Chris Kiegerl, Stephan Schlögl, Aleksander Groth
User Acceptance of Continuous Glucose Monitoring Systems Chris Kiegerl1, Stephan Schlögl1, Aleksander Groth1
Technology acceptance is an important field in information systems research. While it is mainly affected by Davis’ Technology Acceptance Model (TAM), re-cent studies increasingly address the need for more qualitative analysis methods. This paper reports on results of an interview study investigating the acceptance of Continuous Glucose Monitoring (CGM) systems with elderly diabetes patients. CGM systems offer the potential to improve glucose monitoring. However, their potential depends heavily on the users’ willingness to actually use them. Thus, it seems important to investigate potentially prohibiting factors. Interviews with twelve elderly diabetes mellitus patients identified a number of critical acceptance factors. Among these are known determinants such as perceived usefulness, per-ceived ease of use, subjective norms, trial-ability, financial factors, computer anx-iety, quality, and self-efficacy. Acceptance, however, seems to be also influenced by users’ physiological conditions as well their age.
1 Introduction Currently, Europe has to cope with several different demographic challenges, one of which is the aging of its population. The so-called demographic aging describes the proportion of older people within the total population as a result of a significant economic, social and medical progress. This trend is the outcome of numerous simultaneous demographic trends, which include the decrease of the average number of children per woman, the increased life expectancy, and the decline in fertility as well as the increased amount of immigration towards Europe [1]. The impact of the phenomenon of the aging population is severe and undeniable and the reduction in the working-age population requires a constructive response to the acute demographic challenge. Especially noncommunicable diseases and results of these diseases are the leading cause of mortality in the western world [2]. One of the most prevalent non-communicable diseases among seniors is diabetes mellitus. Diabetes mellitus requires adequate care and individually adjusted therapy to minimize its impact. Possible solutions and support systems for diabetes care are enabled by technologies developed within the so-called Ambient Assisted Living (AAL) domain. AAL solutions aim to address the different needs of elderly people helping to increase their quality of life, facilitating their independence and, at the same time, reducing the 1 MCI Management Center Innsbruck, Innsbruck, Austria, Interaction Lab, Dep. Management, Communication &IT
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cost for society and the health care system [3]. Yet despite their potential technical merits, these technologies can only be effective if they are actually used. Research has shown that actual system use is determined by the overall acceptance of the technology [4]. User technology acceptance has received extensive attention from researchers, leading to respective theories and models which try to explain the acceptance of various systems [5]. One system which may help elderly diabetes patients is the so-called Continuous Glucose Monitoring (CGM). The technology facilitates individual glucose care and improves the diabetes therapy by continuously monitoring the glucose level of a patient. As acceptance can be seen as premise which determines the actual value of said CGM systems it is necessary to investigate its influencing factors.
2 Theoretical Background Globally, diabetes is being diagnosed as a considerable concern for national health care systems and creates numerous difficult challenges regarding adequate and effective care for the aging population. Diabetes increases the patient’s risk of developing multiple different health complications such as heart failure, kidney failure, increased likelihood of strokes, cardiovascular risks as well as diabetic retinopathy. These complications can be reduced significantly by controlling the blood glucose level of patients.
2.1 Types of Diabetes Mellitus Diabetes mellitus describes a group of metabolic diseases which are characterized by hyperglycemia resulting from defects in insulin secretion, insulin action or both simultaneously [6]. Most cases fall into two defined etiopathogenetic categories: Type 1 Diabetes Mellitus (T1DM) – This form of diabetes only accounts for five to ten percent of all diabetes patients. It is described as a condition in which pancreatic betacell destruction leads to a complete insulin deficiency. The cause is a combination of resistance to insulin action and an inadequate compensatory insulin secretion response [7]. The patients can no longer produce their own natural insulin, which makes the blood glucose level rise. The autoimmune destruction of the beta-cells has multiple genetic predispositions and is also influenced by environmental factors which are not sufficiently defined and explored so far. Research on the representation of the epidemiology of T1DM has shown that this disease occurs predominantly in children and young adults [6]. Type 2 Diabetes Mellitus (T2DM) – The second class of diabetes represents the majority of all diabetes patients. Nine out of ten diabetes cases can be categorized as T2DM. In contrast to T1DM, the patient’s immune system does not attack its own beta-cells. It is diagnosed by an insulin resistance. The etiology of the insulin resistance is not clarified yet but most patients have an increased percentage of body fat distributed predominantly in the abdominal region in combination with insufficient physical activity [8]. Often, the
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condition is unseen for several years due to its gradual, continuing progress. Overall, the likelihood of micro- and macro-vascular complications increases even in the early stages of the disease [6]. Even though the causes for T1DM and T2DM are not identical, both categories require proper medical supervision in form of adequately adjusted individual care.
