Benefits and Risks of Insulin Pumps and Closed-Loop Delivery Systems

A case study in reverse causality and moral hazards

A closed-loop insulin delivery system

Listen to this really fun AI-generated audio summary of this article.

If you’re a T1D, I’m sure you’ve heard about—or even use—either a fully automated closed-loop insulin pump, or a semi-automated (hybrid) pump, where algorithms read glucose levels from a CGM and administer insulin depending on glucose levels. When your levels rise too high, more insulin is administered; when levels are too low, insulin infusion stops.

The premise behind Automated Insulin Delivery (AID) systems is great—who wouldn’t want to relieve the burden of the daily management of T1D if an automated system can take care of it for you? This feels like a big win for T1Ds, right?

To start, AID technologies are in very early in development. At the time of this writing, the FDA has approved of only a couple systems, one of which being The MiniMed™ 770G, a hybrid closed loop system, discussed in detail later in this article, but many more are coming on the market fast, along with the DIY (home-brew) software programs that run older pumps. As many datasets cite in scholarly literature, these users are achieving A1c levels at 7%, and time-in-range percentages up to 70%.

The greatest beneficiaries are those whose A1c levels prior to using an AID system are north of 9%, which is fantastic. Because this mostly represents children, teens, and adults with either the inability or unwillingness to self-manage their T1D, AID systems have been praised as being the safety net for a large number of users. In fact, almost all scholarly articles that praise AID systems cite studies showing the enormous benefit to this cohort of users.

But, as I always caution readers, it’s not that simple. This article aims to tease out the details behind these numbers and consider the longer and wider implications therein. We identify these key criteria:

• There’s a difference between “target” glycemic ranges and “healthy” ranges, and there are limitations to how well AID systems can possibly perform, given the physiological properties of glucose detection and insulin administration through interstitial tissue.

• Not everyone is able to achieve these target ranges of glycemic control—who does and who doesn’t is essential to understand.

• Pumps have certain mechanical failure profiles that are largely overlooked as anomalies, despite their ubiquity and frequency.

• Lastly, there’s the moral hazard, where dependency on automated systems may keep users from learning (through both training and empirical experience) how to optimize glycemic control in ways that cannot be achieved by AID systems.

Let’s take these individually.

Glycemic Targets

The first consideration—and arguably the most important one—is whether an AID system can help you achieve glucose targets.

Again, the headlines and anecdotal data show some users are able to achieve A1c levels of 7%, but the majority is still within the mid-7s.

So, is that good?

To answer that, we need to differentiate between a specific desired A1c level (or time-in-range percentages), and what is actually healthy.

The American Diabetes Association’s recommendation is to aim for an A1c level <7% and a time-in-range target of >70%, but the ADA did not intend to imply that those levels are “healthy” ranges—rather, they are what medical care teams discerned to be achievable targets. Big difference.

For the past few decades, medical literature finds that only 20-30% of T1Ds achieve A1c levels of 7% (with socioeconomic factors being determining factors of success). Care teams like to use A1c of 7% and 70% time-in-range because patients can see those levels as something they could achieve without totally giving up, and without experiencing severe hypoglycemia, which can be as harmful as hyperglycemia, but in different ways.

Physiologically speaking, however, A1c levels of 7% are still quite unhealthy—long-term complications happen at those levels, too, just more slowly than higher levels. As years and decades go by, the risk of cardiovascular disease from elevated glucose levels rises precipitously, including for A1c levels of 7%. (See my article, “T1D and Health: How Long Will You Live?”)

The correlation between health outcomes and A1c levels is discussed in the paper, “The association of chronic complications with time in tight range and time in range in people with type 1 diabetes: a retrospective cross-sectional real-world study.” Here, the authors found that the more time spent in “tight” range (70-140 mg/dL), the rate of long-term complications dropped significantly. The following graphic from that paper shows that a 10% increase for time in tight range resulted in a decrease of 23.8% of cardiovascular disease, and 34.9% of strokes.

The UK Prospective Diabetes Study found A1c levels between 6% and 8% were linearly paired to all cause mortality. The longer one’s glucose levels were higher, the worse the risk became.

