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International Journal of Mechanical Engineering and Technology (IJMET)
Volume 9, Issue 12, December 2018, pp. 943–947, Article ID: IJMET_09_12_094
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=12
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
©
IAEME
Publication
Scopus Indexed
THE ADVANTAGES AND DISADVANTAGES OF
THE MHEALTH APPLICATIONS AND THE
INTENTION TO USE AMONG SMARTPHONE
USERS
Zuhal Hussein
Faculty of Business Management, Universiti Teknologi MARA Kota Bharu Campus, Lembah
Sireh, 15050, Kota Bharu, Kelantan, Malaysia
ABSTRACT
The use of mobile computing and communication technologies in health care and
public health called mHealth could greatly improve health-care delivery processes and
bring benefits to the people. However, there is a limited research that looking at the
perception of users towards mHealth from the benefits and barriers perspectives. The aim
of this study is to explore the perception of Malaysians on the intention to use mHealth
whether the usage of it will be a barrier or benefit to them. This quantitative study
randomly recruited four hundred eighty respondents who were smartphone users in the
six states in Malaysia include Kelantan, Penang, Selangor, Johore, Sabah and Sarawak
using purposive sampling. Survey method and a questionnaire were used as a tool for
data collection. Consent were obtained from participants before starting the survey.
Findings indicate that both perceived barrier and perceived benefits are positively and
significantly correlated with intention to use. However, among the two independent
variables, only perceived benefits significantly predict respondents’ intention to use. In
conclusion, study suggests that people are ready to use the mHealth technology when they
feel the technology can benefit them.
Keywords: mHealth, intention to use, perceived benefits, perceived barriers
Cite this Article: Zuhal Hussein, the Advantages and Disadvantages of the Mhealth
Applications and the Intention to Use among Smartphone Users, International Journal of
Mechanical Engineering and Technology, 9(12), 2018, pp. 943–947
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=12
1. INTRODUCTION
The advancement in mobile communication technologies have led to the development of mobile-
health (mHealth)—the use of mobile computing and communication technologies in health care
and public health. Many experts believe that mHealth could greatly improve health-care delivery
processes and bring benefits to the people. mHealth intervention can reduce cost, saving time,
better accessibility, useful in patients and doctors communication and easier as well as faster to
send messages regarding the diseases and health. Adoption of mHealth can improve the lifestyle,
Zuhal Hussein
http://www.iaeme.com/IJMET/index.asp 944 editor@iaeme.com
nutrition, health, other physiological states, behaviors and quality of life (Hoque, 2016). It has
been regarded as best tools for curing diseases and improving health condition (Kumar et al,
2013; Cole-Lewis & Kershaw, 2010). However, there is a limited research that looking at the
perception of users towards mHealth from the benefits and barriers perspectives. The aim of this
study is to explore the perception of Malaysians on the intention to use mHealth whether the
usage of it will be a barrier or benefit to them.
Since this study will look at the health promoting behaviours using a technology, therefore it
has used Health Belief Model (HBM) to measure the health promoting behaviours and looking
at the intention to use of the technology. The health belief model, developed in 1950, is used to
predict whether people can prevent and become aware of diseases, and it can facilitate resolving
problematic behavior and prompting public health responses. Two out of six items in HBM
include perceived barriers and perceived benefits. Perceived benefits, which indicate the
assessment of the positive benefits of participating in health-promoting behaviour. Perceived
barriers, which refer to the beliefs on the difficulty and cost of participating in health-promoting
behaviour. According to the reasoned action theory, attitudes and subjective norms result in the
formation of behavioral intention, thereby influencing behaviors. Behavioral intention is a
necessary step in the behavior implementation process. In other words, behavioral intention refers
to the action tendency to adopt a certain behaviour (Fishbein and Ajzen, 1975). Several researches
have been done that looking at the relationship between perceived barriers and benefits and
behavioural intention in the health promoting behaviours studies. According to Chen et al (2013)
study on salt restriction spoon use found that perceived benefits of salt-restriction spoon use
indirectly influence the use of these spoons, whereas perceived barriers directly influence salt-
restriction spoon use. When perceived barriers increase, the use of salt-restriction spoons
decreases substantially. In addition, a study on condom use among female sex workers indicated
that intention to use indirectly influences condom use through perceived barriers and perceived
benefits, respectively (Zhao et al, 2012). Study in the context of mHealth, researchers found that
perceived benefits is a vital factor which determines the adoption of mHealth. Patients thought
the app appeared straightforward and easy to use. Patients found benefits in the portability of
health information when interacting with physicians and emergency medical technicians
(DiDonato et al, 2015). Besides, study done by Lee & Rho (2013) on the acceptance of mobile
health monitoring services found that the users perceived benefits and the behavioural intention
on the usage of the mobile health was higher than non-users.
Therefore, understanding the benefits and barriers that encourage and discourage individuals
from engaging in health promoting activities by using technology and the acceptance that leads
the intention to use is crucial to explore in this study.
