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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 12  |  Issue : 4  |  Page : 464-471

A cross-sectional survey of diabetes care providers’ perceptions and potential barriers to the use of continuous glucose monitoring technology in Singapore


Department of Pharmacy, National University of Singapore (NUS), Singapore, Singapore

Date of Submission18-Apr-2021
Date of Decision16-Aug-2021
Date of Acceptance21-May-2021
Date of Web Publication12-Jan-2022

Correspondence Address:
Ms. Phoebe X C Yap
Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore 117559.
Singapore
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jod.jod_49_21

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  Abstract 

Context: Given the large disease burden of diabetes in Singapore, we felt a need to study the reasons for the low uptake of continuous glucose monitoring (CGM) in Singapore, despite its recognized benefits and international recommendation. Aims: The aim of this article is to identify perceptions of diabetes care providers (DCPs) and extent of barriers to promoting CGM uptake in Singapore. Settings and Design: This is a cross-sectional study in Singapore. Materials and Methods: A cross-sectional online survey of DCPs over 21 years of age, who provided direct outpatient care to diabetes patients in Singapore and spent minimally 50% of their total patient care time on diabetes care services, was conducted. Statistical Analysis Used: K-means cluster analyses grouped respondents by their diabetes technology and CGM attitudes, barriers to employing CGM clinically, and perceived barriers by patients for CGM uptake and adherence. Fisher’s exact tests and Kruskal–Wallis tests assessed for inter-group demographic differences. Results: From a total sample of 47 respondents, analyses formed three clusters, describing DCPs’ willingness to recommend CGM uptake. All respondents agreed cost is the top barrier. Willing (48.9% of sample) and Unwilling (23.4%) DCPs reported minimal and many barriers to employing CGM clinically, respectively, whereas Eager DCPs (27.7%) reported none. Eager and Willing DCPs, who perceived cost as the only barrier by patients, had more positive diabetes technology and CGM attitudes versus the Unwilling DCPs that perceived many barriers by patients. Conclusion: Local DCPs agreed on the role of CGM in optimizing glycemic control, and that cost is the top barrier against CGM uptake.

Keywords: Blood glucose, diabetes mellitus, healthcare personnel, Singapore, technology


How to cite this article:
Yap PX, Koh L, Gallagher PJ, Lum ZK. A cross-sectional survey of diabetes care providers’ perceptions and potential barriers to the use of continuous glucose monitoring technology in Singapore. J Diabetol 2021;12:464-71

How to cite this URL:
Yap PX, Koh L, Gallagher PJ, Lum ZK. A cross-sectional survey of diabetes care providers’ perceptions and potential barriers to the use of continuous glucose monitoring technology in Singapore. J Diabetol [serial online] 2021 [cited 2022 May 26];12:464-71. Available from: https://www.journalofdiabetology.org/text.asp?2021/12/4/464/335616




  Key Messages: Top


Diabetes care providers in Singapore were surveyed to describe their willingness to recommend continuous glucose monitoring (CGM) uptake. Despite varying stance, all acknowledged the role of CGM in optimizing glycemic control, and that the cost of the device is the biggest barrier against uptake of and continued adherence to CGM.


  Introduction Top


In Singapore, the prevalence of diabetes among adults aged 20–79 was estimated to be higher than the global average at 14.2% in 2019,[1] up from 12.8% in 2015.[2] There was a 20% increase in disability-adjusted life years (DALY) due to diabetes from 2009 to 2019, where diabetes was the sixth largest cause of death and disability in 2019.[3]

Currently in Singapore, self-monitored blood glucose (SMBG) and HbA1c are the common methods for diabetes management, whereas continuous glucose monitoring (CGM) usage remains uncommon. SMBG is a comparatively older technology with well-known drawbacks including pain, inconvenience, sleep disturbance for night-time tests, and social embarrassment,[4] while providing only a snapshot of current glucose levels.[5] In contrast, HbA1c has been the established indicator of overall glycemic control since it can predict long-term microvascular complications.[6],[7] Being an average however, it fails to provide insights on glycemic variability or hypoglycemia risk[8],[9] and correlates poorly with macrovascular complications.[10] Measuring glycemic variability is particularly important as large glucose fluctuations have been found to have more detrimental effects on cardiovascular outcomes than sustained hyperglycemia.[11],[12],[13],[14],[15]

