Determinants of Patients’ Intention to Adopt Diabetes Self-Management Applications Ananthidewi Maniam
College of Information Technology Universiti Tenaga Nasional, Selangor, Malaysia
[email protected]
Jaspaljeet Singh Dhillon
College of Information Technology Universiti Tenaga Nasional, Selangor, Malaysia
[email protected]
ABSTRACT
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can lead to heart disease, stroke, kidney failure, high blood pressure, and blindness. The prevalence of diabetes in Malaysia has increased at an alarming rate [1]. It is noted that there were 3.2 million cases of diabetes in Malaysia in 2014 [2]. Recent National Health and Morbidity Survey showed that 2.6 million adults in the country (which makes 15.2% of the adult population) already have diabetes [3]. The figures are way ahead from the projections made by the World Health Organisation (WHO) that indicated Malaysia to have 2.48 million people with diabetes in 2030 [4]. The direct cost of managing patients with diabetes in Malaysia is overwhelming, costing close to RM 1.4 billion in 2012 [5]. Diabetes mellitus requires continuing medical care and patient’s commitment to reduce the risk of long-term disability and prevention problems.
Despite significant advances in medicine, diabetes mellitus remains a major health problem among diabetes patients (diabetics). Diabetes in Malaysia has become increasingly critical along with diabetes complications. Diabetes Self-Management Applications (DSMA) are impactful patient-centered tools that has immense potential in enabling diabetics to manage their health conditions and thereby prevent complications. This study identifies factors that influence the intention to adopt DSMA by diabetics in Malaysia. The aim is to develop a research model to represent the adoption of DSMA amongst diabetics in Malaysia. Previous work is reviewed to develop the proposed model which comprised of constructs from established models and othper constructs from the literature. To test the developed model, a quantitative approach was employed and established questionnaires were administered as research instrument for data collection. The findings indicate that Perceived Financial Risk, Perceived Privacy and Security Risk, Technology Anxiety and Facilitating Conditions have significantly positive relationship with the intention to adopt DSMA. Findings from this study serve as a guideline for DSMA developers in understanding the core factors that influence the adoption and use of diabetes health applications by diabetics.
One solution to overcome the aforementioned issue is to encourage diabetics to play an active role in improving their lifestyle for a better health in preventing diabetes complications. A growing number of healthcare applications aim to help people stay healthy and to avoid diabetes complications. Emerging patient-centred solutions has made patients more empowered to take responsibility towards their own health and to participate in making important decisions about what to do next about their health [6].
Keywords
Diabetes self-management refers to personal actions towards diabetes mellitus, its treatment and progress of disease prevention. These actions include with medical management, role management, and emotional- and self-management. Selfmanagement can be achieved by leveraging Diabetes SelfManagement Applications (DSMA) that are designed and developed to manage diabetes mellitus. These applications are tools that specially offer functionality and services to control diabetes. DSMA are commercially available to consumers to assist with managing their health and are known to have immense potential in achieving better health outcomes. However, DSMA are not meant to replace doctors, but in fact complement doctors in providing better care to the patients. These applications are offthe-shelf tools designed to interact directly with patients, with or without the presence of healthcare professionals. DSMA have great potential in enabling diabetics to track their health status and to actively participate in treatment regimens and preventive strategies, i.e. consolidate effective self-care habits into their daily lives. They provide the necessary knowledge and skills needed to modify their and successfully manage diabetes and its related conditions.
Diabetes Self-Management Applications, Technology Acceptance, Consumer Health, Consumer Health Informatics.
ACM Classification Keywords
• Applied computing~Consumer health • Human-centered computing~User studies • Human-centered computing~User centered design • Human-centered computing~Ubiquitous and mobile devices • Software and its engineering~Requirements analysis.