2.2 Diabetes Care Hyperglycemia caused by any diabetes variation contributes significantly to the pathogeneses of vascular complications. Therefore, the predominant goal of every diabetes therapy is to regulate a patient’s blood glucose level. Glucose control counteracts possible acute complications, improves the quality of life and limits long term effects of the disease. Often, the early stages of T2DM do not require explicit glycemic control. However, oral or intravascular medications are regularly used to intervene with the blood glucose level of the patient. Managing the therapy is a complex task, especially for elderly people. Functional impairment, additional medication and possible cognitive dysfunction should be considered. In Austria alone, there are more than half a million people who suffer from diabetes mellitus. The current demographic development and the rising prevalence of life-style conditioned risk factors such as obesity, sub-optimal nutrition and insufficient physical activity implicates that the number of diabetes patients will increase further over the next years, especially if no adequate counteractions are taken. Diabetes mellitus is a serious challenge to the health care system. However, the consequences of the disease can be addressed by improving the health care situation of affected patients. Furthermore, a standardization of preventive, care related measures would improve the situation [9].
2.3 Blood Glucose Monitoring Insulin dependent diabetes mellitus is accompanied by long-term microvascular, neurologic and macrovascular complications, which can cause major morbidity and mortality in patients. These complications can be reduced and sometimes even be completely prevented by carefully monitoring the blood glucose level of a patient [10]. The American Diabetes Association recommends that T1DM patients should check their individual blood glucose level at least four times per day. T2DM patients are advised to check on their glucose level two times per day [11]. The common method for testing the glucose level requires a glucose meter and a blood sample by pricking a finger with a lancet. This procedure, which is also called ‘Self-Monitoring of Blood Glucose (SMBG)’ is recommended as a core component of a patient’s disease management but is often associated with discomfort. Test strips are not sufficient to predict trends in the individual blood glucose level and the finger pricking interferes with a patient’s daily routines and habits, and is often perceived as an annoying, unavoidable necessity [12]. Nevertheless, tests for
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the glycated hemoglobin and the self-management of blood glucose build up the foundation of almost every diabetes therapy. In recent times, however, a third pillar of modern diabetes glucose monitoring emerged in CGM systems. Continuous Glucose Monitoring (CGM) – CGM systems typically consist of a glucose sensor that continuously measures the physiological glucose level (either with blood or interstitial fluid), an electronic processing unit and a data display unit. The glucose sensor’s placement and the communication with the electronic unit define the invasiveness of a CGM sensor. Sensors can be divided into three major categories: (1) implantable and invasive sensors; (2) minimally invasive sensors; (3) Non-invasive sensors; [12] Even though non-invasive sensors exist and are subject to previous research, only invasive and minimally invasive technologies have been approved and used in clinical practice. Invasive CGM systems consist of a subcutaneous glucose sensor that measures the glucose level of the interstitial fluid, which is osmotically diffused from the pericappilary tissue, using an enzymatic or microdialysis technique. Invasive sensors can continuously measure the blood glucose level up to fourteen days and record the value every couple of minutes [13]. An invasive sensor does not measure the glucose level directly from the blood of a patient, but rather uses interstitial fluid to calculate the amount of blood glucose. The calculated value lags temporally behind the actual blood glucose level and can lead to lower accuracy when the glucose concentration of the blood is changing rapidly (e.g. physical exercises, intake of carbohydrate, etc.). Minimally invasive sensors are located outside the body and draw the interstitial fluid out to the sensor after the initial opening of the skin. As with any other analytic medical device, accuracy is the most important requirement. The required accuracy of a CGM sensor is defined by the ISO 15197. Even though this standard is exclusive for SMBG, similar requirements should be employed for CGM systems to ensure adequate accuracy [12]. According to Figueira et al., more than 90% of CGM readings are in the allowed range, even if the blood glucose level fluctuates rapidly [14]. CGM is a useful diagnostic tool to evaluate the glycaemic profile in patients with inadequate controlled diabetes, detecting and preventing unrecognized hypoglycaemic events and helps adjust insulin dosages according to a patient’s physical activity and nutrition intake. Recent CGM devices also provide retrospective information about the overall glycaemic profile, which helps the patient to learn about diabetic subjects. Furthermore, it provides an effective alarm mechanism for unusual glucose levels based on predetermined values [15]. Effectiveness of CGM for T1DM and T2DM Patients – A study by the Endocrinology Society affirmed the effectiveness of continuous glucose monitoring in helping maintain the target levels of glycemia. Additionally, it was shown that a CGM device limits the risk of hypoglycemia in T1DM patients, which ultimately leads to a decrease in secondary micro- and macrovascular complications [16]. Furthermore, evidence supports the improvement of therapeutic choices based on the real-time readings from a CGM device. The accuracy of insulin supplementation can be improved even if the device is added to a common insulin pump therapy [17]. The general consensus over the practicality of continuous glucose monitoring devices in T1DM seems thus evident. The effectiveness of
