CC BY-NC-ND 4.0 · Libyan International Medical University Journal 2019; 04(02): 74-81
DOI: 10.4103/LIUJ.LIUJ_25_19
Original Article

Health professional students' preparedness for E-Health

Adel A. Al-Tawaty
Department of Pediatrics, Faculty of Medicine, Libyan International Medical University, Benghazi, Libya
,
Ehab Omar Elfallah
1   Department of Software Engineering, Faculty of Information Technology, Libyan International Medical University, Benghazi, Libya
› Author Affiliations
 

Abstract

Background: E-Health is one of the recent major developments in health-care provision. Today's health professional students are considered digitally oriented, and this may endow them with the necessary capabilities to implement E-Health on graduation. Aim: This study aimed to assess students' views, use, confidence, and need for training on E-Health. Participants: Fourth-, 5th- and internship-year students of the medical and dental schools at the Libyan International Medical University constituted the study population. Methodology: This is a cross-sectional study conducted using an online administered survey. Prior to implementation, the questionnaire was reviewed by experts and then piloted on a group of research-targeted students. Likert scale was used for most questions and few were in the form of short answers. Descriptive statistics were reported using SPSS software version 23.0. Results: One hundred and two students responded, and all responders were included for most select-response questions. The male-to-female ratio was 2:3, with a mean age of 24 ± 1.8 years. Medical students accounted for 52% of the participants. An average of 45% reported proficiency in written and spoken English. Only 12% have taken IT-related courses. Their view on E-Health was moderately positive with a mean of 3.5 ± 0.34 of 3.1 ± 1.029. In spite of this, 43% ± 3.9% had negative views on E-Health. Nearly 58% of the participants used digital tools and software with a mean score of 2.43 ± 0.6. Most students reported using social media, especially Facebook (mean 4.95 ± 1.7). The students reported a confidence level of information and communication technology (ICT) use of 3.4 ± 1.2. They also described their confidence in learning a new technology with a value of 3 ± 0.3. Almost 32.9% of the participants expressed an overall need for training on ICT tools. Conclusion: The overall preparedness of this group for ICT is moderate and needs improvement. This could be achieved through introducing changes in the taught curriculum.


#

Introduction

The term E-Health is variably defined and used as it is the case with new terminologies. It is almost impossible to reach an unanimous definition of eHealth.[[1]] eHealth is defined by the WHO, in very simple terms, as “the use of information and communication technologies for health.”[[2]] This definition, though simple, is wide and without clear boundaries. In spite of this uncertainty, the term is firmly grounded in academic literature.

Eighty-five percent of the member states of the United Nations have an eHealth strategy and 55% have a legislation to protect patient data.[[2]] The implementation of eHealth facilitates communication between patients and health-care professionals as it is the case in managing diabetes mellitus, cardiac disease, smoking, and cancer prevention.[[3]],[[4]],[[5]],[[6]]

One of the main aims of eHealth is to improve health-care efficiency and cost-effectiveness. Liaw et al. found out that clinical governance could be supported by clinical record systems as clinical indicators of eHealth. On the other hand, an eHealth web application for laboratory information system was designed and implemented in order to gather laboratory results to monitor HIV epidemic.[[7]] Besides, computer-based interventions have been used to provide self-management training in order to increase cost-effectiveness to patients with Type 2 diabetes.[[8]]

Strategies used to implement eHealth vary from using traditional methods to the use of mobile application for disease management, monitoring, and decision-making.[[9]],[[10]]

Some of the developing countries are already taking initiatives in implementing eHealth. Kenya, for example, has multiple initiatives in this regard even though its efficacy was evaluated only sparingly.[[11]] Telemedicine is highly recommended for developing countries because of the scarcity of health professional numbers and the need for distance consultations.[[12]]

For all these reasons, health professionals need to learn and practice using information and communication tools in the setting of health-care delivery. Unfortunately, intended competencies for health profession graduates mostly do not emphasize the need for mastering information and communication technology (ICT) tools in the context of health care. At best, the competency of using ICT is vaguely mentioned.[[13]]

