One month Control-IQ results

Anna has finished one month (36 days technically) on Control-IQ.

She has pretty much operated on sleep mode 24-7 because we have a good idea of her settings from years of Loop experience and observation/testing of settings, therefore didn’t believe the tighter range would yield an increase in potential for low BG issues. If we hadn’t had a good grasp on settings going into Control-IQ, I would’ve opted for the normal target range (112.5-160 mg/dL).

Nothing in life had changed that much between the month on Loop vs the month on Control-IQ…same school schedule, same weekends. Health was the same between the two months.

As you can see, the results are pretty similar between the two. The average BG on both systems was just about 112 mg/dL…but Anna’s correction targets while using Loop were 95-100 mg/dL. So even though we used lower targets in settings, we didn’t achieve any better BG average on Loop as a result.

My biggest take aways in terms of experience has been noticing the standard deviation drop (39 vs 33 mg/dL). If you aren’t familiar with standard deviation, it’s a measure of the variability in your data set. The larger your standard deviation, the more spread there is in your data.  So a lower standard deviation means more of your data is closer to the average…aka a smoother ride of BGs. We’ve had a decent increase in time in range as a result.

The second take away was the decrease in low percentages, in particular overnight lows. We have pretty much eliminated the pesky lows from situations where we accidentally had slightly too high of scheduled basals. I’m not advocating intentionally setting basals too high on Control-IQ, but rather remarking on Control-IQ’s ability to compensate more effectively than Loop did for us when this situation happens (that’s life with the human body after all, it happens). On both systems, the low percentage is artificially higher than we truly experience due to the first 4-6 hours of new sensor sessions. We get an awfully lot of false lows during brand new sensors…so every 10 days we usually add to our low percentage without really being low.

I hope this helps answer some questions for Loop users about how Control-IQ might measure up against Loop. In our experience, you can get pretty similar end results between the two.

Safety and risk analysis in a closed loop algorithm

As we started to begin our order of the Tandem Control-IQ system, the sales rep was touting “safety”. I thought to myself “yeah, yeah…safety. I don’t think my definition of safety is the same definition that a commercial pump company uses.”

My primary thought was biased by the 670G system.  A system designed to keep the person “safe” would instead leave many people significantly higher than their programmed target of 120 mg/dL for long periods of time.  A system that because of “safety” would require heaps of calibrations for the sensor, and getting kicked out of automode if the sensor got nervous. And that lovely “safety” of people setting dangerously aggressive (short) insulin durations of 2 hours and super strong carb ratios in order to trick the overly “safe” algorithm that left them higher than targets so consistently. Having such artificially aggressive settings in order to overcome a bad algorithm means you’re exposed to a pretty great risk if your loop stops working. So when I heard “safe” as a selling point, I was admittedly more apt to say “ummm, that might not be the strongest word to choose as a marketing choice.”

I totally agree that closed loops are more “safe” than just winging it on normal pump therapy. And the clinical data supports that statement…more time in range and fewer incidences of hypo/hyperglycemia in the clinical studies. But, the problem I had is that we already have been in a “safe(er)” zone using Loop (and OpenAPS) for years now. We have benefitted from closed loop technology’s “safety”. Did Tandem just mean “safer” because it was raising the BG targets compared to Loop, and therefore avoiding more “dangerous” lows? (That’s what I thought they might have meant…but the answer apparently isn’t that.)

But some things happened recently that got me pushed into researching if there was something more behind the rep’s use of the word “safe”. It seemed as though she was insistent on Control-IQ having something about “safety” beyond just simply trying to sell me on closed loop in general or BG targets alone, but rather Control-IQ algorithm in particular. I wasn’t quite grasping what she was meaning, and it wasn’t something she could explain in detail (not her fault…I have a pretty demanding threshold for the details of technology. It was a hard bar to meet for an average rep, so I’d need to do my own digging). Around this same time, I attended the 2020 ATTD conference on diabetes technologies as part of JDRF’s platform to discuss the Open Protocols Initiative. While there, I had the chance to have some really interesting, in-depth discussions about algorithm developments, pitfalls, and successes with researchers and clinicians who run trials for the devices/systems.

So I rolled up my sleeves and started researching. Here’s what I learned…and it was a lot.

The HISTORY OF CONTROL-IQ

I kind of worked backwards in my Control-IQ development research because I knew current info the most, but I’ll present this discussion in chronological order for clarity of information. The University of Virginia (UVA) and JDRF play a large role in the history of Control-IQ. So, let’s start there.

In December 2005, the FDA (along with NIH and JDRF) held an open public workshop titled “Obstacles and Opportunities on the Road to an Artificial Pancreas: Closing the Loop“. About 75-80 of the industry’s leading researchers, regulators, and clinicians were in attendance to discuss the critical path items needed to move closed loop technologies forward. FDA offered to review research proposals from JDRF to assist them in progressing closed loop technologies. Shortly after in 2006, JDRF announced the Artificial Pancreas Project and funded six centers to carry out closed loop research. Among the funding recipients was a group of interdisciplinary researchers from the UVA Schools of Medicine and Engineering and the University of Padova in Italy.

Things were really getting interesting by 2008. Modeling of insulin and blood glucose regulation in the human body was an area of a lot of active research. In 2008, the UVA-PADOVA partnership produced a Type 1 Diabetes Simulator to simulate insulin-glucose changes during a meal…in other words this simulator could “pretend” to be a person with T1D (actually, about 300 virtual T1D patient profiles were available in simulator).  This is loosely referred to as the “metabolic model”. The simulator was updated in 2013 based on newer data and modeling, which improved its accuracy. How complex is the metabolic modeling?  Check out this figure (source):

Modeling allowed researchers to test control algorithms on virtual patients (in silico) before ever moving to tests on real humans (in vivo).  The FDA accepted it as a substitute for animal trials, which advanced the work of the UVA team and the entire JDRF Artificial Pancreas Project.

As the pace of research progressed, things had miniaturized and improved. Algorithms and controls were now small enough to fit on an Android phone. In 2014, UVA had an in vivo nighttime-only closed loop interventional trial using its DiAs (Diabetes Assistant) system and the Unified Safety System (USS) Virginia algorithm…basically 42 participants spent 5 nights in a hotel using a closed loop system for overnight control only. After that trial’s success, they moved on with in-home trials for a longer period of time. The UVA algorithm was moving along well.

Meanwhile during this 2014/2015 timeframe, UVA licensed their algorithm to TypeZero Technologies. TypeZero Technologies began using “inControl” for their technology’s name, replacing the DiAs name…but very similar algorithm.

The International Diabetes Closed Loop (IDCL) Trial began in 2016 and was the pathway for Control-IQ’s eventual FDA approval. As outlined in the IDCL protocols, there would be three studies leading to the pivotal trial (in support of Control-IQ’s submittal to the FDA) using Tandem’s t:slim x2 pump, Dexcom G6 sensor, and the inControl algorithm:

The initial pilot study with Dexcom G6, Tandem t:slim x2 pump, and TypeZero’s inControl was completed in December 2017. This was a supervised 36 to 48-hour pilot study in 5 subjects conducted at the University of Virginia.

The Nightlight study was completed April 2019.

The last phase, a pivotal trial, started in June 2018, completed in April 2019, and had participants use the system at home for 6 months. By now the system was termed “Control-IQ” (see paragraph below for brief explanation of the name change from inControl). The results of this trial were submitted in August 2019 to the FDA for approval of the Control-IQ algorithm.

