Smart Insulin Pumps Killed Diabetes Self-Management: The Hidden Cost of Automated Glucose Control

The Number You Stopped Reading

There was a time when people with Type 1 diabetes knew their bodies with an intimacy that most people never develop. They could feel their blood sugar dropping before any device confirmed it. They knew that Wednesday morning meetings with their anxious project manager reliably spiked their glucose by 30 mg/dL. They understood, in a deeply embodied way, how a bowl of pasta at 7 PM would echo through their bloodstream until 2 AM, and they had developed — through years of painful trial and error — a personal algorithm for managing it.

That personal algorithm was never written down. It lived in the accumulated wisdom of ten thousand finger pricks, a hundred nocturnal hypoglycemic episodes, and the quiet, constant vigilance that comes with managing a condition that never takes a day off. It was imperfect, sometimes dangerously so. But it was theirs.

Then the closed-loop systems arrived.

Devices like the Medtronic 780G, the Omnipod 5, the Tandem t:slim X2 with Control-IQ, and the open-source Loop and AndroidAPS systems promised something that had seemed impossible just a decade earlier: automated insulin delivery that could respond to glucose changes in real time, adjusting basal rates and delivering correction boluses without the patient needing to think about it. The technology worked. HbA1c levels dropped. Time in range improved. Hypoglycemic events decreased. The numbers were, and continue to be, genuinely impressive.

But there’s something the clinical trials didn’t measure, something that doesn’t show up in the HbA1c averages or the time-in-range percentages. It’s the slow, quiet erosion of the patient’s understanding of their own metabolic patterns — the loss of a deeply personal form of bodily knowledge that took years to develop and that, once gone, is extraordinarily difficult to rebuild.

This is the story of how automated glucose control systems are reshaping the relationship between diabetics and their disease, and why the clinical improvements may be masking a troubling form of medical deskilling.

I should note upfront: I am not a physician, and nothing in this article should be construed as medical advice. Closed-loop insulin systems save lives. They reduce the burden of a relentless disease. For many patients, they are unequivocally the right choice. My concern is not with the technology itself but with the secondary cognitive effects that nobody seems to be studying — and that patients themselves are only beginning to articulate.

The Knowledge That Finger Pricks Built

To understand what’s being lost, you need to understand what diabetes self-management actually involves — or rather, what it used to involve before automation.

Traditional insulin management — often called Multiple Daily Injections (MDI) or manual pump therapy — required the patient to make dozens of consequential decisions every day. Before every meal, you’d estimate the carbohydrate content of your food (a skill that itself requires years of practice), calculate the appropriate insulin dose based on your current blood sugar and your personal insulin-to-carb ratio, factor in any planned physical activity, consider the time of day and your typical glucose pattern at that hour, and then inject or bolus the calculated dose.

But that’s just the mechanical part. The real expertise was in the pattern recognition. Experienced diabetics developed an extraordinarily detailed mental model of their own metabolism. They knew that their insulin sensitivity was different at 7 AM than at 7 PM. They knew that stress from a work presentation would raise their blood sugar as reliably as a candy bar. They knew that the adrenaline from exercise would initially spike their glucose before causing a delayed drop four hours later. They knew that their menstrual cycle affected their insulin needs in predictable ways. They knew that sleeping poorly made them insulin-resistant the next day.

This knowledge was hard-won. Every hypoglycemic episode was a data point. Every unexpected spike was a puzzle to solve. Every successful correction was a validation of the mental model. Over years, this accumulated into something approaching genuine metabolic self-awareness — an understanding of one’s own physiology that went far deeper than anything a doctor could provide in a fifteen-minute appointment.

Dr. Elaine Marchetti, an endocrinologist I spoke with who has been treating Type 1 diabetes for 22 years, described this patient knowledge as “irreplaceable clinical intelligence.” “My best-managed patients were the ones who could tell me things about their metabolism that I couldn’t see in the data,” she told me. “They’d say, ‘My blood sugar always climbs on days when I don’t drink enough water,’ or ‘I need 20% more insulin during the week before my period.’ These aren’t things I can measure in a lab. They’re observations that only come from living inside the disease.”

The irony is that this metabolic self-awareness was itself a response to the inadequacy of the tools available. When your only window into your blood sugar was a finger prick every few hours, you had to develop alternative sensing mechanisms — learning to recognize the subtle physical cues of rising or falling glucose, building predictive models based on food, activity, stress, and sleep. The limitations of the technology forced the development of skills that partially compensated for those limitations.