Current students are expected to be masters of technology tools because they are generally considered as digitally native. These students grew up in a world of digital equipment, and they are using ICT in their daily life. It could be speculated that these students will be naturally capable of using ICT once graduated from health professional schools. On contrary, it was found that there is no good correlation between student mastery of ICT and their preference for using their ICT skills in learning. It is also not clear whether health profession students are capable of transferring their skill of using social media to the real world of health service. This study aims at assessing students' knowledge of eHealth, their general use of ICT tools, and their confidence in transferring this knowledge to the health-care services upon graduation.


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Methodology

Setting

This study was conducted at Libyan International Medical University (LIMU).


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Participants

The study population comprised 4th-, 5th-, and internship-year students at medical and dental schools (DSs).


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Type of the study

This was a cross-sectional descriptive study.

A questionnaire published by Lam et al., 2016,[[14]] was reviewed by the authors and adopted to the local context. It was then further reviewed by four high-rank academics who suggested some changes. The changes were made and an online form using Google Forms was prepared. It was then piloted by a small group of students who belong to the student population under study. Their suggestions were taken into account and the questionnaire was modified accordingly. The link to the final form of the questionnaire was then distributed to the participants through Facebook groups and Moodle. Each student was allowed one entry either through Facebook or Moodle. The questionnaire was available to participants for 12 weeks (from December 24, 2017, to February 24, 2018).


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Questionnaire description

It included 111 anonymous questions divided into the following five sections: demographic data (Section I), students' knowledge of eHealth (Section II) using open-ended questions, students' view of eHealth (Section III) using a 4-point Likert scale, use of ICT (Section IV) using a checklist and time category sheet to select from, and confidence in using ICT software and devices for eHealth (Section V) through a 4-point Likert scale. Because of interdependence of Sections 2 and 3, it was made impossible to access Section 3 before completing Section 2.


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Statistics

An IBM SPSS Statistics for Windows, Version 23.0. (Armonk, NY: IBM Corp., USA) was used in the analysis. Counts and percentages were used to express results for Sections I and IV. Section II was analyzed by grouping responses into themes. Means and standard deviation were used to express results in Section III. Counts, percentages, and unpaired t-test were used for Section V. P < 0.05 was considered statistically significant. Essay responses were theme categorized.


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Ethical issues

The study protocol was presented to the Ethical Committee at LIMU and approval was obtained. Students' participation was voluntary and answering the questionnaire is considered as a consent to participate.


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#

Results

Section I: General and demographic data

A total of 102 students responded, giving an overall response rate of 47.4%. Fifty-three (51.9%) respondents were from the faculty of medicine and the rest were from DS. Year distribution by faculty is shown in [[Table 1]]. The mean age of the respondents was 24.06 ± 1.792 years and females formed 70.6% of all respondents. Nearly 85.4% of the students reported that they are either proficient or very proficient in written and spoken English, with a mean of 3.4 ± 0.51 on a 5-point Likert scale. Eighty-eight percent of the students did not take any IT-related courses. The courses taken by the remaining 12 students included International Computer Driving License, power point, and programming.

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Table 1: Frequency distribution of students by faculty and year, n (%)

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Section II: Students' knowledge of eHealth

The 85 responses in this section were divided into five themes, two of these were discarded because of nonrelevance (ten responses). The nonrelevant themes were about university studies of IT and the other were about the use of TV programs for health promotion. The remaining 75 responses were grouped into the following themes: use of ICT in providing medical care reported by thirty, no knowledge by forty, and organization of patient data by five respondents.


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Section III: Students' view of eHealth

One hundred students (98%) responded to the 16 questions of this section. The overall mean was 3.5 ± 0.9 on a 5-point Likert scale. The mean for the six positively phrased statements was 3.9 ± 0.8, whereas the corresponding figure for the negatively phrased statements was 3.28 ± 0.97 [[Table 2]].

