AID systems

The IOB Trade-Off: How Four AID Systems Make Different Bets on Insulin

Real-world registries covering more than 190,000 AID users show that fewer than 10% consistently use optimal settings — yet those users achieve time in range 10–15 percentage points above the population mean. The difference is not which system they use. It is how it is configured — and why that requires understanding the IOB trade-off.

AID systems Insulin on board Clinical practice

60 seconds — the key points

Every AID system uses IOB as a brake. High IOB = hold back. Low IOB = act. This is universal. The settings that govern IOB calculation are therefore the primary responsiveness lever on every system.
More aggressive settings = better TIR, less visible IOB. As systems are configured more aggressively, they deliver more insulin by modelling existing insulin as cleared sooner. TIR rises from ~61% (protective) to ~70% (balanced) to ~78.8% (optimal) — while the IOB display progressively diverges from physiological reality.
Fewer than 10% use optimal settings. In the 780G global registry (101,629 users), only 6.4% were on optimal settings — yet those users achieved 78.8% TIR and 2.2% time below range. Among Control-IQ users, 67% had correction factors weaker than guideline.
The gap is safe to close. In a clinical cohort of 91 users on the lowest 780G target, transitioning from AIT 3h to AIT 2h produced +3pp TIR and +3.7pp tight-range gain with no increase in hypoglycaemia (Bassi et al. 2025).
Exercise is where the trade-off becomes urgent. At high responsiveness settings, device IOB can show near zero while 40–45% of a typical meal dose is still physiologically active — enough to produce a 3–4.5 mmol/L glucose drop in 30 minutes of moderate activity.

This content is for educational exploration only. It is based on clinical data and real-world patterns describing average responses across populations. It is not a prescription, not a medical device, and must not be used as either. It cannot replace individual clinical guidance from your diabetes care team.

The question every AID system must answer

When you take a bolus of rapid-acting insulin, it does not act instantly and it does not disappear on a schedule. It absorbs over minutes. It peaks. It tails off over hours. The shape of that curve varies by dose size, injection site, body temperature, and activity level.

An AID algorithm needs to know — at every five-minute CGM reading — how much of that insulin is still working. If it overestimates, it holds back and glucose stays high. If it underestimates, it stacks more insulin on top of what is already circulating and causes a low.

This is the IOB trade-off. Every system makes a bet about the shape of insulin action, and that bet defines how the system behaves. More aggressive configurations model insulin as clearing faster — giving the algorithm more freedom to act, at the cost of showing the user less of what is physiologically circulating.

The core tension: settings that maximise glycaemic control simultaneously minimise the transparency of the variable you most need to see before exercise. This is not a flaw — it is an engineered design choice. Understanding it is the foundation of safe AID optimisation.

What the real-world data shows: a configuration gap, not a system gap

The IOB trade-off is not theoretical. Real-world registry data from more than 190,000 AID users makes the configuration gap concrete.

System	Registry size	Population mean TIR	Optimal-settings users	TIR on optimal settings
MiniMed 780G	101,629 users	72.3%	6.4% (6,531 users)	78.8% — TBR 2.2%
Control-IQ	20,764 users	~67%	67% had CF weaker than guideline	Top CF quartile: 79.1% vs 65.0% (14pp gap)
Omnipod 5	69,902 users	64.2%	53.8% on lowest target — but only 36.7% achieving ≥70% TIR	Lowest target: 68.8% vs ≥7.2 mmol/L: 53.6%
UK open-source AID	Real-world cohort	60%	Below 70% consensus target	Liarakos et al. 2025 DTT

The 14 percentage point TIR difference between the strongest and weakest correction factor quartiles in Control-IQ users — with negligible change in hypoglycaemia — is among the largest real-world evidence bases for the impact of a single setting change on glycaemic outcomes.

It is safe to close the gap

In a clinical cohort of 91 780G users, only 5.5% were using AIT 2 hours at baseline — despite 81.3% already using the lowest glucose target. When the remaining users were transitioned to AIT 2 hours (Bassi et al. 2025):

TIR improved from 71.9% to 75.0% (+3 percentage points)
Time in tight range improved from 47.2% to 50.9% (+3.7 points)
No increase in hypoglycaemia
The mechanism was redistribution: automated corrections increased from 0.14 to 0.18 U/kg/day while total daily insulin remained stable

AIT reduction redistributes how insulin is delivered, not how much. The insulin budget stays constant; the algorithm uses it more intelligently.