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CGM for T2DM patients indicates a similar position. Reducing glucose variability represents the main challenge of T2DM management. A continuous glucose monitoring system may reduce such inconsistencies, preventing long-term secondary complications, which ultimately results in improved glycaemic control [18]. Additionally, further research has shown that the aplication of such devices also helps elderly T2DM patients in managing their blood glucose level by providing real-time glucose profiles [19]. Continuous glucose management reveals insights beyond the self-monitoring of blood glucose, although the reliability of invasive CGM sensors still needs improvement. Nevertheless, it represents a reliable monitoring tool to minimize glycaemic variability and helps to achieve glycaemic targets in T1DM as well as in T2DM patients. Lastly, a CGM system can have a powerful motivational impact on a patient’s lifestyle, assist in the management of hypoglycemic awareness, and improve the overall effectiveness of an individual diabetes therapy [13]. Disregarding all these advantages, the potential of the CGM technology depends on its actual use. Sometimes, people do not use systems that could help; independent of their technical merits [4]. This also applies to elderly diabetes patients and the CGM technology, wherefore it is essential to investigate human behavior in the context of CGM acceptance and usage.
2.4 Acceptance and Acceptance Models Information technology offers the potential to bring significant performance improvements to the user. These improvements are, however, connected to the actual use of a given technology, for which gains are often obstructed by the end-users’ unwillingness to accept a system [4]. Back in 1988, E. Burton Swanson stated that the understanding of why people accept or reject a given system has proven to be one of the most challenging issues in information systems research [20]. Several researchers tried to investigate the impact of users’ internal belief systems and attitudes on their usage behavior and how these constructs are influenced by external factors. However, the results have been inconclusive due to the wide array of different belief, attitude and satisfaction measures, which have been used without adequate empirical and theoretical justification. As response to the criticism, researchers suggested intention models from the area of social psychology as potential theoretical foundation for further research. The theory of reasoned action was proven successful in predicting and explaining behavior across a wide variety of domains [4]. Theory of Reasoned Action – The Theory of Reasoned Action (TRA) was developed by Fishbein and Ajzen with the intention to better understand the relationship between intention, attitude and behavior within human action [21]. At the time, previous studies indicated a low correlation between behavior and attitude and some theorists even proposed to completely exclude attitude as a factor of an underlying behavior [22]. Unwilling to disregard the influence of attitude, Fishbein and Ajzen showed, however, that it is important to distinguish between an attitude towards an object and an attitude towards a behavior with respect to that object. The result was a model which posits that behavioral
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intention is the direct antecedent of actual behavior. That is, behavioral intention is formulated as a function of salient information or beliefs about the likelihood of performing a behavior which then leads to a specific outcome. The attitude is mainly determined by an individual’s belief system about the outcome of performing the behavior. This implies that if a person holds a positive belief towards a certain outcome resulting from the behavior, they will automatically have a positive attitude towards the behavior. Conversely, if someone values the outcome of a behavior negatively, their attitude will also be negative towards the behavior. Subjective norm is determined by the normative belief system of a person, whether they approve or disapprove of performing the behavior weighted by the person’s motivation to comply with those referents [21]. Central to the TRA is that the direct determinant of behavior is the intention to perform the behavior. This leads to the assumption that the success of this model depends on the degree to which a particular behavior is under volitional control [22]. Thus, Ajzen modified his initial model and added perceived behavioral control to account for factors outside a person’s control that may affect intentions and behaviors, leading to the Theory of Planned Behavior. Theory of Planned Behavior – The Theory of Planned Behavior (TPB) can be seen as an extension to the initial TRA made necessary by the limitation of the original model. That is, TRA presupposes that the behavior