Counter-strike Condition Zero System Requirements
Rocki Stenger
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In hybrid closed loop you gave at least one bolus prior to meals. This pushed your loop often into a temporary shut-off (temporary zero basal) and generally only into a co-management role while that given bolus was strongly active.
Also, prior to each meal, you made inputs about the carb, fat, and protein intake, and also gave indications (generally in settings, plus every day related to each meal) related to estimated carb absorption times.
While meal management gets very easy, activity management might get a bit more difficult, especially considering most of us probably like to limit sports snacks, in an attempt to control body weight.
If the user does not bolus for meals, clearly a very fast insulin is needed so, upon realization of a starting meal-related glucose rise, the loop has any chance to eventually keep glucose in range (by common definition, under 180 mg/dl (10 mmol/l)).
You do not give a mealsize-related bolus any longer; that leaves ALL insulination jobs to the algo! As glucose values are the very basis for this, please inform yourself well about how your CGM 1) principally performs 2) whether and how this may depend on data flow and intermediate apps you use, or differ between days of sensor usage 3) specifically, how and where any smoothing is done, and what this might imply for your tuning, notably for how you define a delta that is a true sign of a starting meal.
Then, but even more importantly in all other day and night times, the CGM should not produce any artefacts (jumpy values) that the loop could misinterpret as sign of a starting meal. Note that also calibrations could produce jumps.
The best way currently is to use Dexcom G5 or G6, and to ensure via overlapping right and left arm sensor and transmitter utilization, that always good quality values can be used by the loop. Other ways are possible, but come with a lot of monitoring effort (via watch) and occasional time-outs for the loop.
Setting up a full closed loop is relatively easy for people whose diet does not consist mainly of components with rapid high effect on blood glucose, and whose meal patterns do not wildly vary day-to-day. They do not have to be low carb.
Full closed looping requires a 24/7 technically stable system, especially regarding reliable CGM signals, but also Bluetooth stability with the pump, and avoiding (or at least early recognition of) occlusion. This could require attention to details like keeping all components well charged and in close proximity; making cannula (or pod) changes always early enough to lower the risk of occlusion; having always potentially needed parts with you. Depending on your system, your experience with it, but also on your acceptance and general lifestyle, these aspects may or may not limit you.
With your full closed loop, the algorithm is tuned to detect meals and to give you insulin to counter glucose rises automatically. Setting a high temp. target and lower %profile right away (effective already around meal start) would be a problem.
There are a couple of parents and kids pioneering this area, too. This paper highlights areas that would require some minimum compliance; in the end it comes down to whether the achieved results are at least comparable to where everyone stood in her/his everyday hybrid closed loop.
However, in full closed loop application, loopers themselves do no longer give a sizeable bolus around meal start. Then clearly size limits on SMBs must be widened to make the loop capable of giving large enough SMBs.
As you are operating with AAPS Master release, it is suggested you set in AAPS preferences the largest setting for maximum allowed SMB size the loop can give (maxUAMSMBBasalMinutes=120, i.e. 2 hours worth of basal at that daytime).
If your basal rate is very low, the resulting SMB limits might be too low to allow good-enough control of your post-prandial glucose rises. In that case the solution might be to avoid diets with strong spikes and later switch to a AAPS dev variant that offers a new parameter in SMB delivery settings: smb_max_range_extension. It expands the standard maximum of 2 h worth of basal by a factor of >1. (Additionally, the default 50% SMB delivery ratio might be elevated in dev variants).
Going to the max. limits on SMB size in AAPS Master will not make the FCL mode inheritantly less safe. In contrary, you replace your big meal bolus by several smaller ones that you enable your loop to give you, and even with some minutes of delay. This virtually eliminates the hypo risk in the first 1-2 hours of any meal. In hour 3 and thereafter, there should not be much difference because in HCL and FCL the loop manages with the same algorithm.
For successful full closed looping, ISF is the key tuning parameter. When utilizating AAPS Master + Automations, a > 100% profile change must automatically be triggered upon meal recognition (via glucose deltas), and provide the sharpened ISF.
In the list of all your Automations, you can tick the check-mark (to left of each field) OFF => This de-activates that Automation. For instance you could do this for all breakfast related FCL Automations to go to Hybrid Closed Looping for breakfast(s).
The following section shows in detail how you can bundle a whole series of conditions to describe situations in which the AAPS loop should increase (or decrease) insulin delivery. As ISF cannot directly be tuned, raising profile% over 100% will do the same for our purposes.
But you can also make use one of the average deltas. By comparing the deltas in the conditions of your Automations you could even define actions of different aggressiveness depending on whether the glucose rises in an accelerated way or not.
If conditions apply, the loop would give 1 or 2 SMBs in the next 12 minutes, using a boosted ISF according to the set elevated profile% (in the example, a 30% boost of insulinReq). As long as these conditions apply, the Automation rule extends by another 12 minutes. A low carb meal might have slower bg rise characteristics. It would benefit from another Automation (#2) that kicks in at lower delta, and gives a weaker insulin boost.
With large meals there is sometimes a second increase. By then, usually also the iob has dropped a bit, and the more aggressive Automations take effect again. (Check that your iob condition in Automation #2 is not set too low to for this to happen).
Soon after a few intial SMBs were given comes a balanced phase where moderate addition of insulin should cover the additional carbs absorbed. (Except in low carb meals, where the loop might see too weak of a bg rise, and go into zero-temping right away already now).
The AAPS main screen (where you always see cob=0 in UAM full loop) might in this phase ask for more carbs required. In UAM mode that simply means, you could make a very rough plausibility check: Is that amount of carbs likely in your body, un-absorbed from your meal just about an hour ago (about which you gave your loop no info)?
Often, Automations #1 and/or #2 make iob rise to heights that typically are enough for your meals. For personalized tuning, look in your hybrid closed loop data at the max iob values that occur with well-managed meals (often: your meal bolus), and above which magnitude a hypo (or requirement for extra carbs) occured at the end.
Sensible iob thresholds at which you should reduce aggressiveness of your loop, might not be the same for every meal. But especially in the first hour after the start of a meal, which is very crucial in the UAM mode, these data differ little for me: Just about 30g/hour get absorbed, and to define a meaningful iob independent of the exact meal can be possible.
Ways to work with EatingSoonTT Some loopers set (by pressing the TT button, or automated via a lowered profile glucose target if eating time slots are fairly fixed) an EatingSoonTT roughly an hour or more before meal start, just to guarantee a low starting glucose and a bit of positive iob. But, assuming the FCL is anyways always en route towards target, this might not yield much and you might rather just define an Automation that sets an EatingSoonTT at recognition of meal start (glucose delta, or acceleration = delta > avg delta). A low TT is important in this stage because any SMB is calculated by your loop using (predicted glucose minus TT)/sens, so a small TT makes the resulting insulinReq bigger.
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