The IOB visibility gap: what the device shows vs what is circulating

A 0.1 U/kg bolus is a typical meal dose — 7 units for a 70 kg person. The table below shows how much of that dose remains physiologically active compared to what AIT 2h and AIT 3h register as remaining IOB at each hour post-bolus.

Time post-bolus	Physiological estimate	AIT 2h display	AIT 3h display
30 minutes	~80% — insulin peaking, most still to act	75% — broadly aligned	83% — broadly aligned
1 hour	~65% — still ascending toward peak action	50% — already underestimating	67% — closely aligned
90 minutes	~50% — at or near peak action	25% — significantly underestimating	50% — closely aligned
2 hours	~40–45% — still substantially active	0% — device shows no IOB	33% — closely aligned
3 hours	~15% — tapering but present	0%	0% — device shows no IOB
4 hours	~5% — trace amount	0%	0%

Physiological estimate based on standard rapid-acting insulin analog pharmacokinetics at a dose of 0.086–0.10 U/kg (Heise 2017, Plank 2005). At 2 hours, approximately 38–45% of physiological insulin activity remains for this dose range. Individual duration varies with insulin type, infusion site, temperature, and circulation.

At AIT 2h for a 70 kg person: at the two-hour mark the device shows zero IOB, while physiologically approximately 2.8–3.0 units is still working — roughly 5.5–6.0 mmol/L (100–110 mg/dL) worth of glucose-lowering potential. The algorithm stopped counting almost an hour before that insulin finished acting.

Line chart comparing device-reported IOB at AIT 2h and AIT 3h against physiological insulin activity over 8 hours after a 0.1 U/kg bolus for a 70 kg person. The physiological curve peaks at 90 minutes and resolves over 6 to 8 hours. The AIT 2h device curve reaches zero at 2 hours. The shaded area between the curves represents hidden circulating insulin. — Device-reported IOB vs physiological insulin activity. The shaded area is the hidden insulin gap — present and working physiologically, invisible to the algorithm and the user. At AIT 2h, the device reports zero IOB from 2 hours onward while 40–45% of the dose remains physiologically active.

Four systems, four different bets

Control-IQ: correction factor as the primary lever — and the most transparent IOB model

Control-IQ has a fixed AIT of 5 hours and a fixed glucose target of 6.1 mmol/L (110 mg/dL) — neither is user-adjustable. The primary responsiveness lever is the correction factor (CF). The algorithm delivers automated correction boluses hourly when glucose is predicted to exceed 10.0 mmol/L within 30 minutes, at 60% of the calculated correction dose. CF governs how much each of those corrections delivers.

In a real-world analysis of 20,764 Control-IQ users, 67% had CFs weaker than the 1700/TDD guideline. The TIR difference between the strongest and weakest CF quartile was 14 percentage points (79.1% vs 65.0%) with negligible change in hypoglycaemia. CF is the most powerful single lever on this system.

The IOB story — a transparency advantage: the fixed 5-hour AIT means the IOB model is more conservative than systems using AIT 2h. At moderate doses, the 5-hour model more closely approximates physiological insulin duration — and may even overestimate residual action for small doses. This reduces the hidden IOB risk seen on shorter-AIT systems. What you see on Control-IQ is closer to what is physiologically present than on AIT 2h systems.

The primary exercise consideration on Control-IQ is not IOB underestimation but negative IOB: when the algorithm has delivered less than the programmed rate, the display can suggest very low circulating insulin while a recent meal bolus may still be substantially active. Negative IOB does not cancel meal insulin.

MiniMed 780G: AIT as the aggressiveness dial

The 780G’s primary responsiveness lever is AIT. Reducing it from 3h to 2h means that at any point more than two hours after a bolus, the device registers zero IOB. The algorithm delivers automated corrections every 5 minutes when glucose exceeds the target — the most frequent correction delivery of any of the four systems.

A lower glucose target (5.5 mmol/L / 100 mg/dL rather than 6.7 mmol/L / 120 mg/dL) compounds this: the algorithm acts earlier and more often. Both meal and correction insulin count toward the same IOB pool.

The IOB story: at AIT 2h, device IOB shows zero from the 2-hour mark onward while approximately 40–45% of a typical meal dose is still physiologically active. This is the widest hidden IOB gap of any of the four systems. Before exercise, the four-hour heuristic — not the device display — is the reliable safety signal.

The Bassi et al. 2025 data confirms the transition is safe: moving from AIT 3h to AIT 2h in users already on the lowest target produced 3pp TIR gain, 3.7pp tight-range gain, and no increase in hypoglycaemia. The algorithm redistributes when it delivers corrections — it does not deliver more total insulin.

