Diabetes mellitus is a condition that results in elevated blood glucose levels (hyperglycaemia). Clinical signs and symptoms include polydipsia, polyuria, weight loss, fatigue, blurred vision and recurrent infections (Holt et al, 2015). Continued elevation of blood glucose contributes to progressive long-term micro- and macrovascular complications across multiple organ systems, potentially leading to renal, nerve and ocular damage, representing a significant contribution to morbidity and mortality (Bilous et al, 2021).
Type 1 diabetes mellitus (T1DM) represents approximately 8–10% of all cases of diabetes (Holt et al, 2015). The pathogenesis of T1DM is complex, principally arising from an autoimmune destruction of insulin-secreting beta cells within the pancreas (DiMeglio et al, 2018).
Optimal glycaemic management, defined as glucose concentrations which mimic those of people without diabetes (normoglycaemia), has long been noted as leading to a reduction in diabetes-related complications (Diabetes Control and Complications Trial Research Group et al, 1993). Frequent monitoring of blood glucose via finger prick testing has historically been established as key in achieving effective glycaemic ranges (Kato et al, 2013).
What is continuous glucose monitoring?
Technologies are available which now enable people with T1DM to quickly monitor and manage their blood glucose without frequent, painful, finger pricking (Leelarathna and Wilmot, 2018), providing glucose readings with accompanying trend arrows via a subcutaneously implanted device, which measures glucose in the interstitial fluid (Messer et al, 2018) (Figure 1).
The device sends the readings to a display device or smartphone and can be worn for between 7–14 days depending on the device chosen.
Continuous glucose monitoring (CGM) has been shown to be superior to finger prick testing in improving glycaemic management in T1DM (reduced HbA1c, reduced hypoglycaemia), particularly for those with elevated HbA1c (Teo et al, 2022; Leelarathna et al, 2022). CGM users report improved quality of life and high levels of satisfaction with these technologies (Pickup et al, 2015; Lind et al, 2017).
CGM provides in-depth data in relation to blood glucose to detect and alert the user to:
CGM can support the user and their health care team to adjust insulin therapy and provide insight into the effect of behaviours (diet, physical activity) on blood glucose levels (Reddy et al, 2020).
The quantity of data generated from such devices can be significant and potentially overwhelming for the person with T1DM and the healthcare provider, so education and support are essential cornerstones to optimise the use of this technology (Alcántara-Aragon, 2019). Yoo et al (2022) demonstrated that users who received structured one-to-one education showed better glycaemic outcomes and treatment satisfaction when commencing CGM systems. CGM education should be incorporated into structured education programmes for all people with T1DM to ensure that people are empowered to use CGM devices (National Institute for Health and Care Excellence (NICE), 2022).
The development of wearable glucose monitors is a rapidly evolving area of health technology. As device use increases, nurses within primary care will encounter more individuals managing their diabetes with CGM, and so knowledge of such technologies may be needed to support these patients in the primary care setting.
Real-time and intermittently scanned CGM
Real-time CGM (rtCGM) systems automatically transmit a continual stream of real-time and predictive glucose data (numerical and trends), enabling alerts and alarms via a receiver, smartphone or smart watch (Milne, 2022).
Intermittently scanned CGM (isCGM), commonly referred to as ‘flash’, provides the same type of data but requires the user to scan (or ‘flash’) the sensor to obtain a reading. Alarms are available for isCGM but only sound when the sensor is scanned (Edelman, 2018; NHS, 2021). isCGM systems must be scanned at least 8 hourly to obtain sufficient data for a complete glucose profile; with 6 scans or more enabling enhanced data (Milne, 2022). A summary of commonly available NHS-funded CGM devices is shown in Table 1.