1. INTRODUCTION
Diabetes is often referred to by doctors as diabetes mellitus that describes a group of metabolic diseases in which there are high blood sugar levels continuing for a long time. Symptoms of high blood sugar include frequent urination, increased thirst, and increased hunger. Diabetes is a chronic metabolic condition that Permission to make digital or hard copies of all or part of this work for Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies personal or classroom use is granted without fee provided that copies are are not made or distributed for profit or commercial advantage and that not made or distributed for profit or commercial advantage and that copies bear this thisnotice notice thecitation full citation onpage. the Copyrights first page. copies bear and and the full on the first Copyrights for components of thisowned work owned by others ACM mustbebe for components of this work by others thanthan ACM must honored. permitted. To To copy copy otherwise, otherwise,oror honored. Abstracting Abstracting with with credit credit is is permitted. republish, on on servers or toorredistribute to lists, prior specific republish,totopost post servers to redistribute to requires lists, requires prior specific permission and/or permissions a fee. Request permissions from permission and/or a fee. Request from
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[email protected]. CHINZ 2015, September 3–4, 2015, Hamilton, New Zealand CHINZ 2015, September 03-04, 2015, Hamilton, New Zealand Copyright is held by the owner/author(s). © 2015 ACM. ISBN 978-1-4503-3670-3/15/09…$15.00 Publication rights licensed to ACM. DOI: 978-1-4503-3670-3/15/09…$15.00 http://dx.doi.org/10.1145/2808047.2808059 ACM DOI: http://dx.doi.org/10.1145/2808047.2808059
Nilufar Baghaei
Unitec Institute of Technology Private Bag 92125, Auckland, New Zealand
Increasingly, more applications are being developed to enable diabetics to take control of their health conditions and to prevent diabetes complications. DSMA are available in the form of smartphone, web-based and desktop-based applications. Popular examples include SugarStats.com, SiDiary, and Glucose Buddy.
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Although DSMA have great potential in managing diabetes, usage of DSMA among diabetics in Malaysia is relatively low. There is lack of awareness about patient self-management among the Malaysian community [7]. Apart from focusing on the innovation and implementation aspects of emerging novel DSMA, it is fairly necessary to ensure that these applications are well adopted and accepted by healthcare consumers and patients. DSMA are progressively gaining popularity among diabetics in Malaysia. The focus of improving health outcomes is shifting gradually from doctors to patients, which is apparent from the increasing numbers of patient-centric applications being designed, developed and leveraged by users. In short, the new opportunities and increased use of DSMA makes it vital to determine the factors that influence patients’ intention to adopt these applications for managing their care. The purpose of this study is to determine the factors influencing the adoption of DSMA by diabetics in Malaysia. Identification of these factors can aid healthcare stakeholders (e.g. providers, payers and patients) to comprehend the essential requirements of diabetics towards DSMA and eventually these applications could be connected to the health-IT ecosystem. This study aim to answer the following research questions: (1) which factors influence the adoption of DSMA amongst diabetics, (2) what is the relationship between the identified factors and the intention to adopt DSMA by diabetics, and (3) what is the perception of diabetics in Malaysia towards DSMA for diabetes self-management. Existing technology acceptance models are too general and they do not encompass essential factors that affect the acceptance of DSMA. Previous studies are mostly focused on the Western society, i.e. the results obtained may not be applicable in developing countries like Malaysia. Furthermore, previous studies are mostly focused on specific health applications for managing diabetes, e.g. smartphone-based applications. In this study, we developed a model that will be able to represent the adoption of any DSMA by diabetics in Malaysia. The remainder of this paper is organised as follows. Section 2 reports on the current literature. Section 3 describes our proposed DSMA model. We then present the research methodology followed by discussion of the results. Limitations and conclusions are presented in Sections 7 & 8 respectively.
A sample of the literature that has been reviewed (presented in Table 1). The table presents models that were employed to investigate the acceptance and effective use of diabetes management systems. These studies examined the acceptance of diabetes management systems and related technologies.
Qualitative
Triangulation: Qualitative and quantitative methods Qualitative and quantitative methods
DeLone and McLean Information System Success Model SCOT and MAST
Repeatedmeasures modelling
Reviewed Literature
3.1 THE PROPOSED MODEL
In developing the adoption model, this research mainly extracted factors from three key theoretical frameworks from the extant literature: Theory of Reasoned Action (TRA) [8], Technology Acceptance Model (TAM) [10] and Unified Theory of Acceptance and Use of Technology (UTAUT) [9]. Table 2 summarizes the variables of the adoption model and provides a brief description for each construct included. The constructs and interrelated propositions are described below.
Table 1. Existing Models in the Literature Intend to identify factors influence the acceptance of diabetes applications amongst patients age 50 years or older [21].
Intend to examine whether applications can enhance adherence to remote monitoring and selfmanagement of diabetes, hence behavior change [24]. Intend to determine the effect of a web-based patient selfmanagement intervention on psychological (self-efficacy, quality of life, self-care) and clinical (blood pressure, cholesterol, glycemic control, weight) outcomes [25]. Intend to evaluate the evidence on the impact of cell phone interventions for persons with diabetes and/or obesity in improving health outcomes and/or processes of care for persons with diabetes and/or obesity [26].