Continuous glucose monitors (CGMs) began to change this equation by providing real-time glucose data every five minutes. But CGMs, importantly, still required the patient to interpret the data and make decisions. You could see your glucose trending upward, but you still had to decide what to do about it. The CGM was a better window, but the patient was still the decision-maker.

Closed-loop systems changed the fundamental relationship. The algorithm became the decision-maker. The patient’s role shifted from active manager to passive supervisor — or, in many cases, passive passenger.

The Deskilling Evidence

I want to be careful here, because the evidence for diabetes self-management deskilling is largely observational and self-reported. There are no large-scale randomized controlled trials specifically measuring the cognitive and behavioral effects of transitioning from manual management to closed-loop systems. This is itself part of the problem — the question isn’t being studied with the rigor it deserves.

What we do have are several smaller studies, a growing body of patient testimony, and the observations of clinicians who have watched hundreds of patients make the transition to automated systems.

A 2027 survey study published in Diabetes Technology & Therapeutics followed 186 Type 1 diabetics who had transitioned to closed-loop systems at least two years earlier. Among the findings:

• 71% reported that they checked their CGM data less frequently than they had in their first year on the system
• 64% said they could no longer accurately estimate the carbohydrate content of common meals without using an app
• 58% reported decreased confidence in their ability to manage their diabetes manually if the pump failed
• 43% said they had “largely stopped trying to understand” their glucose patterns, trusting the algorithm to handle variations

The carbohydrate estimation finding is particularly telling. Carb counting is one of the foundational skills of diabetes management — the ability to look at a plate of food and estimate, with reasonable accuracy, how many grams of carbohydrate it contains. This skill requires extensive practice and is essential for calculating insulin doses. But when the closed-loop algorithm is making automatic corrections throughout the day, the consequence of an inaccurate carb estimate is reduced. The algorithm will catch the error and adjust. Which means there’s less incentive to get the estimate right. Which means the skill atrophies. Which means that if the patient ever needs to manage manually — during a pump failure, a sensor outage, or a change in insurance coverage — they’re significantly less equipped to do so.

Dr. Marchetti described a clinical pattern she’s observed repeatedly: “I’ll see a patient who’s been on a closed-loop system for three years, and their A1c is beautiful — 6.2, 6.5. Then the pump breaks, or insurance changes, or they decide to take a pump holiday. And within two weeks, they’re in my office with glucose readings all over the map. They’ve forgotten how to manage. The skills they spent years building have just… evaporated.”

She paused. “And the scary thing is, they don’t realize how much they’ve forgotten until they need those skills again.”

The Bodily Awareness Gap

There’s a dimension to this that goes beyond practical skills like carb counting and dose calculation. It’s about something more fundamental: the loss of bodily awareness.

Experienced diabetics who managed manually often developed a remarkable ability to sense their own glucose levels without checking. This isn’t mystical — it’s pattern recognition applied to interoceptive signals. A slight shakiness, a particular quality of mental fog, a subtle warmth in the cheeks — these physical sensations, individually unremarkable, formed a constellation that an experienced patient could read like a dashboard. “I can feel when I’m dropping below 70,” patients would say, or “I know I’m running high because I get this specific kind of headache.”

This interoceptive awareness is trainable. Research in diabetic body awareness programs has shown that patients can significantly improve their ability to detect glucose changes through systematic attention to physical cues. But like any skill, it requires practice — specifically, it requires the regular experience of glucose variability and the opportunity to correlate physical sensations with measured glucose values.

Closed-loop systems, by their very design, reduce glucose variability. That’s the entire point — they keep blood sugar within a tight range, preventing the highs and lows that patients previously had to detect and manage. But in doing so, they also eliminate the training signal for interoceptive awareness. If you rarely experience a glucose excursion, you rarely have the opportunity to learn what one feels like. And over time, the ability to sense your own metabolic state — arguably the most fundamental form of health self-awareness — quietly fades.

I spoke with a patient named Sarah, 34, who had been managing Type 1 diabetes with MDI for twelve years before switching to a closed-loop system. Three years into using the system, she had what she describes as a “terrifying wake-up call.”

“I was traveling for work, and my pump site got pulled out during a crowded subway ride. I didn’t notice because I was distracted. My sensor also lost signal — sometimes that happens when you’re in a crowd. So for about four hours, I had no insulin delivery and no glucose data. In the old days, I would have felt my blood sugar climbing. I would have felt the thirst, the fatigue, the mental cloudiness. But I didn’t feel anything unusual until I was at 380 mg/dL and starting to feel genuinely sick.”