| Freestyle Libre (2) | Dexcom ONE | GlucoRx AiDEX | GlucoMen Day | |
|---|---|---|---|---|
| CGM type | isCGM | rtCGM | rtCGM | rtCGM |
| Sensor wear time | 14 days | 10 days | 14 days | 14 days |
| Sensor warm up time | 60 minutes | 120 minutes | 60 minutes | 55 minutes |
| High and low alarms | Yes | Yes | Yes | Yes |
| Predictive alarms | No | No | No | Yes |
| Calibration needed | No | No | No | Every 48 hrs |
| Data share with health care practitioner | LibreView | Clarity | CGM viewer | GlucoLog web |
| Data share with friends/family | Yes | No | Yes | Yes |
| Wear site | Upper arm | Buttocks/abdomen/upper arm | Abdomen/upper arm | Lower back/abdomen/upper arm |
Access to CGM
Until recently, access to CGM systems was variable (Crabtree et al, 2022). Now, all adults with T1DM should be offered a choice of rtCGM or isCGM based on their individual needs, preferences, characteristics and the functionality of the device (NICE, 2022); if multiple devices meet the individuals’ requirements, the device with the lowest cost should be selected. NICE (2022) detail the considerations for device selection as part of a shared decision-making process, which should include:
CGM data
Figure 2 shows typical data from a CGM device; in this example, a smartphone shows the current sensor reading, trend arrow, high and low alarm levels and trends over the selected numbers of hours represented as a graph.
Trend arrows show the rate of sensor glucose rise or fall, allowing for the prediction of impending hypo and hyperglycaemia. Trend arrows for each CGM system show different rates of glycaemic change (eg while a double up arrow conveys a rise of >3 mg/dL/minute for Dexcom rtCGM systems, the isCGM Freestyle Libre system does not have a double up arrow); guidelines for trend arrow-based insulin dose adjustments are specific to each system and users should familiarise themselves accordingly (Marks et al, 2022).
Retrospective CGM data are reported in a standardized format known as the ambulatory glucose profile (AGP). The AGP captures the mean sensor glucose, glucose variability (fluctuations in glucose levels), percentage of CGM wear time, and the percentage of time in range (TIR), above range (TAR), and below range (TBR) (Marks et al, 2022). Figure 3 details the targets for these parameters (Wilmot et al, 2020). CGM data and the APG (Figure 4) can be viewed and shared with clinicians and, in some cases, friends and family, via web-based data sharing.
HbA1c is the current gold standard marker of plasma glucose concentrations over the previous 8–12 weeks and is widely used to estimate the efficacy of diabetes management interventions; HbA1c levels in isolation do not reflect day-to-day glucose variability. CGM data allow the observation of daily variations in glucose, time in glycaemic target range and time in hypoglycaemia. These data in addition to HbA1c can enhance diabetes management and self-care; TIR is now an integral component of diabetes risk assessment and therapy (Chehregosha et al, 2019; Wilmot et al, 2020).
CGM accuracy
People using CGM should continue to finger-prick test (but frequency can be significantly reduced) in order to (Driver and Vehicle Licensing Agency, 2019; NICE, 2022):
Glucose levels in the interstitial fluid are closely correlated with, but not identical to, blood glucose levels via finger-prick testing; glucose flows down a concentration gradient between the vascular space and the interstitial fluid, creating a delay in CGM readings in comparison to finger prick testing known as ‘lag time’; when glucose levels are not changing rapidly, there is a minimal difference but when levels are rising or falling rapidly, CGM may read falsely high or low, leading to potential over or undertreatment of hypo/hyperglycaemia. Lag time for isCGM is typically 2.4 minutes (in adults) and 2.1 minutes in children and for rtCGM approximately 4–5 minutes depending on the device (Alva et al, 2022; Marks et al, 2022; Milne, 2022). A common example of device interference is ‘compression hypoglycaemia’ where direct pressure is applied to the sensor (for example during sleep), which reduces perfusion to the sensor resulting in false hypoglycaemia; the user, carers and clinicians should be mindful of device location when interpreting results; removal of pressure will quickly normalise CGM values (Forlenza, 2017).
Support for CGM use in clinical practice
Regardless of the device chosen, the importance of support and education regarding CGM use is central to ensuring user engagement; increased wear times are associated with greater glycaemic benefit; the frequency of user interaction and appropriate use of data is of vital importance (Barnard-Kelly and Polonsky, 2020). Box 1 provides ‘nine tips’ to optimise initiation of CGM systems for people with T1DM.
Conclusion
CGM is a technology that is now standard of care for people living with T1DM, with potential usage for other forms of diabetes or where monitoring of blood glucose levels is required. Crabtree (2022) posits that as technology evolves and improves, finger prick testing may soon be seen as outdated and impractical, similar to the current perception today of urine testing for blood glucose monitoring. With this in mind, practice nurses will increasingly encounter people with these devices meaning knowledge of CGM, the data generated and the support needed for people living with T1DM will become of key importance in contemporary nursing practice.