Quantitative
To proceed with the investigation of the factors affecting the adoption of DSMA, an adoption model was formulated to test and validate relationships among independent variables and dependent variable. The factors explored in this study include Perceived Ease of Use, Perceived Usefulness, Resistance to Change, Perceived Financial Risk, Perceived Privacy and Security Risk, Technology Anxiety and Facilitating Conditions. Figure 1 depicts the adoption model for this research study.
Limited work that focused on diabetes patients' acceptance of DSMA is noted, despite the fact that these applications have significant role to play in the success of the self-management. Besides the studies on the diabetes management technology acceptance, researchers have used models to explain the acceptance and use of medical applications or systems.
Methodology
Quantitative
3. DSMA ADOPTION MODEL
2. RELATED WORK
Dependent Variable
Intend to examine the effectiveness of a freely available smartphone applications combined with text-message feedback from a certified diabetes educator to improve glycemic control and other diabetes-related outcomes in adult patients with Type 1 diabetes in a twogroup randomised controlled trial [22]. Intend to evaluate the perceptions and user acceptance of mobile diabetes monitoring among Japanese physicians [23].
Model Theorybased and uniform interview
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personal skills. When the users perceive that the system will be easy to use and can help them to complete their work better, they will be confident to use it.
Perceived Ease of Use Facilitating Conditions
Resistance to Change
H7
H1
Diabetics' Intention to Adopt DSMA
H6 H5
Technology Anxiety
Perceived Usefulness
Secondly, the factor Perceived Usefulness is adapted from Davis [10]. Several studies have found that Perceived Usefulness is a primary and utmost the primary predictor of information technology usage [16]. In the context of DSMA, we can define the Perceived Usefulness as the degree to which a person feels that using DSMA for self-management will be advantages to him or her. Thus, if the health professionals or patients believe that DSMA are useful and it will improve their self-care, this usefulness will lead to the intention to use DSMA.
H2 H3
H4
Perceived Financial Risk
Perceived Privacy and Security Risk
Thirdly, Perceived Financial Risk which is the fear of wasting money towards to DSMA. Perceived Financial Risk is adapted from Stone and Gronhaug [11]. According to Featherman and Pavloub [12], Financial Risk is the potential monetary outlay associated with the initial purchase price as well as the subsequent maintenance cost of the product.
Figure 1. DSMA adoption model for this study To characterize the adoption of DSMA, we utilize Behavioral Intention as the dependent variable in our research model. This construct has its original basis within the TRA model [8] which measures a person's relative strength of intention to perform a behavior while attitude consists of beliefs about the consequences of performing the behavior multiplied by his or her evaluation of these consequences.
The fourth factor is Perceived Privacy and Security Risk. Perceived risk constructs have been incorporated in models of technology acceptance and empirical research showed that, as expected, risk negatively affects user intention to accept a technology directly or indirectly [17], [12]. In the context of DSMA, we can define the Perceived Privacy and Security Risk as the fear of sharing private data through DSMA.
Table 2. Factors of the proposed adoption model Variables Independent Variables
Constructs Perceived Ease of Use Perceived Usefulness Perceived Financial Risk Perceived Privacy and Security Risk Technology Anxiety Resistance to Change Facilitating Conditions
Dependent Variables
Behavioral Intention
Definitions Degree to which using the equipment is free from effort [10]. Extent to which the person believes that the technology will assist them [10]. Fear of wasting money towards to DSMA [11].
The next factor is Technology Anxiety can be defined as the tendency of an individual to feel uneasy, apprehensive, or aversive at the prospect of using DSMA. This factor is adapted from Simonson [13]. Many users especially elderly worried about using technology for delivering healthcare services, unexpected errors caused by the applications, and making mistakes they cannot correct. Technology Anxiety can lead to the resistance to change.
Fear of sharing private data through DSMA [12].
The following factor is Resistance to Change which plays an important role in the adoption a new technology. Whenever the Resistance to Change is low, the user will give relatively high evaluation on usefulness and there will be a positive impression towards intention to use and vice versa. Resistance to Change is related to people’s behavior under conditions of change in a variety of contexts. Some people would like to keep their routines and do not like any activities that can change their usual practices. Introducing a new technology often involves some form of change for users who would like to keep their routines and do not like any activity that can change their life style [18].
