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Type 1 diabetes

With type 1, every meal is read with the insulin next to it

GlucoBeat puts every meal, every dose, every workout and every night on the same timeline as your sensor, measures what happened next and tells you when insulin or exercise are mixed into the response. It never suggests doses.

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Why it fits type 1

In type 1 the response to a meal depends on the dose, the timing and the insulin on board. GlucoBeat doesn't hide that: it flags it.

Insulin, as an event

You log each dose (rapid or basal) with one tap. On each meal you see if there was a dose nearby, and the response is flagged as “other events nearby” instead of being attributed to the meal alone.

Exercise and the following night

Each session with its kind and intensity, what changed during the session and whether there was any low in the next 24 hours, compared with days without exercise.

Your usual response, with caveats

Repeated meals with median, range and n, and how many of those times were clean of nearby insulin or exercise: confidence takes it into account.

AI analysis that asks, not orders

Patterns in your data, written as associations, and concrete questions to take to your endocrinologist. Never doses, ratios or correction factors.

What it looks like in the app

A day with its doses and exercise on the curve, the sessions by kind and intensity with what happened in the next 24 hours, and a real, complete AI analysis. Example data except for the analysis.

A day with GlucoBeat

Example
Your glucose Target range 70–180 Sleep

Exercise by kind and intensity

Example
ExerciseSessionsDuring the sessionLow in the next 24 h
Walking · moderate8−14 mg/dL1 of 8
Running · intense4+9 mg/dL2 of 4
Padel · intense3−22 mg/dL2 of 3

Reference: on days without exercise there was a low on 2 of 14 days. Associations in your data, not causes.

Analyse how you're doing

Real example

Pick a period and the AI goes through your readings and everything you've logged to tell you what keeps repeating.

AI analysis

Real example A GlucoBeat result with a real user's data, in full as the AI wrote it (translated from Spanish). No dates: they're personal data.

Good overall control this week, with a few peaks after dinner and an overnight drop to keep an eye on

Glucose over the analysed period

Your glucose Target range 70–180

Summary

Overall your week shows good control: you spent 98.2% of the time within your target range, with an average of 111 mg/dL and variability (CV 19.7%) within the usual. The estimated GMI (6%) is only an approximation based on average glucose; it is not the same as a lab HbA1c.

Looking at the meals you logged, some dinners (like hard-boiled egg with Yatekomo noodles, or Saturday's grill) were associated with sharper rises, while others (chistorra, longaniza sausage) barely moved your glucose. As there are few examples of each and some coincide with nearby exercise, it's best taken as a clue rather than a firm conclusion.

There's also a notable drop during the night from Friday to Saturday, starting from a higher level than usual before bed and getting close to the low limit. The moderate walks you logged (only 2 sessions) showed no clear difference from days without exercise in terms of later drops, but the sample is small.

Day 4 stands out for having more variability and somewhat more time above 180 mg/dL, but no meals or exercise were logged that day, so it can't be linked to a specific cause from this data.

What we found

Food ObservationLow confidence

Some dinners were followed by bigger rises

In your data, three meals (a salad with a sandwich right after a walk, hard-boiled egg with Yatekomo noodles, and a grill) were followed by rises of between +66 and +91 mg/dL over the next two hours. One of them happened very close to an exercise session, which adds uncertainty about what had more influence.

+88, +73 and +66 mg/dL peak within 120 min (n=3 meals)

Food Works for youLow confidence

Other meals barely moved glucose

In contrast, other meals from the same weekend (chistorra with pork secreto, longaniza sausage, hard-boiled eggs with gazpacho) showed much smaller rises or even slight drops, which suggests that not all meals have the same effect for you.

peak between -15 and +27 mg/dL (n=3 meals)

Lows Keep an eye onLow confidence

Notable overnight drop after a higher-level dinner

On the night from Friday to Saturday, starting from 164 mg/dL at bedtime, your glucose fell steadily until it came close to the low end of the range (an approximate nadir of 68 mg/dL, 96 mg/dL below the starting point). It's a single episode in your week, so it's worth seeing whether it happens again.

-96 mg/dL from the start, touching the low limit (n=1 night)

Daily pattern ObservationMedium confidence

Usual rise shortly after waking up

On the three days with a wake-up event logged, your glucose rose consistently in the first hour after getting up, before any meal.

