Methods Inf Med 2019; 58(04/05): 131-139
DOI: 10.1055/s-0040-1701607
Original Article
Georg Thieme Verlag KG Stuttgart · New York

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

Ali Garavand
1   Department of Health Information Management and Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
,
Mahnaz Samadbeik
2   Department of Health Information Technology, Lorestan University of Medical Sciences, Khorramabad, Iran
,
Hamed Nadri
3   Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran
4   Student Research Committee, Urmia University of Medical Sciences, Urmia, Iran
,
Bahlol Rahimi
3   Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran
,
Heshmatollah Asadi
5   Department of Public Health, School of health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, Iran
› Author Affiliations
Funding This study was done by financial support of Lorestan University of Medical Sciences with code no. A-10-1330-1.
Further Information

Publication History

31 March 2019

18 December 2019

Publication Date:
13 March 2020 (online)

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).

Note

This article does not contain any studies with human participants performed by any of the authors.


 
  • References

  • 1 Baldwin LP, Low PH, Picton C, Young T. The use of mobile devices for information sharing in a technology-supported model of care in A&E. Int J Electron Healthc 2007; 3 (01) 90-106
  • 2 Jebraeily M, Fazlollahi ZZ, Rahimi B. The most common smartphone applications used by medical students and barriers of using them. Acta Inform Med 2017; 25 (04) 232-235
  • 3 Ruskin KJ. Mobile technologies for teaching and learning. Int Anesthesiol Clin 2010; 48 (03) 53-60
  • 4 Mosa ASM, Yoo I, Sheets L. A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak 2012; 12 (01) 67
  • 5 Boruff JT, Storie D. Mobile devices in medicine: a survey of how medical students, residents, and faculty use smartphones and other mobile devices to find information. J Med Libr Assoc 2014; 102 (01) 22-30
  • 6 Walton G, Childs S, Blenkinsopp E. Using mobile technologies to give health students access to learning resources in the UK community setting. Health Info Libr J 2005; 22 (02) (Suppl. 02) 51-65
  • 7 Safdari R, Jebraeily M, Rahimi B, Doulani A. Smartphone medical applications use in the clinical training of medical students of UMSU and its influencing factors. Eur J Exp Biol 2014; 4 (01) 633-637
  • 8 Deng Z. Understanding public users' adoption of mobile health service. Int J Mobile Comm 2013; 11 (04) 351-373
  • 9 Free C, Phillips G, Felix L, Galli L, Patel V, Edwards P. The effectiveness of M-health technologies for improving health and health services: a systematic review protocol. BMC Res Notes 2010; 3 (01) 250
  • 10 Ramanathan N, Swendeman D, Comulada WS, Estrin D, Rotheram-Borus MJ. Identifying preferences for mobile health applications for self-monitoring and self-management: focus group findings from HIV-positive persons and young mothers. Int J Med Inform 2013; 82 (04) e38-e46
  • 11 Forrest JI, Wiens M, Kanters S, Nsanzimana S, Lester RT, Mills EJ. Mobile health applications for HIV prevention and care in Africa. Curr Opin HIV AIDS 2015; 10 (06) 464-471
  • 12 Lim MSC, Hocking JS, Hellard ME, Aitken CK. SMS STI: a review of the uses of mobile phone text messaging in sexual health. Int J STD AIDS 2008; 19 (05) 287-290
  • 13 Holubar S, Harvey-Banchik L. A review of the use of handheld computers in medical nutrition. Nutr Clin Pract 2007; 22 (04) 428-435
  • 14 Jayawardhena C. Personal values' influence on e-shopping attitude and behaviour. Internet Res 2004; 14 (02) 127-138