Tendency of an individual to feel uneasy, apprehensive, or aversive at the prospect of using DSMA [13]. People’s behavior under conditions of change in a variety of contexts [14]. Degree to which a patient believes they have the necessary resources, knowledge and skills to use of DSMA [15]. Measure of the strength of one's intention to perform a specified behavior [10], [15].
The last factor is Facilitating Conditions which is the perception that organisational and technical infrastructure exists to support the use of technology [9]. In the context of DSMA, the Facilitating Conditions refers to as the degree to which a patient believes they have the necessary resources, knowledge and skills to use of DSMA. This factor is adapted from UTAUT
model.
Independent variables are set of variables that are assumed to effect the adoption of DSMA. Firstly, Perceived Ease of Use is one of the major construct of TAM [10]. It has been identified as one of the major factors that motivate individuals to accept and use specific technology. The complexity and difficulty of IT products can affect the ease of use. The difficulty faced in using an application may be caused by many factors such as information technology literacy, less experience, and lack of training and
3.2 RESEARCH HYPOTHESES
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HI: There is a positive relationship between Perceived Ease of Use and patients’ intention to adopt DSMA. H2: There is a positive relationship between Perceived Usefulness and patients’ intention to adopt DSMA. H3: There is a negative relationship between Perceived Financial Risk and patients’ intention to adopt DSMA.
consisted of the following sections:
H4: There is a negative relationship between Perceived Privacy and Security Risk and patients’ intention to adopt DSMA. H5: There is a negative relationship between Technology Anxiety and patients’ intention to adopt DSMA. H6: There is a negative relationship between Resistance to Change and patients’ intention to adopt DSMA. H7: There is a positive relationship between Facilitating Conditions and patients’ intention to adopt DSM
4. METHODOLOGY
This research is built upon two approaches: analytical reviews and a quantitative survey. The former approach was used to identify the factors that influence the adoption of DSMA amongst diabetics, and to develop a model to measure the adoption of DSMA amongst diabetics. The second approach is a quantitative survey which was executed to empirically test the proposed model. A questionnaire was employed as an instrument to collect the data from the respondents. The data was analysed and the findings showed the degree to which the developed model can be accepted. Several statistical tests were conducted. Factors that appeared to be insignificant in the model were removed accordingly to achieve a valid model which is in line with our research goal.
Part 1: Demographic Information. This part was about collecting demographic information about participants. It comprised of seven questions. Part 2: Attitude towards Healthcare. This section was made up of nine statements adapted from the Multidimensional Health Locus of Control (MHLC) scale that assesses respondents’ perception whether health is controlled by internal or external factors. Table 3 shows the Subscales of MHLC and respective items. Part 3: Assessing Patients Adoption of DSMA. This section comprised of 36 statements that were related to the projected factors influencing the adoption of DSMA.
Table 4 shows the sources of the measurement of the instrument of this study of five response choices, ranging from strongly disagree (1) to strongly agree (6). These measurements were selected as the Cronbach’s alpha of the measurement exceeds 0.7 (see Table 7), indicating that the constructs are indeed reliable [19]. SPSS version 22.0 was employed for data analysis in this work. Descriptive analysis was used to determine the demographics of the respondents, along with the descriptive information of the respective variables. Reliability analysis was carried out to determine the reliability of the measurement and internal consistency of the items of the measurements. Furthermore, Pearson Correlation, regression analysis and coefficients were done to determine the relationship between the variables, and to find what degree the variation could be explained, respectively.
The study sample involved diabetics living in Malaysia. Due to time and resource constraints we deployed the convenience sampling method in selecting the study participants. The initial sample size was 120 respondents. We prepared two versions of the survey: online and printed. Data was collected for a month between March and April 2015. The online version of the survey was created using SurveyMonkey. We prepared a small write up about the study that was complemented with a diagram (shown in Figure 2. and description about DSMA.
Table 3. Subscales of MHLC and respective items Subscales Internal
Powerful Others
Diabetes SelfManagement Applications
Chance
Items If I take care of myself, I can avoid illness. If I take the right actions, I can stay healthy. The main thing which affects my health is what I do myself. Having regular contact with my doctor is the best way for me to avoid illness. Whenever I don’t feel well, I should consult a medically trained professional. Health professionals control my health. No matter what I do, if I am going to get sick, I will get sick. My good health is largely a matter of good fortune. If it’s meant to be, I will stay healthy.