Around the same time as the pivotal trial began, Dexcom announced it had acquired TypeZero Technologies. This gave Dexcom closer access to the closed loop algorithm, which had already been a part of over 30 successful trials with more than 450 participants at that point, to go along with its G6 iCGM system which had been FDA-approved just months before. This acquisition also explains some of the name-changing between inControl and Control-IQ as the business relationships between all the parties were changing. From the pivotal trial’s protocol “The Tandem X2 insulin pump with Control-IQ Technology is a third-generation closed-loop control (CLC) system retaining the same control algorithm that was initially tested by UVA’s DiAs system and then implemented in the inControl system.” This gives us a pretty good idea that the algorithm wasn’t changing significantly even though the naming was evolving during this time.

The FDA approved the Control-IQ algorithm in December 2019, clearing the way for Tandem to start marketing and selling the Control-IQ system in early 2020. (Note: the pivotal trial originally did not include pediatric enrollments, but that trial is finishing up now for kiddos between 6-13 years old. Initial results presented at ATTD looked in-line with the adult trial results.)

So What’s Different?

Well, all that history didn’t get me any closer to understanding what would be “safer” other than Control-IQ had undergone clinical trials. Which since we’d run our own n=1 clinical trial with my daughter for the last 4 years…all of this research hadn’t much helped me understand the differences in algorithms that might explain a “safety” difference yet. Merely doing clinical trials, while great, wasn’t exactly enough of a sway for me.

And so now I started to read all the research papers that I could find about UVA’s (and other group’s) algorithms and models. I read about the underlying metabolic model. I read about algorithm development in general, and specific. I read about control systems. It’s been non-stop.

Ultimately, there has been a difference that I learned about…and it does actually make me feel “more safe” with Control-IQ than Loop/OpenAPS. But, to explain it I need to go backwards a bit again.

Loop’s Algorithm

Loop’s algorithm has 4 contributions; insulin effects, carb effects, retrospective correction, and blood glucose momentum. Those four effects are summed together to form the predicted BG curve. Loop then automates insulin delivery adjustments based on where that predicted curve is for the next 6 hours relative to your correction range and your suspend threshold.

An important piece to understand in Loop’s algorithm is this: The algorithm does not differentiate between a unit’s potential risk at a low/high blood sugar vs normal blood sugar. In other words, so long as you are above suspend threshold, there is no “risk” assessment of Loop’s actions that would change depending on where you are in the BG range. The two “safety” factors for low BGs below correction targets are:

  1. The suspend threshold being the full-brakes on insulin delivery, and
  2. A rising BG (predicted to stay above suspend threshold through DIA) will get scheduled basals while below the correction range; in other words, high temp basals will not enact until the BG crosses above the bottom of your correction range.

This means at times when you are below targets, but carrying negative IOB because you have your basals scheduled too high, you’ll get a pattern of suspends alternating with scheduled basals.  If you cross the correction range’s minimum value, then you may even get some strong high temp basals to try to “replace” the negative IOB that has lead to a high predicted BG now.  That negative IOB can be a pesky term in Loop if it accumulates significantly while you are in lower BG range. That’s one reason why settings are so important in Loop. Making basals artificially high in the hopes of making Loop more aggressive will backfire with a pattern of lows and lots of negative IOB to be covered later when the low is treated.

RiSK terms in Algorithms

BIG DISCLAIMER HERE: All the commercial closed loop systems are black-box…meaning we don’t really know for sure what all their controls systems are comprised of. I am only working from the published literature and available research. So, while the papers describe the research that lead to the systems…they do not necessarily represent the final versions that end up in a commercial system. 

Much of my recent research has revealed that most algorithms, including the work by UVA’s published papers, include a component of risk-based dosing in their strategies.

What do I mean by that? Well, let’s use an example where you have the time and inclination to be a helicopter parent. If your kid’s BG was 100 mg/dL but headed down…you may choose to conservatively set a smaller temp basal of about 80% to help head off that low that appears to be coming. If your kid was 60 mg/dL, you might be more aggressive in your treatment decisions. You might set a temp basal, but also treat with some small carbs. That situational awareness is because you’re evaluating risk…and you are recognizing the inherent risk from low blood sugar events grows as the lower the blood sugar gets. Similar for increasing blood sugars getting into a high BG range. You would probably choose more aggressive treatments as you see higher blood sugars because you are perceiving a greater risk in those BG ranges. We have all rage-bolused…our instinctive desire to mitigate the high bg risk? There’s a sweet spot in your life, probably near 100 mg/dL, where you feel little risk from maintaining that BG.

This risk concept has been written in many papers about closed loop and diabetes (such as here, here, here, and here.) The concept can be visualized as shown in these graphs:

 

(Interesting side note: Notice where the low point of the risk curve is in that first graph? That’s where Control-IQ likely gets its 112.5-120 mg/dL nighttime targets…you’re in the lowest risk area during a time when the biggest variables, like exercise and food, aren’t in play. The same figure can be found in UVA paper here.)

So what does this mean in terms of making an algorithm “safer”? Well, if your algorithm incorporates some “situational awareness” as part of its dosing strategy and risk mitigation, then you will more effectively have a way of mitigating that risk. While you’re between 70-90 mg/dL, you should perhaps make more conservative decisions that reflect your BGs are in an area of greater risk. Same for if you are between 200-300 mg/dL…you should have an algorithm recognizing the risk of remaining that high is undesirable and it might be time to be more aggressive.

Control-IQ’s algorithm, while I haven’t seen the inner guts of its details for sure…I have seen the research papers on UVA’s algorithm development and watched presentations at conference discussing risk mitigations. So, I feel pretty sure that Control-IQ has risk awareness incorporated into the algorithm. (Disclaimers still apply)

This risk mitigation makes sense to me in a very gut-feeling way. Let’s look at one example where I feel like risk awareness may/could play a role…when Anna has overnight basals that are scheduled too high. What happens in Loop with that situation? Loop will suspend when the BG prediction goes below her suspend threshold (we had that set at 75 mg/dL). So, she’d get a BG dropping for awhile, hit the suspend threshold and then often times tip-toe touch her low BG alarm of 65 mg/dL.  We would treat the low conservatively…but even that action would usually just bring her (prediction curve) up enough so that scheduled basals would turn back on. She would have a lot of negative IOB built up from all the previous suspended basals…her prediction would say she would be going high, but instead the scheduled basals being on would soon send her low again. We’d be stuck in that repeat cycle of off/on scheduled basals with small treatments.

It would be nice in those situations if Loop had some situational awareness that we were in the low BG area of the risk curve…that maybe this would be a time to not just treat all my settings as the “gold standard” of math to work from. Perhaps if there was something that made the algorithm instead say “hey, I see all that negative IOB and yeah yeah…but you’re still low now and have been. Let’s ease back into this until the issue has really passed.” In other words, not jump right back to scheduled basals. And really please don’t jump to aggressive high temps if we treated a little more…enough to cross over the correction range minimum.

Visually what does this situation look like? Here’s an example shown in the orange box below. That represents about 9 hours of overnight BGs for Anna. Her scheduled basals were clearly too high (exercise before bed? Hormones changed? I don’t remember the cause that particular night, but this happened about 2-3 times a month usually). She kept getting BGs pulled back down after we treated lows with minimal carbs. The negative IOB was the source of crushing high temps if we treated enough to bring BGs above the correction range minimum (early in the orange box). And if we just let things play out, as later in that orange box, then scheduled basals would resume and stay on…eventually she still went low again because the scheduled basals were just too much.

Now, let’s compare with how Control-IQ works with its logic in a very similar situation. Here’s a graph, below, from a night recently. Anna was using a transmitter that had very low battery and we had gotten signal loss during the times in the red boxes. You can see the system, in those situations, would revert to her scheduled basal and she would have a small low afterwards. Not too surprising. BUT, look at what happens when Control-IQ turns on in the blue box. Notice how it does not go to scheduled basals despite having negative IOB accumulated? Instead, Control-IQ uses a fraction of the scheduled basal rate. Control-IQ is likely adjusting its insulin delivery not just based on a correct-to-range adjustment alone…there’s likely also a component of situational awareness about the low BG range it is in. We aren’t getting full suspend (not predicted to go below 70 mg/dL in next 30 minutes) but also are not automatically dropped off at scheduled basals like we would be in Loop. We coast along at a lower-than-scheduled basal rate.

