|Year : 2017 | Volume
| Issue : 2 | Page : 69-75
Using the Unified Theory of Acceptance and Use of Technology model to analyze cloud-based mHealth service for primary care
Fatema Khatun1, Md. Jahir U Palas2, Pradeep K Ray3
1 Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh; WHO Collaborating Centre on eHealth, The University of New South Wales, Kensington, NSW, Australia
2 Department of Banking and Insurance, Faculty of Business Studies, University of Dhaka, Dhaka, Bangladesh
3 WHO Collaborating Centre on eHealth, The University of New South Wales, Kensington, NSW, Australia; Centre For Entrepreneurship, University of Michigan-Shanghai Jiao Tong University Joint Institute, China
|Date of Web Publication||18-Sep-2017|
WHO Collaborating Centre on eHealth, The University of New South Wales Sydney, Kensington, NSW 2052, Australia; Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh
Source of Support: None, Conflict of Interest: None
Background and Objectives: Cloud-based mHealth services have the potential to make quality healthcare available in remote locations in the world. A practical deployment will involve medicolegal issues involving physicians and patients in different within and across countries. However, the first step is to evaluate such a cloud-based mHealth (MyOnlineClinic). This study aimed to understand and find out the factors that influence the end-user intention to use this new technology in Australia. Materials and Methods: We surveyed 167 end-users in 2015 and performed a Structural Equation Model analysis using Smart PLS to identify the intention to use the system among the participants. Results: The study revealed that the Unified Theory of Acceptance and Use of Technology construct, particularly facilitating condition (FC) (β = 0.355, P = 0.002), has yielded a significant influence on the behavioral intention to use MyOnlineClinic. However, the relationships between performance expectancy and behavioral intention (β = 0.162, P = 0.141), effort expectancy and behavioral intention (β = −0.004, P = 0.971), and social influences and behavioral intention (β = 0.164, P = 0.100) were insignificant. Further, age showed moderating effect on these variables. The majority of the respondents agreed or strongly agreed that technological issues such as sound (92.2%), video qualities (88.6%), and interaction with doctor (89.8%) are good. Conclusion: The end-users’ intentions to use MyOnlineClinic system were particularly influenced by FCs such as hardware, software, and the information technology knowledge/familiarity of users. These factors may get further accentuated when these systems are deployed across countries with different languages, technological infrastructures, and medicolegal environments. Therefore, cloud-based mHealth would help in removing some barriers, such as differences in software versions and interoperability problems of systems at physician and patient ends.
Keywords: Cloud-based mHealth service, MyOnlineClinic, telehealth, Unified Theory of Acceptance and Use of Technology
|How to cite this article:|
Khatun F, Palas MU, Ray PK. Using the Unified Theory of Acceptance and Use of Technology model to analyze cloud-based mHealth service for primary care. Digit Med 2017;3:69-75
|How to cite this URL:|
Khatun F, Palas MU, Ray PK. Using the Unified Theory of Acceptance and Use of Technology model to analyze cloud-based mHealth service for primary care. Digit Med [serial online] 2017 [cited 2019 Jan 18];3:69-75. Available from: http://www.digitmedicine.com/text.asp?2017/3/2/69/215028
| Introduction|| |
Worldwide telehealth has expanded and targeted to meet the health-care needs for rural and remote habitants. Telehealth is important to leverage health-care gap when it is necessary and urgent.,,, This new technology creates huge opportunity for both users and health-care service providers. In Australia, MyOnlineClinic, a telehealth services has been serving since 2015 as a pilot phase (http://myonlineclinic.com.au). Cloud-based mHealth (healthcare enabled by mobile phones) opens up a new dimension in the deployment of telehealth services across regions and countries, where a physician and the patient are located in different places though there are challenges due to differences in regulatory environments in different countries.