  • 15 Lindquist AM, Johansson PE, Petersson GI, Saveman B-I, Nilsson GC. The use of the Personal Digital Assistant (PDA) among personnel and students in health care: a review. J Med Internet Res 2008; 10 (04) e31-e31
  • 16 Tavares J, Oliveira T. Electronic health record patient portal adoption by health care consumers: an acceptance model and survey. J Med Internet Res 2016; 18 (03) e49-e49
  • 17 Blaya JA, Fraser HSF, Holt B. E-health technologies show promise in developing countries. Health Aff (Millwood) 2010; 29 (02) 244-251
  • 18 Emerging mHealth: paths for growth. Available at: https://www.pwc.com/gx/en/healthcare/mhealth/assets/pwc-emerging-mhealth-full.pdf . Accessed January 7, 2020
  • 19 Rahimi B. , et al., A Systematic Review of the Technology Acceptance Model in Health Informatics. Appl Clin Inform 2018; 9 (03) 604-634
  • 20 Coiera E. Guide to Health Informatics. New York, NY: CRC press; 2015
  • 21 Sunyaev A, Dehling T, Taylor PL, Mandl KD. Availability and quality of mobile health app privacy policies. J Am Med Inform Assoc 2015; 22 (e1): e28-e33
  • 22 Wu AC, Carpenter JF, Himes BE. Mobile health applications for asthma. J Allergy Clin Immunol Pract 2015; 3 (Suppl. 03) 446-448 ; e1–e16
  • 23 van Loenhout JAF, le Grand A, Duijm F. , et al. The effect of high indoor temperatures on self-perceived health of elderly persons. Environ Res 2016; 146: 27-34
  • 24 Smith C, Vannak U, Sokhey L, Ngo TD, Gold J, Free C. Mobile technology for improved family planning (MOTIF): the development of a mobile phone-based (mHealth) intervention to support post-abortion family planning (PAFP) in Cambodia. Reprod Health 2016; 13 (01) 1
  • 25 Scalvini S, Giordano A, Glisenti F. [Telecardiology: a new way to manage the relation between hospital and primary care] [in Italian]. Monaldi Arch Chest Dis 2002; 58 (02) 132-134
  • 26 Rahimi B. , et al., A systematic review of the technology acceptance model in health informatics. Appl Clin Inform 2018; 9 (03) 604-634
  • 27 Dwivedi YK, Shareef MA, Simintiras AC, Lal B, Weerakkody V. A generalised adoption model for services: A cross-country comparison of mobile health (m-health). Gov Inf Q 2016; 33 (01) 174-187
  • 28 Sánchez-Prieto JC, Olmos-Migueláñez S, García-Peñalvo FJ. Informal tools in formal contexts: development of a model to assess the acceptance of mobile technologies among teachers. Comput Human Behav 2016; 55 (A): 519-528
  • 29 Kim S, Lee K-H, Hwang H, Yoo S. Analysis of the factors influencing healthcare professionals' adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital. BMC Med Inform Decis Mak 2016; 16 (01) 12
  • 30 Garavand A, Samadbeik M, Kafashi M, Abhari S. Acceptance of health information technologies, acceptance of mobile health: a review article. J Biomed Phys Eng 2017; 7 (04) 403-408
  • 31 Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. Manage Inf Syst Q 2003; 27 (03) 425-478
  • 32 Briz-Ponce L, García-Peñalvo FJ. An empirical assessment of a technology acceptance model for apps in medical education. J Med Syst 2015; 39 (11) 176
  • 33 Mohamed AHHM, Tawfik H, Al-Jumeily D, Norton L. MoHTAM: A Technology Acceptance Model for Mobile Health Applications. Paper presented at: 2011 Developments in E-systems Engineering; 6–8 Dec. 2011, 2011
  • 34 Jen-Her W, Shu-Ching W, Li-Min L. What Drives Mobile Health Care? An Empirical Evaluation of Technology Acceptance. Paper presented at: Proceedings of the 38th Annual Hawaii International Conference on System Sciences; 6–6 Jan. 2005, 2005
