013;Hoque and Sorwar, 2017;Garavand et al, 2019;Ndayizigamiye, Kante and Shingwenyana, 2020). Again, similar results have been revealed on accepting several different technological developments (Mun, Jackson, Park and Probst, 2006;Schaper and Pervan, 2007).…”

The Adoption of Mobile Health Applications Among University Students in Health Colleges

“…Both perceived usefulness, ease of use, and accessibility are a strong predictor of user’s intention to use and adoption of information technologies. 22–24 Our study found that 71% users agreed that health application can aid in reducing health related cost and improve their health. We also did not find any difference in the perceived usefulness between gender or between the groups using mobile applications for the different purposes such as physical activities, counting calories, and losing weight, managing chronic disease, or mentstrual tracking.…”

“…Studies that examined the adoption of information technologies in general or mobile health have shown that perceived usefulness, ease of use, and accessibility are strong predictors of adoption and intention to use. 22–24 Those studies uncovered many facilitators and barriers to adoption of mobile health applications among health-care professionals. 3 , 9 Studies also investigated the use and attitude of mobile health apps among medical students as they are the future health-care professionals.…”

Willingness to Adopt Health Information Among Social Question-and-Answer Community Users in China: Cross-sectional Survey Study

“…On the basis of these features [36], we proposed three questionnaire items, as follows: ( 1) "I have the resources, hardware and knowledge reserve to effectively use the health information on Zhihu," (2) "I can get help from others when I encounter problems while browsing and consulting health information on Zhihu," and ( 3) "seeking health information on Zhihu is one of the common ways for me to understand and solve health-related problems." Garavand et al [41] found that FCs had a positive effect on major students' adoption of mHealth apps. On the basis of these research findings, we proposed the following hypothesis: FCs positively affect SQAC users' WAHI (H4).…”