MyOnlineClinic is a telehealth platform which provides video consultation (telehealth) for Australian patient. The online platform plays an integral role to connect patients to their own general practitioner (GP) as well as other GPs through utilizing the technology. Patients use their own computer, laptop, tablet including Smartphone to make an appointment with their doctor, receive advices from doctor, and pay online. MyOnlineClinic also collects medical data (such as blood pressure, blood sugar, and temperature) for patients and doctors. In addition, MyOnlineClinic provides benefits to doctors who prefer working remotely online due to being away from their clinic or hospital, during on personal leave, or even traveling. This innovative platform would help to connect and engage online medical transaction between doctors and their own patients as well as any new patient who is seeking for medical services. Apart from above advantages of using the telehealth platform, MyOnlineClinic also provides benefit to people who unexpectedly encounter with health related-issues while they are away for a holiday or enjoying their daily life activities. Given the innovative approach, MyOnlineClinic brings competitive advantages and better services to travelers and clients from industry perspective as well. Telemedicine technology is not new in the digital era; MyOnlineClinic offers opportunity to work remotely with mobile device and in a flexible time, which benefits both patients and health-care providers. The greater adoption of MyOnlineClinic may have significant reduction in face-to-face doctors visit that reduce travel time and cost. This positive feature of telemedicine could be capitalized for the field of preventive and clinical medicine.
Adoption of new technology and its functional features with advanced techniques have been focused on the users’ perspective and the end-users’ need. However, the need of a feature for end-user is important for the successful use of the technology. At the same time, low-usage rate, resistance, and abandonment of use of new technology occurs when end-user reaction are not thoroughly considered. Several models have been developed in the past few decades to explain and understand the user acceptance and use of new technology. The most important of them are theory of reasoned action, the Technology Acceptance Model (TAM), the Theory of Planned Behavior, and Unified Theory of Acceptance and Use (UTAUT). The UTAUT is considered as the most sensitive and updated one that explains variance of technology acceptance and explain intention to use. TAM model has developed a concept of social psychology and tool that define the intention of end-user to use new technology. In the industries, TAM is a gold standard but for health sector, UTAUT model suits best as it incorporates methods for human behavior theory. Consequently, low-usage rates, resistance, abandonment of the use of health information technology (Health IT), and requests for alternative methods have occurred. Therefore, to elicit substantial effects, the reactions of end-users must be thoroughly considered.,,
The objective of present study was the assessment of end-user perception toward telehealth implemented using cloud-based mHealth primary care service. The study was conducted only in Australia and hence issues related to differences (technological infrastructure and medicolegal factors) across countries are not discussed in this paper. The technological environment involved smart phones (Android and iOS), their apps, and wireless primary care sensors for weight, height, blood pressure, temperature, electrocardiogram, and blood oxygen levels. We analyzed the factors that influenced the intentions of end-user to use telehealth (MyOnlineClinic) using the data collected in 2015. The findings will useful for practitioners, industries, and scholars to understand the context and issues related the telehealth technology and help to improv the existing services.
| Materials and Methods|| |
There have been several models (e.g., TAM) for the assessment of information systems and technologies in various application domains including healthcare and UTAUT seems most appropriate as evidenced by its use in recent studies. The main objective of the project was to assess and evaluate the experience of end-user while using MyOnlineClinic about the episode of care. Based on literature review, we constructed model for this study. In this study, we used UTAUT to study acceptance and use of MyOnlineClinic by end-user in Australia. According to UTAUT, four factors influence use of telehealth: performance expectancy (PE), effort expectancy, social influence, and facilitating conditions (FCs). We considered only age and gender for moderating the effect. Therefore, we made some alterations to our research model [Figure 1].
Our hypotheses were:
- Hypothesis 1: Performance expectancy (PE) has positive effect on behavior intention to use MyOnline Clinic
- Hypothesis 2: Effort expectancy (EE) has positive effect on behavior intention to use MyOnline Clinic
- Hypothesis 3: Social influence (SI) has positive effect on behavior intention to use MyOnline Clinic
- Hypothesis 4: Facilitating conditions (FC) has positive effect on use behavior intention to use MyOnline Clinic
- Hypothesis 5a-d: Gender has a moderating effect on the positive effects of PE, EE, SI, and FCs on behavior intention
- Hypothesis 6a-d: Age has a moderating effect on the positive effects of PE, EE, SI, and FCs on behavior intention.
Definition of the constructs
PE: The degree to which an individual believes that using the system will help him/her to attain gains in job performance.
EE: The degree to which an individual believes that ease is associated with use of system.
SI: The degree to which an individual perceived that important others believe he/she should use the system.
FCs: The degree to which an individual believes that organizational and technical infrastructure exist to support use of the system.
Behavior Intention to use (BI): The degree to which an individual intends to use the system.