Table 4. Source of measurement table Variable Perceived Usefulness (PU)
Figure 2. Diabetes self-management applications Then, we shared the link of this online survey to people via email, Facebook and WhatsApp. Seventy five (75) completed the online survey. We approached 45 diabetics personally with a printed copy of the questionnaire at selected hospitals and clinics. Although we had 120 respondents, only 105 responses were useful as they were errors (e.g. skipped sections) by 15 respondents, which were excluded from the sample. The survey
PU1 PU2 PU3 PU4 PU5
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Questions Learning to use DSMA will be easy for me. I will find my interaction with DSMA clear and understandable. I can easily become skilful at using DSMA. It will be easy to remember how to use DSMA. Overall, I anticipate that DSMA will be easy to use.
Perceived Ease of Use (PE)
PE1 PE2 PE3 PE4 PE5 PE6 PE7
Perceived Financial Risk (FR)
FR1 FR2 FR3
Perceived Privacy and Security Risk (PP)
PP1 PP2
PP3 Technology Anxiety (TA)
TA1 TA2 TA3 TA4
Resistance to Change (RC)
RC1 RC2 RC3
Facilitating Conditions (FC)
FC1 FC2 FC3 FC4 FC5
Behavioural Intention (BI)
Using DSMA will make it easier to manage my health. Using DSMA will give me greater control over my disease. DSMA will make my health information more accessible. Using DSMA will allow me to quickly accomplish tasks related to managing my disease. I believe it will be helpful to have an electronic copy of my blood glucose record. DSMA will make obtaining health information more convenient. Overall, I will find DSMA useful in managing my disease. Signing up for DSMA would be a poor way to spend my money. I would be concerned about how much I would pay to use DSMA. If I bought DSMA, I would be concerned that I would not get my money’s worth. My use of DSMA can cause me to lose control over the privacy of my information. Signing up for and using DSMA can lead to a loss of privacy for me because my personal information could be used without my knowledge. Internet hackers (criminals) might take control of my information if I use DSMA. Using DSMA for health selfmanagement would make me very nervous. Using DSMA for health selfmanagement makes me worried. Using DSMA for health selfmanagement may make me feel uncomfortable. Using DSMA for health selfmanagement may make me feel confused. I consider that using DSMA for health self-management is a negative idea. I don’t want DSMA to make any change in my routine doctor-centred lifestyle. Although there are potential benefits of using DSMA for health selfmanagement, I do not want to use them. I have the resources necessary to use DSMA. I have the knowledge necessary to use DSMA. DSMA are compatible with other technologies I use. I can get help from others when I have difficulties using DSMA. Using DSMA is entirely within my control.
BI1 BI2 BI3 BI4 BI5 BI6
I intend to use DSMA in the future. I will always try to use DSMA in my daily life. I plan to use DSMA frequently. I will often use DSMA in the future. I will recommend others to use DSMA. I intend to use DSMA in the future.
5. RESULTS
This section reports the results of several statistical tests that were conducted on the data generated by respondents. The sample consisted of 105 diabetics living in Malaysia (mean age 52, SD = 8.22). With respect to demographics, there was a good balance of distribution (almost 50%) for both males and females among the respondents. Seventy five out of 105 respondents (71.43%) completed secondary level education and majority of the respondents (43.81%) had Type 1 diabetes. Table 5 shows characteristics of the study participants. Table 5. Participant’s characteristics Characteristic n Gender Male 54 Female 51 Highest level of education Primary 11 Secondary 75 Tertiary 19 Types of diabetes the participants had Prediabetes 22 Type 1 46 Type 2 3 Gestational 0 Not sure of the type of diabetes. 34 Computer usage 5 days a week or more 18 1 - 4 days/ week 28 1 - 3 times a month 33 Less often than once a month 21 Never 5 Computer skill level Expert 6 Advanced 16 Intermediate 77 Novice 6 Experienced using DSMA Web-based DSMA 6 Desktop-based DSMA 4 Smartphone-based DSMA 5 Others 0 None 95
% 51.43 48.57 10.48 71.43 18.10 20.95 43.81 2.86 0.00 32.38 17.14 26.67 31.43 20.00 4.76 5.71 15.24 73.33 5.71 5.71 3.81 4.76 0 90.48
With respect to Attitude towards Healthcare, respondents’ overall mean score for the Internal subscale is higher (4.87) than Powerful Others (4.67) and Chance (4.65). This indicates that the respondents have a positive attitude towards managing their health. They are aware that health it is not a matter of chance, it is not controlled by external forces (such as health professionals), but it is primarily internal.