This night above was actually the impetus for me to start researching Control-IQ’s algorithm possibilities. We had gone nearly a whole month without having one of those nights where her basals were peskily too high waking us up with nagging repeat low alarms. Usually we’d have a night or two like that during hormone shifts each month on Loop. I’d deal with it by finally recognizing the pattern was sticking around (usually after two low treatments wouldn’t fix it), and I’d edit her basal schedule lower. When this happened to us on Control-IQ that night, it kind of stood out like a sore thumb since we hadn’t had it happen in nearly a month. I downloaded the pump data to try to figure out what the difference was for that night and that led me to do the digging that I’ve tried to summarize in this post. Now, this lower-than-scheduled-basals Control-IQ behavior is not necessarily entirely the result of the risk component of an algorithm…there may be other things at play, too. But I am quite happy with the results in the end, no matter whatever combination of effects lead to it.

So…UVA’s algorithm likely has this insulin adjustment modified by a risk component. Low and high BG risks would yield appropriately modified insulin adjustments to mitigate that risk. Interesting, right? You can read this paper and the associated equations presented for this idea:

I am by no means an expert on any of this. And certainly, nobody has shared the actual equations/algorithm in Control-IQ or UVA in particular. But, I have noticed that our low and high extremes seem to be better managed (restored more quickly to targets) on Control-IQ…I’m still trying to explore why and quantify it. It’s the same kid, in the same body, with the same habits…so I feel pretty confident that our experience is at least in some part simply the difference in algorithms. The risk component that I believe is in Control-IQ in some fashion seems like a reasonable cause.

In short…I think now I can finally say that I am more appreciative of the marketing word “safe” and what it probably means for Control-IQ. I can see that whatever they are doing has resulted in some improvement in BG results for us over Loop…which had us quite happy for years before. In particular, we really notice it avoiding lows for those accidentally too-high-of-scheduled-basals times at night. We are happy to see Control-IQ choose lower basals rather than off/on scheduled basals in those situations. That is a safety measure.

 

Control-IQ vs Loop FAQs

Just a summary post to address a lot of the questions that I’ve gotten about Control-IQ, and also specifically about how it differs from Loop. This is not a QUALITATIVE kind of post…meaning I’m trying my best to not make pros/cons judgements in this post. Certainly I have opinions about the pros/cons, but this is more just meant to be straight dry info.

Also: Just to be clear, I have no affiliation with Tandem. I have not been provided any compensation, either in money or gear. We were out-of-warranty on our old original pod system and knew that system was not what Anna could go back to using (her insulin needs are too high and she gets more cannula leaks on pods than she wants to deal with). We paid for the t:slim x2 system and supplies through my insurance (regular co-pays and deductibles). Hopefully the reason for our trying out the Control-IQ system was well-explained in my previous post, check that out if you are wondering why.

Target Range

Control-IQ has three different sets of  target ranges. The standard range is 112.5-160 mg/dL. There are two “activity” modes that will change the target range…sleep mode is 112.5-120 mg/dL and exercise mode 140-160 mg/dL. You can schedule sleep based on days of week so that the system automatically switches to the sleep targets at those times/days. Sleep targets can also be turned off/on manually at anytime by the user. Exercise targets cannot be scheduled, but instead must be manually turned off/on by the user specifically. The target ranges work as the triggers for when Control-IQ will maintain your profile basal* (up to 3.0 U/hr…see discussion below on system-initiated changes to settings) or when it will increase/decrease basals. For when exercise or normal modes are active, the predicted 180mg/dL within 30 minutes threshold also will trigger an automatic correction bolus. In normal and sleep modes, your basal insulin delivery will be suspended if predicted BG in 30 minutes is below 70 mg/dL. In exercise mode that basal suspend is raised to 80 mg/dL.

NORMAL MODE

EXERCISE MODE

SLEEP MODE

[Note: recent investor call with Tandem reportedly included the information that users would be able to specify targets in an update to Control-IQ. I have no specifics on that info. It’s possible it could be something like exercise mode could be 150-180 for some people…or that normal mode could be 112.5-140…or something entirely different. It’s too soon to know, but interesting for sure.]

Loop allows you to set your own target range between minimum of 60 mg/dL and maximum of 180 mg/dL. Loop will also let you save target ranges differently based on time of day.

Kinds of Insulin Adjustments

Control-IQ uses temporary basal adjustments. If you are in normal targets mode or exercise mode, you will also have access to an automatic bolus correction if you are predicted to go above 180 mg/dL within 30 minutes. An automatic bolus aims to give 60% of the insulin needed to achieve 110 mg/dL in normal mode, 140 mg/dL in exercise mode. Automatic bolus corrections are not available in sleep mode.  Automatic boluses are also only available if there hasn’t been a bolus within the previous 60 minutes…doesn’t matter if it was a food bolus or a correction bolus…any bolus within previous 60 minutes will prevent an automatic bolus.

Loop uses temporary basal adjustments. *If you build an experimental branch of Loop, you can try a version that uses automatic boluses instead of temporary basal adjustments, for correcting high blood sugars. If using automatic boluses, you will not get any increased basal rates for correcting high BGs…all corrections are in the form of boluses.

Prediction Timeframes for Adjustments

Control-IQ basal adjustments (and automatic boluses) are based on the BG value predicted in 30 minutes time. The user will not see the value of the 30-min predicted BG, but you can see whether the basal is being raised, lowered, stopped, or kept at the user’s profile. A few clicks on the pump screen will also show you the exact active basal rate if you wanted to know. (Note: I hear that Tandem will be soon be releasing a mobile app that will allow the user to see all that info on your mobile device without needing to pull pump out.)

Loop uses a 6-hour predicted BG curve to make insulin adjustment decisions. The full prediction curve is shown on Loop’s main screen. If any part of the 6-hour prediction goes below the user’s suspend threshold, Loop will suspend basals. And, Loop will not add additional insulin, even if you are above targets, if your BG is predicted to be in range within 6 hours. In fact, if you are above target and predicted to go below targets within 6 hours, Loop will start to decrease basals. That full 6-hour window is often difficult for new users to be “in agreement with” and probably is the most common FAQ in Looped group. You can read more about the details for when Loop increases/decreases basals here.

USER-initiated settings changes

The ability to let a closed loop system know about a change from “standard operating conditions” is an important design point. For example…taking a new medication, doing an exercise, or hormonal shift in insulin needs can all require a “heads up” to your closed loop that the usual settings for basal, carb ratio, or correction factor/ISF are not going to apply for awhile. By telling your closed loop about these changes, your loop should do a better job at predicting BG movements and therefore keeping you in range more successfully.

Control-IQ has the user enact/turn-off the sleep and exercise modes to change your default targets. This can help influence when (or better said “at what BG”) your temp basal adjustments will kick in. Sleep mode can be pre-programmed on a daily schedule so that it automatically turns off/on at certain hours on certain days of the week. Exercise mode is turned off/on by the user intentionally and cannot be pre-programmed. Both modes can be left “on” for as long as the user would like…24/7 is not prevented for either sleep or exercise modes. In the event of a conflict (like you stayed at the gym late one night…later than your sleep mode profile was due to turn on per a scheduled time that evening), exercise mode will remain on until you turn it off. You will have to manually turn on sleep mode after that since the window for the “automatic” start of sleep mode was missed.

With Control-IQ, you also have the ability to save six user profiles with different basals, carb ratios, and correction factors to help quickly change between different overall insulin needs. This can help with weekdays vs weekends, hormonal cycles, and illness/medications.