Online survey was conducted for this study among the users of MyOnlineClinic in Australia. The questionnaire is based on the pretested questions presented in the original UTAUT model. The respondents needed to fulfill the following criteria for participating in the survey:
- MyOnlineClinic user and used MyOnlinClinic in last 28 days
- Age 18 years and above.
Participants and data collection
The study recruited patients aged 18-year-old and above. The project utilized MyOnlineClinic website and facilities. On patients’ side, there was a person who had been trained for the use of the system and he or she helped patients through MyOnlineClinic. Therefore, patients were assisted to use the system. As such, this project aimed at understanding patients’ opinions only on episode of care and the features of the technology. The study looked at how patients see their experience of meeting online with their physician.
Participants were invited within the 4 weeks of use of MyOnlineClinic. List of potential participants were prepared from MyOnlineClinic patient's pool. Before inviting the potential participants, office manager sent a poster over E-mail to explain the objective and procedure of the study. A total of 167 participants was recruited for the online survey. A web-based questionnaire was administered to the participants. The questionnaire had a Participant Information Sheet (PIS) and a consent form at the beginning of the survey. The PIS explained the purpose of the study and contact details of the study team while the consent form implied if they agree to participate in the survey. If participants decide to go with the survey, they could simply click the “submit” button on the online form. The field testing of the questionnaire was conducted during May–June, 2015 and necessary changes have been made since then. Final data collection was conducted between July 2015 and November 2015. Data were analyzed under the UTAUT theoretical framework.
The study was approved by the Human Research Ethics Advisory Panel UNSW Business School, UNSW Australia. Ethics approval number (no.: 1460121). Informed consent was obtained from each participant, and confidentiality and anonymity were assured.
Partial Least Squares (PLS) was the statistical tool used for measuring the research model. Microsoft Excel and SmartPLS GmbH, Ahornstr. 54, 25474 Boenningstedt, Germany ('SmartPLS') were used to analyze the survey data. Outliers in the dataset were screened, and missing values were imputed, whenever possible, to have validated findings. Structural equation modeling (SEM) technique used for evaluating the relationships in the UTAUT model and also for testing the hypotheses among the variables. The reason for selecting SEM is because this statistical methodology allows a hypothesis testing (confirmatory) approach to structural analysis of data that represent a phenomenon. The statements used in the questionnaire were based on pretested questionnaires used in Venkatesh et al. on UTAUT. The variables were measured with a 5-point Likert scale as compared to the 7-point scale used in the original UTAUT since a 5-point scale proved to be more robust for this survey. According to the scale, 1 equaled the negative end (strongly disagree) and 5 equaled the positive end (strongly agree). For testing the moderating effects of gender and age, the data of gender were categorized into two different groups, namely, “male” and “female” denoted by 1 and 2, respectively. The age related data were categorized into two different groups, namely, below 50 years and more than or equal to 50 years, which were denoted by 1 and 2, respectively.
| Results|| |
In total, 167 study participants completed the survey. Demographic characteristics of the study participants are given in [Table 1]. Most of the respondents belonged to the age categories 31–59 (55%) and 33% were from aged 60 and above. Around 86% of the respondents were male. Eighty-seven percent of the respondent used other telehealth services before using MyOnlineClinic.
[Table 2] describes all variables used in the research, the item description, and descriptive statistics (mean and standard deviation (SD)) of the measured item. The mean values of all the items ranged from 3.84 to 4.12 while the SD has a range of 0.46 to 0.74. Of the survey questions, UTAUT questionnaire was composed of 19 questions and 5 constructs.
|Table 2: Mean score and standard deviation of each item in the questionnaire related to the research model constructs|
Click here to view
Internal reliability, convergent validity, and discriminant validity are the ways to examine a model's robustness. Our measurement model's internal reliability was tested through Cronbach's Alpha and construct composite reliability where 0.70 remains the benchmark as an acceptable internal consistency. Average variance extracted (AVE) is calculated to measure the convergent validity where a value of 0.50 should be available to have convergent validity. [Table 3] presents the calculated the results of Cronbach's alpha and construct composite reliability. Cronbach's alpha values ranged from 0.653 to 0.908 where Behavioral Intention (BI) has a marginal value. In case of construct composite reliability, values ranged in 0.812–0.935 which is an indicator of strong internal reliability. The estimated construct loadings and the AVE are all in the satisfactory level. Thus, the convergent validity conditions are adequately satisfied in the study's measurement model.
