Episode 40, Accu-Chek SmartGuide

Why this episode exists

People living with type 1 diabetes on multiple daily injections have, for years, had a CGM that tells them what their glucose is doing right now and a 15-minute trend arrow. People on pumps have had layer after layer of automation added underneath the same sensor. The Accu-Chek SmartGuide is the first device to seriously attempt a second generation of CGM for the majority of people with T1D, who remain on injections. This episode is about whether it delivers, and how to teach it.

Amy Jolley is not a manufacturer representative. She is an independent clinician educator who put 17 people onto the SmartGuide in a single week and asked them to come back and tell her what actually happened. That is a different conversation to the manufacturer-led episodes earlier in this series. This episode is not affiliated with or endorsed by Roche Diagnostics; it reflects the independent clinical perspectives of John Pemberton and Amy Jolley.

In this episode

John and Amy cover what separates the SmartGuide from a standard CGM, how its three predictive layers work in practice, what 17 early adopters reported, and where the technology is heading. They also discuss the DTN Competency Assessment Framework Amy developed with Erica Richardson and the Leicester team, and Amy’s clinical experience using GNL Grace in a specialist service that now supports 700 people on AID systems.

Amy is Lead Educator at the Diabetes Technology Network UK and runs the Young Adult and Transition Service at Salford NHS Foundation Trust. She was among the first clinicians in the UK to run a structured group evaluation of the SmartGuide in clinical practice, and she co-authored the position paper on implementing the national diabetes technology competency framework alongside John.

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Key themes

The MDI generation that has been missing

Pump therapy has had several technology iterations, each adding a layer of automation. MDI has had one: a CGM showing a 15-minute trend arrow. Around 400,000 people in the UK have type 1 diabetes and, even on an optimistic projection, fewer than half will reach an AID system in the foreseeable future. The SmartGuide is the first device to seriously attempt a second generation for the majority who will remain on injections. Amy, who ran the young adult and transition service at Salford when 800 people were waiting for AID, is direct about the stakes: “Should we not expect more from the technology for people using this type of therapy?”

What the SmartGuide is

A 14-day disc sensor worn on the upper arm, factory-calibrated but requiring two paired finger-prick calibrations on day one (taken at approximately 12 and 14 hours after insertion) to activate the predictive features. Readings every five minutes via Bluetooth, waterproof, adults 18 and older only by labelling, with no current paediatric indication and no compatibility with any AID system. It runs across two apps (SmartGuide and Predict) that link automatically, though Amy notes it would be cleaner as one. mySugr integration is in the pipeline. What separates it from a standard CGM is not the glucose readings themselves; it is what it does with them to look forward.

Three prediction layers, not one

The Predict app packages three independent machine-learning models, each with a different time horizon and a different clinical purpose (Herrero 2024, Journal of Diabetes Science and Technology). The framing is the part to get right at the start of the conversation. None of these prevents an event; each gives an earlier window to act.

Night Low Predict, sleep without the 3am alarm

Between 9pm and 2am, the SmartGuide delivers a RAG-rated (Red, Amber, Green) prediction for overnight hypoglycaemia risk. A “green” reading corresponds to the model’s high-confidence low-risk class; in the pivotal evaluation that class had around 92% specificity (Herrero 2024). It is a population-level confidence band, not a personal probability. The system also distinguishes between the first and second half of the night because the appropriate response differs depending on when the risk is likely to materialise; the model’s discrimination is materially stronger for the first half (ROC AUC 0.902) than the second (ROC AUC 0.730), and that asymmetry should land at onboarding. Fast-acting carbohydrates are the option most aligned with first-half-of-night risk; protein, slow-acting carbohydrates, or a temporary basal reduction (if on a pump) are the conversation to have for second-half risk, with your care team. Predictions can be updated every 20 minutes, and the system needs around 28 days of input data to reach its full personalised accuracy.