Loop has override presets that you can program to signal an overall needs change. HOWEVER, those overrides are simplified to a single overall adjustment…so basals, carb ratios and ISF/correction factor are all adjusted at the same time and in the same percentage adjustment.  You don’t have the ability to unlink those adjustments. For example, you can’t have a preset that only makes basal rates 20% higher…you will also be making carb ratio 20% stronger and correction factor/ISF 20% more aggressive. This may or may not work well for all situations…so care needs to be taken, as the more aggressive you stray from defaults you will be getting a lot of adjustment since all three settings are moving. The override presets can be active for any period of time, including indefinitely, and can be scheduled to start in advance. However, you cannot pre-schedule more than one override at a time. Additionally, activating any override manually BEFORE a pre-scheduled override was due to start will end up canceling the pre-scheduled override’s start. You can also use IFTTT integrations to have a preset enact based on alarm clock or location (e.g., arrive at school).

With Loop, you do not have the ability to save profiles for different regularly encountered patterns such as weekdays vs weekends, hormones, or illness. Instead you will either need to adjust your settings (basal, carb ratios, and ISF/correction factor) manually or try a preset to see if that will suffice. If you work swing/rotating shifts, you would need to manually change your settings to accommodate the change in work schedules.

SYSTEM-initiated changes from settings

Another consideration is whether your closed loop system is automatically changing or limiting any of YOUR specified settings without your ability to stop that adjustments. For example, is your closed loop deciding that your personal profile of 2.0 U/hr basal rate between 1:30pm-5:00pm isn’t “right” and instead decides to use 1.75 U/hr as the basis for calculating your predictions? That would be a pretty important thing to know about.  While the excitement of a closed loop being able to detect these settings changes automatically is understandable…it’s also a long way off from being done well in reality from what I’ve seen so far. The Medtronic 670G‘s automatic basal profile is super clunky in practice and too slow to react. The adjustments it has been making for many people have overly conservative and leaving many users suck on high blood sugars.  Additionally, I’ve heard rumors that the Horizon system does a bit of an “override” of your user-inputed basal profile and instead uses a distributed basal schedule calculated as a 50% allotment of your previous day’s total daily insulin deliveries. Not super keen on a system that assumes Anna uses 50% basal and bolus ratio since her data has consistently been around a 65-70% basal needs. We’d be left pretty high from a system that assumed a 50% split.

The problem with all commercial systems is that we don’t REALLY know all those automated decisions because they are internal and proprietary. At least with open-source closed loop systems, we have all the information about those operations. So, when evaluating commercial systems…it gets a little difficult to say FOR SURE what’s going on in guardrails or limits that we might not be seeing. I scoured the Control-IQ manual to learn what I could as well as any published articles on the system that I could find that may have helpful info.

The Control-IQ manual doesn’t have a nice easy section about any automatic overrides/limitations that it does for you, this is all conjecture and observational for the most part. I did see one little interesting nugget tucked in here on page 280 of the manual. It’s highlighted in blue.

That little statement led me wonder if maybe the system restricts “active profile” situations to 3.0 U/hr in general…beyond just times of CGM loss. So, I rooted around for a bit and sure enough…it does. Below is a screenshot of when her scheduled basal rate was 3.7 U/hr, and due to 30-min predicting BG not requiring a needed basal adjustment, the system should have been using a “active profile” basal rate. However…as you can see…Control-IQ dropped her off at a limited 3.0 U/hr basal rate instead of 3.7 U/hr. This is pretty curious limit and worth noting if you are someone who has basal rates greater than 3.0 U/hr and want to use Control-IQ. It means that when you are predicted to be in range for 30 minutes, you’ll be dropped off at a maximum of 3.0 U/hr, even if your schedule profile would’ve had you at a greater rate. Probably really good to keep in mind if you have to go on steroids or medicines that push your basal needs over 3.0 U/hr on the regular…you may want to temporarily consider turning off Control-IQ in some really high-needs situations, and run with regular pump mode. Regular pump operations allow for basal profiles up to 15 U/hr.

Another point I was interested in comparing with Loop was any maximum temp basal rate in Control-IQ. This is a user-specified setting in Loop, but there is nowhere to manually enter that setting in Control-IQ.

I’m quite certain there is a limit on the maximum temp basal rate that Control-IQ can access and I’m also sure that the limit is not necessarily the same day-to-day or hour-to-hour perhaps. For example, here’s February 12th’s data and the temp basals were limited to 5 U/hr. Pretty evident by the prolonged highest temp basal lasting more than 5 minutes around 1pm in that graph.

The next day on February 13th, the max temp basal rates are looking a bit higher, closer to 5.8 U/hr. Notice, the scheduled basals for the day were also changed so that may affect the maximum basal limit. There was a higher average and total basal scheduled for February 13th than for the previous day.

So, what influences the maximum basal that the system is allowed to use? Hard to know for certain right now…but I’d guess it is in part based on the scheduled profile basal and some multiplier on top of that. I’ve seen temp basal rates as high as 2.5x the scheduled basal. This is consistent with the not-using-Control-IQ limit of 250% for temp basals. But there are also times where the limit appears to be a more hard limit (like the 5.0 and 5.8 limits on the previous example), even if that is less than 2.5x the scheduled basal…and that hard limit could be a function of the average basal scheduled to be delivered for the entire day.

To start Control-IQ mode, you have to enter a weight and total daily dose of insulin. From what I’ve heard those values are only used to get you started with Control-IQ the first day and after that are not used significantly. BUT…again…I don’t have any solid information to substantiate exactly how those are used on the first day (or not used later). I suspect they play a part in how the maximum temp basal is set for sure…but beyond that I’d love to know.

Those are the biggest “hidden” things that I’ve noticed. Everything else thus far seems to be operating according the user-saved profile values. Meaning, adjusting a setting will result in an expected change in your insulin delivery as well. If you change a carb ratio, you’ll see different boluses recommended. If you change a scheduled basal rate (below 3 U/hr), that basal rate will be respected and used as a the basal to use when no adjustment is needed (because you are predicted to be within target range for next 30 minutes).

Loop doesn’t have any “hidden” constraints such as discussed above. And since the code is open source, any user with questions about the priority decisions or function of the algorithm could look up the code themselves.

What Stops Automated Adjustments?

Control-IQ allows you to manually exit automated adjustments by sliding a slider in the pump menu to turn Control-IQ off. If you do that, you will be reverted to scheduled profile basal immediately.

Control-IQ will stop automatically adjusting insulin deliveries if your CGM readings discontinue for 20 minutes or longer, for example with sensor error or signal loss. When that happens, you will be reverted to the active profile setting (limited to 3 u/hr, as discussed above). Control-IQ technology will automatically resume automated insulin dosing once the pump has CGM data again.

Robustness check? Control-IQ has been rock solidly on except one period of time…we did have one prolonged stretch where Anna’s app was receiving CGM data, but her pump was showing signal loss. Prolonged like a whole day because…well…teen brain? She just thought it was coincidentally off each time she looked as opposed to long-term off and ignored the alert on the pump that was pretty helpful. The signal loss was likely because we are using a very old transmitter that had sat on the shelf long after its scheduled “start before this date or Dexcom doesn’t promise it will work the full 90 days”. I suspect it has a fairly weak battery. Regardless, the fix was easy although not published in the Control-IQ manual, and likely not going to be told to me by the Tandem technical support reps either I would guess. I posted the short version of the instructions on twitter…so if you get the “you’ve lost CGM on the pump for more than 20 minutes” alarm…follow these steps in the pump’s CGM menu and CGM should reconnect within 5 min or less:

Loop allows you to manually exit automated adjustments by sliding to “Open Loop” mode from within Loop settings. If you do that, your existing temp basal will continue to run until it expires (up to 30 minutes in duration). Therefore, it is good practice to cancel any active temp basal (by suspend insulin and resume insulin commands back-to-back) before turning on Open Loop mode. There is also a consideration around bolusing while in Open Loop mode that is discussed in the bolusing section later.