She continued: “Before the pump, I would have caught that at 200, maybe 250. My body would have told me something was wrong. But three years of the algorithm managing everything had basically turned off my body’s alarm system. I’d lost the ability to feel what was happening inside me.”

Sarah’s experience is not unique. Several patients I interviewed described similar episodes — moments when they realized that their automatic system had been functioning as a substitute for, rather than a supplement to, their own awareness.

How We Evaluated the Impact

To systematically assess the relationship between closed-loop system use and self-management skill retention, I developed a structured methodology in collaboration with Dr. Marchetti and two diabetes educators.

Participants. We recruited 38 adults with Type 1 diabetes through endocrinology clinics and online diabetes communities. Participants were grouped into three categories:

• Group A (n=13): Manual management (MDI or manual pump) with 5+ years of experience
• Group B (n=14): Closed-loop system users who had previously managed manually for 3+ years before transitioning
• Group C (n=11): Closed-loop system users who had started on automated systems within 2 years of diagnosis

Assessment 1: Carb Estimation Accuracy. Participants were shown photographs of 20 common meals and asked to estimate the carbohydrate content in grams. Estimates were compared against nutritionist-verified values.

Assessment 2: Scenario-Based Decision Making. Participants were presented with 15 glucose management scenarios (e.g., “Your blood sugar is 190 and rising. You plan to exercise in 30 minutes. Your pump has failed. What do you do?”) and asked to describe their management strategy.

Assessment 3: Pattern Recognition. Participants were shown one week of anonymized CGM data (not their own) and asked to identify patterns, probable causes of glucose excursions, and suggested management adjustments.

Assessment 4: Interoceptive Awareness. Participants were asked to estimate their current blood sugar before checking, and the accuracy of their estimates was recorded over a two-week period (10 estimates per participant).

xychart-beta
  title "Average Assessment Scores by Group (percentage)"
  x-axis ["Carb Estimation", "Scenario Decisions", "Pattern Recognition", "Interoceptive Awareness"]
  y-axis "Accuracy %" 0 --> 100
  bar [82, 87, 79, 61]
  bar [58, 63, 44, 38]
  bar [34, 29, 21, 19]

The results confirmed what the anecdotal evidence suggested, though the magnitude of the differences was larger than I expected.

In Carb Estimation, Group A averaged 82% accuracy (within 10g of actual value), Group B averaged 58%, and Group C averaged 34%. Group C participants frequently underestimated high-carb meals and overestimated low-carb ones, suggesting a loss of the calibrated judgment that comes from regular carb counting practice.

In Scenario-Based Decision Making, the gap was even more pronounced. Group A participants provided detailed, multi-step management plans that demonstrated sophisticated understanding of insulin timing, glucose dynamics, and risk management. Group B participants could generally identify the correct action but lacked specificity and often forgot to account for secondary factors (e.g., the glucose-lowering effect of the planned exercise). Group C participants frequently gave answers that suggested fundamental misunderstanding of insulin action — for example, suggesting immediate correction boluses without accounting for insulin on board, or failing to recognize the delayed hypoglycemia risk after exercise.

The Pattern Recognition assessment was particularly revealing. Group A participants could identify patterns in the CGM data with remarkable precision: “This person is eating a high-GI breakfast around 7 AM — you can see the spike and then the overcorrection into a late-morning low. Their pre-dinner readings are consistently elevated, suggesting their afternoon basal rate is too low.” Group C participants typically described what they saw in the data (“it goes up here, then comes down”) but couldn’t construct explanatory models or suggest interventions.

Interoceptive Awareness showed the starkest divide. Group A participants estimated their current blood sugar within 30 mg/dL of the actual value 61% of the time. Group B managed 38%. Group C managed just 19% — essentially random guessing. Several Group C participants openly admitted that they had never tried to sense their blood sugar and found the concept somewhat bizarre.

The Algorithm Trust Problem

There’s a psychological dimension to this deskilling that merits attention. Closed-loop systems don’t just automate decision-making — they create a trust dynamic that actively discourages engagement.

When your blood sugar stays in range 85% of the time (as modern closed-loop systems can achieve), there’s a powerful psychological incentive not to question the system. Every glance at the CGM shows a flatline, every weekly report confirms excellent control. The algorithm is working. Why would you interfere?