Having an A1c of 7% for forty years is going to impose a great deal of irreversible harm on your cardiovascular system. You can’t decide later in life to get glucose under control and undo past cardiovascular damage.

If you really want to be healthy, a lifetime of A1c of 6% had the lowest risk profile for all-cause mortality, provided that you also minimize hypoglycemia (time-below-range (70 mg/dL) should be <3%).

And therein lies the challenge for T1Ds. Exceedingly few people can achieve an A1c of 6%, which is having an average glucose level of 136 mg/dL, with very few hypoglycemic events, and similarly rare hyperglycemic spikes. There’s no shame in this—even healthy non-diabetics have a hard time doing this, as explained in my article, “Why Controlling Glucose is so Tricky.” There are over 30 million type 2 diabetics in America, with over 98 million undiagnosed, and an additional 100 million Americans who have “pre-diabetes,” or what I lovingly call aspiring diabetics.”

Don ‘t forget, the definition of “diabetes” (of any type) is an A1c of 6.5%, which means that “prediabetes” goes all the way up to 6.4%. High glucose levels is directly associated with at least three of the four horsemen of all-cause mortality: cardiovascular disease, cancer, neurodegenerative disease, and metabolic disease. (Glucose doesn’t “cause” cancer the way some social media misinformation sometimes makes it appear. Cancer uses excessive amounts of glucose in its voracious quest for cellular growth, but you can’t stop that. Your body will break down all your other tissues to create that glucose to feed the cancer, which is why people with advanced cancers lose so much weight.)

In short, excessive levels of glucose in the bloodstream—any amount beyond what’s needed at any given time—starts to do bad things, primarily to the cardiovascular system, which is why heart attacks and strokes are the leading cause of death for all Americans, including (and especially) T1Ds.

This is what brings us to the slipperiness of “health” and glycemic control. It’s hard—for everyone—not just T1Ds. It requires good diet, yes, but especially exercise. That’s the best way to not just keep glucose levels lower, but to keep the metabolic system healthy. Again, see the article, “T1D and Health: How Long Will You Live?”

But exercise is far more difficult for T1Ds because it requires a level of insulin dosing precision that is quite challenging—as if regular dosing protocols aren’t challenging enough, right? Yeah, it’s hard, and fear of hypoglycemia is why most T1Ds don’t exercise enough, or properly. So, most T1D lifestyles do not involve that much exercise, which brings us back to daily management of T1D.

Knowing how much insulin to administer at any given time has to be based on knowing several things: You current glucose levels, their rate of change (up, down, curves, etc.) and what you’re about to do over the next 30-90 minutes (eat, sleep, exercise, etc.).

What automated delivery systems can do is look at your current glucose levels and rates of change to estimate where you’re headed. It does its best at guessing based on statistical analysis of these glucose numbers, and sometimes taking historical patterns into account. But the “automation” part means that you, the user, are not telling it additional information, such as how many carbs you’re about to eat, or what (if any) activity you’re about to do. If you did, that would be “semi-automated,” because you would then be part of the decision-making process.

Note: semi-automated is good. You should be part of the process, but to do so, you have to be a bit more informed than what most people want and expect from an AID system. They don’t want to be bothered with the task or the level of knowledge needed to tell the system, “I just ate a donut with 50g of carbs, so give me another 5u of insulin.” Again, the more people are engaged with their disease, the healthier they are.

But most people don’t want that, or at least, to minimize it. Therefore, automated systems are inherently capped in how well they can keep glucose levels in control, because they can only react to glucose movements after they have moved higher or lower. They cannot act preemptively ahead of events to avoid glucose swings in the first place.

There is speculation that AID users who report having A1c levels <7% are highly likely announcing meals and exercise to the pump, giving the “automated” algorithm a chance to anticipate glucose movements by adjusting dosing appropriately. This is obviously wise—and one could argue the “correct thing to do”—but it also blurs the line as to whether the system is truly “automated.”

MDI users are forced to do the “correct” thing by manually dosing in the same manner as described here, and accordingly, achieve largely similar results, presuming they are doing so consistently.