Loop will exit automated adjustments if your CGM loses data for 20 minutes. Loop will also exit automated adjustments if your RileyLink fails to properly communicate with the pump to retrieve pump history or confirm commands. Loop will also exit automated adjustments if any of your settings are deleted or missing (either manually or as a result of an iOS glitch).

Robustness check? Loop’s dependence on RileyLink for pump communications is a critical element and probably the most likely source of losing automated insulin delivery. Interference from other wireless sources, a long standing bug in the RileyLink firmware (that seems more prevalent during major iOS updates, oddly), and simply range/transmission issues cause the majority of red loops stretches of Loop-not-looping. Although, there are also a lot of reports that have to do with Dexcom issues. Often times users have reported the Dexcom app is still getting current CGM readings, but Loop is failing to gather those readings until the person has restarted RileyLink, restarted phone or rebuilt Loop app. I spent a couple hours at ATTD troubleshooting someone’s Loop app that was failing often after months of working fine. No discernible cause was found; tried new batteries, new pump, new RileyLink. Ultimately, the only solution was the rebuild the app entirely. Loop’s problems “staying green” have come in waves for us personally. Major iOS updates were the most problematic times for us as they seem to affect Loop’s ability to stay connected both for Dexcom and RileyLink. Lately my iPhone has been randomly shutting itself off in the middle of the night (only happened to Anna’s phone once) over the last several months and that would concern me if I were looping for myself.

Bolusing

Control-IQ allows you to bolus for meals in a pretty traditional way. You enter the carbs, your CGM value can be automatically used for the BG entry for the meal, and a recommended bolus based on carb ratio is provided. You then can make two choices…(1) Do you want to increase/decrease the bolus amount based on Control-IQ’s determination that you need more/less to get to range? and (2) Do you want to extend any portion of the total bolus?

Control-IQ provides the exact information on the screen about what part of the bolus is the carb ratio alone vs. any +/- corrective insulin beyond that amount. You are limited to a maximum extended bolus time of 2 hours…no 4 hour extended boluses. If you have an extended bolus running when Control-IQ determines that you need basals suspended (predicted to go below 70 mg/dL in next 30 min), your extended bolus is not canceled and will continue (unlike how that worked in Basal-IQ).

Loop offers bolus recommendations quite differently than most all other systems. You use a “food type” (lollipop, taco, or pizza icons are the defaults) to indicate whether the carbs to be consumed will be quick, medium, or slow digestion. Loops algorithm will use that food type to predict quicker/harder vs slower/sustained impacts on blood sugar. As a result, bolus recommendations from Loop are never simply a function of carb ratio or insulin on board alone. They are always a function of the predicted blood glucose curve, your suspend threshold, food type, and how all that information plays together…such that Loop offers an initial bolus that will be predicted to keep you from going lower than suspend threshold in the next 3 hours, and target range between hours 3-6. Any additional bolus that might be needed beyond that initial amount is provided via high temp basals when the prediction curve allows for increased basals.

As a result of Loop’s unique bolus ways, if you are using Open Loop mode…you may have times where you need to more actively engage in providing bolus insulin later in a meal because the system won’t have the ability to automatically cover longer/slower carb impacts after an initial (smaller-than-carb-ratio) bolus. Loop will not automatically alarm when any additional bolus is useful in those situations.

Correcting for Stubborn Highs

I don’t know how to compare the two systems on this topic because they are just so different of an experience.

Control-IQ is using a prediction is only 30-min ahead for adjusting actions, you aren’t fighting a long 6-hour prediction. This means, if you think that you want to add insulin to correct a stubborn high BG, you won’t find yourself automatically zero-basaled as a counter-balance like you often do in Loop. Control-IQ holds off on lowering basals for a longer period of time. For Loop, most of the time you’d use fake carbs, edit old carbs, or set an override for a short time to deal with stubborn high BGs. If those didn’t work, maybe a settings adjustment was in order.

That said, we have had less instances of needing to manually intervene with Control-IQ so the lack of “tricks” isn’t missed. Why fewer interventions? I think part of that is probably due to the more direct carb-ratio-based bolus recommendations (so we are more often getting full boluses up front as opposed to covered later). Anna is less apt to spike with meals overall, but she is having to remember bolus splits again for slower carb meals like pizza. At least when we do intervene with a manual correction, it feels like a more straight-forward process…a small (critical adjective there) bolus correction and we stop. No overrides, no fake carbs, and no editing carbs.

Anna had one meal that was 44g on the nutrition label but was bolused as 6g (don’t ask, teen brain issue). I can safely tell you that just like our Loop experiences, Control-IQ did not “prevent a spike” such a mismatch. Still broke 200 mg/dL, but recovery was smooth and all was fine in the end. Insulin is still slower than we’d all like (hello Afrezza…someday!) and carb counting is still needed.

Anna also had full hormonal shifts on both systems now. On Control-IQ, we saw some stubborn highs (just like we saw on Loop during similar times) that helped tip us off to the need for a settings change. For Control-IQ, we dealt with it through switching to her “higher needs” profile. For Loop, we’d typically use an override preset until we had a chance to adjust her profile settings. Most often, Anna’s carb ratios don’t need adjusting due to hormones…just mostly basals changes with a little bit of ISF/correction factors.

Data Access

Control-IQ data in live format is (so far) only accessible from the pump screens itself. Tandem does have a mobile app in limited testing currently that allows users to see all their pump info on the mobile device. That app should be released this year, and a follow-up app update is expected shortly after that would allow for remote bolusing through the app…don’t need to access the pump to bolus then. Nightscout isn’t going to work with your Control-IQ pump right now, sorry.

For endo appointments or settings adjustments, you’ll want to use either Tidepool’s uploader tool or Tandem’s t:connect system. Both are free to use. Both require using a cable connected between the pump and the computer to upload the pump’s stored information. Uploads take about half a minute if you do them about once a week. If you want a longer period of time in-between uploads, it might get up to a couple minutes. One pooper to be aware of…t:connect won’t install on Mac computers that are using Catalina macOS (at least not as of the date of this blog posting). I’m dusted off an old PC computer with old Windows version for t:connect uploads.

If you’ve never seen t:connect data, it looks like these:

Loop data is remotely available in Tidepool and Nightscout live time…you just have to create the accounts and link them with a couple steps. Nothing hard and instructions are all online. For endo appointments, Tidepool and Nightscout are what you’ll need to show comprehensive data from looping.

Both Control-IQ and Loop still have Dexcom clarity reports just the same, as that is provided separately from any closed loop system…just need to have a Dexcom mobile app uploading data.

Batteries and Charging

Control-IQ seems to need a pump charge about every 4-5 days or so? It charges fairly fast and is easily little micro-B cable that you have around the house with many devices. Most people charge the pump while they are in the shower or driving in the car. You don’t have to take it out of the case to charge. Admittedly, I am a little removed from the hands-on experience with this since Anna does this work. From what I hear, charging about 15 minutes a day is enough to keep going if you don’t want to do a 1-2 hour full blown 0-100% charge on a less often basis.

Loop is running on your iPhone so there’s nothing really extra required there. Loop doesn’t seem to drain batteries any faster from what we experienced. RileyLink needs a charge nightly and charges in about an hour.

Wireless Headphones

Someone asked about wireless audio devices…we haven’t had problems on either system. I have heard of others having problems with Loop failing when people use their car audio bluetooth though…that seems to be the most common wireless failure point and is more related to the iOS resources than Loop itself. Loop is simply the victim of iOS then.