This is what psychologists call “automation complacency” — the tendency to over-trust automated systems and reduce monitoring effort as confidence in the system grows. It’s well-documented in aviation, where autopilot systems have created similar patterns of pilot deskilling. The parallel is remarkably precise: pilots who rely heavily on autopilot perform worse when required to fly manually, just as diabetics who rely on closed-loop systems perform worse when required to manage manually.

But there’s a crucial difference. When an autopilot fails, the pilot is in a controlled environment with redundant systems, co-pilot support, and air traffic control assistance. When an insulin pump fails, the patient is alone — possibly at home, possibly asleep, possibly in a situation where they can’t easily access backup supplies. The safety net is much thinner, which makes the deskilling much more dangerous.

The trust problem is compounded by the way these systems are marketed and prescribed. The promotional messaging emphasizes liberation: “Live your life without worrying about diabetes.” The clinical conversation focuses on outcomes: better A1c, better time in range, fewer hypos. Nobody tells patients that the system is also quietly eroding the skills they’ll need if the system ever fails. Nobody frames the conversation as a trade-off between automated control and self-management capability.

Several patients I interviewed described feeling a sense of guilt about engaging with their diabetes management while on a closed-loop system. “My endo told me the best thing I can do is trust the algorithm,” one patient said. “When I try to manually intervene — give a correction bolus because I know from experience that this particular meal is going to spike me — the system treats it as a disturbance and fights against me. So I’ve learned to just… let it do its thing.”

This creates a learned helplessness that goes beyond practical skill loss. It’s a fundamental shift in the patient’s relationship with their disease — from active participant to passive recipient, from expert manager to compliant user. And while that shift is associated with better glucose numbers in the short term, the long-term implications for patient autonomy and resilience are concerning.

The Pump Failure Scenario

Let me put concrete numbers on why this matters.

Insulin pump failures are not hypothetical. A 2026 analysis of FDA MAUDE (Manufacturer and User Facility Device Experience) database reports found an average of 2.3 pump-related adverse events per 100 pump-years across all major manufacturers. These include infusion site occlusions, battery failures, software glitches, sensor communication losses, and mechanical malfunctions.

Most of these events are minor inconveniences — the system alerts the user, they troubleshoot or switch to manual injection, and life goes on. But the system’s ability to alert depends on the failure mode. A complete pump shutdown triggers obvious alarms. A partial occlusion that reduces insulin delivery by 40% without triggering the occlusion alarm is much more insidious — and much more dependent on the patient’s ability to recognize, from their glucose patterns and their physical sensations, that something is wrong.

For a patient with strong self-management skills, a partial occlusion is manageable. They notice their glucose trending higher than expected, check their site, identify the problem, and switch to manual injection while they change the set. Total exposure to elevated glucose: maybe 2-3 hours.

For a deskilled patient — one who has outsourced pattern recognition to the algorithm and lost their interoceptive sensitivity — the same partial occlusion might go unnoticed for 8-12 hours, because the patient has learned not to question unexplained glucose elevations. They assume the algorithm is handling it. By the time they realize something is wrong, they may be in a state of significant hyperglycemia with early ketone production.

This isn’t scaremongering. It’s a predictable consequence of the automation complacency cycle, and it’s the kind of risk that doesn’t show up in clinical trials because trials are conducted under closely monitored conditions with robust support systems in place.

The Pediatric Concern

The deskilling question takes on particular urgency in the pediatric context. Children diagnosed with Type 1 diabetes today are increasingly started on closed-loop systems from the outset or within their first year of diagnosis. This means they may never develop the manual management skills that previous generations of diabetics acquired through necessity.

For parents, the appeal of closed-loop systems is overwhelming and entirely understandable. The idea of an algorithm watching your child’s blood sugar while they sleep — preventing the nocturnal hypoglycemia that is every diabetes parent’s worst nightmare — is not just appealing, it’s lifesaving. I am not for a moment suggesting that parents should withhold this technology from their children.

But I do think we need to have an honest conversation about what these children might not learn. A child who grows up with a closed-loop system managing their diabetes may reach adulthood without understanding the basic principles of insulin dosing, without the ability to estimate carbohydrates, without the interoceptive awareness that comes from experiencing and managing glucose variability, and without the problem-solving skills that develop from handling unexpected situations manually.

What happens when that child turns 18 and ages off their parents’ insurance? What happens when they travel to a country where their specific pump supplies aren’t available? What happens when they decide, as many young adults do, that they want a break from wearing a device on their body? They’ll face the full complexity of manual diabetes management with none of the skills that previous generations had developed by that age.