This then leads to the trade-off: You can do better on your own if you manually bolus for meals and take other preemptive actions before glucose levels move, but it comes at a cost of having some basic knowledge about how to do that, and, well, convenience. Having to pay attention to do these things is, to some, “hard.”

So, there’s your choice: Convenience vs. less-healthy glucose levels. Pick One.

A very strong and valid argument in favor of the AID systems is that not everyone is going to be very good at self-management, in which case, an AID system may well be better, which leads to the question: Who among us would benefit most from closed-loop systems, and who are not good candidates?

Ideal candidates for AID systems

Perhaps you’re one of those who would say of yourself, or perhaps your child, “I do great with these automated systems!”

Hey, I believe you. I’ve even met many of you. But anecdotal data is not a scientific study, and what the literature tells us that those who do the best with these systems are not necessarily the general T1D population. I don’t mean to be using Orwellian double-speak, so I’ll quote what researchers call it: healthy user bias.

Those who do well with automated systems continue to use them. Those who do not do well, give them up (especially because they’re quite expensive). This imbalance of users contributes to the illusion that those who use closed-loop systems all achieve ADA recommendations. That’s not only an erroneous conclusion, but it’s also correlation, not causality.

It’s also the case that many users are so bad at self-management, either because they can’t (too young or cognitive impairment), or won’t (psychological stress), that the AID will do better than they can do on their own. Indeed, this is exactly what the medical literature tells us: Young children and teens (up to the mid-20s), the elderly, those in denial of their disease, and those with certain mental health concerns may do better with AID systems. And while that may well be a broad swath of T1Ds, the question is whether these people remain in those groups. Might they grow out of it or overcome the barriers that can allow them to take better care of themselves than what the AID system can do?

And that leads us to the contrarian question: Who should not attempt to use such systems? That’s the question that few people really ask, but to put it bluntly, people who should NOT use automated systems are those who wish to achieve “healthy” A1c levels, and who are willing and capable of engaging more closely with their disease. In fact, using automated systems may impair one’s ability to achieve healthier outcomes, not due to technical reasons, but psychological ones.

(Parents: Do you have a transition plan for your kids to one day learn about T1D? I know—tough question. Oops… Too soon?)

If not pumps, then what—insulin pens? Well, yes, as that’s the only other option (besides the very old disposable syringe). But, it’s not that simple either. Pen users are subject to the same errors and challenges of engagement and self-discipline, but at last pen users are not under the illusion that the device will do their work for them, or that it will relieve them of work. Pen users know that they have to be the ones in control of their T1D management. Over the course of time, these users learn better than pump users do.

An article published in the June, 2024, issue of Diabetes Care magazine titled, “Association Between Treatment Adherence and Continuous Glucose Monitoring Outcomes in People With Diabetes Using Smart Insulin Pens in a Real-World Setting,” the authors analyzed data from 3,945 T1Ds that use smart insulin pens (NovoPen 6 or NovoPen Echo Plus) and CGMs. They found that those who pay close attention to their daily management (as measured by reliably administering meal-time bolus injections) were able to achieve better A1c levels that are comparable to—or better than—AID systems. The key, of course, is that this group remembered to do meal-time boluses.

Attributes of a healthy T1D

When considering whether you should use an AID system, researchers say that, regardless of their method of administering insulin, these are the attributes of healthy T1Ds: They are more informed about T1D management, they’re more engaged with daily administration, they have more support systems (family and medical resources), and—most importantly—they are more willing invest a great deal amount of their time learning to use and work with their systems. This can and does apply to both pump and pens alike.

Obviously, those personal attributes are not necessarily going to be found in everyone. Indeed, it requires a level of maturity and intelligence, not typically seen until people reach their 20s and 30s. The fortunate thing about today’s technology is that people can live decades without serious complications, even with high glucose levels. The unfortunate thing about today’s technology is that people will get so used to it, they never learn the lessons that takes years to master, but lead to longer, healthier lives.

Sure, it is definitely worthwhile for kids to use automated systems to minimize long-term damage. Let’s let our kids grow up to be harmed by the usual things, like social media and video games instead.