The new glucose monitoring technology, CGM, is a device that continuously monitors interstitial glucose levels via a subcutaneous implantable sensor and transmits the data to a monitor device for display. CGM technology has various advantages over SMBG. CGM is more user-friendly, without the pain and inconvenience of routine finger-pricking required by SMBG.[4] Next, CGM provides immediate glycemic data with trend information about the direction and rate of change of glucose levels,[16] empowering individuals to make informed therapy, dietary, and exercise adjustments accordingly.[9],[17] CGM technology also gives rise to a new potential monitoring marker in the form of time-in-range (TIR). As opposed to the 3-month average value that HbA1c provides, TIR provides clinicians specific information that can be acted on.[9],[18],[19],[20],[21],[22] Notably, an international consensus report published in 2019[9] recommended the easily comprehensible TIR targets as a metric of glycemic control for day-to-day glucose control.

While CGM is indeed gaining traction globally, its uptake is still low due to cost and access reasons.[23],[24],[25],[26],[27] Other reasons include the concerns about the device’s accuracy and reliability, dislike of having devices on one’s body by patients, and providers’ uncertainty regarding how to best use CGM data in therapeutic decisions.[24],[25],[26],[27],[28],[29] In Singapore, Abbott’s Freestyle Libre is the only CGM readily available for sale in pharmacies, sold at a retail price of SGD92 for each sensor or reader. Since diabetes care providers (DCPs) are key in introducing new diabetes management technology to patients, as well as educating and ensuring its effective use,[24],[26] it is important to understand DCPs’ perception of CGM and to identify any barriers they may have regarding the promotion of CGM uptake. Studies have been conducted on providers’ perception of CGM in other countries such as the USA[24],[25] and Australia,[26] but none yet in Singapore.

Hence, the primary objective of this study is to identify DCPs’ perceptions and extent of barriers to the promotion of CGM technology uptake in Singapore.


  Subjects and Methods Top


Study design and participants

This study was a cross-sectional electronic survey, consisting of 15 Likert scale-based questions and a free-response question at the end for any other comments. As there is currently no validated survey regarding providers’ perceptions of CGM, the survey was self-developed based on literature and adapted to reflect the local context.[24],[25],[26],[27],[28],[29] Prior to survey dissemination, the survey instrument was sent to two endocrinologists with 10 years of clinical practice experience for face and content validity evaluation. No changes were made to the survey instrument. A waiver of informed consent was granted by the National University of Singapore Institutional Review Board.

This survey targeted Singapore-registered physicians, nurse practitioners, or pharmacists over 21 years of age, who provided direct outpatient care to patients of all years with diabetes of any type in Singapore and spent at least 50% of their total patient care time on diabetes care services. Providers from restructured hospitals and outpatient polyclinics were included. Since there was no objective way to determine whether DCPs were sufficiently informed with regard to CGM, we relied on the strict inclusion criteria of 50% time that DCPs had to spend on diabetes management to select for participants presumably aware of CGM and its concerns locally. The survey was operated via Qualtrics (Qualtrics, Provo, UT, USA).

Recruitment and study procedures

Data were collected from August 24, 2020 to October 2, 2020 through two recruitment approaches: convenience snowball sampling and purposive sampling. The survey was anonymous and no personal identifiable information was collected.

Convenience snowball sampling of healthcare clusters

The Clinical Services Offices of the healthcare clusters, National University Health System, and National Healthcare Group were contacted to disseminate the survey to all DCPs in their cluster. This included an e-mail invitation to participate in the survey with a web-based link and QR code to the survey, which could be forwarded on to other providers.