Alarms

Control-IQ has a lot of the usual alarms and alerts that you’d expect…but the unexpected one I like a lot is that the pump has a quick beep alarm if insulin delivery has been suspended for 15 minutes. AND…it’s loud enough that Anna actually hears it (as opposed to her old Medtronic pump). We know call this the “Anna your shower has gone on long enough” alarm. Our water bill is so happy. Loop users have often requested a similar alarm option if insulin delivery has been suspended for 15 minutes or more…and now I can see why. Very useful.

Loop mainly relies on the pump system itself to supply the alarms (like Medtronic Loop) or emulates a limited set of alarms (like Omnipod Loop). The alarms specific to Loop are loop-not-looping alarms at 20, 40, 60, and 120 minutes.

Cortisol

This blog post is about my new found appreciation for cortisol:

“Cortisol is a steroid hormone also secreted from the adrenal gland. It makes fat and muscle cells resistant to the action of insulin, and enhances the production of glucose by the liver. Under normal circumstances, cortisol counterbalances the action of insulin. Under stress or if a synthetic cortisol is given as a medication (such as with prednisone therapy or cortisone injection), cortisol levels become elevated and you become insulin resistant.”

One of the large frustrations we were facing during this school year has been a wicked spike during school mornings…but not every morning. But, many mornings I’d be seeing her spike to 200+ without food and invariably starting around 9:30-9:40am. The spikes were so fast and aggressive that they have outpaced any looping algo despite the settings we’d have. How many diabetes frustrations start with “I spike when _____, but not every time. I wish I knew the pattern. If there is a pattern. Oh damn, hand me a diet coke, my head hurts. This sucks.” We were there.

I knew it wasn’t food. Anna is a really diligent food boluser and was actively doing manual actions (on top of looping) to try to blunt the spikes in the mornings. It was just stumping us. LUCKILY for me, as I’ve been discussing algorithms with my friend Ken, I bemoaned that looping is not very effective against this school spike thing. He mentioned that this was looking like a cortisol issue and it suddenly lit a lot of lightbulbs in my head. Here’s what I was FINALLY able to put together:

(Note: I am [un]lucky to have a kid that doesn’t really eat breakfast on school mornings…so the data was uncomplicated by food/boluses for most days.) Mondays are way worse than Fridays..and these Monday graphs include extra insulin from looping and manual corrections. Weekends were zero issue. Here’s the data…

What was happening for months now? Weekends were great. I’d send her to school on Monday. We’d see a huge spike starting at 9:30am (school starts at 8am), but get there too late to prevent going above 200. (and even while Looping…we never avoided the spikes above 200) Loop wasn’t enough and why would we be late? Well, dosing 3 units of insulin proactively at 90 mg/dL is a bit daunting without food involved. Especially if you aren’t seeing it every day. Some days that spike wouldn’t materialize. So we wouldn’t really correct with “extra” until she got to 180 mg/dL and climbing still aggressively. By the time we’d corrected for the spike, we’d have a problem usually fighting a low between 2-4pm.

So, after seeing that big spike on a day, I’d increase basals and adjust ISF and try to account for what we’d seen the day before. The day after a spike would be a little better. Then the next day might have not so much of a spike. Then suddenly I’d be pulling back on the morning basal adjustments because the spike seemed gone, and still overshooting the adjustments and having lows 2-4pm.

Here’s the problem though…my brain never realized this was happening on a Monday-Friday pattern. I just wasn’t realizing it until Ken mentioned cortisol and sleep patterns. So until I realized that, it just seemed like we were chasing a semi-random spike and I was very hesitant to give large corrective actions without knowing exactly what was going to cause them.

Addressing this spike meant near constant basal/profile adjustments and this was destroying our relationship. “Anna, can I see your phone again?” or “Anna can you change your 8am basal to 9am and make it 2.7 instead of 2.5?”…either one of those texts is NOT how I wanted to spend my time. I didn’t want to hound her to pay attention to the spike either. I was utterly SUPER frustrated with Loop’s interface to make these adjustments (hourly changes to basals and ISF on back-to-back days are NOT Loop’s strong point in terms of quick actions). I desperately wanted an easier way to make these changes faster with multiple profiles…because I definitely knew that weekends vs weekdays were one of the underlying needs to address.

Frustrations were at an all time high until cortisol understanding came along.

So, armed with the new info…I watched for patterns. I realized Anna’s spike is like clock work…I just hadn’t been recognizing the clock! DOH! Worse on Monday…decreasing day-to-day until Friday when it is minimal. Non-existent on weekends when she sleeps in about 3 hours later than a school day.

Reading the cortisol/sleep cycle literature and research…Anna’s experiencing insulin resistance tied to her morning spike of cortisol upon waking up. I hadn’t realized that a 2 hour delay from getting up out of bed was still possibly a cortisol issue. I’d always thought of it only tied to feet hitting the ground getting out of bed…but turns out there’s quite a bit of research showing that cortisol-related blood glucose spikes can be at play 2 hours after wake up. The phenomenon is worse on Mondays since (1) her sleep pattern is most disturbed suddenly that day (3 hours shift from the previous day) and (2) highest stress as it is the first day back at school. By Friday, she has acclimated to the wake up time and stressors of school…and the cortisol impact is less pronounced.

Armed with this knowledge, Anna has a better chance of avoiding lows/highs in this scenario.

Option 1: She could choose to wake up early on weekends and therefore avoid the extra cortisol response from the inconsistent sleep schedule…but she’s opted to not use that option lol.

Option 2: She can now more confidently choose to keep a moderate school vs. weekend profile and just manually correct on Monday and Tuesday at 9:40am-ish when she sees a spike building. We know Mondays are an early 3 unit correction, Tuesdays are an early 2 unit correction and Wednesdays are 1 unit correction, with Thursdays/Fridays can be handled pretty well by a looping option with a school profile with increasing time-in-range as those days go.

Option 3: She can choose to accept that Mondays and Tuesdays will be spikes over 200 for an hour or more if she doesn’t want to manually correct…that’s her choice too.

No matter which option she chooses, at least now we have a much safer operation for the 2-4pm timeframes now that we know what’s happening earlier. That’s the most important part. And we have been having increasing success with Mondays and Tuesdays spikes too…which is also nice.

Cortisol. What a beast.

If you want interesting reading on cortisol to start down your rabbit hole of research:

Cortisol and the menstrual cycle

Cortisol on weekdays vs weekends

If you want to really amp up the reading, add some reading about the effect on cortisol from disruption of natural sleep cycles/circadian rhythms. There’s a lot to unpack in the research, but I sure learned a lot and it’s made a tangible difference for us now.

 

Why Control-IQ?

The overwhelming response when I posted our first 6 days results on Control-IQ was “Why are you trying Control-IQ?” and “Does this mean you are giving up Loop?”

The second question is an easy answer: We have always been a family willing to try new things to see what works best for Anna. We haven’t always used Loop…we tried OpenAPS before as well for 6 months. Anna has used pods and Medtronic pumps. From pumps to CGMs to algorithms…we believe that trying new things is usually worth while. We don’t try everything (*cough* *cough* that 670G never tempted us…we like Dexcom’s CGM much better than Medtronic’s), but we do try to keep an open mind if something has a potential to lessen Anna’s diabetes burden. So are we giving up Loop? Not necessarily. If Control-IQ and the t:slim x2 pump don’t prove to be a better experience in Anna’s evaluation, she can easily go back to Loop use.  We usually give things a full month before concluding anything about BG control of any system…but if the overall experience doesn’t work on an everyday-living basis before then, we would quit before a month.

The first question though is a little longer of a response and there are several layers to that onion 😉 Probably to start with, I need to go into a little bit of history.