But for those who wish to live longer and healthier, learning to manage the disease requires personal engagement in ways that automated systems just can’t do, and may even prevent or delay people from learning that essential lesson of personal engagement. (For a personal story on this, see my article, “The Sound of Diabetes.”)

Believe it or not, that’s just the topline discussion. Where things get interesting is in the weeds, where the real data supports the above suppositions.

Today’s AID Technology

As with everything in medicine, not to mention technology, the devil is in the details, one of which being the rate of incidents in “adverse events,” such as severe hypoglycemia or hospitalization due to diabetic ketoacidosis (DKA). As more T1Ds get on the bandwagon, these adverse events emerge and the details start showing cracks.

There are two areas where adverse events can be triggered: hardware malfunction, and software (algorithm) miscalculations. Hardware malfunctions (or just misdesigns) has been known since the first one was commercially available in 1976, creating a great number of adverse events. I cover the topic more thoroughly in the article, “Conditions Where Insulin Pumps May Not Deliver Intended Doses,” which delves deeply into the subject. Among the many citations provided, the one that applies most in this context is a paper published by Jan S. Krouwer titled, “More Focus is Needed to Reduce Adverse Events for Diabetes Devices.” Krouwer reviewed publicly available reports from the Food and Drug Administration’s (FDA) search tool and found that “the percent of adverse events due to diabetes devices as a percentage of adverse events from all medical devices increased from 20.4% in 2018 to 30.5% in 2019 and is the largest contributor of any medical device.” 

Since that article was written, the number of adverse events has skyrocketed given the advent of AID systems. This chart, from 2022, shows the number of adverse events that resulted in hospitalization from insulin pumps.

Even researchers are surprised by this. In the June 2024 issue of Diabetes Care is a paper titled, “Severe Hypoglycemia and Impaired Awareness of Hypoglycemia Persist in People With Type 1 Diabetes Despite Use of Diabetes Technology: Results From a Cross-sectional Survey.” The authors collected data from 2,074 T1Ds, half of whom used AID systems, whereas the rest were non-AID pump systems. Among all, “only 57.7% reported achieving glycemic targets of A_1c <7%, but the number of severe hypoglycemic events from automated systems reaching 16% for those using AID systems versus 19% of non-AID pump users.”

Yes, the AID systems performed better than non-AID pumps, but not by a significant margin, and certainly far worse than the authors had expected.

One of the leading contributors to the idea that pumps are universally beneficial is pump manufacturers themselves, such as those who presented at the October, 2023 convention of the European Association for the Study of Diabetes. An example is the following figure produced by Medtronic for their MiniMed 780G system, which included data from over 100,000 users, showing reduced incidents of hypoglycemia and hyperglycemia, while also improving time-in-range (TIR) to the sought-after threshold of >70%. 

Other manufacturers showed similar results at the conference.

As noted, these are not clinical trials, nor is this data subject to independent scrutiny. But even taken at face value, this is clearly an example of what researchers call selection bias, which is when the users in a dataset is not representative of the target population. In this case, T1Ds as a group.

Most studies conducted by objective researchers not affiliated with, or financed by device manufacturers show that user engagement is the determining factor, not the technology itself.

The other problem is that we don’t know how these same users performed without using the AID system. If, as objective trials have indicated, they didn’t really perform better, the systems net effect was negligible, raising the question of whether the systems are worthwhile. Remember, they’re expensive, and they require considerable administration (dealing with changing parts, managing software, obtaining supplies, etc.)

A valid consideration is whether T1Ds like and want to use a pump more than insulin pens.

Being motivated to use a new/hot technology is hardly anything new these days. Just look at the rate in which people upgrade to the latest smartphones or other devices.

This is a phenomenon known as the Christmas Tree Effect, where users’ attraction to (and satisfaction of) new technology is often driven by the illusion that it is better than the older model/method, and it has lots of “bells and whistles.”

A Christmas tree is more fun if you just add more lights. And make them blink. Even better if they blink to Taylor Swift. Imaging an insulin pump doing that.