in question is completely in volitional control of the individual and lacks accuracy if that circumstance is not present. It could, however, not predict and explain all manner of socially significant behavior. In 1991, Ajzen thus added the variable of perceived behavioral control to the model, which describes factors outside an individual’s control. This new variable is determined by two antecedents, i.e. control beliefs and perceived power. Control beliefs address the presence or absence of facilitators and barriers to the performance of a certain behavior, weighted by the perceived power [23]. The more resources and opportunities individuals think they possess over a behavior, the greater is their perceived behavioral control of that behavior. TRA can be applicable when the behavior in question is under volitional control. However, when the behavior violates the assumption of volitional control, TPB has shown to be superior to its predecessor [24]. Despite the overall applicability of this model, some critics mentioned the possible drawback of focusing purely on cognitive factors in the decision making process [25]. The continuous progress in information technology led researchers to look for further ways to specifically explain computer usage behavior. In 1989, a new model was proposed, which was capable of explaining user behavior across a broad range of computing technology while at the same time being both parsimonious and theoretically justified – the Technology Acceptance Model [4]. Technology Acceptance Model – The Technology Acceptance Model (TAM), adapted from the previously described TRA, was originally proposed by Davis [4]. The model poses one of the most influential and well-recognized theories about acceptance within the context of information systems [26]. TAM states that an individual’s information system acceptance and use is a response that can be explained by the user’s motivation. The
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motivation is directly influenced by an external stimulus consisting of the actual system’s features and capabilities [27]. TAM assumes that an individual’s information system acceptance is determined by two distinct variables. These determinants or beliefs are Perceived usefulness (PU) and Perceived Ease of Use (PEOU); the latter directly influencing the former. Both variables were hypothesized to be directly influenced by various system design characteristics [27]. PU describes to which extend or degree a person believes that using a particular system would enhance the performance of a certain task. This follows directly from the definition of the word ‘useful’. The other determinant PEOU refers to the degree to which a person believes that using that system would be free of any effort. Effort in this context describes a finite resource that a person may allocate to the various activities connected to a task. This leads to the conclusion that an application which is perceived to be easier to use than others is more likely to be accepted by the user [4]. Davis continuously refined his model to include new variables and modified their relationships. Additional work on the model resulted in the exclusion of Attitude as a construct, which was later substituted by Behavioral Intention. Another change to the initial model was the consideration of additional factors which may influence the beliefs of a person towards the information system. These external variables typically included characteristics such as user training and user participation in the design and the nature of the implementation process [28]. Four years after the initial development of TAM, Vankatesh and Davis proposed the second version of the model. Due to limitations regarding the predictability of PU, they proposed various antecedent variables to account for that circumstance. Those variables included characteristics like Subjective Norm, Image, Job Relevance and Output Quality [29]. The revised model was simply called TAM2. Finally, a third iteration of the model, i.e. TAM3, was proposed. In addition to the variables of TAM2, TAM3 includes variables which would constitute to the PEOU of a system [29]. TAM in the Context of the Elderly – Empirical evidence indicates that the acceptance of technology by elderly people is heavily influenced by the perceived usefulness of devices. In this context, important components involve socio-demographic factors, psychological preconditions and the anxiety-related construct known as technophobia. Different age groups tend to think differently about technology use. This can lead to different decisions regarding the adoption of technology. Furthermore, the interrelation between perceived usefulness and technology use seems to be much stronger among seniors than within groups of younger people [30]. Overall, the variables PU and PEOU seem to be the most critical factors for older people in accepting a system. Some older individuals may find a given technology irrelevant to their daily life without considering possible merits. Seniors tend to value systems based on the impact to their individual independence and quality of life. Also, changes in the psychological and physiological structure of the human body as well as age related impairments need to be addressed when discussing and investigating systems to be used by elderly [31]. It can thus be concluded that the theoretical underpinnings used by TAM are also effective when applied to seniors, even though additional age-related characteristics may need to be considered [31].