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The descriptive statistic of the variables of the study was identified using mean score interpretation [20]. Table 6 summarises the descriptive statistic of the variables of the study. Cronbach’s Alpha [19] was used as measure of reliability (i.e. internal consistency) that explains how closely related a set of items are as a group. Table 7 presents the Cronbach’s Alpha of the variables, showing that all possess a Cronbach’s Alpha exceeding 0.9.
significantly influencing the adoption of DSMA. Figure 3 presents the final model, depicting the four factors affecting the adoption of DSMA by diabetics in Malaysia.
Table 6. Descriptive statistic of the variables
Diabetics' Intention to Adopt DSMA
Variables PU PE PF PP TA RC FC BI
Mean score value 4.00 2.29 3.97 4.12 3.97 1.85 3.93 4.08
Interpretation Agree Disagree Agree Agree Agree Disagree Agree Agree
Technology Anxiety
PU PE PF PP TA RC FC BI
No. Items 5 7 3 3 4 3 5 6
Cronbach's Alpha (α) 0.995 0.981 0.991 0.976 0.953 0.993 0.985 0.993
6. DISCUSSION
This research was motivated by the recognition that DSMA are impactful patient centered tools that enable diabetics manage their health conditions (i.e. prevent diabetics complications) and DSMA developers and researchers need to better understand what drives a diabetics’ intention to adopt DSMA. Concerning that an individual’s adoption and usage decision is not simple, but rather complex, the researcher sought to offer a holistic perspective on the factors that constitute all intention to adopt DSMA.
Status Excellent Excellent Excellent Excellent Excellent Excellent Excellent Excellent
Theoretically, the findings of this study can help us to measure the adoption of DSMA amongst diabetics by identifying the relationship between Perceived Ease of Use, Perceived Usefulness, Perceived Financial Risk, Perceived Privacy and Security Risk, Technology Anxiety, Resistance to Change, and Facilitating Conditions and intention to adopt DSMA. This study has provided findings which will serve as a guideline for DSMA developers in understanding the core constructs that influence the adoption and use of diabetes health applications by diabetics in Malaysia.
From the table, the entire constructs possess Cronbach’s Alpha exceeding 0.9, which indicative of the fact that internal consistency between the items is excellent. Pearson’s correlation coefficient is used to measure the association between variables of interest because it is based on the method of covariance. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship. Table 8 shows the coefficients value of coefficients of the variables.
The model highlights the essential factors influencing the adoption of DSMA. It became apparent that Perceived Financial Risk (fear of wasting money towards to DSMA), Perceived Privacy and Security Risk (fear of sharing private data through DSMA), Technology Anxiety (tendency of feeling uneasy, apprehensive, or aversive at the prospect of using DSMA) and Facilitating Conditions (degree to which a patient believes they have the necessary resources, knowledge and skills to use of DSMA) are the most influencing factors.
Table 8. Coefficients Model Constant PU PE PF PP TA RC FC
Unstandardised Coefficients B Std. Error -.040 .112 -.045 .031 -.031 .098 .146 .066 .802 .064 -.359 .084 .028 .030 .465 .084
Standardised Coefficients Beta
-.067 -.027 .166 .728 -.305 .038 .470
Perceived Privacy and Security Risk
Figure 3. DSMA adoption model
Table 7. Reliability analysis testing between variables Variables
Perceived Financial Risk
Facilitating Conditions
T
Sig
-.356 -1.422 -.316 2.229 12.535 -4.268 .934 5.525
.722 .158 .752 .028 .000 .000 .352 .000
It is assumed that the identification of these factors will aid healthcare stakeholders (e.g. providers, payers and patients) to comprehend the essential requirements of diabetics in Malaysian consumers towards DSMA. In return, DSMA designers and researchers will be able to produce better solutions (targeted towards the local need). Furthermore, DSMA will then be able to leverage effective DSMA in preventing diabetes complications, which could eventually lead to lower healthcare expenditure. Perceived Financial Risk significantly influences the perceived intention to adopt DSMA (p = 0.028). Diabetics in Malaysia are concerned about the money they will be spending to purchase DSMA and want to ensure value for their money. DSMA
The final model of this study is produced based on the results of hypotheses testing. Based on the results obtained, only four out of the seven proposed independent variables were found to be
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presented in this study may not represent the general population of diabetics worldwide.
designers and developer got to ensure that the applications are cost effective. At least the basic functionalities (e.g. tracking blood glucose) should be provided for free to encourage diabetics to use these applications. Applications service providers should provide DSMA for free or provide trial version for a month so that the users can come up with a better outcome of DSMA.