  • 35 Cilliers L, Viljoen KL-A, Chinyamurindi WT. A study on students' acceptance of mobile phone use to seek health information in South Africa. Health Inf Manag 2018; 47 (02) 59-69
  • 36 Zhao Y, Ni Q, Zhou R. What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age. Int J Inf Manage 2018; 43: 342-350
  • 37 Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int J Med Inform 2017; 101: 75-84
  • 38 Hoque MR. An empirical study of mHealth adoption in a developing country: the moderating effect of gender concern. BMC Med Inform Decis Mak 2016; 16 (01) 51
  • 39 Dou K, Yu P, Deng N. , et al. Patients' acceptance of smartphone health technology for chronic disease management: a theoretical model and empirical test. JMIR Mhealth Uhealth 2017; 5 (12) e177
  • 40 Chib A, van Velthoven MH, Car J. mHealth adoption in low-resource environments: a review of the use of mobile healthcare in developing countries. J Health Commun 2015; 20 (01) 4-34
  • 41 Barutçu S, Barutçu E, Adigüzel DÜ. A technology acceptance analysis for mhealth apps: the case of Turkey. Balkan Near Eastern J Social Sci 2018; 4 (04) 104-113
  • 42 Illiger K, Hupka M, von Jan U, Wichelhaus D, Albrecht U-V. Mobile technologies: expectancy, usage, and acceptance of clinical staff and patients at a university medical center. JMIR Mhealth Uhealth 2014; 2 (04) e42
  • 43 Han S, Mustonen P, Seppanen M, Kallio M. Physicians' acceptance of mobile communication technology: an exploratory study. IJMC 2006; 4 (02) 210-230
  • 44 Schomakers E-M, Lidynia C, Ziefle M. Exploring the Acceptance of mHealth Applications-Do Acceptance Patterns Vary Depending on Context?. Paper presented at: International Conference on Applied Human Factors and Ergonomics2018
  • 45 Aggelidis VP, Chatzoglou PD. Using a modified technology acceptance model in hospitals. Int J Med Inform 2009; 78 (02) 115-126
  • 46 Wills MJ, El-Gayar OF, Bennett D. Examining healthcare professionals' acceptance of electronic medical records using UTAUT. Issues Inf Syst 2008; 9 (02) 396-401
  • 47 Krein S. Understanding nurse perceptions of a newly implemented electronic medical record system AU - Holtz, Bree. J Technol Hum Serv 2011; 29 (04) 247-262
  • 48 Quaosar GMAA, Hoque MR, Bao Y. Investigating Factors Affecting Elderly's Intention to Use m-Health Services: An Empirical Study. Telemed J E Health 2018; 24 (04) 309-314
  • 49 Schepers J, Wetzels M. A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Inf Manage 2007; 44 (01) 90-103
  • 50 Lee SJ, Choi MJ, Rho MJ, Kim DJ, Choi IY. Factors affecting user acceptance in overuse of smartphones in mobile health services: an empirical study testing a modified integrated model in South Korea. Front Psychiatry 2018; 9: 658
  • 51 Nadri H. , et al., Factors affecting acceptance of hospital information systems based on Extended Technology Acceptance Model: a case study in three paraclinical departments. Applied clinical informatics 2018; 9 (02) 238-247
  • 52 Sambasivan M, Esmaeilzadeh P, Kumar N, Nezakati H. Intention to adopt clinical decision support systems in a developing country: effect of physician's perceived professional autonomy, involvement and belief: a cross-sectional study. BMC Med Inform Decis Mak 2012; 12: 142-142
  • 53 Yi MY, Jackson JD, Park JS, Probst JC. Understanding information technology acceptance by individual professionals: toward an integrative view. Inf Manage 2006; 43 (03) 350-363
  • 54 The mHealth apps market is getting crowded. Available at: https://research2guidance.com/mhealth-app-market-getting-crowded-259000-mhealth-apps-now/ . Accessed January 7, 2020