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Table 2: Students view of eHealth

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Section IV: Students' use of information and communication technology devices

The students were asked about ICT devices they are using from a prepared list. As shown in [[Figure 1]], around two out of three participants own a desktop or a tablet computer or a smartphone, and more than 85% of the participants own a laptop. Smaller percentage of the participants own an E-reader and a simple phone, and around a third of them own an MP3, an MP4, and/or console games.

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Figure 1: Use of information and communication technology devices by the medical students of Libyan International Medical University

When they were asked about the frequency of the use of common computer software, nearly 40% of the students never used spreadsheets (e.g., Excel), or any other data analysis software. Nearly 49% of the students never used databases and 32.4% of the students never used audio editing, video editing (23%), or other software related to image editing (18.6%). The most commonly used software was PowerPoint (94.1%) [[Table 3]].

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Table 3: Percentage in the frequency of the use of different computer software by medical students of LIMU

Social media and other online activities are being used on a regular basis by most of the participants as shown in [[Table 4]]. Facebook (93.1%) and video (84.4%), document (81%), and photo (83.3%) sharing were most commonly used. LinkedIn scored the lowest (48.1%).

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Table 4: Frequency of the use of social media and online activity by medical students of LIMU

[[Table 5]] shows the students' involvement in online entertainment, online information gathering, shopping, and communication. On an average, 74.8% ±7.4% of the students are using online entertainment. Respective values for information gathering and communication were 78.8 ± 5 and 81.3 ± 1.4. Online shopping scored the lowest (53.9%). Nearly 10%–14% of the students do not use E-mails or instant messaging.

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Table 5: The percentage of online activity of medical students of LIMU

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Section V: Confidence in using information and communication technology software and devices for eHealth

The participants were asked about how confident they will be in learning how to use new ICT skills [[Table 6]]. The mean score on a 5-point Likert scale for all students was 3.1 ± 1.0. The mean percentage of not being sure of confidence in learning the skills was 25.8 ± 4.29, whereas the mean percentage of being confident or extremely confident was 51.1 ± 5.4. [[Table 6]] shows that students' confidence in learning new ICT skills increased if they get different types of support.

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Table 6: Percentage frequency of confidence in learning new information and communication technologies skills

Regarding the students' personal characteristics in learning a new computer technology or an online tool, the average for the five favorable personal characteristics was 3.7 ± 0.2, whereas the average for the five unfavorable characteristics was 2.7 ± 0.2, as measured on a 5-point Likert scale. There was a statistically significant difference between unfavorable and favorable characteristics in favor of positive ones (t-test: P = 0.004). [[Table 7]] shows the percentages for each of these characteristics.

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Table 7: Percentages of personal characteristics in learning new computer technology

The students were also asked about the need for training on several software, as indicated in [[Figure 2]]. On an average, less than a third felt the need for training with a mean of 29.25% ± 14.4% (95% confidence interval = 26.46–32.05). More than half of the students felt a need to receive training in many software including creation of a spreadsheet, managing data with spreadsheet, and blogging [[Figure 2]].

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Figure 2: Need for training

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Discussion

This article aims at determining how prepared are the students of two health professional schools at the LIMU for using ICT on graduation. The graduates of these two schools are expected to work in digitally laden environments where graduates face challenges to cope with the new work demands. It took about 3 months to get the responses from students. We had to stop receiving responses because it was clear that it is unlikely to get meaningful increase in responses. Slow and poor response rate is a common phenomenon in questionnaire-based researches.[[15]] We have noticed that persistence in seeking response contributed to increasing the response from participants, a finding noticed by others as well.[[16]] Females' responses predominated in the group, which is contradictory to other reports.[[17]],[[18]] Surprisingly, 88% of the students did not take any IT-related courses. This figure is obviously high and probably results from the lack of a need to take such courses because the IT skills needed to study in medical and DSs are not so dependent on such skills. This calls for a real change in the IT skills used by students for searching and learning. The English mastery overall was good.