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3 Methodology TAM remains a proven and reliable model for researchers in the acceptance domain. This fact is based on well-defined procedures and general accepted interpretations of previous results [32]. Yet most data stems from quantitative research methods. Overall, more than 90% of acceptance studies used questionnaire-based field studies [26]. The here presented research, however, aims to explain the technology acceptance of elderly, for which a quantitative approach may proof sub-optimal, as elderly people often have problems handling questionnaires due to their physical and psychological conditions. Also, past research showed that a qualitative approach to investigating technology acceptance can achieve results which move beyond well-known theories. These results can include technology specific factors, personality related factors, as well as social factors [32]. Furthermore, qualitative data analysis holds the potential to comprehensively analyze complex relationships like the interaction between humans and technology, where questionnaire-based surveys may miss such relevant factors of influence [33]. Finally, a qualitative research method offers the opportunity to identify possible external variables and categories which are not adequately defined in existing models.
3.1 Participants Twelve interviews were conducted with different seniors who all suffer from diabetes mellitus. All participants agreed to being recorded. Recordings were transcribed and analyzed following Mayring’s qualitative content analysis method [34]. All conversations started with a short introduction. Information was given about the purpose and goals of the interview. The term technology acceptance was briefly explained to ensure a common understanding. Following, a short video was shown to the participants, introducing the continuous glucose monitoring technology. The video was in German and showed a minimal invasive glucose sensor which is wearable up to fourteen days. Emerging questions about the functionality and characteristic of the CGM device were answered and possible lack of knowledge eradicated as clearly as possible before the actual start of the interview. Every participant was asked a set of questions following some basic constructs identified by previous acceptance research (cf. Section 2.4). This semi-structured interview approach served as a framework to facilitate the comparability of the data [35]. Furthermore, it provided a necessary level of security during the interview [36]. Interviews lasted between five and ten minutes, depending on a participant’s talkativeness.
3.2 Analysis Following Mayring’s approach to analyzing qualitative data, interviews were first fully transcribed and then analyzed for relevant statements [34]. Found statements were then encoded to core-statements and categories. The underlying categories were taken from
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the relevant TAM literature and extended by additional determinants and variables discussed in Section 2.4. In case the statements implicated another external variable, not previously considered, a new category was formed. The collected material was screened and revised throughout several iterations. This process ended when all statements were clearly assigned to at least one category. Next, categories were ranked by frequency (i.e. by the number of times they were mentioned), and their relevance (i.e. the strength of a statement’s content). Four different values for relevance were distinguished: (-1) = explicit rejection; (0) = no reference at all; (1) = reference; (2) = explicit reference or reference with accent. Indicators for the references with accent were expressions such as: “it is important”, “it is crucial”, or statements which were formulated as imperatives. In addition to frequency and relevance it was counted in how many different interviews a particular category was positively mentioned (i.e. a category’s interview-frequency).