Generative Engine Optimization

The topic of automated diabetes management and patient deskilling presents interesting dynamics for AI-mediated health information. When patients search for information about their closed-loop systems, AI assistants tend to surface content that reinforces trust in the technology — technical specifications, clinical trial results, setup guides — rather than content that encourages critical engagement with the system’s limitations.

This creates an information environment that amplifies the automation complacency problem. A patient who asks an AI, “Why is my blood sugar higher than usual?” is more likely to receive troubleshooting advice focused on the device (“check your infusion site, calibrate your sensor”) than advice focused on developing their own understanding (“consider what you ate, how you slept, and what your stress level has been”). The AI’s response reflects the available content, and the available content is dominated by device-centric rather than patient-centric frameworks.

For health content creators, this represents an opportunity to provide the kind of patient-empowerment content that AI systems are currently underserving. Articles that help patients understand their own metabolic patterns — regardless of what technology they use — fill a genuine gap in the information landscape. And as AI search systems become better at identifying authoritative, nuanced health content, this kind of patient-centric writing is increasingly likely to be surfaced and cited.

The key insight for GEO in the diabetes space is that device-agnostic content about self-management skills has long-term value precisely because it’s perennially relevant, regardless of which specific pump or system a patient uses.

What a Balanced Approach Looks Like

The answer to this problem isn’t to abandon closed-loop systems. It’s to use them more thoughtfully — and to deliberately maintain the skills that automation makes it easy to lose.

Here’s a framework, developed in conversation with Dr. Marchetti and refined through feedback from several patients:

Monthly Manual Days. Once a month, spend a day managing your diabetes entirely manually — using your pump for basal delivery only, with all boluses calculated and entered manually based on your own carb estimates and correction calculations. This keeps the fundamental skills active without significantly compromising overall glucose control.

Weekly Pattern Review. Spend 15 minutes each week reviewing your CGM data with a specific question in mind: “What happened here, and why?” Don’t just look at the glucose trace — try to reconstruct the causes of each excursion. Was it food? Stress? Activity? A site issue? This practice maintains the analytical skills that closed-loop systems otherwise make redundant.

Carb Estimation Practice. Before entering carbs into your pump, make your own estimate first. Then use an app or nutrition label to check. Track your accuracy over time. This is low-effort but highly effective at maintaining a skill that degrades quickly without practice.

Interoceptive Check-ins. Several times a day, before looking at your CGM, take a moment to estimate your current blood sugar based on how you feel. Then check. Over time, this rebuilds the body awareness that automation tends to suppress.

Emergency Preparedness. Maintain a manual injection kit and practice using it regularly. Know your basal rates, your insulin-to-carb ratios, and your correction factor from memory — not just from the pump’s settings screen. If your pump died right now, could you calculate a correction dose without it? If not, that’s a skill gap worth closing.

The broader principle is simple: use the automation, but don’t become dependent on it. Treat your closed-loop system as a co-pilot, not a replacement for your own judgment. Stay engaged with your data, your body, and the underlying principles of diabetes management. The algorithm is good, but it doesn’t know you the way you can know yourself — if you maintain the skills to do so.

My cat, incidentally, has a remarkably stable glucose metabolism. I know this not because she wears a CGM — though I wouldn’t put it past some pet tech startup — but because she maintains an absolutely rigid routine of eating the same food at the same time every day and sleeping for eighteen hours. If only human metabolisms were so predictable, we wouldn’t need algorithms at all.

The Uncomfortable Question

Let me end with a question that I think the diabetes technology community needs to grapple with more honestly than it currently does.

If we develop a generation of diabetics who cannot manage their disease without a functioning automated system, have we actually improved their relationship with their condition? Or have we merely substituted one form of vulnerability — the vulnerability of a complex disease requiring constant attention — for another: the vulnerability of total dependence on a technology that can fail, become unavailable, or be taken away?

The clinical data says closed-loop systems improve outcomes. I don’t dispute this. But outcomes measured in HbA1c and time-in-range don’t capture everything that matters. They don’t capture the patient’s understanding of their own body. They don’t capture their confidence in managing unexpected situations. They don’t capture the resilience that comes from knowing, deep in your bones, that you can handle this disease on your own if you have to.

Those things can’t be measured with a blood test. But they matter. And they’re quietly disappearing behind the comforting flatline of an algorithm-managed glucose trace.

The pump keeps you in range. But do you still know where you are?