CamAPS FX: algorithm insulin is invisible — and that changes everything

CamAPS FX has a structural IOB rule that makes its responsiveness mechanism distinct from the other three systems. According to the EASD/ISPAD 2025 position statement on AID and exercise: only bolus insulin entered via the bolus calculator counts towards IOB (displayed as Active Insulin). Algorithm-directed delivery does not count.

The carb ratio (ICR) is the lever that directly controls how much bolus IOB accumulates — and therefore how much headroom the algorithm has. For a 60g carbohydrate meal: with a stronger ICR of 1:15 (1U per 15g), 4.0 units enter the bolus IOB pool; with a weaker ICR of 1:25 (1U per 25g), only 2.4 units do. The algorithm sees less IOB and has more freedom to deliver its own corrections — the proportion of insulin delivered algorithmically rises as a direct result. Strengthen the ICR too much, and the bolus IOB brake throttles the algorithm’s ability to manage highs autonomously.

The IOB story: CamAPS’s overall IOB model is more physiologically accurate than shorter-AIT systems for the insulin it does track. But the exclusion of algorithm delivery from the display means the portrait-mode Active Insulin screen can substantially understate total circulating insulin at high responsiveness levels. Rotating to landscape view reveals the algorithm’s full delivery history — a considerably more complete picture. Activate Ease Off mode 90 minutes before planned activity.

Omnipod 5: target-driven AIT — the same gap, internally managed

Omnipod 5 uses AIT in a similar way to the 780G, but AIT is not directly user-adjustable. The algorithm selects an internal AIT (estimated range 2–4 hours) based on the chosen glucose target. A lower, more responsive target drives a shorter effective AIT — creating the same physiological IOB gap seen in the 780G at AIT 2h.

At the lowest target (6.1 mmol/L / 110 mg/dL), median TIR in the Omnipod 5 registry was 68.8% — and only 36.7% of the 69,902-user registry achieved the ≥70% consensus target. Consistent bolusing matters as much as target setting: users with ≥4 boluses per day achieved 72.4% TIR vs 59.9% for those with fewer.

The IOB story: at the most responsive target, the physiological IOB gap is structurally similar to the 780G at AIT 2h. The fact that AIT is internally managed rather than user-set does not reduce the gap — it makes it less visible. Activity mode should be started 90 minutes before exercise.

Comparison table showing IOB transparency, primary responsiveness lever, correction frequency, and exercise implications for MiniMed 780G, Control-IQ, Omnipod 5, and CamAPS FX — Four-system comparison: IOB transparency, primary lever, correction frequency, and exercise implication. The IOB transparency row shows the key design difference — from low (780G at AIT 2h) to moderate (Omnipod 5) to high (Control-IQ 5h) to algorithm-excluded (CamAPS).

Exercise: where the trade-off becomes urgent

Exercise is where the IOB trade-off becomes visible — and sometimes urgently so. During aerobic activity, insulin action is amplified through muscle glucose uptake pathways that are partly independent of insulin. The algorithm responds by reducing basal delivery — but it can only reduce what it is currently delivering. It cannot recall insulin that has already been absorbed.

The glucose-lowering effect of activity is directly proportional to how much physiological insulin is circulating. A glucose of 8.0 mmol/L with low IOB is a fundamentally different pre-exercise state from the same glucose with high IOB.

Expected glucose response during moderate aerobic activity (per 20 minutes)

Physiological IOB (U/kg body weight)	Expected glucose drop per 20 min moderate aerobic activity	Risk level
≤0.02 U/kg	0.3–0.5 mmol/L (5–10 mg/dL)	Safe
0.02–0.05 U/kg	0.5–1.0 mmol/L (10–20 mg/dL)	Low risk
0.05–0.08 U/kg	1.0–2.0 mmol/L (20–35 mg/dL)	Moderate — published guidance supports carbohydrate intake in the 10–15 g range before activity in this scenario on average
0.08–0.12 U/kg	2.0–3.0 mmol/L (35–55 mg/dL)	High — in this scenario, carbohydrate cover or delaying activity is the approach reflected in the published evidence on average
>0.12 U/kg	>3.0 mmol/L (>55 mg/dL) — with continued decline after stopping	Very high — in this scenario, deferring vigorous activity is the pattern reflected in published guidance on average

Based on moderate-intensity aerobic activity (brisk walking, steady cycling at conversational pace). High-intensity interval training (HIIT) produces different patterns — catecholamine-driven glucose rise during activity, followed by a hypoglycaemia window 4–8 hours later. Published guidance notes that Exercise Mode on Control-IQ is not designed for HIIT-type sessions. Checking CGM at regular intervals during activity — commonly every 20 minutes — is a widely used approach reflected in clinical guidance.