8. CONCLUSION & FUTURE WORK
Diabetes is a serious health condition that can lead to other complications. The number of people affected with this disease is increasing rapidly and the cost of managing diabetics is overwhelming. Diabetics should be encouraged to play an active role in improving their lifestyle and to take preventive measures to avoid complications caused by diabetes. DSMA are great tools that have immense potential in enabling diabetics to actively participate in treatment regimens and preventive strategies (e.g. effectively monitor their glucose levels). DSMA are available on many different platforms such as smartphones applications, weband desktop-based solutions. Increasingly, more DSMA are being designed and developed in meeting the requirements and changing needs of diabetics. Based on our review and understanding, previous work mostly focussed on the innovation and implementation aspects of novel DSMA, i.e. often overlook the adoption and acceptance of their target users.
Perceived Privacy and Security significantly influences the perceived intention to adopt DSMA (p = 0.000). Diabetics have the fear of sharing their private data through DSMA. It can be concluded that security and privacy appear to be the top concerns among diabetics in Malaysia to use DSMA. Developers got to find ways to ensure that the privacy and security aspects of DSMA are taken into account. Applications service providers should have privacy policies in place to gain confidence among potential users of DSMA. They should check policies for clear statements on who owns the data, how the data might be "shared" with others. Technology Anxiety significantly influences the perceived intention to adopt DSMA (p = 0.000). Diabetics in Malaysia seem to be quite anxious in using DSMA. Developers should include tutorials (possibly video tutorials) and provide necessary trainings or hands on experience in enabling users to be familiar with the proposed applications.
In this paper, we developed a model to represent the adoption of DSMA by diabetics living in Malaysia. A quantitative survey was conducted with 120 diabetics which was analysed with SPSS version 22.0 using descriptive analysis along with Pearson correlation and multiple regression. Results showed that Perceived Financial Risk, Perceived Privacy and Security Risk, Technology Anxiety, and Facilitating Conditions are important determinants of diabetics’ intention to adopt diabetes self-management applications in improving or managing their health conditions.
Facilitating Conditions significantly influence the perceived intention to adopt DSMA (p = 0.000). Diabetics in are confident that they have the necessary resources, knowledge and skills to make effective use of DSMA. Perceived Ease of Use does not influence the perceived intention to adopt DSMA. This means that no matter how easy it is to use DSMA, it does not affect the intention of diabetics to adopt or use these applications. We can conclude that diabetics have the ability explore new technologies, regardless of their complexity.
In future, qualitative interviews with diabetics can be conducted with diabetics to confirm the results achieved in this study. Openended questions could be included to investigate their perception towards DSMA, i.e. to determine the factors that encourage or discourage them to use DSMA.
Perceived Usefulness does not influence the perceived intention to adopt DSMA. The amount and type of functionality that is offered by DSMA does not affect the intention of diabetics to adopt or use these applications. Diabetics are aware that health support applications do offer functionalities that could be useful to them.
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Resistance to Change does not influence the perceived intention to adopt DSMA. Diabetics are flexible to change and willing to adopt DSMA. Their daily routines do not affect the intention of diabetics to adopt or use these applications.
[2] International Diabetes Federation. (2014). Western Pacific. Retrieved from http://www.idf.org/membership/wp/malaysia. [3] Institute for Public Health. (2011). National health and morbidity survey.
Overall, the results highlighted the importance of DSMA to diabetics. It is clear that diabetics in Malaysia see the potential of DSMA in enabling them to manage their conditions as to prevent diabetes complications. This particular finding is not very innovative but it can help DSMA developers to understand important requirements of diabetics towards DSMA so they could enhance or develop these applications accordingly.
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7. LIMITATIONS
There are some limitations that must be taken into account in this study. Firstly, due to resource constraints, we did not provide real DSMA to the study participants to try out before completing the quantitative survey. Instead, we provided a description of what is DSMA which was complemented with a diagram showing the applications which falls under diabetes self-management. Secondly, the respondents of this study are diabetics living in Malaysia. Thus, their perception towards DSMA is might be different from those living in foreign countries. Hence, the model
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