Purposive sampling via known DCPs

A second recruitment approach was initiated after receiving low response numbers from the first approach. As the low response was likely attributable to the inclusion criterion which excluded any provider who spent less than 50% of their total patient care time on diabetes care services, targeted cluster sampling was done to specifically invite providers known to fulfill this criterion.

The interdisciplinary diabetes care teams, comprising of doctors, pharmacists, and nurse clinicians, at restructured hospitals and polyclinics known to have such a service were contacted via email. This included National University Hospital, Tan Tock Seng Hospital, Singapore General Hospital, Khoo Teck Puat Hospital, National Healthcare Group Polyclinics, and National University Polyclinics.

Measures

Clustering variables

Five clustering variables were utilized to represent various aspects of DCPs’ perceptions and potential barriers to the use of CGM technology in Singapore. Each clustering variable was assessed with three items, forming the 15 Likert scale-based questions. All items were evaluated on the same five-point Likert scale [Table 1].
Table 1: Items used according to the clustering variables

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Demographic variables

Participants were asked to provide general demographics (e.g., age, gender), characteristics specific to their clinical experience (e.g., professional status, years in profession, practice setting, current academic appointment), and general characteristics of their patient population (e.g., adults, pediatrics, estimated percentage of patients with type 1 diabetes mellitus (T1DM), on insulin pump, CGM).

Statistical analysis

K-means cluster analysis was used to segment DCPs into both two and three groups based on the five clustering variables. K-means cluster analysis is a procedure that endeavors to identify a pre-specified number of relatively homogeneous clusters based on given characteristics. Distances from cases to cluster centers were computed using simple Euclidean distance. The responses of the 15 Likert scale questions were centered from a range of 1 to 5 to (−2) to 2 for interpretability. After deciding to go with a three-cluster solution for this study, Fisher’s exact and Kruskal–Wallis tests were used to test for significant (P < 0.05) differences between the three clusters against the demographic variables collected. SPSS version 26 software (IBM Corporation, Armonk, NY, USA) was used for data analysis.


  Results Top


After excluding non-completers, we had a total sample of 47 DCPs. Participants included physicians (55.3%), nurse clinicians (27.7%), and pharmacists (17.0%). Respondents had a median age of 40 years, with a median of 15 years in practice. On average, the respondents reported having about 10% of the patients with T1DM and 5% of the patients on CGM. Characteristics of survey participants are detailed in [Table 2].
Table 2: Demographic characteristics of all participants and differences by cluster

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Selection of cluster solution

[Figure 1] presents an overview of the two- and three-cluster solutions. The clusters were descriptively named according to their “willingness” to recommend CGM at this point of time: Willing and Unwilling DCPs in the two-cluster solution; Eager, Willing, and Unwilling DCPs in the three-cluster solution.
Figure 1: Overview of the two- and three-cluster solutions

Click here to view


The three-cluster solution consisted of the Unwilling DCPs group (n = 11, 23.4%), with less positive attitudes toward diabetes technologies and many perceived barriers to CGM in practice; the Eager DCPs group (n = 13, 27.7%), with more positive attitudes toward diabetes technologies, no perceived barriers to employing CGM in practice, and the perception of cost as the only barrier by patients; and a third intermediate group, the Willing DCPs (n = 23, 48.9%), with positive attitudes toward diabetes technologies, minimal perceived barriers to employing CGM in practice, and the perception of cost as the only barrier by patients.