The 20-by-2 rule: 20 minutes of moderate-intensity activity produces a glucose reduction of approximately 2 mmol/L (35 mg/dL) when physiological IOB is low to moderate. This principle — featured in the GNL Activity to Lower Highs Explorer — makes activity a non-insulin route to glucose reduction. At high IOB (>0.10 U/kg), the same 20 minutes can produce considerably larger drops with continued decline after stopping.

AID users and exercise carbohydrate: approximately half of MDI guidelines

AID algorithms detect falling glucose during exercise and progressively suspend basal delivery. This algorithm-driven basal suppression reduces effective circulating insulin by approximately 30–50% compared with multiple daily injection (MDI) therapy under the same exercise conditions. As a result, carbohydrate requirements for AID users are approximately half those recommended for people on MDI for the same activity type, duration, and intensity — consistent with ISPAD 2022 (Adolfsson et al.) and the EASD/ISPAD 2025 position statement on AID and physical activity.

Standard exercise carbohydrate tables designed for MDI will substantially overestimate the carbohydrate most AID users need. The Exercise IOB Explorer uses AID-specific estimates based on IOB, activity type, and duration.

Heat map showing exercise risk by insulin on board per kilogram and starting glucose level, with colour coding from green (safe) through amber (caution) to dark red (defer activity). Rows show IOB bands from below 0.02 to above 0.12 U/kg. Columns show starting glucose from below 4 to above 14 mmol/L. — Exercise risk matrix: expected glucose response by insulin exposure and starting glucose level during moderate aerobic activity. Green cells are generally safe to proceed. Amber cells warrant carbohydrate cover. Red and dark-red cells advise delay or carbohydrate loading. Use the Exercise Planning Explorer to calculate your own values.

The four-hour heuristic — for all systems: “Have I given a bolus for food in the last four hours?” If yes, there is physiologically circulating insulin — regardless of what any IOB display shows. Time elapsed since the last meal bolus is the most reliable exercise safety signal when using AID systems at high responsiveness settings. The EASD/ISPAD position statement notes that IOB on AID systems does not accurately reflect peak insulin action, which typically occurs 1–2 hours after the prandial bolus dose.

Settings are levers, not prescriptions

Each system gives clinicians and people with diabetes different levers to pull. Understanding which lever does what — and why — is where optimisation begins.

System	Primary responsiveness lever	IOB model	Correction frequency	Exercise preparation
Control-IQ	Correction factor (CF)	Fixed 5h — most transparent; conservative model rarely underestimates	Hourly when predicted >10.0 mmol/L in 30 min	Four-hour heuristic; watch negative IOB
MiniMed 780G	AIT (2–3h user-set) + glucose target	0% display at 2h; ~40–45% physiological still active	Every 5 minutes	Four-hour heuristic critical; Bassi 2025 confirms AIT 2h safe
CamAPS FX	Target glucose + carb ratio (ICR)	Bolus IOB only displayed; algorithmic proportion rises as ICR weakens — none of it counted	Continuous adaptive	Ease Off 90 min before; landscape view for full delivery history
Omnipod 5	Glucose target (drives internal AIT 2–4h)	AIT internally managed; similar gap to 780G at lowest target	Continuous adaptive	Activity mode 90 min before; four-hour heuristic

The AID System Explorer lets you enter age, weight, and total daily dose and see exactly how each system responds at five different responsiveness levels — including algorithm-calculated basal rates, correction doses, and glucose targets. The differences between systems at the same responsiveness level are often striking.

A note on CGM metrics and HbA1c

When using time in range (TIR) to guide AID configuration decisions, be aware that HbA1c and TIR can give different signals for the same person. Research indicates that in Black ethnic groups and some children, HbA1c may be approximately 5 mmol/mol (0.5%) higher for a given TIR compared with other populations. Escalating AID settings based on HbA1c alone when TIR is already adequate risks delivering excess insulin without glycaemic benefit.

Use CGM metrics — TIR, time below range, coefficient of variation — as the primary measure of glycaemic control when optimising AID settings. If HbA1c and TIR appear discordant, explore this with your diabetes care team before changing settings.