Forty-seven percent of the participants had no clear idea about what eHealth is. This is more than twice of what has been reported by Lam et al., where only one in five health professional students did not know exactly what eHealth is. This highlights the importance of tackling students' illiteracy of eHealth and calls for changes in the taught curricula.[[14]]

Although 47% of the students did not know exactly what eHealth is, their overall perception of it was good. The students showed more confidence in agreeing with positive statements (mean score 3.9) but less so with negatively phrased statements (mean score 3.2). In spite of that, this result shows that work needs to be done to improve students' perception of eHealth.

A large percentage of students own digital devices and 85% own a laptop. This confirms that we are dealing with digitally oriented students. Other studies reported similar ownership rates.[[19]] Ownership of digital devices gives the students a direct access to scientific and educational resources and help connect them with educational forums. However, students owning such devices also use them for noneducational purposes even in classrooms.[[20]] They may even have a negative impact on learning in classroom. A probable solution for this is to construct instructional activities based on the use of such devices.

Nearly 27.5% of the students never used software among those included in the questionnaire. Strangely enough, 2% of the students never used Word Processing. However, the main gap was in using data management software such as Spreadsheets, database, and data analysis, where around 40% of the students reported not using them. This finding is probably explained by the lack of instructional activities using data management software. Involving students in research projects might foster learning such software.

An expected finding is the high frequency of the use of online activities. This high frequency of the use of online activities by students underlies the label given to the present-day students as digital natives. These students use laptops, tablets, smart phones, etc., in their daily life, so it was postulated that it would be easier for them to transfer these capabilities to their learning activities and later to the work environment.[[21]],[[22]] This trend was observed among dental and medical students alike, simply because these are regular activities undertaken by all students irrespective of their field of education. Nowadays, students seek information from the Internet rather than regular books. Providing access to the Internet in classrooms may help increase student engagement in learning activities. Therefore, any recommendation coming out of this study should include reference to changes in classroom setup to allow for the use of digital equipment and software.

An important aspect of this article is the students' confidence in leaning new ICT skills. The self-determination theory focuses on three psychological needs which interact to foster motivation. These include need for competence, relatedness, and autonomy.[[23]] The perception of confidence helps satisfying the need for competence. For this reason, it could be speculated that confident students feel more competent and so have better mastery of their educational environment. The mean confidence in learning new ICT skills by the students included in this study was 3.1 ± 1.0, which shows almost a neutral trend but also shows that the range is wide. Even though around half of the students feel either confident or extremely confident, the other half feel either not sure or unconfident. This calls for implementing strategies that could help enhancing students' confidence in learning new ICT skills.

On testing the personal characteristics of students in learning new ICT skills, favorable characteristics predominated with a mean score of 3.7 ± 0.2. These include persistence, exerting more effort, spending more time, and seeking help from others. These favorable characteristics need to be strengthened in any learning program on ICT skills. Changing the mindsets of students could foster their adaptability and response to different challenges.[[24]]

An important dimension in learning is the student perception for the need of training. Students having this perception are more likely to be motivated in order to satisfy their need. The students in this study perceived this need only a third of the time. This issue is complicated because measuring perception may not truly reflect what is actually measured. Unfortunately, perception addresses only the first level in the Kirkpatrick training evaluation model. The need for training was highly expressed for data management software, and these are the same types of software on which many students do not have experience with, as shown in [[Table 3]].


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Conclusion

This article shows that the students at LIMU are digital natives. It also shows that there is a need for training on ICT skills for them to be readied for work in health-care services, especially on spreadsheets and data management software. In spite of their positive attitude toward eHealth, overall they lack an understanding of what eHealth is exactly about.

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Conflict of Interest

There are no conflicts of interest.

Financial support and sponsorship

Nil.