The centered mean scores of the 15 Likert scale questions according to cluster for the two- and three-cluster solutions are provided in [Figure 2] and [Figure 3], respectively. We decided on the three-cluster solution for our analysis as it revealed a majority intermediate cluster, the Willing DCPs, that was not present in the two-cluster solution. From the pool of providers with positive attitudes to diabetes technology and CGM, and the perception of cost as the only barrier by patients for CGM uptake and adherence, the three-cluster solution distinguished the DCPs with some perceived barrier to employing CGM in practice, the Willing DCPs, from those with none such perceived barriers, the Eager DCPs. Using the two-cluster solution would have led to an over-reporting and over-interpretation of barriers to employing CGM in practice and perceived barriers by patients.
Figure 2: Centered mean scores according to cluster, of the two-cluster solution

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Figure 3: Centered mean scores according to cluster, of the three-cluster solution

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Willingness of DCPs to recommend CGM

All respondents generally had positive attitudes toward diabetes technology and CGM (D1-3, C1-3), although such positive attitudes were particularly weaker among the Unwilling DCPs. Notably, all three clusters agreed to a similar extent that the cost of the CGM device (P1) and of buying regular supplies for continued CGM use was too much for the patient (A1), thus making cost the top perceived barrier by patients against CGM uptake and adherence.

The Eager DCPs and Willing DCPs were similarly positive toward diabetes technology and CGM, and their perception of cost as the only barrier by patients to CGM uptake and adherence. Nevertheless, there were some differences. Eager DCPs indicated noticeably greater ease with staying updated with advances in diabetes technology (D1) than the Willing DCPs. Eager DCPs also more strongly disagreed that patients would be physically uncomfortable using CGM (P3) than the Willing DCPs. Unlike the Eager DCPs, Willing DCPs reported to perceive barriers to employing CGM in practice, though minimal. Barriers indicated include insufficient time to review CGM data (B1), troublesomeness of setting up the platforms to retrieve CGM data (B2), and insufficient manpower to guide patients on CGM usage (B3).

Between the clusters, the Unwilling DCPs had the least positive attitudes toward diabetes technology and CGM. In particular, they reported the least ease keeping up with advances in diabetes technology (D1) and indicated that SMBG and HbA1c are sufficient for optimal diabetes management (D2). Unwilling DCPs also reported the weakest belief that there is sufficient evidence to recommend TIR as a glycemic metric (C1). Regarding barriers to employing CGM in practice, the Unwilling DCPs reported barriers similar to the Willing DCPs (time, manpower, and inconvenience), but to a markedly larger degree. As for perceived barriers by patients, Unwilling DCPs felt that patients would experience a steep learning curve with CGM (P2), inconvenience with managing the CGM device (A2), as well as physical and social discomfort using the device (P3, A3).

Demographic analyses

There were no significant differences between the three clusters in terms of general demographics, characteristics specific to their clinical experience, and general characteristics of their patient population. Results from Fisher’s exact and Kruskal–Wallis tests used to examine the differences between the clusters are shown in [Table 2].


  Discussion Top


This is the first study in Singapore to identify the perceptions and perceived barriers of DCPs to CGM usage locally. Our analysis yielded three groups of DCPs, describing their willingness in recommending CGM uptake.

Similar to findings by Tanenbaum et al.,[28] Bergloff et al.,[25] and Health Quality Ontario,[27] cost has been reported to be the top barrier to CGM usage. This is the one perception reported to similar extents by all DCPs, regardless of cluster grouping and demographic characteristics. This is consistent with the report of financial worries of local diabetes patients by Diabetes Singapore,[30] given the lack of insurance coverage for diabetes monitoring. In comparison, other countries such as Canada and Switzerland have private insurance companies that cover for CGM devices, whereas countries such as Slovakia and Czech Republic require co-payment for the device.[27],[31] In the free-response comment question, a number of DCPs emphasized that cost is a major barrier and indicated the need for potential solutions such as subsidies and/or policy change to allow the purchase of CGM devices to be claimable via Medisave, the national medical savings scheme in Singapore. Given the general consensus found regarding the benefits of CGM, the results of this study provided rationale to initiate pharmacoeconomic studies for CGM.