4 Discussion of Results In twelve interviews, more than 109 statements concerning the acceptance of a CGM system could be identified. On average, nine statements per interview were extracted and assigned to a total of twelve distinct acceptance categories. Four of these categories were inductively created i.e. not deduced from the existing TAM literature. Following all twelve categories are discussed in some more detail. Perceived Usefulness – Confirming previous research PU provided the highest values for relevance, frequency and interview-frequency. It was discussed both positively and negatively. That is, several participants mentioned their advanced age as well as their prevalent satisfaction with existing measurement processes as limiting a CGM device’s usefulness. Also, a general resentment towards change would inhibit adoption. Next to these rather conservative views, participants also highlighted possible advantages they would see in such a system; for example the accurate knowledge of their blood glucose level and the related overall understanding of their health condition. Furthermore, some mentioned that they see general usefulness for their diabetes therapy. Here it was particularly the CGM’s minimal invasive sensor which received positive feedback, as it would replace the traditional finger pricking procedure. Perceived Ease of Use – Next to PU, it was especially PEOU which scored high relevance and frequency values. Relevant statements were found nine times in five different interviews. Those included positive feedback, such as that using a CGM device seems rather easy, that placing the CGM sensor on the upper arm would not pose any additional effort, or that seeing the blood glucose level displayed on the computing device would be convenient, as well as negative statements, which would not see a great advantage over the traditional finger pricking method. While TAM describes PEOU as one of the two most important attitudinal factors in explaining the acceptance and use of technology [4], critiques argue that PEOU is a rather unstable measure in predicting behavioral intention, highlighting that no amount of PEOU will compensate for low usefulness [37]. The analysis results seem to confirm this criticism.
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Subjective Norm – Subjective Norm was the second most mentioned category in terms of interview frequency, showing the highest relevance scores after the TAM core determinants. Participants mentioned relatives and caregivers as important influencing factors towards the use of a CGM device. Even in cases where the measurement is performed by seniors themselves, a need for third party judgments from relatives was highlighted. These results are in line with previous findings. Elderly people tend to be particularly selective in their social relationships, where especially family members seem to influence opinions and behavior [38]. In addition, the relationship with caregivers and nursing staff was found to be important. Various statements about overall value and trustfulness highlighted the relevance of this role. Trial-ability – The inductively created category Trial-ability was derived from eight different statements. It was formed to highlight the participant’s request for a trial period where they could practically experience the system. Such a trial phase is required so as to more realistically evaluate the CGM’s usefulness and suitability. The term ‘trialability’ was originally used by Rogers. In his work about diffusion of innovations, he stated that trialability refers to the degree to which an innovation may be experimented with before it is adopted [39]. Trialability, which revolves around the idea that systems which can be tested in advance are more often adopted [40], has already been used as an external variable in previous studies [26]. Here it was identified as a component influencing both PEOU (directly) and PU (indirectly). The interview data confirms this connection between a CGM device’s trialability and its eventual adoption. Financial Concerns – Financial Concerns are not yet included in the different TAM constructs. The category was derived from four interviews which particularly highlighted this aspect. It stems from the fact that participants questioned the affordability of the device. It was explicitly mentioned that CGM would have to be subsidized for it to be used, even if possible savings might emerge from the reduced need for other tools such as lancets and test strips. While cost and financial factors have been neglected in many previous acceptance studies [31] Mallenius et al. highlight their importance for the technology acceptance of elderly people. They argue that older generations are in general more price-sensitive and consider spending more carefully than younger generations [41]. Chen and Chan even propose that cost is one of the most critical factors in determining the acceptance of technology for the elderly [31]. The interview data supports this position. Physiological Factors – The new category Physiological Factors was formed due to various statements linked to the participants’ prevalent health condition. Four different interviewees reported factors which were directly related to the anatomy and abnormality of the physiological or anatomical structure or function of the human body. Concerns were mentioned from people with both motor as well as mental impairments. They centered on the obliviousness of properly using the device. Motor impairments would, for example, imply that qualified health care personnel assist in operating the CGM device.