In a Roche real-world cohort of 249 users (86% Germany, data on file, EASD 2025), Night Low Predict was associated with around a 31% lower likelihood of nocturnal time below 3.9 mmol/L, with no increase in time above range. Hypothesis-generating, awaiting peer-reviewed RCT replication. What Amy’s group reported in clinic was not the trial statistic; it was a different feeling. People went to bed without worrying. When the prediction turned red, the response was a workable plan, not panic.

Glucose Predict, action window and awareness window

The Glucose Predict feature shows a projection of where glucose is heading over the next two hours, displayed as a worm on screen. The worm is surrounded by confidence bands representing the 50% most likely futures; a narrow worm means high confidence, a wide one means wide uncertainty. The accuracy at different time horizons is the part to teach: Parkes Error Grid A+B agreement is 99.3% at 45 minutes and 96.3% at 2 hours (Herrero 2024). John draws a clear line that Amy endorses: the 45-minute window is an action window where accuracy is good enough to inform decisions; the 45-minute-to-two-hour window is an awareness window where the information is directional rather than prescriptive. The most useful real-world cases were predictable but telling: meetings, lectures, exams, and driving. John is explicit about the risk of overselling: “Do not oversell the two-hour accuracy. If you do, people will hate it.”

The rage bolus and what changes

Two people in Amy’s evaluation group reported the same thing: they stopped post-meal correction boluses because Glucose Predict showed them the glucose was already coming down. One of the most persistent frustrations in managing T1D is reactivity, seeing a double up-arrow and acting before there is information about what comes next. The 45-minute action window directly addresses this. John is frank about his own experience: “I see a nine with a double arrow up, I’m doing something right there and then.” A look 45 minutes ahead does not eliminate that impulse, but it gives it something to work with other than anxiety.

Calibration as an educational opportunity

The two paired finger pricks on day one (around 12 and 14 hours after insertion) were the feature Amy expected to cause friction. They did not, and the reason is instructive. The SmartGuide’s calibration requirement gave her a concrete reason to re-establish a conversation about when and why finger prick testing still matters: when glucose is very high, very low, or when a dosing decision feels uncertain. That conversation landed better in a group setting where people who had not done a finger prick in years heard from those who still did them routinely. After day one, no further finger pricks are required.

CGM quality standards, not a commodities market

Amy reports receiving enquiries from three CGM companies in recent weeks asking for formulary support for devices she has never reviewed. The DTN has a clear standard: a five out of five on the CGM study design score, at least 90% of readings within 20/20, and no more than 1% outside 40/40. The SmartGuide just clears that bar (90.5% within 20/20, 1.2% outside 40/40; Mader 2024), with the outside-40/40 figure marginally above the rule but within rounding tolerance. The CGM series on GNL covers only devices that meet that bar. John’s position is direct: “Insulin dosing with CGM is not a commodities market. When the ultimate risk is death, you have to apply the precautionary principle.” Price matters, but only once quality is established.

The DTN Competency Assessment Framework

Amy developed a self-assessment tool in partnership with Erica Richardson and the Leicester team, aligned to a four-tier national framework covering diabetes technology competency. The tiers are not hierarchical in a status sense; a consultant diabetologist might operate at tier two for certain technology decisions, while a lead DSN or dietitian works at tier four. The tool links to annual appraisal documentation, asks for evidence rather than self-declaration, and is being uploaded to the DTN website shortly. Industry is coming on board: study days are now being pitched at specific tiers. John and Amy co-authored the implementation position paper. The framework means evidence of competence travels with the clinician, not stays buried in one service.

GNL Grace in clinical practice

Amy was given access to Grace Max during a period preparing for an ATTD symposium and nearly missed it. She is glad she did not. She used Grace for pump setting problems (a Control-IQ user with exercise-mode hypoglycaemia despite not exercising; she entered weight and total daily dose and received a structured set of next steps), for evidence searches (780G in SmartGuard with TPN, no evidence exists, confirmed immediately), and for thinking through algorithm drivers across multiple AID systems. What she valued most was that the database is closed to T1D evidence only: “There’s not any nuance there, there’s not anybody’s kind of opinion in it. It’s all evidence-based.” Salford has moved from 100 to 700 people on AID systems. The practical pressure that creates for a small specialist team is exactly what Grace is designed for.