CGM data provided lessons

When we first got a Dexcom, we were only one month into diabetes. The benefits were instant. Fewer finger sticks. Alarms to help me know when to check on her. Probably most significant to me was that it shortened our diabetes learning curve tremendously. We were able to see connections between our food choices and boluses like we would never have been able to see on finger sticks alone. I quickly learned how to make settings adjustments based on blood glucose trends.

The lessons from having CGM data access saved us years of effort in learning diabetes management.

Loop data saved relationship

About a year after getting a CGM, we started Loop. And you know what that added to our data stream? EVERYTHING. We now had automatic uploading of every carb entry and bolus. Every temp basal. We could log site changes more easily, I could see her cell phone’s battery level, and I could see the remaining units in her pump reservoir.  It was every little bit of diabetes information.

At the time we started Looping, Anna was starting her freshman year of high school and was 14 years old. Before Loop, I would often text her during the day with instructions about how to treat BGs that were trending up or down. I would ask questions about “I see a rise, did you just eat? Did you prebolus? Or maybe your site is bad? We just changed it this morning.” When she was at school, I wasn’t sure whether or not she was doing things the same way as she was at home (which she was doing very well). I just felt such a burden to try to keep her BGs in range and not burden her with needing to make all those decisions. As a result, I interrupted her school day a lot, asking a lot of questions first so that I could make the best decision for the situation once I understood it.

When Loop brought us carbs, boluses, and temp basals…I didn’t need to ask all those questions anymore. Glorious!!! It literally healed a lot of relationship issues between us because I didn’t interrupt as much. Hands down that was my favorite part of Looping for a very long time. I could see that my kid was doing things fine, and it was a positive feedback loop that gave me comfort.

Loop allowed me to see my kid grow into her diabetes management like a true diabadass.

Therapist Jenny

Around our second year of Loop use, Anna and I started seeing a therapist, Jenny, together. I just really wanted to make sure our relationship could survive the “teen years” and especially wanted help through the challenges of diabetes responsibilities. How long should I continue to make adjustments for her? Does Anna want to take the duties over? Was she capable of it? What level of “give” should I expect as she took over her own management?

We started the discussions, but admittedly we didn’t get far on some of the homework. But, the two most important take-aways from therapy for me were:

  1. Anna does not view my stepping back from such an interactive diabetes management as a “burden” thrust on her. Instead, she welcomed the shift. She WANTED to be more in charge and have me involved less. It wouldn’t be a burden, it would be a blessing. Those conversations were invaluable. I recommend every parent of a teen with t1d start those conversations. Find out if your kid will want to be sharing their health data, and if so…how/what data they are comfortable with sharing. I have a kid that doesn’t want to share unless she’s asking for help. And that is a decision I intend to respect. Closed looping certainly makes that an easier option because the health impacts don’t necessarily have to suffer as they take on more responsibilities. 🙂
  2. Setting up that transition is challenging. Teen brain is real. Parental overstep is real. Sitting on your hands and watching things NOT happen is hard when you are finally ready for her to start doing things. “Why isn’t she doing _______? Look at that data…she doesn’t see this?” was a frequent thought during these efforts to shift responsibilities to her.

So therapy helped us start down the path, but it would take a couple more lessons before I finally identified that the data that previously healed us, was instead now getting in our way.

Anna is growing up

I was having a hard time defining short- and medium-term changes we could implement for this transition of care. As a result, I just kind of ignored it for the most part. I tried simple things like making her take over the overnight low treatments when needed, as opposed to ME having the alarms in my room…we moved the LOUD alarms to her room to wake her up (she sleeps like the dead).

It was easier to see my long-term goal for how I fit in based on Anna’s discussions in therapy…I want to only be there as a fail-safe for low blood sugar alarms. Said another way, I want her to have healthy boundaries of privacy around how she chooses to manage her diabetes. But, I was not close to that long term goal. I see too much of the details of her diabetes management with all the data that Loop provides. I’m like a data addict. If it’s served up, I look. And if I look, it’s really, really hard not to judge or second-guess her actions. And by and large her actions are just fine…and I just need to chill. Really, I’ve had years of seeing her work and it’s really good. But the data just sucks me in to look at it regardless.

Two things happened to really help me change the long-term vision into more actionable medium and short term actions. The first change came as a result of living with adults with type 1. The second big change came from talking to two close parent-friends who had recently sent their t1D kiddos off to college (they are sophomores now).

What adults with t1D taught me: As part of my Tidepool work last year, I lived for a week with about 17 adults with type 1. I watched them so quickly estimate their carbs, give their boluses and then resume our conversations in the blink of an eye. I also realized that they had ZERO parents watching that entry. No mom on the other side looking that her son had just entered 65g and 8.5 units. And the adults had no desire for their parents to be seeing that kind of detail. They’d all grown up without CGMs and Loops, so it was never an option for their parents to be that involved. Yet for me, it was MY normal because of how quickly we’d started CGM and Looping. It’s this current generation’s normal now with Nightscout access and Loop data.

I asked them if any of them shared carb and bolus info with their parents or a significant other. They all said no, it was private. They did say that they had a couple trusted people that were trusted with access to CGM data. They shared that data with ground rules, like “don’t call me unless you see I’m under 55 for more than 15 minutes”.

Like a light-bulb, I realized just how good that will feel from a mental health perspective FOR MYSELF. To not know every time Anna eats and boluses. To just let a meal entry be a private thing she was doing for herself. Even if she is already doing that work herself now…there’s something about always SEEING those carbs and boluses that sucks me into a place that I ultimately am not comfortable having access to when she’s an adult. That should be part of her private personal “life habits” that she gets total control over. I won’t know that food minutia about my non-T1D daughter…so why would I have that info for Anna? If Anna wanted me to be a part of that data as an adult, we could do that…but we’ve already talked in therapy about how much she wants me pretty much as only a low-BG backup alarm.

Long-term, I know that I want to give adult Anna the same kind of privacy and non-judgmental space that I will provide to my other daughter (that does not have t1D). If I have access to her carbs and boluses info, I won’t be building that privacy. It will open the door to spaces that it should be private and only opened by her IF she wants to. So, I recognize that at some point…I will need to make sure I cut off that flow of information and only see blood sugars.

What the college kids’ parents taught me: Luckily, I have two great friends who have recently sent their kiddos off to college with t1D. They’ve been brutally honest about how difficult is has been. These are smart, good kids. These are smart, good parents.  And yet, it’s just a tremendously hard adjustment.

One of the common themes between their two sets of advices has been to (1) prepare earlier than you expect for this transition, and (2) expect a decrease in time in range/increase in A1C. The advice is good. In fact, if you look at any clinical trials the late teens to early/mid-20s is statistically the time of life where the time in range is the lowest of any age range. The total insulin use per day is raging for that group compared to other ages. Hormones are starting to make things really difficult. College and social commitments make life more variable and diabetes is harder to balance.

The good news is that the statistics improve after this age. They recover. Their lives settle in. Their biology settles down. It all gets better. So that made me think that really my goals for the college years needed to flex a bit if our relationship were to come out unscathed. Above all, I want to make sure that we find a way to continue to have a great relationship through this incredibly difficult time coming up.

ACTIon items for change

So that brings me to why we are trying Control-IQ. The reasons are pretty straight-forward. I’ve covered a lot of the background about this move being part of the intentional transition of care. The t:slim x2 pump offers some things that Loop doesn’t do well for Anna.