No clinician can do this maths in their head

This is the uncomfortable truth behind AID optimisation. The interactions between IOB decay curves, correction factors, basal modulation, meal absorption, and activity are too complex for mental arithmetic. A 15-minute clinic appointment cannot model how a correction factor change on Control-IQ will alter the stacking risk compared to shortening AIT on the 780G.

This is not a criticism of clinical practice — it is a recognition that the systems themselves are more sophisticated than the tools traditionally available to discuss them. The algorithms run continuous calculations every five minutes. The conversations about them happen every three months.

This is exactly why the GNL Explorers exist. They bridge that gap. They let clinicians and patients see — visually and numerically — what changing a setting actually does to the system’s behaviour. Not as a prescription, but as a starting point for an informed conversation.

When a young person asks “Why does my pump keep giving me corrections at night?” the answer is different on every system. On Control-IQ, it may be a basal profile issue. On the 780G, it may be the AIT setting. On CamAPS, it may be the overnight target. Without a tool that shows these differences, the conversation defaults to generic advice. With the explorers, it can become specific.

Matching the system to the person

There is no universally best AID system. The evidence shows all four improve time in range and quality of life compared to non-AID therapy. The question that matters is: which system’s design philosophy matches this person’s life?

If transparency and IOB accuracy matter most

Control-IQ’s 5-hour IOB model is the most physiologically representative of the four systems. The correction factor is the primary lever and the evidence is clear that strengthening it is both effective and safe. For people who want to understand what the algorithm is doing and why, Control-IQ rewards that engagement.

If aggressive automated correction matters most

The 780G delivers the most frequent automated correction logic — every 5 minutes at AIT 2h. It auto-learns basal rates and sensitivity and tends to produce excellent overnight results with minimal manual tuning. The trade-off is the widest IOB visibility gap during activity. The Bassi 2025 data confirms that moving to AIT 2h is safe for users already on the lowest target.

If adaptability and variable schedules matter most

CamAPS FX offers the widest range of target customisation (4.4–11.0 mmol/L by time of day), Ease Off for exercise, and Boost for short-term intensification. The algorithm delivers up to 65% of total daily insulin autonomously at the most responsive level — the highest algorithmic proportion of the four systems. For people with unpredictable schedules, variable activity, or pregnancy, its adaptability is a genuine advantage.

If simplicity and discretion matter most

Omnipod 5 is tubeless, requires minimal settings, and runs the algorithm inside the Pod itself. For people who want effective AID without complexity — or who need a discreet system for sport, work, or personal preference — it delivers. The responsiveness level is the main adjustment; everything else is automated. Consistent bolusing frequency matters as much as target level for achieving optimal TIR.

Explore the differences yourself

The GNL Explorers were built to make these invisible differences visible. Each tool addresses a different aspect of the IOB trade-off — from algorithm behaviour to exercise risk to glucose recovery.

AID System Explorer

Enter age, weight, and total daily dose. See how all four systems behave at five responsiveness levels — including basal rates, correction doses, and glucose targets.

Open the AID System Explorer

Exercise IOB Explorer

Model evidence-based carbohydrate estimates for activity based on physiological insulin on board and body weight — using AID-specific values, not MDI guidelines.

Open the Exercise IOB Explorer

Exercise Planning Explorer

Plan exercise before, during, and after — with AID-specific guidance on IOB, carbohydrate timing, and glucose targets for different activity types.

Open the Exercise Planning Explorer

Activity to Lower Highs Explorer

Model how glucose responds to moderate activity based on true insulin exposure — using the 20-by-2 principle with your own weight and settings.

Open the Activity Explorer

The bottom line

AID systems are remarkable. They have changed what is achievable for hundreds of thousands of people with type 1 diabetes. But they are not identical, and treating them as interchangeable misses the point.

The IOB trade-off — the design choice each system makes about how to track active insulin — shapes everything downstream. It determines how aggressively the algorithm corrects, how visible the remaining insulin is to the user, and how safe it is to start exercising after a meal bolus. Real-world data from more than 190,000 users confirms that the gap between those who understand and apply these principles and those who do not is 10–15 percentage points in time in range.

That gap is closeable. The evidence is there. The tools exist. The conversation just needs the right framework.

Registry and clinical trial data cited: Choudhary et al. (780G global registry); Messer et al. / Shah et al. (Control-IQ); Forlenza et al. (Omnipod 5); Liarakos et al. 2025 DTT (UK open-source AID); Bassi et al. 2025 (780G AIT transition cohort); EASD/ISPAD 2025 position statement on AID and physical activity; Heise 2017, Plank 2005 (insulin pharmacokinetics). Source manuscripts: Pemberton JS & Uday S (in submission).