The problem is, this effect is short-lived. Being enamored with technology is highly volatile, which explains why many tech gadgets fail—Google Glass, VR headsets, and hundreds of other products. (Fun read!)

This has happened with insulin pumps too. An example that illustrates this is the paper, “Glycemic Outcomes in Adults With T1D Are Impacted More by Continuous Glucose Monitoring Than by Insulin Delivery Method: 3 Years of Follow-Up From the COMISAIR Study.” The authors showed that insulin pumps – both automated and not – initially spurred engagement with users, but tapered off quickly, and ended up having little effect on overall glycemic control versus MDI.

But here’s the catch: The better outcomes were restricted to those who wore CGMs.

Indeed, the rise of CGM use has mirrored the rise of pump use (automated and not), which also leads to the impression that it’s the pump that drives improved outcomes, when in fact, CGM use is the strongest contributor of improved glycemic control over any other technology or method of insulin intake. 

A more robust study that teases out these differences CGM use, pumps and MDI can be found in this 2018 study by the T1D Exchange, where researchers engaged over 21,000 subjects who used a combination of these. The results are differentiated, along with separations for age groups, as shown in the following figure:

The researchers wrote that “there isn’t a substantial difference between A1c outcomes for those who manually take insulin injections compared to those who use pumps” when the users are over the age of 26 and use CGMs. In fact, CGM+MDI users slightly outperformed CGM+pump users.

Once again, there’s nuance to appreciate: As with many other studies, the T1D Exchange study showed that CGM+pump use for people under 26 years old benefitted more than MDI. But over the age of 26, the advantage disappeared.

Young people lack years of experience living with T1D, their bodies are undergoing rapid changes, and they lack the maturity to understand a difficult disease or to develop the wherewithal to remain engaged. Yes, automated systems did better for them than they could do on their own, but their A1c levels were still dangerously high. Teaching them how to manage themselves is the real goal. When and how will that be managed isn’t being discussed right now, because the perception is that AID systems will be the be-all-end-all solution. Only when the nuances emerge will this problem be addressed.

User demographics and insulin pump outcomes

Perhaps the most important detail to consider about pump users is demographics. Given the high expense of pumps and CGMs (and the education and medical infrastructure to deliver them), pump users tend to be one or more of the following: They’re well-insured, come from higher socioeconomic households, have greater access to medical care, have higher income levels, and/or are typically more educated. Perhaps most importantly, they come from families that promote healthy lifestyles. The more any of those conditions apply, the more likely that person is to be engaged and informed about managing T1D, which is in keeping with the theme of this article.

The following paper, “Racial, ethnic, socioeconomic disparities in insulin pump use have persisted over 20 years,” breaks down this data accordingly:

“insulin pump use was 67% among non-Hispanic whites, 41% among Hispanics, 29% among Blacks, and 46% among other racial and ethnic groups. In addition, 70% of people with bachelor’s degrees or higher used the pumps, compared with 56% among those with some college, 40% among holders of high school degrees, and 18% among those with no high school education. By income level, 74% of those with household incomes of $75,000 or more, 66% with $50,000 to 74,999, 51% with $25,000 to $49,999, and 41% with less than $25,000, used the pumps.

Many attribute this as being the inequality of our healthcare system. That is, people who are white, rich, and educated have better access to technologies like automated insulin pumps.

True, but again, it’s not the pump doing the real work; it’s all the other factors.

To test whether this is the case is to review trials where subjects’ A1c levels are measured before and after introducing them to pumps and then evaluating their outcomes according to the various socioeconomic groups.

The paper, “Time in Range for Closed-Loop Systems versus Standard of Care during Physical Exercise in People with Type 1 Diabetes: A Systematic Review and Meta-Analysis,” analyzed dozens of random controlled trials (RTCs) among a wide range of socioeconomic groups, and also factored studies involving T1Ds who exercise. The authors concluded that pump users were only able to achieve a 1.07% improvement over control groups who didn’t use an an insulin pumps (and did use CGMs), irrespective of their background.