  1. Multiple saved profiles. Loop lacks the ability to save multiple profiles and this has been a very difficult reprogramming of settings every Friday and Sunday nights to prepare for the differences between school and weekend needs. As well, we were having difficulties dealing with the insulin resistance that happens as the result of hormone cycles every month. Put simply, it was just too time consuming of a process for either of us to do so much. We both grew quite frustrated with that. The t:slim x2 lets us save 6 profiles and she can (and DOES) switch between them with just a couple buttons clicks. A huge problem has been alleviated by switching to t:slim x2.
  2. Simplicity of gear. Anna doesn’t have to carry a RileyLink anymore. She also doesn’t need to have a phone with her to get automated basal changes. While she didn’t have much of an issue with those things with Loop, I know that there were many times it was an issue…seemingly always happened at a time when you’d really want to be looping. Like she’d walk across campus to hang posters and be trending low…but she left her RileyLink behind in class because “it was only going to be for an hour”. Or at the beach, she would leave range of her phone/RL to go play smash ball…then no looping could go on. Plainly said, by not NEEDING extra gear, we’ve eliminated another potential failure point.
  3. Eliminating Red Loops. We have been lucky and not badly afflicted with red loops…but I’m not naive. I know they can happen suddenly and could require quite a bit of attention to resolve if not readily fixed with a reboot of phone/RileyLink. So eliminating this potential stress for Anna is probably a good idea as she moves out. I could easily see her choosing to ignore a red loop for a long time when she is on her own, and especially at night. Dorms are notorious for having a lot of wireless interference and overnights in dorms aren’t necessarily a “loop friendly” experience for some.
  4. Eliminating my need to follow Loop developments. The other nice thing about this is that I no longer would have to stay super current on bugs or issues in Loop. I don’t have to watch for bugs in displays or behaviors. There’s been a recent increase in bugs that I find particularly kind of confusing for kids since they are display related and kind of hard to identify if you aren’t spending a lot of time deeply understanding Loop.
  5. Eliminate worries about building Loop. Let’s face it…when Anna is in college, she will not be building her own Loop. She won’t be stopping to deal with Loop rebuilds if it needs to be done at an inconvenient time. Using the Tandem system eliminates the worry for both of us about iPhone/RileyLink failures or replacements. It also eliminates the worries that comes with every iOS update about whether it will cause an issue for Loop.
  6. Infusion site changes have built-in reminders. Oh lordy, site change reminders have been hard. I desperately want to not nag about them, so we set up really cool little IFTTT actions while Looping, so that if she pushes a button in an app, she would get a text 72 hours later to remind her that it was time to change the site again. But, if she doesn’t press that button…nothing reminds her to change a site except high blood sugars or site irritation. The t:slim x2 has a reminder built-in for every three days after each site change. It’s been working gloriously. Like night and day difference. This was one of the “parental nags” that I was really looking to eliminate prior to college. We needed a system that she could use to reliably keep track of site changes without a personal nag from me.
  7. Lack of data. This was one of the hardest things to put on the positives of trying Control-IQ, but one that I knew I needed. For Anna’s age and soon to be “grown and flown” to college, the data flow needs to pair down to just CGM data now as discussed above. I don’t want to be looking at her food. Quite simply, it is just not necessary and only tempts me to question what doesn’t need questioning. Ironically, this is the opposite of how I felt when she was 12-15 years old. That data then was very valuable for me to learn about diabetes and become comfortable with how she was managing herself. Now the utility of the data is gone. It is not serving to make her diabetes any easier, and is only a potential stress point in our relationship. I just need to be there as back-up for low blood sugar issues for safety. Of course, I expect Tandem will eventually have live data uploading to the cloud and it will be available again…but hopefully by then my cold-turkey efforts will have weaned me off of looking at it anymore.
  8. Bolus from phone still. While it’s not here yet (planned later this year), Tandem does have plans for releasing an app that will allow Anna to bolus from her iPhone (similar to how Loop works). I’m glad she will have that option, but she didn’t cite that in her reasons for liking Loop previously so I can’t really say that is a big influence one way or another for us. For other people, it may be a big deal.
  9. Algorithm differences. This one is going to be expanded on later, I’m sure. But for now I’ll summarize one part of Loop’s algorithm that I am most frustrated by…stuck on highs. Anna knew how to edit her carbs while she was stuck on a high BG or to add carbs…but this still seems a bit cumbersome for a kid. She find it easier to just give a small correction. With Loop, a small correction in those “stuck on high” situations will often result in Loop suspending basals and basically reversing the correction you applied. Control-IQ does not automatically drop you to low basals. Instead, since Control-IQ is looking at a 30-minute prediction (as opposed to a 6-hour prediction) for actions, you tend to get more “trust” from the system that your actions are reasonable for a time. Now I’ll undo a little bit of that and say that overall, we are experiencing far fewer times of being stuck on high.
  10. In-warranty pump. This hasn’t been a huge deal for me…but I do feel slightly better having an in-warranty pump that can be swapped out easily if it breaks. I feel better sending her to camp with a system that I’m sure the medical staff understands well. Again, not my biggest concern, but still was a factor in the decision as Anna goes to college.
  11. Rechargeable pump battery. I can just imagine college years that she will not have a AAA battery near her when she needs one at some point. The rechargeable battery on the t:slim x2 means that we have eliminated a likely failure point. She will be far more likely to be near or carrying a mini-usb charging cord than near a spare AAA battery these days in an emergency. I’m sure adults do a great job at placing AAA batteries around for themselves as needed…but college kids that could prove quite challenging.

So, in summary, that’s why we are trying Control-IQ. I believe it will help us transition into the next phase of her life better than we could do with Loop. I think there’s a bunch of us parents that are “growing up” with lots of data flowing from our kids constantly. I worry about how much that data is affecting their eventual privacy rights. I worry about how much that data is unknowingly able to negatively impact my relationship with Anna as she grows more independent and moves out of the house. She is at the age where her desired level of “control” is going to be driving the boat. Control-IQ allows her more autonomy and me to take a graceful step backwards. I won’t feel the need to make sure her or I are Loop current. We eliminate more diabetes conversations this way. She’s already shown me that those conversations aren’t needed nearly as often as I initiate them. If we can achieve a smaller diabetes footprint in our lives at very little/no detriment to time-in-range…why not give it a try?

So far…the results are super promising. Anna has reported the following negatives after a couple weeks on Control-IQ:

  1. The screen doesn’t auto-dim when you are in a dark room. It’s bright and kind of obvious in a movie theater or dark classroom.
  2. The pump clip sucks.
  3. The cartridge fill process is more clumsy than filling a Medtronic reservoir.

But, for the positives…she has NOTICED that we are talking less about diabetes. And that means the world. She is doing profile switching herself. She is doing all of it with less talking between us. That’s exactly what I was aiming for. Added benefit, she’s achieving better BG metrics than with Loop…but that wasn’t our aim to start with.

I challenge you all to start communicating and make sure that you are planning long term for your own kid’s transition. Find out if you are on the same page as them about data sharing. Start thinking about what you both need to do to get there. You hopefully have some time…but it goes by quicker than you’d imagine. I have spent a year trying to wrap my brain around this transition, identifying data addiction was part of the problem, and find a way to unhook myself from data addiction. I’m fine with just BGs…but I needed to rip the bandaid off and make it happen. Control-IQ is letting me do that plus addressing some things that Loop wasn’t doing well for us (especially that multiple profiles problem).

At one month or so…I’ll do a post showing the BG results comparison. We need to go through a full month before the comparison is very useful, but so far the results are showing that we aren’t doing any worse on Control-IQ. Standard deviation is down, average is only a tiny bit higher, % lows is cut in half, and time in range is improved. And, I’m learning a bit more about the algorithm to know which knobs do what.  Will we switch full-time to Control-IQ? I don’t know…that’s up to Anna. I’ll support whichever she wants to do. If she wants to go back to Loop, I know I will disable Nightscout as my primary viewing/alarm platform and instead use Sugarmate app so that I’m limited to just BGs. As of now, I’m thinking she may want to stay on Control-IQ, but she needs to use it longer to know. This weekend she is going on an school-related overnight wilderness camping trip with Control-IQ and I think that will help her decide, too.