Teaching the three prediction layers

A clinic-ready breakdown of what each layer does, what it does not do, when to teach it, and what to do when it fires. Open each layer in turn; the order below is the order the wearer should meet them in their first onboarding session. For the printable A4 version that drops into a group session pack, use the link below.

Open the printable A4 teaching resource

SmartGuide, what’s in the box

For anyone considering the device or trying to picture how it sits alongside the other CGMs in this series, the practical specifics matter. The detail below is consolidated from the GNL SmartGuide guide (the same source the CGM Series episodes work from) and the pivotal accuracy evidence (Mader 2024).

Practical exploration

For people living with type 1 diabetes and their families

The SmartGuide rewards a 28-day onboarding pattern that current CGM marketing does not always reflect.

• Night Low Predict needs around 28 days of input data before it reaches its full personalised accuracy. The first few weeks may be less reliable; do not draw conclusions from the first night.
• If the prediction shows red in the first half of the night, fast-acting carbohydrates are the option most aligned with that risk; discuss with your care team. If red in the second half, the model’s confidence is lower; protein, slow-acting carbohydrates, or a temporary basal reduction (if on a pump) are the conversations to have, again with the care team.
• You can re-request a Night Low Predict every 20 minutes between 9pm and 2am. If you take action, check whether the risk rating changes.
• The 45-minute Glucose Predict window is more reliable than the two-hour window (Parkes A+B 99.3% at 45 min vs 96.3% at 2 hours; Herrero 2024). Treat 45 minutes as an action window and the two hours as awareness and context.
• The predictions do not account for alcohol, exercise, or illness. Adjust your interpretation if any of these applies.
• If the worm is wide at two hours, confidence is lower. Do not treat it as a reliable forecast.
• The two day-one finger pricks (at approximately 12 and 14 hours after insertion) take a few minutes each and activate the full predictive features. Good hand washing and technique matter.

For clinicians and educators

The teaching shape of the SmartGuide differs from a standard CGM in ways that matter at the first appointment.

• Teach the first-half versus second-half distinction for Night Low Predict from the outset. The model’s ROC AUC is meaningfully higher for first-half risk than second-half (0.902 vs 0.730; Herrero 2024); the action menu and the confidence band differ accordingly.
• Group onboarding sessions generate richer learning than one-to-one. The calibration conversation lands differently when people hear from peers who still finger prick routinely.
• The mandatory day-one calibration is an opportunity to re-establish the value of finger prick testing, not a device drawback.
• Send people away to use it and come back to report. The most useful education comes from early adopters describing what they actually did, not from the manual.
• Current indication is adults 18 and older only. No paediatric licence and no AID compatibility in current form.
• The DTN Competency Assessment Framework is being uploaded to the DTN website shortly. It maps team skill mix against a four-tier national standard and links to annual appraisal documentation.

About the guest

Amy Jolley is a Highly Specialised Dietitian and Lead for the Young Adult and Transition Service at Salford NHS Foundation Trust, where her service has grown from supporting 100 to 700 people on AID systems. She is Lead Educator at the Diabetes Technology Network UK (DTN-UK), where she developed the national diabetes technology competency assessment tool in partnership with Erica Richardson and the Leicester team, and co-authored the implementation position paper alongside John Pemberton. She was among the first clinicians in the UK to run a structured group evaluation of the Accu-Chek SmartGuide in clinical practice, putting 17 people onto the device in one week and using their feedback to shape how it is taught. She has presented at ATTD and contributes to the DTN expert views series on device onboarding. She is currently developing educational video resources for the SmartGuide with the DTN.

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