  • References

  • 1 Oh H, Rizo C, Enkin M, Jadad A. What is eHealth (3): A systematic review of published definitions. J Med Internet Res 2005;7:e1.
  • 2 Available from: [“http://www.who.int/eHealth%20/en/%20accessed%20on%2031/05/201”].
  • 3 Lewis J, Ray P, Liaw ST. Recent worldwide developments in ehealth and mhealth to more effectively manage cancer and other chronic diseases – A systematic review. Yearb Med Inform 2016;(1):93-108.
  • 4 Vuong AM, Huber JC Jr., Bolin JN, Ory MG, Moudouni DM, Helduser J, et al. Factors affecting acceptability and usability of technological approaches to diabetes self-management: A case study. Diabetes Technol Ther 2012;14:1178-82.
  • 5 Kuijpers W, Groen WG, Aaronson NK, van Harten WH. A systematic review of web-based interventions for patient empowerment and physical activity in chronic diseases: Relevance for cancer survivors. J Med Internet Res 2013;15:e37.
  • 6 Bock BC, Graham AL, Whiteley JA, Stoddard JL. A review of web-assisted tobacco interventions (WATIs). J Med Internet Res 2008;10:e39.
  • 7 García PJ, Vargas JH, Caballero N P, Calle V J, Bayer AM. An e-health driven laboratory information system to support HIV treatment in Peru: E-quity for laboratory personnel, health providers and people living with HIV. BMC Med Inform Decis Mak 2009;9:50.
  • 8 Pal K, Dack C, Ross J, Michie S, Murray E. Integrating theory and data to create an online self-management programme for adults with type 2 diabetes: HeLP-Diabetes. Front. Public Health. Conference Abstract: 2nd Behaviour Change Conference: Digital Health and Wellbeing; 2016. doi: 10.3389/conf.FPUBH.2016.01.00005.
  • 9 Hebden L, Balestracci K, McGeechan K, Denney-Wilson E, Harris M, Bauman A, et al. 'TXT2BFiT' a mobile phone-based healthy lifestyle program for preventing unhealthy weight gain in young adults: Study protocol for a randomized controlled trial. Trials 2013;14:75.
  • 10 Pellegrini CA, Duncan JM, Moller AC, Buscemi J, Sularz A, DeMott A, et al. A smartphone-supported weight loss program: Design of the ENGAGED randomized controlled trial. BMC Public Health 2012;12:1041.
  • 11 Njoroge M, Zurovac D, Ogara EA, Chuma J, Kirigia D. Assessing the feasibility of eHealth and mHealth: A systematic review and analysis of initiatives implemented in Kenya. BMC Res Notes 2017;10:90.
  • 12 Combi C, Pozzani G, Pozzi G. Telemedicine for developing countries. A Survey and some design issues. Appl Clin Inform 2016;7:1025-50.
  • 13 Zeind CS, Blagg JD Jr., Amato MG, Jacobson S. Incorporation of Institute of Medicine competency recommendations within doctor of pharmacy curricula. Am J Pharm Educ 2012;76:83.
  • 14 Lam MK, Hines M, Lowe R, Nagarajan S, Keep M, Penman M, et al. Preparedness for eHealth: Health sciences students' knowledge, skills, and confidence. J Inf Technol Educ 2016;15:305-34. Available from: http://www.informingscience.org/Publications/3523. [Last accessed on 2018 Jan 05].
  • 15 Galesic M, Bosnjak M. Effects of questionnaire length on participation and indicators of response quality in a web survey. Public Opin Q 2009;73:349-60.
  • 16 Manfreda KL, Berzelak J, Vehovar V. Web surveys versus other survey modes: A meta-analysis comparing response rates. Int J Market Res 2008;50:79-104.
  • 17 Kehoe CM, Pitkow JE. Surveying the Territory: GVU's five WWW User Surveys; 1996. p. 77-84.
  • 18 Smith MA, Leigh B. Virtual subjects: Using the Internet as an alternative source of subjects and research environment. Behav Res Method Instrum Comput 1997;29:496-505.
  • 19 McCoy BR. Digital distractions in the classroom phase II: Student classroom use of digital devices for non-class related purposes. J Med Educ 2016;7:5-32.
  • 20 Chen B, deNoyelles S. Exploring students' mobile learning practices in higher education. Educ Rev 2013. Available from: https://er.educause.edu/articles/2013/10/exploring-students-mobile-learning-practices-in-higher-education. [Last accessed on 2018 Feb 18].
  • 21 Prensky M. Digital natives, digital immigrants. Horizon 2001;9:1-6.
  • 22 Prensky M. How to Teach with Technology: Keeping Both Teachers and Students Comfortable in an Era of Exponential Change. Emerging Technologies for Learning. Vol. 2. Coventry, UK: BECTA; 2007. p. 40-6. Available from: http://xploit-eu.com/pdfs/PRENSKY%20%20How%20to%20teach%20with%20technology.pdf. [Last accessed on 2018 Jan 05].
  • 23 Deci EL, Vallerand RJ, Pelletier LG, Ryan RM. Motivation and education: The self-determination perspective. Educ Psychol 2011;26:3-4. Available from: https://www.tandfonline.com/doi/abs/10.1080/00461520.1991.9653137. [Last accessed on 2018 Jun 12].
  • 24 Yeager DS, Dweck CS. Mindsets that promote resilience: When students believe that Personal characteristics can be developed. Educ Psychol 2012;4:302-14.