Analysis of the differences between the three groups can inform what is needed for DCPs to feel more comfortable recommending CGM uptake. For example, the Eager DCPs reported experiencing markedly greater ease keeping up with advances in diabetes technology than the Willing and Unwilling DCPs. Consistent with other studies,[26],[29],[32],[33] it has been found that educational resources and training of providers with diabetes technology are potentially beneficial to effective CGM usage. From the USA, Aleppo and Webb[32] proposed a stepped and easy-to-use approach to CGM data review and interpretation for clinicians, detailing the targets of various CGM metrics and actions accordingly. Similarly, a Continuing Professional Education (CPE) course on CGM technology and usage could be designed to assist local DCPs as they navigate this new technology, so that they may be more comfortable recommending CGM.

It is understood that Eager DCPs have sufficient time in clinic to review CGM data and manpower to guide patients on effective CGM usage, whereas the Willing and Unwilling DCPs perceived higher barriers. Such barriers are commonly reported in a number of studies.[26],[28],[29],[34] To streamline efficient CGM uptake clinically, Mullen et al.[34] from the USA designed an optimized glucose download system, using the ambulatory glucose profile (AGP) as a standardized report, and evaluated the system. They found that the optimization system with a work-flow modification to include printing of the AGP report prior to provider–patient appointments to be time-saving in two centers.[34] Studies from other countries such as Australia[26] and France[33] have similarly reported such a need, so as to increase efficiency of the data retrieval process and help alleviate time and manpower inadequacies.

We observed that the Unwilling DCPs reported to perceive many barriers by patients against CGM uptake and adherence to continued use when compared with the Eager and Willing DCPs. Although we cannot verify as yet if local patients would indicate the same barriers as perceived by DCPs (learning curve, convenience, physical and emotional comfortability with the device), it would not be surprising if DCPs overestimated such barriers from a lack of mutual understanding between the providers and patients. In fact, a study by Tanenbaum et al.[28] reported that clinicians generally endorsed barriers more frequently than adults with T1DM. Regarding comfortability with the device, a Canadian study reported positive user experience, where adult patients expressed appreciation of its discreetness, whereas parents of children with T1DM spoke of social freedom provided by CGM.[27] To have a more accurate understanding of the perceptions of local patients on CGM, another study has been planned as part of future directions from this study. It will survey and examine the barriers experienced by patients who had used CGM before, as well as barriers perceived by patients naive to CGM.



Limitations and future directions

The greatest challenge for this study was with the survey response numbers and resulting small sample size. As low response rates are not uncommon among surveys targeting healthcare providers,[25],[35],[36] it was not surprising. Besides, our inclusion criterion of providers spending at least 50% of their total patient care time on diabetes care services may have significantly reduced the target population size and hence the sample size. However, this stringent criterion is essential to ensure that survey respondents have sufficient clinical experience in diabetes management.

Next, the survey was conducted online, to examine technology attitudes; thus, the results may be biased to include responders with more positive technology attitudes. Clinic-based recruitment was not done in this study as it was conducted during the COVID-19 pandemic, when social distancing was recommended. Lastly, there was a lack of qualitative questions, which may be more effective in surfacing perceived barriers. However, this survey format via 15 Likert scale questions was chosen as it requires minimal time for completion and thus is more likely to receive responses from busy healthcare professionals.


  Conclusion Top


Despite the potential benefits of CGM as a glycemic monitoring tool, CGM uptake is low. Our study found that DCPs in Singapore agreed on the role of CGM in optimizing glycemic control in diabetes management and that the cost of the device was the top barrier against CGM uptake. From the perspective of DCPs, financial strategies to make CGM more affordable are vital. Educational resources and training for DCPs, optimization of CGM data retrieval process, as well as improved time and manpower allocation may be needed to better equip DCPs as they are key to facilitating effective CGM usage. To examine whether the perceptions and barriers of patients are aligned to those perceived by DCPs, the next study to this has been planned to survey local patients on their perceptions and barriers toward CGM usage.

Acknowledgements

We would like to thank all healthcare providers who helped with survey dissemination and all DCPs who participated in this study.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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