+14 to +24 mg/dL in the first 60-120 min after waking (n=3 days)

Exercise ObservationLow confidence

Moderate walks with no clear difference from days without exercise

The two moderate walks logged showed a slight fall in glucose during the session, and neither was followed by a low in the next 24 hours, similar to the four reference days without exercise (which had no lows either). The sample is too small to draw firm conclusions.

average change of -9 mg/dL during the session, 0 of 2 sessions followed by a low; 0 of 4 reference days with a low

Data quality ObservationMedium confidence

Higher variability on day 4 with no events logged

Day 4 had the highest variability of the week and somewhat more time above 180 mg/dL, but there's no meal, exercise or other event logged that day that would let it be linked to a specific cause.

CV 26.8%, 6.5% of the time above 180 mg/dL, 0 events logged

Questions for your healthcare team

  • Would you expect the overnight drop seen after a dinner with a higher than usual level before bed?
  • What kinds of meals or combinations could explain why some dinners show higher peaks than others in my data?
  • Would it help to log the type and intensity of exercise in more detail to better understand its effect on my glucose?
  • Does it make sense to look into why day 4 showed more variability, even though no events were logged that day?

About your data

  • Only 7 days of data are available and just 2 exercise sessions of the same type and intensity, so the exercise comparisons have little statistical power.
  • Day 4 shows the highest variability of the week but has no event (meal, exercise, etc.) logged, which makes it impossible to link that rise to a specific cause.
  • Event e16 has lower data coverage (56%) and several missing delta values, which limits the reliability of that observation.
  • The daily sensor coverage percentage appears empty on several days, although the overall summary shows 100%; it's worth checking whether the daily log is complete.
  • Several meals happened close to other events (for example, exercise), which makes it hard to know for sure what had more influence on the glucose rise observed.
AI-generated. Content generated by artificial intelligence. It may contain errors and is not medical advice.

Everything you need to understand yourself

AI analysis

Pick some dates and get concrete patterns: what doesn't suit you, what does, when you tend to go low.

Nothing slips through

If you forget to log a meal, GlucoBeat spots the rise it left in your glucose curve and suggests the approximate time. The app asks you about it: all you do is say what it was.

Statistics that matter

Time in range, variability, GMI, lows, AGP profile and comparison with the previous period.

Experiments

What happens if you go for a walk after dinner? {n} different comparisons: log each situation a few times and GlucoBeat compares with your own data, with its n and its confidence.

Your usual response

The same dish, seven times: median rise, usual range, time to peak and what changed the times you walked afterwards. With its n and its confidence.

How would this affect me?

Type what you fancy: first what happened in your diary with similar meals and, if you want, an AI estimate with a range and what tends to make a difference.

What a repeated meal looks like

With insulin nearby in 3 of the 5 times, confidence drops and it says so. Associations in your data, not causes.

Margherita pizza

Example, made-up figures
Times logged
5
Median rise
+72 mg/dL
Usual range
+51 to +98
Peak at
95–140 min
No other events nearby
2 of 5
Confidence
Low

What GlucoBeat does not do

  • It never suggests, calculates or adjusts doses, ratios or correction factors, neither through the AI nor through any calculation. What you log about insulin is there to read your data, not to decide.
  • It is not a medical device: it does not diagnose, give doses or change any treatment. That is your care team's job.
  • It does not alert on values and does not work in real time: your sensor and its app do that. GlucoBeat shows you the evolution and what goes with it.
  • It does not predict: every figure describes what happened in your data, with how many times it was seen, in what range and with what confidence.

Frequently asked questions

Does it take insulin on board into account?

It takes it into account as an event: if there was a dose near a meal, the response is flagged as not attributable to the meal alone and confidence drops. It does not calculate insulin on board or use it to recommend anything.

Does it work with a pump?

Yes. You log boluses as insulin doses and the rest works the same. The pump's basal does not need logging.

What about hypos?

Every low stays as an episode in the statistics, and treating a low is logged as its own event. GlucoBeat does not alert on lows: your sensor does.

How much does it cost?

The diary, charts, statistics, usual response, report and sharing are free forever. The paid plans widen the limits, such as those of the AI or experiments.

Start with this week's diary

Bring in your sensor readings and log meals and doses for a few days. Free and no card.

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FreeStyle, Libre, LibreView and LibreLinkUp are trademarks of Abbott; GlucoBeat is not affiliated with Abbott.