Corresponding author

Prof. Adel I. A. Al.Tawaty
Consultant Pediatrician and Health Professional Educationist, Libyan International Medical University
Kairawan Street, Benghazi
Libya   

Publication History

Received: 22 October 2019

Accepted: 09 December 2019

Article published online:
10 June 2022

© 2019. Libyan International Medical University. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 Oh H, Rizo C, Enkin M, Jadad A. What is eHealth (3): A systematic review of published definitions. J Med Internet Res 2005;7:e1.
  • 2 Available from: [“http://www.who.int/eHealth%20/en/%20accessed%20on%2031/05/201”].
  • 3 Lewis J, Ray P, Liaw ST. Recent worldwide developments in ehealth and mhealth to more effectively manage cancer and other chronic diseases – A systematic review. Yearb Med Inform 2016;(1):93-108.
  • 4 Vuong AM, Huber JC Jr., Bolin JN, Ory MG, Moudouni DM, Helduser J, et al. Factors affecting acceptability and usability of technological approaches to diabetes self-management: A case study. Diabetes Technol Ther 2012;14:1178-82.
  • 5 Kuijpers W, Groen WG, Aaronson NK, van Harten WH. A systematic review of web-based interventions for patient empowerment and physical activity in chronic diseases: Relevance for cancer survivors. J Med Internet Res 2013;15:e37.
  • 6 Bock BC, Graham AL, Whiteley JA, Stoddard JL. A review of web-assisted tobacco interventions (WATIs). J Med Internet Res 2008;10:e39.
  • 7 García PJ, Vargas JH, Caballero N P, Calle V J, Bayer AM. An e-health driven laboratory information system to support HIV treatment in Peru: E-quity for laboratory personnel, health providers and people living with HIV. BMC Med Inform Decis Mak 2009;9:50.
  • 8 Pal K, Dack C, Ross J, Michie S, Murray E. Integrating theory and data to create an online self-management programme for adults with type 2 diabetes: HeLP-Diabetes. Front. Public Health. Conference Abstract: 2nd Behaviour Change Conference: Digital Health and Wellbeing; 2016. doi: 10.3389/conf.FPUBH.2016.01.00005.
  • 9 Hebden L, Balestracci K, McGeechan K, Denney-Wilson E, Harris M, Bauman A, et al. 'TXT2BFiT' a mobile phone-based healthy lifestyle program for preventing unhealthy weight gain in young adults: Study protocol for a randomized controlled trial. Trials 2013;14:75.
  • 10 Pellegrini CA, Duncan JM, Moller AC, Buscemi J, Sularz A, DeMott A, et al. A smartphone-supported weight loss program: Design of the ENGAGED randomized controlled trial. BMC Public Health 2012;12:1041.
  • 11 Njoroge M, Zurovac D, Ogara EA, Chuma J, Kirigia D. Assessing the feasibility of eHealth and mHealth: A systematic review and analysis of initiatives implemented in Kenya. BMC Res Notes 2017;10:90.
  • 12 Combi C, Pozzani G, Pozzi G. Telemedicine for developing countries. A Survey and some design issues. Appl Clin Inform 2016;7:1025-50.
  • 13 Zeind CS, Blagg JD Jr., Amato MG, Jacobson S. Incorporation of Institute of Medicine competency recommendations within doctor of pharmacy curricula. Am J Pharm Educ 2012;76:83.
  • 14 Lam MK, Hines M, Lowe R, Nagarajan S, Keep M, Penman M, et al. Preparedness for eHealth: Health sciences students' knowledge, skills, and confidence. J Inf Technol Educ 2016;15:305-34. Available from: http://www.informingscience.org/Publications/3523. [Last accessed on 2018 Jan 05].