One can get even more granular still: In a 2021 study titled, “Predictors of the effectiveness of insulin pumps in patients with type 1 diabetes mellitus,” the authors conclude that the subjects whose A1c levels were >8% going into the study saw an improvement of 0.9 ± 1.2% when started on insulin pumps, whereas those whose A1c levels were <8% saw no benefit to pumps at all, irrespective of age, race, and income levels.

This is another example of statistical significance, but clinically irrelevant.

What about “lifestyle” factors?

Lastly, there are those who claim that, even if pumps offer no clinical benefit, the lifestyle benefits make them worthwhile.

Don’t take this the wrong way, but are they really having a better lifestyle with pumps? Fortunately, there are RCTs that study that too.

An interesting paper is “The DIAMOND Randomized Trial,” which initially aimed to assess the relative difference in costs associated with pumps versus MDI therapy. The authors found that pump users’ lifetime costs are $112,045 greater than those on MDI.

But researchers unexpectedly found that pump users’ quality-of-life factors decreased by 0.71 compared to MDI, and (even more unexpectedly) life expectancy dropped by 0.48 years (based on higher risk of DKA, among other risk factors that rose). Minimal differences from MDI to be sure, but these numbers certainly didn’t improve, as pump advocates would like to believe. 

The reduction of efficacy AND the higher costs of insulin pumps (compared to pens) are two primary reasons why insurance companies are reluctant to cover insulin pumps.

Similar conclusions are found in multiple studies compiled in the paper, “Systematic literature review: quality of life associated with insulin pump use in type 1 diabetes.” By assessing a much larger set of trials, the authors concluded that there was little change in quality-of-life outcomes for most users due to a subtle psychological bias: Pump users really wanted them to work better, and in so doing, they believed they were. This makes “actual” satisfaction hard to assess, which brings us to the realm of psychology, especially around technology.

In the BMJ paper, “Incidence and prevalence of diabetic ketoacidosis (DKA) among adults with type 1 diabetes mellitus (T1D): a systematic literature review,” the authors aimed to summarize incidence and prevalence of T1Ds who were admitted to hospitals for DKA, and sort them into different subgroups (age, sex, geographical region, ethnicity and type of insulin administration). The paper is excessively long, as the reviewers started with 1082 separate articles in order to characterize each of the criteria. Among the many citations, a clinic in Colorado stood out, where all the patients admitted with DKA used pumps—none were on MDI. Other citations are listed below:

• In the paper, “Insulin pump therapy is associated with higher rates of mild diabetic ketoacidosis compared to injection therapy: A 2-year Swedish national survey of children and adolescents with type 1 diabetes,” the authors found that 85% of admissions were pump users vs. MDI.

• In the paper, “Severe Hypoglycemia and Diabetic Ketoacidosis in Adults With Type 1 Diabetes: Results From the T1D Exchange Clinic Registry,” researchers performed a cross-sectional analysis from the T1D Exchange clinic registry at 70 U.S. endocrinology centers, and found that 2972 incidents of DKA were associated with pump use, versus 2034 for those on MDI (roughly 47% greater risk for pump users).

• In the paper, “Causes of diabetic ketoacidosis among adults with type 1 diabetes mellitus: insulin pump users and non-users,” researchers conducted  a prospective observational study between January and June 2019 at the Cleveland Clinic Fairview Hospital, and found that, among patients admitted with DKA, 55% of the cases were pump users.

The list of similar findings is extensive.

Many people assume that DKA from insulin pumps is largely due to device malfunction, but this isn’t always the case. A more prevalent reason for DKA is user error. In the paper, “Risk and Relevance of Insulin Pump Therapy in the Aetiology of Ketoacidosis in People with Type 1 Diabetes,” the authors found that “an overwhelming majority were those on insulin pump treatment (93%)”, and added, “Overall, patient errors caused more DKA cases than device malfunctions.”

Imagine that: user error on a device that should be either automated or semi-automated, and where people are enamored with the technology, and who prefer to use them over much simpler insulin pens. The authors of this paper, “DKA Prevention and Insulin Pumps: Lessons Learned From a Large Pediatric Pump Practice,” were more blunt: “Most events could have been avoided if users followed standard troubleshooting guidelines.”