2. METHODOLOGY
Four hundred eighty respondents who were smartphone users and knowing about mobile health
systems or ever experience/ever heard with/about mobile health system recruited in this study.
This quantitative study collating data from the six states include Kelantan, Selangor, and Penang,
Johore from the West Malaysia and Sabah and Sarawak from the East Malaysia using purposive
sampling. Data were collected between November 2015 and March 2016. Survey method and a
questionnaire were used as a tool for data collection. A set of questionnaire includes 4 sections
comprise respondent’s socio demographic background, perceived benefits, perceived barriers and
intention to use. These measurement items were adopted and modified from one or two sources
to fit the needs and purpose of the questions. If the participants agreed to participate, they have
to sign an informed consent form and then they were asked to complete the questionnaire
accordingly. Finally, data were analysed using the statistical software package SPSS Version 21
and several analysis were conducted includes frequency, Pearson correlation and multiple
regression analysis.
The Advantages and Disadvantages of the Mhealth Applications and the Intention to Use among
Smartphone Users
http://www.iaeme.com/IJMET/index.asp 945 editor@iaeme.com
3. RESULTS AND DISCUSSION
3.1. Socio-demographic background of respondent
Table 1 shows the summary statistics for the socio-demographic background of the respondents
includes sex, age groups, level of education, and the health problems. The percentage of female
respondents is slightly higher, which accounts for more than half of the total respondents
surveyed (58.5%). Majority of the respondents were in the age group of 21-30 years (51.3%),
followed by age group of 31-40 years (19.0%), age group of 18-20 years (14.6%), age group of
41-50 years (11.7%) and the least was the age group of 51-60 years (3.5%). Most of the
respondents having tertiary education which accounts for almost half of the total respondents
surveyed (66.0%). The lowest percentage of respondents was with primary education (10.6%).
In terms of the health problems, most of the respondents (75.0%) mentioned that they do not have
health problems, and followed by mentioning that they had health problems (18.8%) and do not
know about their health problems (6.2%). It can be speculated that majority of the respondents
did not have health problems because majority of them were below 40 years old.
Table 1: Socio demographic of the respondents
Demographic /
Social Variables Categories Frequency
N=480 (%)
Sex Male
Female
198
281
41.3
58.5
Age (Years)
18-20
21-30
31-40
41-50
51-60
70
246
91
56
17
14.6
51.3
19.0
11.7
3.5
Level of education
Primary
Secondary
Tertiary
51
112
317
10.6
23.3
66.0
Health problems
Yes
No
Don’t know
90
360
30
18.8
75.0
6.2
Table 2: Pearson correlation and multiple regression analyses
Variable M SD
Correlation
with
intention
to use
b SE b ß
Intention to
Use
3.54 1.19 1.00 1.70 .10
Perceived
barriers
3.03 1.50 .57** -.03 .05 -.03
Perceived
benefits
3.55 1.54 .67** .54* .05 .70*
Zuhal Hussein
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Note. R
2
= .45; *p < .05, **p < .01
Pearson correlation and multiple regression analyses were conducted to examine the
relationship between intention to use, perceived barriers and perceived benefits. Table 2
summarizes the descriptive statistics and analysis results. Findings indicate that both perceived
barrier and perceived benefits are positively and significantly correlated with intention to use. A
multiple linear regression was calculated to predict intention to use based on respondents’
perceived barriers and perceived benefits. A significant regression equation was found (F(2,477)
= 195.288, p< .000), with an R2 of .45. This indicates that 45% of variability in intention to use
is explained by the two independent variables (IV). However, among the two independent
variables, only perceived benefits significantly predict respondents’ intention to use.
Based on the mentioned results, this study found the consistency with other studies (DiDonato
et al, 2015; Lee and Rho, 2013) that perceived benefits is a significant factor that can promote
the intention to use the mHealth. This can be postulated that the encouragement of using the apps
emerged when the users found the apps can give more benefits compared to the risk to them. In
addition, this study involves young generation which has a vast knowledge and accessibility
about using a mobile phone, therefore they were perceived benefits of the usage of mHealth.
Deng, Mo and Liu (2014) posited that mHealth provides personalized and tailored the healthcare
services for the young citizen. Meanwhile, perceived barriers was not significant to predict the
intention to use because when the users found the technology is not benefit to them and they are
having so many challenges for them to use it, they will ignore the technology and not get the
technology involved in managing their daily life especially in their health monitoring
(Narasimhan, 2013).
4. CONCLUSION
In conclusion, study suggests that people are ready to use the mHealth technology when they feel
the technology can benefit them. Researchers, educators and healthcare providers need to educate
the people especially non-user about this technology and encourage them to use it regularly in
their daily routine. Besides, healthcare providers need to occupy themselves with a best
knowledge and practices to handle this technology. For the marketers and technology developers
need to focus on robustly establish the ability of mobile technology-based interventions to
improve health-care delivery processes to make it more beneficial to the users.