The discriminant validity of the measurement model was examined by the cross-loading matrix and square root of all the AVEs. To achieve satisfactory level of discriminant validity, the square root of AVE of a construct needs to be higher than its correlation with other constructs. Therefore, the diagonal values in the constructs’ correlation matrix must be greater than the entries in corresponding rows and columns to avail discriminant validity of the model. The result at [Table 4] confirms that all the constructs have sufficient discriminant validity in the data.
The study's measurement model has been tested through a structural equation model, which identified the constructs’ relationships. The path coefficients (β) and P values were used to test the relationship between dependent and independent variables. The PLS results are presented in [Table 5]. The results depicted that the relationship between FC and BI (β = 0.355, P = 0.002) was significant. Thus, H4 was accepted. However, the relationships between PE and BI (β = 0.162, P = 0.141), EE and BI (β = −0.004, P = 0.971), and SI and BI (β = 0.164, P = 0.100) were insignificant and did not support hypotheses H1, H2, and H3 of our study. While testing the moderation effects of age on the relationships, all the P values were higher than the acceptance criteria of P = 0.05. Therefore, the hypotheses H6(a-d) were all rejected. However, due to a limited number of female respondents (only 22 out of 167 respondents), the moderating effect of gender could not be evaluated as it resulted in a singular matrix error.
The majority of the respondents agreed or strongly agreed that technological issues related to MyOnlineClinic such as sound (92.2%), video quality (88.6%), and interaction with doctor (89.8%) are good [Figure 2].
|Figure 2: Technological issue related to MyOnlineClinic platform (n = 167)|
Click here to view
| Discussion|| |
This paper describes a study concerning the utilization of UTAUT model to provide an explanation on end-user acceptance of MyOnlineClinic. It is also noted that UTAUT was developed outside health-care context. However, UTAUT is the most up-to-date form of framework that explains technology acceptance and use. This study provides empirical insight into four constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) of UTAUT model to explain relationship between the constructs and behavior intention for adoption of MyOnlineClinic. Age and gender were considered as moderating factors. In our study, 33% of the study participants were elderly (aged 60+) and have high school and above education. Education has direct association with technology use shown in other studies., The majority of the respondents were male, and moderation with gender could not be employed due to limited number of female participants.
This study shows that performance expectancy, effort expectancy, and social influences do not have a significant influence on behavior intention as shown in original UTAUT model and other related technology acceptance and use.,,, We found that FC has significant relationship with behavior intention to use MyOnlineClinic. Findings suggested that end-users believe that organizational and technical infrastructure are exists to support the systems while they are using the platform. It is important to have available physical and technical support (electricity, wifi/laptop/computer/and family support), respondents IT knowledge, and online nurse/IT support for end-user. These findings are also similar with the line of Vanneste et al. When we moderate FC with age group, FC was insignificant on behavior intention, which allows us to explain that younger age group is independent of the IT technical supports. The most widely used model for end-user technology acceptance is TAM. However, in the healthcare information technology (HIT), UTAUT is the widely used model to test user acceptance and has 20%–30% greater explanatory power compared TAM. From the view of a pilot project, considerable amount of sample (n = 167) obtained in this study will be a valuable reference for improving the MyOnlineClinic and similar project in the future. MyOnlineClinic is a business model of mHealth services. Study result showed that the majority of the end-user was willing to pay for MyOnlinClinic teleconsultation which has positive effect on sustainability of this health program.
The present study had some limitations. First, the sample of potential user of MyOnline Clinic was restricted and there was a very limited female participant that did not allow us to look at gender effect. A second limitation is that for convenience and time reasons, we had to complete the study in a specific time frame due to budget and study participants’ constraints. The third limitation is the fact that the results are only valid for the specific telehealth services (MyOnlineClinic) in Australia. Therefore, it was not possible to evaluate the capabilities of MyOnlineClinic across countries (physician on one country and patient in another country). Finally, we were not able to evaluate the capabilities of a cloud-based mHealth service as against those of normal mHealth service.
| Conclusion|| |
This paper presents the results of our study on the end-users’ intentions to use MyOnlineClinic system (a cloud-based mHealth system). We observed that results were particularly influenced by FCs, mainly the technological infrastructure (mobile apps, sensors, etc.) and its knowledge with patients and their nursing assistants. Thus, success of the implementation of MyOnlineClinic systems will depend on the level of training and familiarity of users (patients, family members, and health professionals) with the mHealth technology. We believe that cloud-based mHealth infrastructure will help in this direction because it eliminates some of the technical problems, thanks to ubiquitous mobile apps and wireless sensors, relating to interoperability of different systems at patient and medical service provider ends.