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Yet, interviewees expressed their desire for being able to handle the device by themselves so as to avoid dependency. Quality – Quality refers to the subjective believe about a CGM device’s applicability and suitability. Four different interviewees mentioned the quality of the sensors or the electronic processing unit. It was stated that sensors must resist water during showers, and must not interfere with other electronic devices such as pacemakers. Also, the need for permanent accuracy and precision of the output was raised and the accompanying regular maintenance highlighted as an influencing factor. Output quality was already identified in past acceptance studies [26]. Age – Their Age was mentioned by three individuals as being hindering. One interviewee, for example, mentioned that his advanced age is the reason why he would completely reject the use of a CGM device. To that respect, it was also stated that elderly people do not want to cause any inconveniences towards their environment, for which an actual system use would only be considered if its benefits are clearly visible. Generally, participants often judge existing solutions as sufficient, which leads to the assumption that there is no use in upgrading tools and processes. Existing habits and experience may thus prevent technology adoption [42]. An additional argument for age being an external factor is put forward by Venkatesh and colleagues in their Unified Theory of Acceptance and Use of Technology. Here, their research shows that age constitutes to the behavioral intention and overall acceptance of technology. The model even suggests that age can be seen as a moderating factor to other variables [43]. Self-Efficacy – The degree to which a diabetes person believes that he or she has the ability to handle a CGM device was mentioned in three separate interviews. They particularly distrust their own abilities to utilize such a system. These doubts came either from past experiences with similar devices or a general disbelieve in their intellectual capacities. Self-Efficacy represents one of the antecedents of PEOU. Experimental evidence showed the causal flow from computer self-efficacy to system specific perceived ease of use [28]. Seniors tend to struggle in certain situations, which leads to a lack of confidence in their own abilities to handle technology [31]. The interview results support this claim. Computer Anxiety – Overall, elderly tend to suffer from high technology anxiety [26]. The interview results, however, do not fully support this assumption. That is, only three interviewees stated their individual fear or apprehension towards the use of a new technology. Those came from a disapproval of today’s youth and its affinity towards technology and the explicitly expressed wish of sticking to current habits and trusted procedures. Intrinsic Motivation – Only one participant shared her excitement and joy for possibly using a CGM system, despite potential performance problems. This feeling was supported by a tendency to be generally open towards new technologies and the continuous progress exhibited in the health care sector. A CGM device improves the measurement process for diabetes care. Elderly see such a device thus first and foremost as medical instrument to improve their glucose monitoring. Neither the interview data, nor the participants’ ratings suggest that they expect enjoyment from this usage. Past studies have,
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however, shown that enjoyment influences the acceptance of technology [30]. Yet, these studies did not focus on medical systems but rather explored the use of entertainment technologies. Result Demonstrability – Finally, the one category which was mentioned the least may be summarized in Result Demonstrability. Solely one interviewee stated that the system should provide the end-user with a written documentation of the glucose level in addition to the results being displayed on the processing unit. Although, result demonstrability was defined by Venkatesh and Davis as an external variable of PU [29] one may argue that with respect to CGM devices such would be of minor importance to the patient.
5 Conclusions and Future Work The presented research aimed to investigate possible factors influencing elderly diabetes patients’ acceptance of a CGM device. An interview study guided by a number of constructs used in previous acceptance research hints to several key ingredients. That is, elderly seem to value familiar procedures and tend to show critical stance towards changing their current (generally working) glucose measurement processes. To overcome rejections it seems crucial to not only engage them, but also their trusted social environment including relatives and care providers. The study showed that the relationships with their social environment tend to heavily influence their opinion, behavior and subsequently potential use of technology. Literature supports this importance of social relationships although other aspects should be considered as well. Especially biological factors seem to be relevant. For example, people’s age, as well as potential cognitive and motor impairments influences their intention to use. Such has to be considered in case the substitution of a CGM process is planned. Furthermore, the health care system would have to financially support seniors so as to lower the entry barrier for such a technology. Results also highlight elder people’s increased apprehension or even fear of using new technology. Hands-on experiences and trial phases would, however, help fight possible concerns and may show the usefulness of a system. Overall, the analysis confirmed previous research in showing that PU and all its antecedents dictate the intention to generally use a CGM system and that PEOU and its external variables dictate the intention to use a particular technology. Findings from the presented analysis provide valuable insights into the acceptance and potential use of CGM systems by elderly. Prior acceptance research has neglected the possible effect of age and other physiological factors; a circumstance which was addressed by Venkatesh in his work on a unified theory of acceptance and use of technology [44]. Yet, additional research is required to better define these newly identified influencing factors. Future work may thus move towards a quantitative study setting so as to validate the presented findings. Furthermore, a similar study design which extends the target group to include seniors living independently might generate additional relevant constructs. Finally, future studies may also turn to secondary users, such as relatives, physicians and other health care personnel, as those stakeholders were revealed as being of particular importance to the interviewed end-users.
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