ACKNOWLEDGEMENTS
The author would like to acknowledge the research project funded by Ministry of Higher
Education (MOHE) under the Fundamental Research Grant Scheme (FRGS) and Universiti
Teknologi MARA (UiTM), Project No: 600- RMI/RFGS 5/3/122 for the publication of this paper.
REFERENCES
[1] Li, C., Unger, J.B., Schuster, D., Rohrbach, L.A., Howard-Pitney, B., Norman, G. (2003).
Youths’ exposure to environmental tobacco smoke (ets): Associations with health beliefs and
social pressure. Addictive Behaviours. 28, 39–53.
[2] Orji, R., Mandryk, R.L. (2014). Developing culturally relevant design guidelines for
encouraging healthy eating behavior. International Journal of Human Computer Study, 72,
207–223.
[3] Gillibrand, R., Stevenson, J. (2006). The extended health belief model applied to the
experience of diabetes in young people. British Journal of Health Psychology, 11, 155–169.
[4] Umeh, K., Rogan-Gibson, J. (2001). Perceptions of threat, benefits, and barriers in breast self-
examination amongst young asymptomatic women. British Journal of Health Psychology, 6,
361–372.
The Advantages and Disadvantages of the Mhealth Applications and the Intention to Use among
Smartphone Users
http://www.iaeme.com/IJMET/index.asp 947 editor@iaeme.com
[5] Radtke, T., Kaklamanou, D., Scholz, U., Hornung, R., Armitage, C.J. (2014). Are diet-
specific compensatory health beliefs predictive of dieting intentions and behaviour? Appetite,
76, 36–43.
[6] Gerend, M.A., Shepherd, J.E. (2012). Predicting human papillomavirus vaccine uptake in
young adult women: Comparing the health belief model and theory of planned behavior.
Annals Behavioural Medicine, 44, 171–180.
[7] Haldre, K., Part, K., Ketting, E. (2012). Youth sexual health improvement in Estonia, 1990–
2009: The role of sexuality education and youth-friendly services. European Journal of
Contraceptive Reproduction Health Care, 17, 351–362.
[8] Fishbein, M., Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to
Theory and Research; Addison-Wesley: Reading, MA, USA, p. 480.
[9] Chen, J., Liao, Y., Li, Z., Tian, Y., Yang, S., He, C., Tu, D., Sun, X. (2013). Determinants of
salt-restriction-spoon using behavior in China: Application of the health belief model. PLoS
ONE, 8, e83262.
[10] Vander Wal, J.S. (2012). The relationship between body mass index and unhealthy weight
control behaviors among adolescents: The role of family and peer social support. Economic
Human Biology, 10, 395–404.
[11] Lajunen, T., Räsänen, M. (2004). Can social psychological models be used to promote bicycle
helmet use among teenagers? A comparison of the Health Belief Model, Theory of Planned
Behavior and the Locus of Control. Journal of Safety Research, 35, 115–123.
[12] Nieminen, T., Prattala, R., Martelin, T., Harkanen, T., Hyyppa, M.T., Alanen, E., Koskinen,
S. (2013). Social capital, health behaviours and health: A population-based associational
study. BMC Public Health, 13, 613.
[13] Simsekoglu¸Ö., Lajunen, T. (2008). Social psychology of seat belt use: A comparison of
theory of planned behavior and health belief model. Transportation Research Part F, 11, 181–
191.
[14] Sun, X., Guo, Y., Wang, S., Sun, J. (2006). Predicting iron-fortified soy sauce consumption
Intention: Application of the theory of planned behavior and health belief model. Journal of
Nutrition Education Behavioural, 38, 276–285.
[15] Zhao, J., Song, F., Ren, S., Wang, Y., Wang, L., Liu, W., Wan, Y., Xu, H., Zhou, T., Hu, T.
et al. (2012) Predictors of condom use behaviors based on the health belief model (HBM)
among female sex workers: A cross-sectional study in Hubei province, China. PLoS ONE, 7,
e49542.
[16] Wang, Y.S, Lin, H.H, Luarn, P. (2006). Predicting consumer intention to use mobile service.
Information System Journal, 16(2):157–79.
[17] Kumar, S., Nilsen, W.J, Abernethy, A, Atienza, A, Patrick, K., Pavel, M., et al. (2013).
Mobile health technology evaluation: the mHealth evidence workshop. American Journal of
Preventive Medicine, 45(2), 228–36.
[18] Cole-Lewis, H., Kershaw, T. (2010). Text messaging as a tool for behavior change in disease
prevention and management. Epidemiology Review, 32(1),56–69.
[19] Deng, Z., Mo, X., Liu, S. (2014). Comparison of the middle-aged and older users’ adoption
of mobile health services in China. International Journal of Medical Informatics, 83(3), 210–
24.
[20] Lee, J.B., Rho, M.J. (2013). Perception of Influencing Factors on Acceptance of Mobile
Health Monitoring Service: A Comparison between Users and Non-users. Health Information
Research, 19(3): 167–176.
[21] Hoque, M.R. (2016). An empirical study of mHealth adoption in a developing country: the
moderating effect of gender concern. BMC Medical Informatics and Decision Making.