  • 15 Galesic M, Bosnjak M. Effects of questionnaire length on participation and indicators of response quality in a web survey. Public Opin Q 2009;73:349-60.
  • 16 Manfreda KL, Berzelak J, Vehovar V. Web surveys versus other survey modes: A meta-analysis comparing response rates. Int J Market Res 2008;50:79-104.
  • 17 Kehoe CM, Pitkow JE. Surveying the Territory: GVU's five WWW User Surveys; 1996. p. 77-84.
  • 18 Smith MA, Leigh B. Virtual subjects: Using the Internet as an alternative source of subjects and research environment. Behav Res Method Instrum Comput 1997;29:496-505.
  • 19 McCoy BR. Digital distractions in the classroom phase II: Student classroom use of digital devices for non-class related purposes. J Med Educ 2016;7:5-32.
  • 20 Chen B, deNoyelles S. Exploring students' mobile learning practices in higher education. Educ Rev 2013. Available from: https://er.educause.edu/articles/2013/10/exploring-students-mobile-learning-practices-in-higher-education. [Last accessed on 2018 Feb 18].
  • 21 Prensky M. Digital natives, digital immigrants. Horizon 2001;9:1-6.
  • 22 Prensky M. How to Teach with Technology: Keeping Both Teachers and Students Comfortable in an Era of Exponential Change. Emerging Technologies for Learning. Vol. 2. Coventry, UK: BECTA; 2007. p. 40-6. Available from: http://xploit-eu.com/pdfs/PRENSKY%20%20How%20to%20teach%20with%20technology.pdf. [Last accessed on 2018 Jan 05].
  • 23 Deci EL, Vallerand RJ, Pelletier LG, Ryan RM. Motivation and education: The self-determination perspective. Educ Psychol 2011;26:3-4. Available from: https://www.tandfonline.com/doi/abs/10.1080/00461520.1991.9653137. [Last accessed on 2018 Jun 12].
  • 24 Yeager DS, Dweck CS. Mindsets that promote resilience: When students believe that Personal characteristics can be developed. Educ Psychol 2012;4:302-14.

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Table 1: Frequency distribution of students by faculty and year, n (%)
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Table 2: Students view of eHealth
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Figure 1: Use of information and communication technology devices by the medical students of Libyan International Medical University
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Table 3: Percentage in the frequency of the use of different computer software by medical students of LIMU
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Table 4: Frequency of the use of social media and online activity by medical students of LIMU
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Table 5: The percentage of online activity of medical students of LIMU
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Table 6: Percentage frequency of confidence in learning new information and communication technologies skills
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Table 7: Percentages of personal characteristics in learning new computer technology
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Figure 2: Need for training
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