The authors would like to thank Dr. Ash Collins, the Industry Leader and his team for this project partly funded by the NSW Government Department of Trade and Industry, for their help with arrangements for data collection on this research. Authors would also like to thank Mr. Teuku Geumpana, a UNSW PhD student for helping in compiling data for this project.
Financial support and sponsorship
This project is funded by NSW Trade and Investment Department through TeleMedicine Australia Pty. Ltd.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Perednia DA, Allen A. Telemedicine technology and clinical applications. JAMA 1995;273:483-8.
Coiera E. Guide to Medical Informatics, the Internet and Telemedicine. London, UK: Chapman & Hall Ltd.; 1997.
Currell R, Urquhart C, Wainwright P, Lewis R. Telemedicine versus face to face patient care: Effects on professional practice and health care outcomes. Cochrane Libr 2000;2.
Roine R, Ohinmaa A, Hailey D. Assessing telemedicine: A systematic review of the literature. CMAJ 2001;165:765-71.
Maeder AJ, Mars M, Scott RE. Global Telehealth 2014. Vol. 206. Ios Press; 2014.
Holden RJ, Karsh BT. The technology acceptance model: Its past and its future in health care. J Biomed Inform 2010;43:159-72.
Ajzen I, Fishbein M. Understanding Attitudes and Predicting Social Behaviour. Engle-wood-Cliffs, N.J.: Prentice-Hall.; 1980.
Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of two theoretical models. Manage Sci 1989;35:982-1003.
Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Q 2003;27:425-78.
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 1989;13:319-40.
Cimperman M, Makovec Brenčič M, Trkman P. Analyzing older users’ home telehealth services acceptance behavior-applying an extended UTAUT model. Int J Med Inform 2016;90:22-31.
Rho MJ, Kim HS, Chung K, Choi IY. Factors influencing the acceptance of telemedicine for diabetes management. Cluster Comput 2015;18:321-31.
Diño MJ, de Guzman AB. Using Partial Least Squares (PLS) in predicting behavioral intention for telehealth use among Filipino elderly. Educ Gerontol2015;41:53-68.
Kline RB. Principles and Practice of Structural Equation Modeling. New York: Guilford Press; 2011.
Hair JF, Ringle CM, Sarstedt M. Partial Least Squares Structural Equation Modeling: Rigorous Applications, better Results and Higher Acceptance. Long Range Planning 2013;46:1-12.
Hair JF Jr., Anderson RE, Tatham RL, Black WC. Multivariate Data Analysis with Readings. New Jersy: Prentice Hall; 1995.
Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing. Adv Int Mark 2009;20:277-319.
Khatun F, Hanifi SM, Iqbal M, Rasheed S, Rahman MS, Ahmed T, et al.
Prospects of mHealth services in Bangladesh: Recent evidence from chakaria. PLoS One 2014;9:e111413.
Khatun F, Heywood AE, Ray PK, Hanifi SM, Bhuiya A, Liaw ST, et al.
Determinants of readiness to adopt mHealth in a rural community of bangladesh. Int J Med Inform 2015;84:847-56.
Venkatesh V, Zhang X. Unified theory of acceptance and use of technology: US vs. China. J Global Inf Technol Manag 2010;13:5-27.
Venkatesh V, Sykes TA, Zhang X. ‘Just What the Doctor Ordered': A Revised UTAUT for EMR System Adoption and Use by Doctors: System Sciences (HICSS), 2011 44th
Hawaii International Conference on: IEEE; 2011. p. 1-10.
Brown SA, Dennis AR, Venkatesh V. Predicting collaboration technology use: Integrating technology adoption and collaboration research. J Manag Inf Syst 2010;27:9-54.
Vanneste D, Vermeulen B, Declercq A. Healthcare professionals’ acceptance of belRAI, a web-based system enabling person-centred recording and data sharing across care settings with interRAI instruments: A UTAUT analysis. BMC Med Inform Decis Mak 2013;13:129.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]