And yet, T1Ds are willing to overlook this due to the Christmas Tree Effect. And that brings us to the ultimate moral hazard: automation.

The perils of automation

When the stakes are high, such as automated cars or insulin pumps, the consequences of taking your eyes off the road can be catastrophic. Automation robs people of the experience and expertise necessary to manage the same task, which are essential when the consequences are high.

According to this T1D Exchange report, the authors state that, although the use of pumps increased from 57% in 2010–2012 to 63% in 2016–2018, “the average A1C overall has risen from 7.8% to 8.4%.” The authors conclude that A1c levels are directly correlated to user engagement. For those who relied on the pump to manage their disease for them, they fell further behind than those who bothered to learn on their own.

This sentiment is similarly expressed by the authors in a 2022 review of closed-loop systems in the Diabetes Journal article, “Diabetes Technology: Standards of Medical Care in Diabetes—2022,” where the authors wrote:

“The most important component in all of these systems is the patient. [...] Simply having a device or application does not change outcomes unless the human being engages with it to create positive health benefits. [...] Expectations must be tempered by reality—we do not yet have technology that completely eliminates the self-care tasks necessary for treating diabetes.”

When it comes to engagement, this is one of the primary advantages of MDI. The physical act of injecting insulin makes one keenly aware of what the patient is doing. This is an example of The Hawthorne Effect, a phenomenon where people modify their behavior (usually positively) when they know they’re being observed. Observing yourself is the base case of the Hawthorne Effect.

In a study titled, “The Importance of the Hawthorne Effect on Psychological Outcomes Unveiled in a Randomized Controlled Trial of Diabetes Technology,” study subjects were exposed to a series of tasks associated with T1D management. The authors found that T1Ds who exhibited fear of hypoglycemia (FOH) saw a significant reduction in FOH, simply because they were being closely watched, even though there was no intervention whatsoever in how they managed their disease. They simply did what they knew they should do on their own. (All the other studies cited in this article do not have subjects “closely watched.”)

Similar outcomes have been shown for T1Ds who are told to log carbohydrates, exercise, and yes, manually take insulin. When you have to stop and think about calculating insulin and carbs, the act of what you’re about to do becomes more present. You proactively think to yourself, “Do I log 40g or 60g for this glazed donut that I know I shouldn’t be eating?” Believe it or not, the physical act of logging those carbs and taking that insulin causes many to be more engaged and deliberative in their T1D choices. Many people–including me–often choose not to eat that glazed donut. Sigh.

Summary and Commentary

I have lived with T1D for over fifty years, so I can attest to the fact that the disease is grippingly difficult to live with, let alone self-manage. Any technology that can ease the burden–even in the slightest–is a blessing. And this is why I struggle with the concept of automated insulin pumps: They are essential for those who are either unable or unwilling to manage their own care beyond a certain threshold, especially the young or those with cognitive impairment. But for those who are able to take care of themselves, one must remain as engaged as possible with the nuances of daily T1D management. One of the risks of automation is disengagement–failing to learn self-management.

The state of closed-loop technology today is, sadly, as good as it can possibly get, unless and until there are better methods of sensing glucose and delivering insulin in real time (not through interstitial tissue), while also delivering other essential regulatory hormones that are also part of the process. (I cover this in my article, “Challenges Facing Automated Insulin Delivery Systems.”)

Without solving the technical barriers that pumps face, today’s closed-loop systems are like a self-driving car equipped with only a hazy rear-view camera (the CGM), a gas pedal (insulin) and a faulty fuel line that may or may not deliver the gas to the engine in a timely manner. No forward view, no side view, no brakes and no reliable navigation system. In automatic mode, there’s not even a driver.  The most powerful AI-driven algorithms that try to calculate proper insulin dosing cannot possibly compensate for these mechanical dysfunctions. At best, the car will go on a very straight road at 5mph, and get pretty banged up along the way. You know, like having an A1c of 8%.

I certainly don’t have any problem with people using these systems, so long as they are acutely aware of what they are doing, and are not under the illusion that it’s the pump making things better. It’s you.